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.

Representation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but these models are targeted towards token- and sentence-level training objectives and do not leverage information on inter-document relatedness, which limits their document-level representation power. For applications on scientific documents, such as classification and recommendation, the embeddings power strong performance on end tasks. We propose SPECTER, a new method to generate document-level embedding of scientific documents based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, SPECTER can be easily applied to downstream applications without task-specific fine-tuning. Additionally, to encourage further research on document-level models, we introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation. We show that SPECTER outperforms a variety of competitive baselines on the benchmark.

The global health threat from COVID-19 has been controlled in a number of instances by large-scale testing and contact tracing efforts. We created this document to suggest three functionalities on how we might best harness computing technologies to supporting the goals of public health organizations in minimizing morbidity and mortality associated with the spread of COVID-19, while protecting the civil liberties of individuals. In particular, this work advocates for a third-party free approach to assisted mobile contact tracing, because such an approach mitigates the security and privacy risks of requiring a trusted third party. We also explicitly consider the inferential risks involved in any contract tracing system, where any alert to a user could itself give rise to de-anonymizing information.

More generally, we hope to participate in bringing together colleagues in industry, academia, and civil society to discuss and converge on ideas around a critical issue rising with attempts to mitigate the COVID-19 pandemic.

Recent single image unsupervised representation learning techniques show remarkable success on a variety of tasks. The basic principle in these works is instance discrimination: learning to differentiate between two augmented versions of the same image and a large batch of unrelated images. Networks learn to ignore the augmentation noise and extract semantically meaningful representations. Prior work uses artificial data augmentation techniques such as cropping, and color jitter which can only affect the image in superficial ways and are not aligned with how objects actually change e.g. occlusion, deformation, viewpoint change. In this paper, we argue that videos offer this natural augmentation for free. Videos can provide entirely new views of objects, show deformation, and even connect semantically similar but visually distinct concepts. We propose Video Noise Contrastive Estimation, a method for using unlabeled video to learn strong, transferable single image representations. We demonstrate improvements over recent unsupervised single image techniques, as well as over fully supervised ImageNet pretraining, across a variety of temporal and non-temporal tasks. Code and the Random Related Video Views dataset are available at https://www.github.com/danielgordon10/vince

Lake ice, as part of the Essential Climate Variable (ECV) lakes, is an important indicator to monitor climate change and global warming. The spatio-temporal extent of lake ice cover, along with the timings of key phenological events such as freeze-up and break-up, provide important cues about the local and global climate. We present a lake ice monitoring system based on the automatic analysis of Sentinel-1 Synthetic Aperture Radar (SAR) data with a deep neural network. In previous studies that used optical satellite imagery for lake ice monitoring, frequent cloud cover was a main limiting factor, which we overcome thanks to the ability of microwave sensors to penetrate clouds and observe the lakes regardless of the weather and illumination conditions. We cast ice detection as a two class (frozen, non-frozen) semantic segmentation problem and solve it using a state-of-the-art deep convolutional network (CNN). We report results on two winters ( 2016 - 17 and 2017 - 18 ) and three alpine lakes in Switzerland. The proposed model reaches mean Intersection-over-Union (mIoU) scores >90% on average, and >84% even for the most difficult lake. Additionally, we perform cross-validation tests and show that our algorithm generalises well across unseen lakes and winters.

High-resolution remote sensing images (HRRSIs) contain substantial ground object information, such as texture, shape, and spatial location. Semantic segmentation, which is an important task for element extraction, has been widely used in processing mass HRRSIs. However, HRRSIs often exhibit large intraclass variance and small interclass variance due to the diversity and complexity of ground objects, thereby bringing great challenges to a semantic segmentation task. In this paper, we propose a new end-to-end semantic segmentation network, which integrates lightweight spatial and channel attention modules that can refine features adaptively. We compare our method with several classic methods on the ISPRS Vaihingen and Potsdam datasets. Experimental results show that our method can achieve better semantic segmentation results. The source codes are available at https://github.com/lehaifeng/SCAttNet.

