By University of Illinois Extension In an era of increasing energy prices, many Americans insulate and seal up their homes during the winter months. Although this can result in savings on the monthly power bill, sealing the home can concentrate … Continue reading →

A set of key visuals for Nike Shanghai

Branding and Visual Identity for Potency Design

Technology has changed the way we live. A few decades back, we couldn’t even imagine making a video call to a person sitting in another corner of the world. With every technological advancement, mankind is treading towards greater development. From smart gadgets to machines, every invention has leveraged the human lifestyle.  There’s no doubt in […]

Original post: Best Alexa Skills That Will Actually Surprise You

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For those who’ve never seen TheCrafsMan SteadyCraftin on YouTube, you’re in for a treat – even if you already understand everything contained within this 25-minute video. For those who have, you know exactly what to expect. I’ve been following this rather unconventional channel for a while now. It covers a lot of handy DIY and […]

The post Watch YouTube’s most informed sock puppet teach you how to shoot with manual exposure appeared first on DIY Photography.

How we read pie charts is still an open question: is it angle? Is it area? Is it arc length? In a study I'm presenting as a short paper at the IEEE VIS conference in Vancouver next week, I tried to tease the visual cues apart – using modeling and 3D pie charts. The big […]

Communication has been quite a challenge during the COVID-19 pandemic, and data visualization hasn't been the most helpful given the low quality of the data – see Amanda Makulec's plea to think harder about making another coronavirus chart. A great example of how to do things right is the widely-circulated Flatten the Curve information graphic/cartoon. […]

In this paper, for a family of second-order parabolic equation with rapidly oscillating and time-dependent periodic coefficients, we are interested in an approximate two-sphere one-cylinder inequality for these solutions in parabolic periodic homogenization, which implies an approximate quantitative propagation of smallness. The proof relies on the asymptotic behavior of fundamental solutions and the Lagrange interpolation technique.

This paper considers the distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of local cost functions by using local information exchange. We first propose a distributed first-order primal-dual algorithm. We show that it converges sublinearly to the stationary point if each local cost function is smooth and linearly to the global optimum under an additional condition that the global cost function satisfies the Polyak-{L}ojasiewicz condition. This condition is weaker than strong convexity, which is a standard condition for proving the linear convergence of distributed optimization algorithms, and the global minimizer is not necessarily unique or finite. Motivated by the situations where the gradients are unavailable, we then propose a distributed zeroth-order algorithm, derived from the proposed distributed first-order algorithm by using a deterministic gradient estimator, and show that it has the same convergence properties as the proposed first-order algorithm under the same conditions. The theoretical results are illustrated by numerical simulations.

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.

The central aim of this paper is to study (regional) fractional Poincar'e type inequalities on unbounded domains satisfying the finite ball condition. Both existence and non existence type results are established depending on various conditions on domains and on the range of $s in (0,1)$. The best constant in both regional fractional and fractional Poincar'e inequality is characterized for strip like domains $(omega imes mathbb{R}^{n-1})$, and the results obtained in this direction are analogous to those of the local case. This settles one of the natural questions raised by K. Yeressian in [ extit{Asymptotic behavior of elliptic nonlocal equations set in cylinders, Asymptot. Anal. 89, (2014), no 1-2}].

We prove that, given a constant $K> 2$ and a bounded linear operator $T$ from a JB$^*$-triple $E$ into a complex Hilbert space $H$, there exists a norm-one functional $psiin E^*$ satisfying $$|T(x)| leq K , |T| , |x|_{psi},$$ for all $xin E$. Applying this result we show that, given $G > 8 (1+2sqrt{3})$ and a bounded bilinear form $V$ on the Cartesian product of two JB$^*$-triples $E$ and $B$, there exist norm-one functionals $varphiin E^{*}$ and $psiin B^{*}$ satisfying $$|V(x,y)| leq G |V| , |x|_{varphi} , |y|_{psi}$$ for all $(x,y)in E imes B$. These results prove a conjecture pursued during almost twenty years.

