Instagram Stories are an integral part of the platform. Though Instagram copied Snapchat’s concept a few years ago, over 500 million accounts use the Stories feature on a daily basis. Some users...

With the debut of Vert.x, the asynchronous framework is reaching an inflection point, suggests Andrew Cholakian. With Vert.x, the software is packaged together in such a way as to be extremely practical, he states. For some JVM zealots, Vert.x may meet needs recently and apparently addressed by node.js. Vert.x is an asynchronous application server – Read the rest...

Some of the problems we work on as UX researchers are simple and are easily solved by getting users in front of our product. But other problems can be complex and it's hard to know how to start solving them. In situations like that, a simple 2x2 diagram can cut through the 'what ifs', the 'how abouts' and the edge cases and provide a simple way of looking at the problem. Here are 10 examples of 2x2 diagrams to simplify UX research discussions.

Instagram is one of the most efficient and fastest growing social networks. Brands and businesses love it and leverage it to promote and market their products and services to billions of users worldwide. More and more brands are competing for declining customer attention whose span is now no more than a Goldfish’s at 8s. Hence, […]

Original post: 7 Simple Ways to Get Even More Engaged Instagram Followers

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With pleasure we can now announce that Xtreme One 1.6 was released as a beta last week! Xtreme One WordPress […]

Ditch the idea of yourself as a starving artist. Throw away the notion that you’re doomed to be another poor creative soul. My long time pal Ramit Sethi is back on the show to remind us we need to get back on track to building and living a rich life. And believe me, that doesn’t have to mean cutting back the lattes. Ramit has been on the show a few times, but if you haven’t caught up yet, let me fill you in. Ramit Sethi is the author of the NYT bestseller “I Will Teach You To Be Rich” and writes for over 500,000 monthly readers on his website, iwillteachyoutoberich.com. It’s one of my favorite go-to finance resources covering psychology, personal finance, careers, and entrepreneurship. No one has single-handedly given me better insight into the business side of art, than Ramit. Ramit has updated & expanded 2nd edition of his book and joined me for a LIVE studio conversation on money confessions. In this episode we get into: A rich life isn’t only about save money, it’s about defining & prioritizing the things you love the most. It’s one thing to manage our own personal finances but navigating that with […]

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In this episode I’m chatting with my long time friend and financial guru, Ramit Sethi. Ramit has been on the show a number of times, and this time we’re connecting virtually from our living rooms during the quarantine. Of course we get into finances during these uncertain times, but more importantly: adaptation and resilience. Over the years, no one has single-handedly given me better insight about the business side of art than the New York Times bestselling author, Ramit Sethi. Take a listen and let us know what you think. Enjoy! This episode was part of CreativeLive TV, a brand-new, free, 24/7 variety show, live-streamed from the very casual living rooms, studios, and kitchen tables of our worldwide community of legendary creators. You can expect musical performances, Q&As, cooking, spoken word, drawing, and more – featuring many of our favorite personalities – all in a safe, virtual space full of joy, shared experiences, and connection via live, interactive chat. The schedule and upcoming broadcasts can be seen at http://creativelive.com/tv FOLLOW RAMIT: instagram | twitter | website Listen to the Podcast  Subscribe   Watch the Episode This podcast is brought to you by CreativeLive. CreativeLive is the world’s largest hub […]

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For the most part, we tend to underestimate things that are familiar to us. It is also very likely that we will underestimate those things that though new, seem very simple to process. And that is correct to some degree. But, when we are faced with complex cases and all measures are taken, a good and solid understanding of the basics could help us to find the right solutions. In this article, we will take a deeper look at one of the most simple, thus, quite often underrated activities in web development that is the design of wireframes.

Have you noticed extra-long and weirdly stretched images on your Instagram feed? It looks like some kind of a glitch has appeared, allowing users to post images like this to their followers. Of course, some Instagrammers have made the use of it to draw attention, and if you want to have some fun (or annoy […]

The post Weird glitch lets you post insanely long photos to Instagram appeared first on DIY Photography.