In recent years there has been a burgeoning interest in the use of computational methods to distinguish between elicited speech samples produced by patients with dementia, and those from healthy controls. The difference between perplexity estimates from two neural language models (LMs) - one trained on transcripts of speech produced by healthy participants and the other trained on transcripts from patients with dementia - as a single feature for diagnostic classification of unseen transcripts has been shown to produce state-of-the-art performance. However, little is known about why this approach is effective, and on account of the lack of case/control matching in the most widely-used evaluation set of transcripts (DementiaBank), it is unclear if these approaches are truly diagnostic, or are sensitive to other variables. In this paper, we interrogate neural LMs trained on participants with and without dementia using synthetic narratives previously developed to simulate progressive semantic dementia by manipulating lexical frequency. We find that perplexity of neural LMs is strongly and differentially associated with lexical frequency, and that a mixture model resulting from interpolating control and dementia LMs improves upon the current state-of-the-art for models trained on transcript text exclusively.

Multimodal Sentiment Analysis is an active area of research that leverages multimodal signals for affective understanding of user-generated videos. The predominant approach, addressing this task, has been to develop sophisticated fusion techniques. However, the heterogeneous nature of the signals creates distributional modality gaps that pose significant challenges. In this paper, we aim to learn effective modality representations to aid the process of fusion. We propose a novel framework, MISA, which projects each modality to two distinct subspaces. The first subspace is modality invariant, where the representations across modalities learn their commonalities and reduce the modality gap. The second subspace is modality-specific, which is private to each modality and captures their characteristic features. These representations provide a holistic view of the multimodal data, which is used for fusion that leads to task predictions. Our experiments on popular sentiment analysis benchmarks, MOSI and MOSEI, demonstrate significant gains over state-of-the-art models. We also consider the task of Multimodal Humor Detection and experiment on the recently proposed UR_FUNNY dataset. Here too, our model fares better than strong baselines, establishing MISA as a useful multimodal framework.

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.

Image-based tracking of medical instruments is an integral part of many surgical data science applications. Previous research has addressed the tasks of detecting, segmenting and tracking medical instruments based on laparoscopic video data. However, the methods proposed still tend to fail when applied to challenging images and do not generalize well to data they have not been trained on. This paper introduces the Heidelberg Colorectal (HeiCo) data set - the first publicly available data set enabling comprehensive benchmarking of medical instrument detection and segmentation algorithms with a specific emphasis on robustness and generalization capabilities of the methods. Our data set comprises 30 laparoscopic videos and corresponding sensor data from medical devices in the operating room for three different types of laparoscopic surgery. Annotations include surgical phase labels for all frames in the videos as well as instance-wise segmentation masks for surgical instruments in more than 10,000 individual frames. The data has successfully been used to organize international competitions in the scope of the Endoscopic Vision Challenges (EndoVis) 2017 and 2019.

Unsupervised learning of hidden representations has been one of the most vibrant research directions in machine learning in recent years. In this work we study the brain-like Bayesian Confidence Propagating Neural Network (BCPNN) model, recently extended to extract sparse distributed high-dimensional representations. The saliency and separability of the hidden representations when trained on MNIST dataset is studied using an external classifier, and compared with other unsupervised learning methods that include restricted Boltzmann machines and autoencoders.

Wireless sensor networks have attracted much attention because of many applications in the fields of industry, military, medicine, agriculture, and education. In addition, the vast majority of researches has been done to expand its applications and improve its efficiency. However, there are still many challenges for increasing the efficiency in different parts of this network. One of the most important parts is to improve the network lifetime in the wireless sensor network. Since the sensor nodes are generally powered by batteries, the most important issue to consider in these types of networks is to reduce the power consumption of the nodes in such a way as to increase the network lifetime to an acceptable level. The contribution of this paper is using topology control, the threshold for the remaining energy in nodes, and two of the meta-algorithms include SA (Simulated annealing) and VNS (Variable Neighbourhood Search) to increase the energy remaining in the sensors. Moreover, using a low-cost spanning tree, an appropriate connectivity control among nodes is created in the network in order to increase the network lifetime. The results of simulations show that the proposed method improves the sensor lifetime and reduces the energy consumed.