We study a class of fourth order nonlinear parabolic equations which include the thin-film equation and the quantum drift-diffusion model as special cases. We investigate these equations by first developing functional inequalities of the type $ int_Omega u^{2gamma-alpha-eta}Delta u^alphaDelta u^eta dx geq cint_Omega|Delta u^gamma |^2dx $, which seem to be of interest on their own right.

In this review paper, we survey Hardy type inequalities from the point of view of Folland and Stein's homogeneous groups. Particular attention is paid to Hardy type inequalities on stratified groups which give a special class of homogeneous groups. In this environment, the theory of Hardy type inequalities becomes intricately intertwined with the properties of sub-Laplacians and more general subelliptic partial differential equations. Particularly, we discuss the Badiale-Tarantello conjecture and a conjecture on the geometric Hardy inequality in a half-space of the Heisenberg group with a sharp constant.

In information theory, the so-called log-sum inequality is fundamental and a kind of generalization of the non-nagativity for the relative entropy. In this paper, we show the generalized log-sum inequality for two functions defined for scalars. We also give a new result for commutative matrices. In addition, we demonstrate further results for general non-commutative positive semi-definite matrices.

Temporal event segmentation of a long video into coherent events requires a high level understanding of activities' temporal features. The event segmentation problem has been tackled by researchers in an offline training scheme, either by providing full, or weak, supervision through manually annotated labels or by self-supervised epoch based training. In this work, we present a continual learning perceptual prediction framework (influenced by cognitive psychology) capable of temporal event segmentation through understanding of the underlying representation of objects within individual frames. Our framework also outputs attention maps which effectively localize and track events-causing objects in each frame. The model is tested on a wildlife monitoring dataset in a continual training manner resulting in $80\%$ recall rate at $20\%$ false positive rate for frame level segmentation. Activity level testing has yielded $80\%$ activity recall rate for one false activity detection every 50 minutes.

Position encoding (PE), an essential part of self-attention networks (SANs), is used to preserve the word order information for natural language processing tasks, generating fixed position indices for input sequences. However, in cross-lingual scenarios, e.g. machine translation, the PEs of source and target sentences are modeled independently. Due to word order divergences in different languages, modeling the cross-lingual positional relationships might help SANs tackle this problem. In this paper, we augment SANs with emph{cross-lingual position representations} to model the bilingually aware latent structure for the input sentence. Specifically, we utilize bracketing transduction grammar (BTG)-based reordering information to encourage SANs to learn bilingual diagonal alignments. Experimental results on WMT'14 English$Rightarrow$German, WAT'17 Japanese$Rightarrow$English, and WMT'17 Chinese$Leftrightarrow$English translation tasks demonstrate that our approach significantly and consistently improves translation quality over strong baselines. Extensive analyses confirm that the performance gains come from the cross-lingual information.

Many efforts of research are devoted to semantic role labeling (SRL) which is crucial for natural language understanding. Supervised approaches have achieved impressing performances when large-scale corpora are available for resource-rich languages such as English. While for the low-resource languages with no annotated SRL dataset, it is still challenging to obtain competitive performances. Cross-lingual SRL is one promising way to address the problem, which has achieved great advances with the help of model transferring and annotation projection. In this paper, we propose a novel alternative based on corpus translation, constructing high-quality training datasets for the target languages from the source gold-standard SRL annotations. Experimental results on Universal Proposition Bank show that the translation-based method is highly effective, and the automatic pseudo datasets can improve the target-language SRL performances significantly.