We show how to efficiently compute the derivative (when it exists) of the solution map of log-log convex programs (LLCPs). These are nonconvex, nonsmooth optimization problems with positive variables that become convex when the variables, objective functions, and constraint functions are replaced with their logs. We focus specifically on LLCPs generated by disciplined geometric programming, a grammar consisting of a set of atomic functions with known log-log curvature and a composition rule for combining them. We represent a parametrized LLCP as the composition of a smooth transformation of parameters, a convex optimization problem, and an exponential transformation of the convex optimization problem's solution. The derivative of this composition can be computed efficiently, using recently developed methods for differentiating through convex optimization problems. We implement our method in CVXPY, a Python-embedded modeling language and rewriting system for convex optimization. In just a few lines of code, a user can specify a parametrized LLCP, solve it, and evaluate the derivative or its adjoint at a vector. This makes it possible to conduct sensitivity analyses of solutions, given perturbations to the parameters, and to compute the gradient of a function of the solution with respect to the parameters. We use the adjoint of the derivative to implement differentiable log-log convex optimization layers in PyTorch and TensorFlow. Finally, we present applications to designing queuing systems and fitting structured prediction models.

In this paper we design suboptimal control laws for an unknown linear system on the basis of measured data. We focus on the suboptimal linear quadratic regulator problem and the suboptimal H2 control problem. For both problems, we establish conditions under which a given data set contains sufficient information for controller design. We follow up by providing a data-driven parameterization of all suboptimal controllers. We will illustrate our results by numerical simulations, which will reveal an interesting trade-off between the number of collected data samples and the achieved controller performance.

We introduce primal and dual stochastic gradient oracle methods for decentralized convex optimization problems. Both for primal and dual oracles the proposed methods are optimal in terms of the number of communication steps. However, for all classes of the objective, the optimality in terms of the number of oracle calls per node in the class of methods with optimal number of communication steps takes place only up to a logarithmic factor and the notion of smoothness. By using mini-batching technique we show that all proposed methods with stochastic oracle can be additionally parallelized at each node.

We consider the most general class of linear inhomogeneous boundary-value problems for systems of ordinary differential equations of an arbitrary order whose solutions and right-hand sides belong to appropriate Sobolev spaces. For parameter-dependent problems from this class, we prove a constructive criterion for their solutions to be continuous in the Sobolev space with respect to the parameter. We also prove a two-sided estimate for the degree of convergence of these solutions to the solution of the nonperturbed problem.

This paper develops a method for obtaining guaranteed outer approximations for global attractors of continuous and discrete time nonlinear dynamical systems. The method is based on a hierarchy of semidefinite programming problems of increasing size with guaranteed convergence to the global attractor. The approach taken follows an established line of reasoning, where we first characterize the global attractor via an infinite dimensional linear programming problem (LP) in the space of Borel measures. The dual to this LP is in the space of continuous functions and its feasible solutions provide guaranteed outer approximations to the global attractor. For systems with polynomial dynamics, a hierarchy of finite-dimensional sum-of-squares tightenings of the dual LP provides a sequence of outer approximations to the global attractor with guaranteed convergence in the sense of volume discrepancy tending to zero. The method is very simple to use and based purely on convex optimization. Numerical examples with the code available online demonstrate the method.

The problem of minimizing an entropy functional subject to linear constraints is a useful example of partially finite convex programming. In the 1990s, Borwein and Lewis provided broad and easy-to-verify conditions that guarantee strong duality for such problems. Their approach is to construct a function in the quasi-relative interior of the relevant infinite-dimensional set, which assures the existence of a point in the core of the relevant finite-dimensional set. We revisit this problem, and provide an alternative proof by directly appealing to the definition of the core, rather than by relying on any properties of the quasi-relative interior. Our approach admits a minor relaxation of the linear independence requirements in Borwein and Lewis' framework, which allows us to work with certain piecewise-defined moment functions precluded by their conditions. We provide such a computed example that illustrates how this relaxation may be used to tame observed Gibbs phenomenon when the underlying data is discontinuous. The relaxation illustrates the understanding we may gain by tackling partially-finite problems from both the finite-dimensional and infinite-dimensional sides. The comparison of these two approaches is informative, as both proofs are constructive.