XSS is a security vulnerability that permits injecting malicious code into the client side of a web application. In the simplest situations, XSS vulnerabilities arise when a web application includes the user input in the web output without due sanitization. Such simple XSS vulnerabilities can be detected fairly reliably with blackbox scanners, which inject malicious payload into sensitive parts of HTTP requests and look for the reflected values in the web output.

Contemporary blackbox scanners are not effective against stored XSS vulnerabilities, where the malicious payload in an HTTP response originates from the database storage of the web application, rather than from the associated HTTP request. Similarly, many blackbox scanners do not systematically handle context-sensitive XSS vulnerabilities, where the user input is included in the web output after a transformation that prevents the scanner from recognizing the original value, but does not sanitize the value sufficiently. Among the combination of two basic data sources (stored vs reflected) and two basic vulnerability patterns (context sensitive vs not so), only one is therefore tested systematically by state-of-the-art blackbox scanners.

Our work focuses on systematic coverage of the three remaining combinations. We present a graybox mechanism that extends a general purpose database to cooperate with our XSS scanner, reporting and injecting the test inputs at the boundary between the database and the web application. Furthermore, we design a mechanism for identifying the injected inputs in the web output even after encoding by the web application, and check whether the encoding sanitizes the injected inputs correctly in the respective browser context. We evaluate our approach on eight mature and technologically diverse web applications, discovering previously unknown and exploitable XSS flaws in each of those applications.

Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of affected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed, and could largely reduce the efforts of clinicians and accelerate the diagnosis process. Chest computed tomography (CT) has been recognized as an informative tool for diagnosis of the disease. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images. To fully explore multiple features describing CT images from different views, a unified latent representation is learned which can completely encode information from different aspects of features and is endowed with promising class structure for separability. Specifically, the completeness is guaranteed with a group of backward neural networks (each for one type of features), while by using class labels the representation is enforced to be compact within COVID-19/community-acquired pneumonia (CAP) and also a large margin is guaranteed between different types of pneumonia. In this way, our model can well avoid overfitting compared to the case of directly projecting highdimensional features into classes. Extensive experimental results show that the proposed method outperforms all comparison methods, and rather stable performances are observed when varying the numbers of training data.

We study the problem of jointly designing a sparse sensor and actuator schedule for linear dynamical systems while guaranteeing a control/estimation performance that approximates the fully sensed/actuated setting. We further prove a separation principle, showing that the problem can be decomposed into finding sensor and actuator schedules separately. However, it is shown that this problem cannot be efficiently solved or approximated in polynomial, or even quasi-polynomial time for time-invariant sensor/actuator schedules; instead, we develop deterministic polynomial-time algorithms for a time-varying sensor/actuator schedule with guaranteed approximation bounds. Our main result is to provide a polynomial-time joint actuator and sensor schedule that on average selects only a constant number of sensors and actuators at each time step, irrespective of the dimension of the system. The key idea is to sparsify the controllability and observability Gramians while providing approximation guarantees for Hankel singular values. This idea is inspired by recent results in theoretical computer science literature on sparsification.

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.

With all due respect to Greta Thunberg and Billie Eilish, nobody had a better 2019 than Baby Yoda. The real star of the Disney+ flagship Star Wars series The Mandalorian, the little green puppeteering/CGI marvel (aka "the Child") might be the most adorable creature ever created.…

ON INLANDER.COM NATION: As meatpacking plants nationwide shutdown due to COVID-19 outbreaks, certain meat products are becoming harder to find at grocery stores and fast-food drive-thrus.…

An apparatus for recognizing a representative user behavior includes a unit-data extracting unit configured to extract at least one unit data from sensor data, a feature-information extracting unit configured to extract feature information from each of the at least one unit data, a unit-behavior recognizing unit configured to recognize a respective unit behavior for each of the at least one unit data based on the feature information, and a representative-behavior recognizing unit configured to recognize at least one representative behavior based on the respective unit behavior recognized for each of the at least one unit data.