Human visual attention is a complex phenomenon. A computational modeling of this phenomenon must take into account where people look in order to evaluate which are the salient locations (spatial distribution of the fixations), when they look in those locations to understand the temporal development of the exploration (temporal order of the fixations), and how they move from one location to another with respect to the dynamics of the scene and the mechanics of the eyes (dynamics). State-of-the-art models focus on learning saliency maps from human data, a process that only takes into account the spatial component of the phenomenon and ignore its temporal and dynamical counterparts. In this work we focus on the evaluation methodology of models of human visual attention. We underline the limits of the current metrics for saliency prediction and scanpath similarity, and we introduce a statistical measure for the evaluation of the dynamics of the simulated eye movements. While deep learning models achieve astonishing performance in saliency prediction, our analysis shows their limitations in capturing the dynamics of the process. We find that unsupervised gravitational models, despite of their simplicity, outperform all competitors. Finally, exploiting a crowd-sourcing platform, we present a study aimed at evaluating how strongly the scanpaths generated with the unsupervised gravitational models appear plausible to naive and expert human observers.

This paper addresses the problem of efficiently achieving visual predictive control tasks. To this end, a memory of motion, containing a set of trajectories built off-line, is used for leveraging precomputation and dealing with difficult visual tasks. Standard regression techniques, such as k-nearest neighbors and Gaussian process regression, are used to query the memory and provide on-line a warm-start and a way point to the control optimization process. The proposed technique allows the control scheme to achieve high performance and, at the same time, keep the computational time limited. Simulation and experimental results, carried out with a 7-axis manipulator, show the effectiveness of the approach.

We present a sound and complete method for the verification of qualitative liveness properties of replicated systems under stochastic scheduling. These are systems consisting of a finite-state program, executed by an unknown number of indistinguishable agents, where the next agent to make a move is determined by the result of a random experiment. We show that if a property of such a system holds, then there is always a witness in the shape of a Presburger stage graph: a finite graph whose nodes are Presburger-definable sets of configurations. Due to the high complexity of the verification problem (non-elementary), we introduce an incomplete procedure for the construction of Presburger stage graphs, and implement it on top of an SMT solver. The procedure makes extensive use of the theory of well-quasi-orders, and of the structural theory of Petri nets and vector addition systems. We apply our results to a set of benchmarks, in particular to a large collection of population protocols, a model of distributed computation extensively studied by the distributed computing community.

Sentence level quality estimation (QE) for machine translation (MT) attempts to predict the translation edit rate (TER) cost of post-editing work required to correct MT output. We describe our view on sentence-level QE as dictated by several practical setups encountered in the industry. We find consumers of MT output---whether human or algorithmic ones---to be primarily interested in a binary quality metric: is the translated sentence adequate as-is or does it need post-editing? Motivated by this we propose a quality classification (QC) view on sentence-level QE whereby we focus on maximizing recall at precision above a given threshold. We demonstrate that, while classical QE regression models fare poorly on this task, they can be re-purposed by replacing the output regression layer with a binary classification one, achieving 50-60\% recall at 90\% precision. For a high-quality MT system producing 75-80\% correct translations, this promises a significant reduction in post-editing work indeed.

During VR demos we have performed over last few years, many participants (in the absence of any haptic feedback) have commented on their perceived ability to 'feel' differences between simulated molecular objects. The mechanisms for such 'feeling' are not entirely clear: observing from outside VR, one can see that there is nothing physical for participants to 'feel'. Here we outline exploratory user studies designed to evaluate the extent to which participants can distinguish quantitative differences in the flexibility of VR-simulated molecular objects. The results suggest that an individual's capacity to detect differences in molecular flexibility is enhanced when they can interact with and manipulate the molecules, as opposed to merely observing the same interaction. Building on these results, we intend to carry out further studies investigating humans' ability to sense quantitative properties of VR simulations without haptic technology.