Recent studies of optimization methods and GNC of spacecraft near small bodies focusing on descent, landing, rendezvous, etc., with key safety constraints such as line-of-sight conic zones and soft landings have shown promising results; this paper considers descent missions to an asteroid surface with a constraint that consists of an onboard camera and asteroid surface markers while using a stochastic convex MPC law. An undermodeled asteroid gravity and spacecraft technology inspired measurement model is established to develop the constraint. Then a computationally light stochastic Linear Quadratic MPC strategy is presented to keep the spacecraft in satisfactory field of view of the surface markers while trajectory tracking, employing chance based constraints and up-to-date estimation uncertainty from navigation. The estimation uncertainty giving rise to the tightened constraints is particularly addressed. Results suggest robust tracking performance across a variety of trajectories.

We study the two-parameter quadratic sieve for a general test function. We prove, under some very general assumptions, that the function considered by Barban and Vehov [BV68] and Graham [Gra78] for this problem is optimal up to the second-order term. We determine that second-order term explicitly.

High-performance implementations of graph algorithms are challenging to implement on new parallel hardware such as GPUs, because of three challenges: (1) difficulty of coming up with graph building blocks, (2) load imbalance on parallel hardware, and (3) graph problems having low arithmetic intensity. To address these challenges, GraphBLAS is an innovative, on-going effort by the graph analytics community to propose building blocks based in sparse linear algebra, which will allow graph algorithms to be expressed in a performant, succinct, composable and portable manner. In this paper, we examine the performance challenges of a linear algebra-based approach to building graph frameworks and describe new design principles for overcoming these bottlenecks. Among the new design principles is exploiting input sparsity, which allows users to write graph algorithms without specifying push and pull direction. Exploiting output sparsity allows users to tell the backend which values of the output in a single vectorized computation they do not want computed. Load-balancing is an important feature for balancing work amongst parallel workers. We describe the important load-balancing features for handling graphs with different characteristics. The design principles described in this paper have been implemented in "GraphBLAST", the first open-source linear algebra-based graph framework on GPU targeting high-performance computing. The results show that on a single GPU, GraphBLAST has on average at least an order of magnitude speedup over previous GraphBLAS implementations SuiteSparse and GBTL, comparable performance to the fastest GPU hardwired primitives and shared-memory graph frameworks Ligra and Gunrock, and better performance than any other GPU graph framework, while offering a simpler and more concise programming model.

Quantum computing has evolved quickly in recent years and is showing significant benefits in a variety of fields. Malware analysis is one of those fields that could also take advantage of quantum computing. The combination of software used to locate the most frequent hashes and $n$-grams between benign and malicious software (KiloGram) and a quantum search algorithm could be beneficial, by loading the table of hashes and $n$-grams into a quantum computer, and thereby speeding up the process of mapping $n$-grams to their hashes. The first phase will be to use KiloGram to find the top-$k$ hashes and $n$-grams for a large malware corpus. From here, the resulting hash table is then loaded into a quantum machine. A quantum search algorithm is then used search among every permutation of the entangled key and value pairs to find the desired hash value. This prevents one from having to re-compute hashes for a set of $n$-grams, which can take on average $O(MN)$ time, whereas the quantum algorithm could take $O(sqrt{N})$ in the number of table lookups to find the desired hash values.

How to generate testing scenario libraries for connected and automated vehicles (CAVs) is a major challenge faced by the industry. In previous studies, to evaluate maneuver challenge of a scenario, surrogate models (SMs) are often used without explicit knowledge of the CAV under test. However, performance dissimilarities between the SM and the CAV under test usually exist, and it can lead to the generation of suboptimal scenario libraries. In this paper, an adaptive testing scenario library generation (ATSLG) method is proposed to solve this problem. A customized testing scenario library for a specific CAV model is generated through an adaptive process. To compensate the performance dissimilarities and leverage each test of the CAV, Bayesian optimization techniques are applied with classification-based Gaussian Process Regression and a new-designed acquisition function. Comparing with a pre-determined library, a CAV can be tested and evaluated in a more efficient manner with the customized library. To validate the proposed method, a cut-in case study was performed and the results demonstrate that the proposed method can further accelerate the evaluation process by a few orders of magnitude.