Disclosed is a method for preparing optically pure (+)-ambrisentan and (+)-darusentan, comprising: firstly catalyzing the asymmetric epoxidation of a β-unsaturated alkene using a chiral ketone derived from fructose or a hydrate thereof as a catalyst, and then subjecting the product to an epoxy compound ring-opening reaction and substitution reaction successively to obtain optically pure (+)-ambrisentan and (+)-darusentan.

A process is presented for the increasing the yields of aromatics from reforming a hydrocarbon feedstream. The process includes splitting a naphtha feedstream into a light hydrocarbon stream, and a heavier stream having a relatively rich concentration of naphthenes. The heavy stream is reformed to convert the naphthenes to aromatics and the resulting product stream is further reformed with the light hydrocarbon stream to increase the aromatics yields. The catalyst is passed through the reactors in a sequential manner.

This invention relates to the oligomerization of olefinic compounds in the presence of an activated oligomerization catalyst. The invention also extends to a particular manner for providing an activated oligomerization catalyst. According to the present invention, there is provided a process for producing an oligomeric product by the oligomerization of at least one olefinic compound, the process including (a) providing an activated oligomerization catalyst by combining, in any order, iii) a source of chromium, ιv) a ligating compound of the formula (R1)mX1(Y)X2(R2)n wherein X1 and X2 are independently an atom selected from the group consisting of nitrogen, phosphorus, arsenic, antimony, bismuth, oxygen, sulphur and selenium or said atom oxidized by S, Se, N or O where the valence of X1 and/or X2 allows for such oxidation, Y is a linking group between X1 and X2 which linking group contains at least one nitrogen atom which is directly bonded to X1 or X2, m and n are independently 0, 1 or a larger integer, and R1 and R2 are independently hydrogen, a hydrocarbyl group, an organoheteryl group or a heterohydrocarbyl group, and the respective R1 groups are the same or different when m>1, and the respective R2 groups are the same or different when n>1, in) a catalyst activator which is an organoboron compound including a cation and a non-coordinating anion of the general formula [(R10)xL*-H]+[B(R20)4]− wherein L* is an atom selected from the group consisting of N, S and P, the cation [(R10)x L*-H]* is a Bronsted acid, x is an integer 1, 2 or 3, each R10 is the same or different when x is 2 or 3 and each is a —H, hydrocarbyl group or a heterohydrocarbyl group, provided that at least one of R10 comprises at least 6 carbon atoms and provided further that the total number of carbon atoms in (R10)x collectively is greater than 12, R20 independently at each occurrence is selected from the group consisting of hydride, dialkylamido, halide, alkoxide, aryloxide, hydrocarbyl, halosubstituted-hydrocarbyl radicals, halosubstituted-alkoxide, halosubstituted-aryloxide and a halosubstituted aromatic ring moiety with at least one halide substituent on the aromatic ring, and vi) an aliphatic solvent, and (b) contacting the at least one olefinic compound with the activated oligomerization catalyst to produce an oligomeric product.

Temperatures in microelectronic integrated circuit packages and components may be measured in situ using carbon nanotube networks. An array of carbon nanotubes strung between upstanding structures may be used to measure local temperature. Because of the carbon nanotubes, a highly accurate temperature measurement may be achieved. In some cases, the carbon nanotubes and the upstanding structures may be secured to a substrate that is subsequently attached to a microelectronic package.

A sensor for biomolecules includes a silicon fin comprising undoped silicon; a source region adjacent to the silicon fin, the source region comprising heavily doped silicon; a drain region adjacent to the silicon fin, the drain region comprising heavily doped silicon of a doping type that is the same doping type as that of the source region; and a layer of a gate dielectric covering an exterior portion of the silicon fin between the source region and the drain region, the gate dielectric comprising a plurality of antibodies, the plurality of antibodies configured to bind with the biomolecules, such that a drain current flowing between the source region and the drain region varies when the biomolecules bind with the antibodies.