Visual localization is a useful alternative to standard localization techniques. It works by utilizing cameras. In a typical scenario, features are extracted from captured images and compared with geo-referenced databases. Location information is then inferred from the matching results. Conventional schemes mainly use low-level visual features. These approaches offer good accuracy but suffer from scalability issues. In order to assist localization in large urban areas, this work explores a different path by utilizing high-level semantic information. It is found that object information in a street view can facilitate localization. A novel descriptor scheme called "semantic signature" is proposed to summarize this information. A semantic signature consists of type and angle information of visible objects at a spatial location. Several metrics and protocols are proposed for signature comparison and retrieval. They illustrate different trade-offs between accuracy and complexity. Extensive simulation results confirm the potential of the proposed scheme in large-scale applications. This paper is an extended version of a conference paper in CBMI'18. A more efficient retrieval protocol is presented with additional experiment results.

Visualization-oriented natural language interfaces (V-NLIs) have been explored and developed in recent years. One challenge faced by V-NLIs is in the formation of effective design decisions that usually requires a deep understanding of user queries. Learning-based approaches have shown potential in V-NLIs and reached state-of-the-art performance in various NLP tasks. However, because of the lack of sufficient training samples that cater to visual data analytics, cutting-edge techniques have rarely been employed to facilitate the development of V-NLIs. We present a new dataset, called Quda, to help V-NLIs understand free-form natural language. Our dataset contains 14;035 diverse user queries annotated with 10 low-level analytic tasks that assist in the deployment of state-of-the-art techniques for parsing complex human language. We achieve this goal by first gathering seed queries with data analysts who are target users of V-NLIs. Then we employ extensive crowd force for paraphrase generation and validation. We demonstrate the usefulness of Quda in building V-NLIs by creating a prototype that makes effective design decisions for free-form user queries. We also show that Quda can be beneficial for a wide range of applications in the visualization community by analyzing the design tasks described in academic publications.

Selecting an appropriate model to forecast product demand is critical to the manufacturing industry. However, due to the data complexity, market uncertainty and users' demanding requirements for the model, it is challenging for demand analysts to select a proper model. Although existing model selection methods can reduce the manual burden to some extent, they often fail to present model performance details on individual products and reveal the potential risk of the selected model. This paper presents DFSeer, an interactive visualization system to conduct reliable model selection for demand forecasting based on the products with similar historical demand. It supports model comparison and selection with different levels of details. Besides, it shows the difference in model performance on similar products to reveal the risk of model selection and increase users' confidence in choosing a forecasting model. Two case studies and interviews with domain experts demonstrate the effectiveness and usability of DFSeer.

Public key encryption with equality test (PKEET) allows testing whether two ciphertexts are generated by the same message or not. PKEET is a potential candidate for many practical applications like efficient data management on encrypted databases. Potential applicability of PKEET leads to intensive research from its first instantiation by Yang et al. (CT-RSA 2010). Most of the followup constructions are secure in the random oracle model. Moreover, the security of all the concrete constructions is based on number-theoretic hardness assumptions which are vulnerable in the post-quantum era. Recently, Lee et al. (ePrint 2016) proposed a generic construction of PKEET schemes in the standard model and hence it is possible to yield the first instantiation of PKEET schemes based on lattices. Their method is to use a $2$-level hierarchical identity-based encryption (HIBE) scheme together with a one-time signature scheme. In this paper, we propose, for the first time, a direct construction of a PKEET scheme based on the hardness assumption of lattices in the standard model. More specifically, the security of the proposed scheme is reduces to the hardness of the Learning With Errors problem.

Unsupervised machine translation (MT) has recently achieved impressive results with monolingual corpora only. However, it is still challenging to associate source-target sentences in the latent space. As people speak different languages biologically share similar visual systems, the potential of achieving better alignment through visual content is promising yet under-explored in unsupervised multimodal MT (MMT). In this paper, we investigate how to utilize visual content for disambiguation and promoting latent space alignment in unsupervised MMT. Our model employs multimodal back-translation and features pseudo visual pivoting in which we learn a shared multilingual visual-semantic embedding space and incorporate visually-pivoted captioning as additional weak supervision. The experimental results on the widely used Multi30K dataset show that the proposed model significantly improves over the state-of-the-art methods and generalizes well when the images are not available at the testing time.