Introduction of spectrum-sharing in 5G and subsequent generation networks demand base-station(s) with the capability to characterize the wideband spectrum spanned over licensed, shared and unlicensed non-contiguous frequency bands. Spectrum characterization involves the identification of vacant bands along with center frequency and parameters (energy, modulation, etc.) of occupied bands. Such characterization at Nyquist sampling is area and power-hungry due to the need for high-speed digitization. Though sub-Nyquist sampling (SNS) offers an excellent alternative when the spectrum is sparse, it suffers from poor performance at low signal to noise ratio (SNR) and demands careful design and integration of digital reconstruction, tunable channelizer and characterization algorithms. In this paper, we propose a novel deep-learning framework via a single unified pipeline to accomplish two tasks: 1)~Reconstruct the signal directly from sub-Nyquist samples, and 2)~Wideband spectrum characterization. The proposed approach eliminates the need for complex signal conditioning between reconstruction and characterization and does not need complex tunable channelizers. We extensively compare the performance of our framework for a wide range of modulation schemes, SNR and channel conditions. We show that the proposed framework outperforms existing SNS based approaches and characterization performance approaches to Nyquist sampling-based framework with an increase in SNR. Easy to design and integrate along with a single unified deep learning framework make the proposed architecture a good candidate for reconfigurable platforms.

For Convolutional Neural Network-based object detection, there is a typical dilemma: the spatial information is well kept in the shallow layers which unfortunately do not have enough semantic information, while the deep layers have a high semantic concept but lost a lot of spatial information, resulting in serious information imbalance. To acquire enough semantic information for shallow layers, Feature Pyramid Networks (FPN) is used to build a top-down propagated path. In this paper, except for top-down combining of information for shallow layers, we propose a novel network called Image Pyramid Guidance Network (IPG-Net) to make sure both the spatial information and semantic information are abundant for each layer. Our IPG-Net has two main parts: the image pyramid guidance transformation module and the image pyramid guidance fusion module. Our main idea is to introduce the image pyramid guidance into the backbone stream to solve the information imbalance problem, which alleviates the vanishment of the small object features. This IPG transformation module promises even in the deepest stage of the backbone, there is enough spatial information for bounding box regression and classification. Furthermore, we designed an effective fusion module to fuse the features from the image pyramid and features from the backbone stream. We have tried to apply this novel network to both one-stage and two-stage detection models, state of the art results are obtained on the most popular benchmark data sets, i.e. MS COCO and Pascal VOC.

A recently introduced classifier, called SS3, has shown to be well suited to deal with early risk detection (ERD) problems on text streams. It obtained state-of-the-art performance on early depression and anorexia detection on Reddit in the CLEF's eRisk open tasks. SS3 was created to deal with ERD problems naturally since: it supports incremental training and classification over text streams, and it can visually explain its rationale. However, SS3 processes the input using a bag-of-word model lacking the ability to recognize important word sequences. This aspect could negatively affect the classification performance and also reduces the descriptiveness of visual explanations. In the standard document classification field, it is very common to use word n-grams to try to overcome some of these limitations. Unfortunately, when working with text streams, using n-grams is not trivial since the system must learn and recognize which n-grams are important "on the fly". This paper introduces t-SS3, an extension of SS3 that allows it to recognize useful patterns over text streams dynamically. We evaluated our model in the eRisk 2017 and 2018 tasks on early depression and anorexia detection. Experimental results suggest that t-SS3 is able to improve both current results and the richness of visual explanations.

Stockhusen and Tantau (IPEC 2013) defined the operators paraW and paraBeta for parameterised space complexity classes by allowing bounded nondeterminism with multiple read and read-once access, respectively. Using these operators, they obtained characterisations for the complexity of many parameterisations of natural problems on graphs.

In this article, we study the counting versions of such operators and introduce variants based on tail-nondeterminism, paraW[1] and paraBetaTail, in the setting of parameterised logarithmic space. We examine closure properties of the new classes under the central reductions and arithmetic operations. We also identify a wide range of natural complete problems for our classes in the areas of walk counting in digraphs, first-order model-checking and graph-homomorphisms. In doing so, we also see that the closure of #paraBetaTail-L under parameterised logspace parsimonious reductions coincides with #paraBeta-L. We show that the complexity of a parameterised variant of the determinant function is #paraBetaTail-L-hard and can be written as the difference of two functions in #paraBetaTail-L for (0,1)-matrices. Finally, we characterise the new complexity classes in terms of branching programs.