Disclosed are a biomolecular sensor and a method of fabricating the same having high sensitivity and resolution by using a plurality of metal plates that change electrical properties of a plurality of nanostructures according to the attachment of biomolecules. The biomolecular sensor includes a substrate, first and second electrodes disposed to be spaced apart from each other on the substrate, a plurality of nanostructures disposed on the substrate to connect the first and second electrodes to each other, and a plurality of metal plates that change electrical properties of the plurality of nanostructures according to the attachment of biomolecules.

A wireless communication system including a sender apparatus having a plurality of transmitting antennas that performs MIMO transmission of a plurality of data blocks; and a receiver apparatus that receives the plurality of data blocks. The sender apparatus transmits a process number via a control channel different from a data channel to the receiver apparatus, and wherein when the MIMO diversity transmission is performed, the receiver apparatus performs HARQ processing in the received data blocks based on not a process number which prevents the data blocks from competing but the received process number from the sender apparatus.

A method for sequestrating arsenic oxides, comprising forming an insoluble and stable glass incorporating a fully oxidized form of arsenic generated by oxidation of an initial lower oxide of arsenic and stabilization by calcium salt formation. The glass composition for sequestration of arsenic comprises from 50 to 75% silica; from 0.5 to 3% Al2O3; from 1 to 15% MnO; from 5 to 15% CaO; from 1 to 20% As2O5 and from 8 to 14% Na2O, less than four percent of iron oxides, magnesium oxide and other oxides.

Provided is a photosensitive composition which can be cured with low energy consumption, even when a substance (such as a colorant) that attenuates or shades an illumination light is contained in a high concentration or even when the photosensitive composition is in the form of a thick film. Specifically provided is a photosensitive composition which comprises the following four components: (1) a radical initiator (A); (2) an acid generator (B) or a base generator (C); (3) a polymerizable substance (D); and (4) a colorant (E), a metal oxide powder (F), or a metal powder (G). Further, the photosensitive composition is characterized in that the radical initiator (A), the acid generator (B), and/or the base generator (C) generates an active species (H) through irradiation with an active ray of light; the active species (H) reacts the radical initiator (A), the acid generator (B), or the base generator (C) to form another species (I); and thus the polymerization of the polymerizable substance (D) by means of the active species (I) proceeds, said active species (H) or (I) being an acid or a base.

The present disclosure provides a method of providing an adhesive composition comprising the steps of combining crosslinkable composition including: a) a (meth)acryloyl monomer mixture with the b) photocrosslinking agent mixture, and irradiating with UVC radiation to polymerize and crosslink the composition.

Systems, mediums, and methods are provided for scheduling input/output requests to a storage system. The input output requests may be received, categorized based on their priority, and scheduled for retrieval from the storage system. Lower priority requests may be divided into smaller sub-requests, and the sub-requests may be scheduled for retrieval only when there are no pending higher priority requests, and/or when higher priority requests are not predicted to arrive for a certain period of time. By servicing the small sub-requests rather than the entire lower priority request, the retrieval of the lower priority request may be paused in the event that a high priority request arrives while the lower priority request is being serviced.

According to an embodiment, based on task border information, and first-type dependency relationship information containing N number of nodes corresponding to data accesses to one set of data, containing edges representing dependency relationship between the nodes, and having at least one node with an access reliability flag indicating reliability/unreliability of corresponding data access; task border edges, of edges extending over task borders, are identified that have an unreliable access node linked to at least one end, and presentation information containing unreliable access nodes is generated. According to dependency existence information input corresponding to the set of data, conversion information indicating absence of data access to the unreliable access nodes is output. According to the conversion information, the first-type dependency relationship information is converted into second-type dependency relationship information containing M number of nodes (0≦M≦N) corresponding to data accesses to the set of data and containing edges representing inter-node dependency relationship.

A system and method is disclosed for communicating with sensors/loggers in integrated radio frequency identification (RFID) tags. An RFID reader uses a Communicate With Data Logger Command to communicate with a data logger in an RFID tag. The RFID reader performs data access processes using an Index Register and a Data Register of the RFID tag. The RFID reader selects one of (1) Index Read access (2) Index Write access (3) Data Write access (4) Data Read access with parity and (5) Data Read access with cyclic redundancy check (CRC). The RFID tag performs the requested data access and then performs an error detection process.