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.

You shouldn't find yourself rewatching some sitcom for the thousandth time or sitting through a vacuous Hollywood blockbuster just because you're stoned and stuck inside during the age of social distancing.…

Another First Friday is here, and you can correctly assume that it will be all online once again. The May 1 Virtual First Friday is from 10 am to midnight.…

By Erica L. Green The New York Times Company…

While events around the country have been put on hold, Coeur d’Alene’s Summer Theatre is still slated for a show at the end of the month. But it won’t be in the usual format.…

Two beloved Spokane-based entertainers are teaming up this weekend for a good cause.…

The Cannabis Issue In a normal year, cannabis stores would be cashing in this April.…

A method for performing relational analysis of parsed input is employed to create a visual map of knowledge information. A title, header or subject line for an input item of information is parsed into syntactical components of at least a subject component and any predicate component(s) relationally linked as topic and subtopics. A search of topics and subtopics is carried out for each parsed component. If a match is found, then the parsed component is taken as a chosen topic/subtopic label. If no match is found, then the parsed component is formatted as a new entry in the knowledge map. A translation function for translating topics and subtopics from an original language into one or more target languages is enabled by user request or indicated user preference for display on a generated visual map of knowledge information.

An approach is provided for contextual content suggestion. A recommendation platform processes and/or facilitates a processing of contextual information associated with at least one device to determine one or more locations, one or more contextual parameter values, or a combination thereof. The recommendation platform also determines popularity data associated with one or more content items with respect to the one or more locations, the one or more contextual parameter values, or a combination. The popularity data is determined from one or more other devices sharing at least substantially the one or more locations, the one or more contextual parameter values, or a combination thereof. The recommendation platform then causes, at least in part, a recommendation of the one or more content items to the at least one device based, at least in part, on the popularity information.

Bioassays for detecting the ability of one sample of a food substance, nutritional supplement, therapeutic agent and/or disease preventive agent relative to that of a second sample of such a substance, supplement and/or agent to inhibit, upregulate or otherwise modulate translation initiation, and thereby demonstrate a disease curative and/or preventive effect in a human and/or animal that consumes a such substance, supplement and/or agent or to whom a such substance, supplement and/or agent is administered are provided.

Disclosed is a method for producing p-xylene and/or p-tolualdehyde with high yield through a short process using biomass resource-derived substances as raw materials. The method for producing p-xylene and/or p-tolualdehyde of the present invention comprises: a cyclization step of producing 4-methyl-3-cyclohexenecarboxaldehyde from isoprene and acrolein; and an aromatization step of producing p-xylene and/or p-tolualdehyde from 4-methyl-3-cyclohexenecarboxaldehyde by gas-phase flow reaction using a catalyst(s).

A process according to various approaches includes flushing an intermediate transfer line between a raffinate stream transfer line and a desorbent stream transfer line away from the adsorptive separation chamber to remove residual fluid including desorbent from intermediate transfer line. The process may include directing the residual fluid flushed from the intermediate transfer line to a recycle stream to introduce the residual fluid into the adsorptive separation chamber.

The generation of individual-specific information having a good reliability and uniqueness is made possible with a little circuit scale. For this purpose, in an individual-specific information generation apparatus, a plurality of digital circuits are in the same circuit configuration. Each of the digital circuits outputs a fixed or a random number output value individually without their output with respect to a certain input being determined unambiguously among the digital circuits. In each of the digital circuit, an order is defined in advance. A random number judgment unit judges whether the output value is a random value or fixed, for each of the plurality of digital circuits. An individual-specific information generation unit generates the individual-specific information based on information of the order defined in the digital circuit judged by the random number judgment unit as having a fixed output value among the plurality of digital circuits and the output value.