We study the language universality problem for One-Counter Nets, also known as 1-dimensional Vector Addition Systems with States (1-VASS), parameterized either with an initial counter value, or with an upper bound on the allowed counter value during runs. The language accepted by an OCN (defined by reaching a final control state) is monotone in both parameters. This yields two natural questions: 1) Does there exist an initial counter value that makes the language universal? 2) Does there exist a sufficiently high ceiling so that the bounded language is universal? Despite the fact that unparameterized universality is Ackermann-complete and that these problems seem to reduce to checking basic structural properties of the underlying automaton, we show that in fact both problems are undecidable. We also look into the complexities of the problems for several decidable subclasses, namely for unambiguous, and deterministic systems, and for those over a single-letter alphabet.

Moving a module in a modular robot is a very complex and error-prone process. Unlike in swarm, in the modular robots we are targeting, the moving module must keep the connection to, at least, one other module. In order to miniaturize each module to few millimeters, we have proposed a design which is using electrostatic actuator. However, this movement is composed of several attachment, detachment creating the movement and each small step can fail causing a module to break the connection. The idea developed in this paper consists in creating a new kind of deformable module allowing a movement which keeps the connection between the moving and the fixed modules. We detail the geometry and the practical constraints during the conception of this new module. We then validate the possibility of movement for a module in an existing configuration. This implies the cooperation of some of the modules placed along the path and we show in simulation that it exists a motion process to reach every free positions of the surface for a given configuration.

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the Blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous and a non-invasive BP measurement system is proposed using Photoplethysmogram (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo pre-processing and feature extraction steps. Time, frequency and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for Systolic BP (SBP) and Diastolic BP (DBP) estimation individually. Gaussian Process Regression (GPR) along with ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root-mean-square error (RMSE) of 6.74 and 3.59 respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.

Despite recent progress on computer vision and natural language processing, developing video understanding intelligence is still hard to achieve due to the intrinsic difficulty of story in video. Moreover, there is not a theoretical metric for evaluating the degree of video understanding. In this paper, we propose a novel video question answering (Video QA) task, DramaQA, for a comprehensive understanding of the video story. The DramaQA focused on two perspectives: 1) hierarchical QAs as an evaluation metric based on the cognitive developmental stages of human intelligence. 2) character-centered video annotations to model local coherence of the story. Our dataset is built upon the TV drama "Another Miss Oh" and it contains 16,191 QA pairs from 23,928 various length video clips, with each QA pair belonging to one of four difficulty levels. We provide 217,308 annotated images with rich character-centered annotations, including visual bounding boxes, behaviors, and emotions of main characters, and coreference resolved scripts. Additionally, we provide analyses of the dataset as well as Dual Matching Multistream model which effectively learns character-centered representations of video to answer questions about the video. We are planning to release our dataset and model publicly for research purposes and expect that our work will provide a new perspective on video story understanding research.

Web services often impose inter-parameter dependencies that restrict the way in which two or more input parameters can be combined to form valid calls to the service. Unfortunately, current specification languages for web services like the OpenAPI Specification (OAS) provide no support for the formal description of such dependencies, which makes it hardly possible to automatically discover and interact with services without human intervention. In this article, we present an approach for the specification and automated analysis of inter-parameter dependencies in web APIs. We first present a domain-specific language, called Inter-parameter Dependency Language (IDL), for the specification of dependencies among input parameters in web services. Then, we propose a mapping to translate an IDL document into a constraint satisfaction problem (CSP), enabling the automated analysis of IDL specifications using standard CSP-based reasoning operations. Specifically, we present a catalogue of nine analysis operations on IDL documents allowing to compute, for example, whether a given request satisfies all the dependencies of the service. Finally, we present a tool suite including an editor, a parser, an OAS extension, a constraint programming-aided library, and a test suite supporting IDL specifications and their analyses. Together, these contributions pave the way for a new range of specification-driven applications in areas such as code generation and testing.