A film formed from a water-soluble polymer matrix within which is contained at least one fragrance is provided. The film is water-sensitive (e.g., water-soluble, water-dispersible, etc.) so that upon contact with a sufficient amount of water, the polymer matrix loses its integrity over time to increasingly expose the fragrance to the ambient environment for releasing its odor. The ability to incorporate a fragrance into the polymer matrix is achieved in the present invention by controlling a variety of aspects of the film construction, including the nature of the fragrance, the nature of the water-soluble polymer, the manner in which the polymer matrix and fragrance are melt processed, etc. For example, the fragrance may be injected directly into the extruder and melt blended with the water-soluble polymer. In this manner, the costly and time-consuming steps of pre-encapsulation or pre-compounding of the fragrance into a masterbatch are not required. Furthermore, to obtain a balance between the ability of the fragrance to release the desired odor during use and likewise to minimize the premature exhaustion of the odor during melt processing, the fragrance is selected to have a boiling point (at atmospheric pressure) within a certain range, such as from about 125° C. to about 350° C.

Various methods, apparatuses and/or articles of manufacture are provided which may be implemented to estimate a trajectory of a mobile device within an indoor environment. In some embodiments, the trajectory may be estimated without the use of any signal-based positioning information. For example, a mobile device may estimate such a trajectory based, at least in part, on one or more sensor measurements obtained at the mobile device, and further affect the estimated trajectory based, at least in part, on one or more objects identified in an electronic map of the indoor environment.

The present invention provides personal care compositions comprising a carrier and a mixture of essential oil components having specific levels of eucalyptol, terpene materials and auxiliary fragrance materials. The compositions herein gentle to skin and have a fragrance and activity similar if the composition were made using the pure extracted essential oil.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for computing numeric representations of words. One of the methods includes obtaining a set of training data, wherein the set of training data comprises sequences of words; training a classifier and an embedding function on the set of training data, wherein training the embedding function comprises obtained trained values of the embedding function parameters; processing each word in the vocabulary using the embedding function in accordance with the trained values of the embedding function parameters to generate a respective numerical representation of each word in the vocabulary in the high-dimensional space; and associating each word in the vocabulary with the respective numeric representation of the word in the high-dimensional space.

An apparatus for decoding data segments representing a time-domain data stream, a data segment being encoded in the time domain or in the frequency domain, a data segment being encoded in the frequency domain having successive blocks of data representing successive and overlapping blocks of time-domain data samples. The apparatus includes a time-domain decoder for decoding a data segment being encoded in the time domain and a processor for processing the data segment being encoded in the frequency domain and output data of the time-domain decoder to obtain overlapping time-domain data blocks. The apparatus further includes an overlap/add-combiner for combining the overlapping time-domain data blocks to obtain a decoded data segment of the time-domain data stream.

A time warp contour calculator for use in an audio signal decoder receives an encoded warp ratio information, derives a sequence of warp ratio values from the encoded warp ratio information, and obtains warp contour node values starting from a time warp contour start value. Ratios between the time warp contour node values and the time warp contour starting value are determined by the warp ratio values. The time warp contour calculator computes a time warp contour node value of a given time warp contour node, on the basis of a product-formation having a ratio between the time warp contour node values of the intermediate time warp contour node and the time warp contour starting value and a ratio between the time warp contour node values of the given time warp contour node and of the intermediate time warp contour node as factors.

Various embodiments present a combined visual entity that represents overlapping visual entities. The combined visual entity can include a primary visualization that represents one of the overlapping visual entities and annotations that represent others of the overlapping visual entities. For example, a map view can include multiple geographical entities that overlap. A primary visualization can be rendered that represents one of the multiple geographical entities. The primary visualization can be visually annotated (e.g., with symbols, letters, or other visual indicators) to indicate others of the multiple geographical entities. In some embodiments, a zoom operation can cause visual entities to be added and/or removed from the combined visual entity.