A durable ambient light curable waterproof liquid rubber coating with volatile organic compound (VOC) content of less than 450 grams per liter made from ethylene propylene diene terpolymer (EPDM) in a solvent, a photoinitiator, an additive, pigments, and fillers, and a co-agent and a method for making the formulation, wherein the formulation is devoid of thermally activated accelerators.

A virtual machine (VM) migration from a source virtual machine monitor (VMM) to a destination VMM on a computer system. Each of the VMMs includes virtualization software, and one or more VMs are executed in each of the VMMs. The virtualization software allocates hardware resources in a form of virtual resources for the concurrent execution of one or more VMs and the virtualization software. A portion of a memory of the hardware resources includes hardware memory segments. A first portion of the memory segments is assigned to a source logical partition and a second portion is assigned to a destination logical partition. The source VMM operates in the source logical partition and the destination VMM operates in the destination logical partition. The first portion of the memory segments is mapped into a source VMM memory, and the second portion of the memory segments is mapped into a destination VMM memory.

In one exemplary embodiment, a computer-implemented method includes receiving, at a remote direct memory access (RDMA) device, a plurality of RDMA requests referencing a plurality of virtual pages. Data transfers are scheduled for the plurality of virtual pages, wherein the scheduling occurs at the RDMA device. The number of the virtual pages that are currently pinned is limited for the RDMA requests based on a predetermined pinned page limit.

An electronic data tablet has a controller and transition manager. The controller is to store in a memory of the tablet virtual configuration space information for a peripheral device of a computer, and the transition manager is to control the controller to operate in a first mode and a second mode. The virtual configuration space information is stored in the tablet memory when the first mode is to be switched to the second mode. When the second mode is switched to the first mode, the virtual configuration space information is accessed to control recognition of the peripheral device of the computer without performing a re-scanning operation.

A method for sharing peripheral devices in dual operating systems for an electronic device having at least one peripheral device is provided. The method includes: receiving a setting value for the peripheral device under the first operating system from a user; activating a second operating system; transmitting the setting value to the second operating system; and switching from the first operating system to the second operating system, wherein the second operating system sets the peripheral device with the setting value and enables the electronic device to operate under the second operating system.

A cloud system may create physical resource tags to store relationships between cloud computing offerings, such as computing service offerings, storage offerings, and network offerings, and the specific physical resources in the cloud computing environment. Cloud computing offerings may be presented to cloud customers, the offerings corresponding to various combinations of computing services, storage, networking, and other hardware or software resources. After a customer selects one or more cloud computing offerings, a cloud resource manager or other component within the cloud infrastructure may retrieve a set of tags and determine a set of physical hardware resources associated with the selected offerings. The physical hardware resources associated with the selected offerings may be subsequently used to provision and create the new virtual machine and its operating environment.

Systems, methods and computer program products may provide managing utilization of one or more physical processors in a shared processor pool. A method of managing utilization of one or more physical processors in a shared processor pool may include determining a current amount of utilization of the one or more physical processors and generating an instruction message. The instruction message may be at least partially determined by the current amount of utilization. The method may further include sending the instruction message to a guest operating system, the guest operating system having a number of enabled virtual processors.

A system, method and computer program product for allocating shared resources. Upon receiving requests for resources, the cost of bundling software in a virtual machine (VM) image is automatically generated. Software is selected by the cost for each bundle according to the time required to install it where required, offset by the time to uninstall it where not required. A number of VM images having the highest software bundle value (i.e., highest cost bundled) is selected and stored, e.g., in a machine image store. With subsequent requests for resources, VMs may be instantiated from one or more stored VM images and, further, stored images may be updated selectively updated with new images.

A server computer system determines that configuring a first virtual machine in a cloud depends on a configuration result of configuring a second virtual machine. The server computer system configures the second virtual machine in the cloud and configures the first virtual machine in the cloud using the configuration result of the second virtual machine.