Bayesian predictive coding is a putative neuromorphic method for acquiring higher-level neural representations to account for sensory input. Although originating in the neuroscience community, there are also efforts in the machine learning community to study these models. This paper reviews some of the more well known models. Our review analyzes module connectivity and patterns of information transfer, seeking to find general principles used across the models. We also survey some recent attempts to cast these models within a deep learning framework. A defining feature of Bayesian predictive coding is that it uses top-down, reconstructive mechanisms to predict incoming sensory inputs or their lower-level representations. Discrepancies between the predicted and the actual inputs, known as prediction errors, then give rise to future learning that refines and improves the predictive accuracy of learned higher-level representations. Predictive coding models intended to describe computations in the neocortex emerged prior to the development of deep learning and used a communication structure between modules that we name the Rao-Ballard protocol. This protocol was derived from a Bayesian generative model with some rather strong statistical assumptions. The RB protocol provides a rubric to assess the fidelity of deep learning models that claim to implement predictive coding.

We consider the problem of protecting and manipulating elections by recounting and changing ballots, respectively. Our setting involves a plurality-based election held across multiple districts, and the problem formulations are based on the model proposed recently by~[Elkind et al, IJCAI 2019]. It turns out that both of the manipulation and protection problems are NP-complete even in fairly simple settings. We study these problems from a parameterized perspective with the goal of establishing a more detailed complexity landscape. The parameters we consider include the number of voters, and the budgets of the attacker and the defender. While we observe fixed-parameter tractability when parameterizing by number of voters, our main contribution is a demonstration of parameterized hardness when working with the budgets of the attacker and the defender.

Feature pyramid has been an efficient method to extract features at different scales. Development over this method mainly focuses on aggregating contextual information at different levels while seldom touching the inter-level correlation in the feature pyramid. Early computer vision methods extracted scale-invariant features by locating the feature extrema in both spatial and scale dimension. Inspired by this, a convolution across the pyramid level is proposed in this study, which is termed pyramid convolution and is a modified 3-D convolution. Stacked pyramid convolutions directly extract 3-D (scale and spatial) features and outperforms other meticulously designed feature fusion modules. Based on the viewpoint of 3-D convolution, an integrated batch normalization that collects statistics from the whole feature pyramid is naturally inserted after the pyramid convolution. Furthermore, we also show that the naive pyramid convolution, together with the design of RetinaNet head, actually best applies for extracting features from a Gaussian pyramid, whose properties can hardly be satisfied by a feature pyramid. In order to alleviate this discrepancy, we build a scale-equalizing pyramid convolution (SEPC) that aligns the shared pyramid convolution kernel only at high-level feature maps. Being computationally efficient and compatible with the head design of most single-stage object detectors, the SEPC module brings significant performance improvement ($>4$AP increase on MS-COCO2017 dataset) in state-of-the-art one-stage object detectors, and a light version of SEPC also has $sim3.5$AP gain with only around 7% inference time increase. The pyramid convolution also functions well as a stand-alone module in two-stage object detectors and is able to improve the performance by $sim2$AP. The source code can be found at https://github.com/jshilong/SEPC.

Recently, the Edge Computing paradigm has gained significant popularity both in industry and academia. Researchers now increasingly target to improve performance and reduce energy consumption of such devices. Some recent efforts focus on using emerging RRAM technologies for improving energy efficiency, thanks to their no leakage property and high integration density. As the complexity and dynamism of applications supported by such devices escalate, it has become difficult to maintain ideal performance by static RRAM controllers. Machine Learning provides a promising solution for this, and hence, this work focuses on extending such controllers to allow dynamic parameter updates. In this work we propose an Adaptive RRAM Variability-Aware Controller, AVAC, which periodically updates Wait Buffer and batch sizes using on-the-fly learning models and gradient ascent. AVAC allows Edge devices to adapt to different applications and their stages, to improve computation performance and reduce energy consumption. Simulations demonstrate that the proposed model can provide up to 29% increase in performance and 19% decrease in energy, compared to static controllers, using traces of real-life healthcare applications on a Raspberry-Pi based Edge deployment.