Techniques are disclosed for supplying users in an online environment with a safe and effective chat facility. The chat facility is “safe” in the sense that the ability of users to compose inappropriate messages is greatly restricted, while “effective” in the sense that users are still allowed a broad range of expressivity in composing and exchanging chat messages.

In a computer-implemented method of generating event notifications, in-home data is received. The in-home data and environment data (e.g., weather, law enforcement, etc.) is generated by, or based on information generated by, a device located at a residence of an individual, and is analyzed to determine whether it is indicative of an event that should be reported to one or more neighboring residences and/or businesses. If the data is indicative of such an event, a notification is generated and transmitted to the relevant residences and/or businesses.

The disclosed device enables the recording of television broadcasts which are preprogrammed in a memory. The presence of data lines of the television signal in combination with the presence of a color television signal is checked. When the data lines stop, the video recorder is switched on in real time by a clock time signal.

A radio paging selective receiver determines that a received message is unread based on the time difference between the message reception time and the current time being larger that some predetermined value of time, and the paging selective receiver provides an indication of the unread message by displaying the reception time of the unread message in a second fashion which is visibly different from a first fashion normally used to display the current time.

According to one embodiment, an actinic-ray- or radiation-sensitive resin composition includes any of the compounds (A) of general formula (I) below that when exposed to actinic rays or radiation, generates an acid and a resin (B) whose rate of dissolution into an alkali developer is increased by the action of an acid. (The characters used in general formula (I) have the meanings mentioned in the description.)

A radiation-sensitive composition includes a compound represented by a formula (1), and a polymer having a structural unit that includes an acid-labile group. In the formula (1), R1 represents a group having a polar group; n is an integer of 1 to 4, wherein, in a case where R1 is present in a plurality of number, the plurality of R1s are identical or different, and optionally at least two R1s taken together represent a cyclic structure; A represents an alicyclic hydrocarbon group having a valency of (n+1); and M+ represents a monovalent onium cation.

Provided herein are methods, compounds, and compositions for reducing expression of PTP1B mRNA and protein in an animal. Such methods, compounds, and compositions are useful to treat, prevent, delay, or ameliorate metabolic disease, for example, diabetes, or a symptom thereof.

An antisense molecule capable of binding to a selected target site to induce exon skipping in the dystrophin gene, as set forth in SEQ ID NO: 1 to 202.

A network storage server implements a method to discard sensitive data from a Persistent Point-In-Time Image (PPI). The server first efficiently identifies a dataset containing the sensitive data from a plurality of datasets managed by the PPI. Each of the plurality of datasets is read-only and encrypted with a first encryption key. The server then decrypts each of the plurality of datasets, except the dataset containing the sensitive data, with the first encryption key. The decrypted datasets are re-encrypted with a second encryption key, and copied to a storage structure. Afterward, the first encryption key is shredded.

A data processing system on an integrated circuit includes a core that performs switching operations responsive to a system clock that draws current from the power supply network. An IR-drop detector includes a resistor ladder having outputs representative of an IR-drop caused by the core during the switching operations. The system further includes a plurality of amplifiers coupled to the outputs indicative of the IR-drop, a plurality of flip-flops coupled to the amplifiers, and a variable clock generator. The variable clock generator outputs a sampling clock comprising a group consisting of a variable phase or a variable frequency to the plurality of flip-flops. The flip-flops are triggered by the sampling clock so that the IR-drop at a time during a clock cycle of the system clock can be detected, and the peak IR-drop value for can be tracked.

A method uses a firmware interface setup program for a selected compute node (“node”) to cause a firmware interface to enable a trusted platform module (TPM) on the selected node to receive a physical presence (PP) signal. The selected node is selected from a plurality of nodes within a multi-node chassis, wherein each node includes a firmware interface and a TPM. A device within the multi-node chassis is manually actuated to transmit a PP signal to each of the plurality of nodes, such that each node receives the PP signal. The PP signal is asserted to the TPM of the selected node in response to both enabling the TPM of the selected node to be able to receive the PP signal and receiving the PP signal. Still further, the method allows modification of a security setting of the selected node in response to the TPM receiving the PP signal.