The increasing ubiquity of low-cost wireless sensors in smart homes and buildings has enabled users to easily deploy systems to remotely monitor and control their environments. However, this raises privacy concerns for third-party occupants, such as a hotel room guest who may be unaware of deployed clandestine sensors. Previous methods focused on specific modalities such as detecting cameras but do not provide a generalizable and comprehensive method to capture arbitrary sensors which may be "spying" on a user. In this work, we seek to determine whether one can walk in a room and detect any wireless sensor monitoring an individual. As such, we propose SnoopDog, a framework to not only detect wireless sensors that are actively monitoring a user, but also classify and localize each device. SnoopDog works by establishing causality between patterns in observable wireless traffic and a trusted sensor in the same space, e.g., an inertial measurement unit (IMU) that captures a user's movement. Once causality is established, SnoopDog performs packet inspection to inform the user about the monitoring device. Finally, SnoopDog localizes the clandestine device in a 2D plane using a novel trial-based localization technique. We evaluated SnoopDog across several devices and various modalities and were able to detect causality 96.6% percent of the time, classify suspicious devices with 100% accuracy, and localize devices to a sufficiently reduced sub-space.

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The invention relates to ceramide dimers which are constructed from two ceramide molecules which are crosslinked to each other via their lipophilic end. The ceramide molecules thereby have at least one hydrophilic group at their hydrophilic end for increasing the hydration shell of the dimer. The ceramide dimers according to the invention can be used as pharmaceutical preparation or as cosmetic preparation.

There are provided a dielectric ceramic having a high Qf value in a relative permittivity ∈r range of 35 to 45, and a small absolute value of a temperature coefficient τf which indicates change of the resonant frequency in a wide temperature range from a low temperature range to a high temperature range, and a dielectric filter having the dielectric ceramic. A dielectric ceramic includes: a main component, molar ratios α, β, and γ satisfying expressions of 0.240≦α≦0.470, 0.040≦β≦0.200, 0.400≦γ≦0.650, and α+β+γ=1 when a composition formula of the main component is represented as αZrO2.βSnO2.γTiO2; and Mn, a content of Mn in terms of MnO2 being greater than or equal to 0.01% by mass and less than 0.1% by mass with respect to 100% by mass of the main component.

A glass ceramic as cooktop for induction heating having improved colored display capability and heat shielding is provided. The cooktop includes a transparent, dyed glass ceramic plate having high-quartz mixed crystals as a predominant crystal phase. The glass ceramic contains none of the chemical refining agents arsenic oxide and/or antimony oxide and has a transmittance values greater than 0.4% at at least one wavelength in the blue spectrum between 380 and 500 nm, a transmittance >2% at 630 nm, a transmittance of less than 45% at 1600 nm, and a light transmittance of less than 2.5% in the visible spectrum.

Shape memory and pseudoelastic martensitic behavior is enabled by a structure in which there is provided a crystalline ceramic material that is capable of undergoing a reversible martensitic transformation and forming martensitic domains, during such martensitic transformation, that have an elongated domain length. The ceramic material is configured as a ceramic material structure including a structural feature that is smaller than the elongated domain length of the ceramic material.

A dielectric ceramic material comprises a primary component of barium titanate (BaTiO3) and at least one additive component. The additive component has a mole percentage from 1% to 50% and is selected from the group consisting of lithium tantalite (LiTaO3), barium cerate (BaCeO3), sodium metaniobate (NaNbO3) and the combinations thereof.

To provide a glass ceramic body wherein the deterioration of the reflectance due to black coloration is suppressed, and the unevenness of the firing shrinkage is suppressed. A glass ceramic body comprising a glass matrix and alumina particles dispersed therein, wherein the glass matrix is not crystallized, a ceramic part composed of the dispersed alumina particles has an α-alumina crystal structure and a crystal structure other than the α-alumina crystal structure.

There is provided a dielectric composition including: a base powder including BaTiO3; a first accessory component including a content (x1) of 0.1 to 1.0 at % of an oxide or a carbonate including transition metals, based on 100 moles of the base powder; a second accessory component including a content (y) of 0.01 to 3.0 at % of oxide or carbonate including a fixed valence acceptor element, based on 100 moles of the base powder; a third accessory component including an oxide or a carbonate including a Ce element (content of z at %) and at least one rare earth element (content of w at %); and a fourth accessory component including a sintering aid, wherein 0.01≦z≦x1+4y and 0.01≦z+w≦x1+4y based on 100 moles of the base powder.

A perpendicular spin-transfer torque magnetic random access memory (STTMRAM) element includes a fixed layer having a magnetization that is substantially fixed in one direction and a barrier layer formed on top of the fixed layer and a free layer. The free layer has a number of alternating laminates, each laminate being made of a magnetic layer and an insulating layer. The magnetic layer is switchable and formed on top of the barrier layer. The free layer is capable of switching its magnetization to a parallel or an anti-parallel state relative to the magnetization of the fixed layer during a write operation when bidirectional electric current is applied across the STTMRAM element. Magnetic layers of the laminates are ferromagnetically coupled to switch together as a single domain during the write operation and the magnetization of the fixed and free layers and the magnetic layers of the laminates have perpendicular anisotropy.

Methods using a sequence of externally generated magnetic fields to initialize the magnetization directions of each of the layers in perpendicular MTJ MRAM elements for data and reference bits when the required magnetization directions are anti-parallel are described. The coercivity of the fixed pinned and reference layers can be made unequal so that one of them can be switched by a magnetic field that will reliably leave the other one unswitched. Embodiments of the invention utilize the different effective coercivity fields of the pinned, reference and free layers to selectively switch the magnetization directions using a sequence of magnetic fields of decreasing strength. Optionally the chip or wafer can be heated to reduce the required field magnitude. It is possible that the first magnetic field in the sequence can be applied during an annealing step in the MRAM manufacture process.

The invention provides a process for monitoring and/or adjusting a dispersion polymerization of an olefin-based polymer, the process comprising monitoring the concentration of the carbon-carbon unsaturations in the dispersion using Raman Spectroscopy. The invention also provides a process for polymerizing an olefin-based polymer, the process comprising polymerizing one or more monomer types, in the presence of at least one catalyst and at least one solvent, to form the polymer as a dispersed phase in the solvent; and monitoring the concentration of the carbon-carbon unsaturations in the dispersion using Raman Spectroscopy.

An approach for introspection of a software component and generation of a conditional memory dump, a computing device executing an introspection program with respect to the software component is provided. An introspection system comprises one or more conditions for generating the conditional memory dump based on operations of the software component. In one aspect, a computing device detects, through an introspection program, whether the one or more conditions are satisfied by the software component based on information in an introspection analyzer of the introspection program. In addition, the computing device indicates, through the introspection program, if the one or more conditions are satisfied by the software component. In another aspect, responsive to the indication, the computing device generates the conditional memory dump through the introspection program.

A method of preparing a simple ceramic ingot of a spent filter having radioactive cesium trapped therein, and a ceramic ingot of a spent filter having improved properties such as leach resistance, thermal stability, and cesium content are provided. The method includes grinding and mixing a spent filter having cesium trapped therein, adding a solidifying agent, and sintering the spent filter. The method of preparing a ceramic ingot of a spent filter can be useful in preparing the ceramic ingot of the spent filter from only the spent filter by means of simple grinding and sintering, and in preparing the ceramic ingot of the spent filter by adding a small amount of a solidifying agent. The ceramic ingot of the spent filter has a high density and improved thermal stability, and shows improved leach resistance since a leach rate of a radioactive material is remarkably low. Therefore, the spent filter having radioactive cesium trapped therein can be effectively used to prepare a stable ceramic ingot.

A data collection unit obtains physiological data from a subject interface on a subject. The subject interface can be connected to the data collection unit. When the subject interface is connected to the data collection unit, subject interface contacts on the subject interface make contact with data collection unit contacts on the data collection unit. Some of the data collection unit contacts are for communicating physiological data from the subject interface to the data collection unit. Some of the contacts are for powering the data collection unit upon the subject interface being connected to the data collection unit and for powering down the data collection unit upon the subject interface being disconnected from the data collection unit.