While the exact definition and implementation of accountability depend on the specific context, at its core accountability describes a mechanism that will make decisions transparent and often provides means to sanction "bad" decisions. As such, accountability is specifically relevant for Cyber-Physical Systems, such as robots or drones, that embed themselves into a human society, take decisions and might cause lasting harm. Without a notion of accountability, such systems could behave with impunity and would not fit into society. Despite its relevance, there is currently no agreement on its meaning and, more importantly, no way to express accountability properties for these systems. As a solution we propose to express the accountability properties of systems using Structural Causal Models. They can be represented as human-readable graphical models while also offering mathematical tools to analyze and reason over them. Our central contribution is to show how Structural Causal Models can be used to express and analyze the accountability properties of systems and that this approach allows us to identify accountability patterns. These accountability patterns can be catalogued and used to improve systems and their architectures.

In recent years, all-neural end-to-end approaches have obtained state-of-the-art results on several challenging automatic speech recognition (ASR) tasks. However, most existing works focus on building ASR models where train and test data are drawn from the same domain. This results in poor generalization characteristics on mismatched-domains: e.g., end-to-end models trained on short segments perform poorly when evaluated on longer utterances. In this work, we analyze the generalization properties of streaming and non-streaming recurrent neural network transducer (RNN-T) based end-to-end models in order to identify model components that negatively affect generalization performance. We propose two solutions: combining multiple regularization techniques during training, and using dynamic overlapping inference. On a long-form YouTube test set, when the non-streaming RNN-T model is trained with shorter segments of data, the proposed combination improves word error rate (WER) from 22.3% to 14.8%; when the streaming RNN-T model trained on short Search queries, the proposed techniques improve WER on the YouTube set from 67.0% to 25.3%. Finally, when trained on Librispeech, we find that dynamic overlapping inference improves WER on YouTube from 99.8% to 33.0%.

Software development has been changing rapidly. This development process can be influenced through changing developer friendly approaches. We can save time consumption and accelerate the development process if we can automatically guide programmer during software development. There are some approaches that recommended relevant code snippets and APIitems to the developer. Some approaches apply general code, searching techniques and some approaches use an online based repository mining strategies. But it gets quite difficult to help programmers when they need particular type conversion problems. More specifically when they want to adapt existing interfaces according to their expectation. One of the familiar triumph to guide developers in such situation is adapting collections and arrays through automated adaptation of object ensembles. But how does it help to a novice developer in real time software development that is not explicitly specified? In this paper, we have developed a system that works as a plugin-tool integrated with a particular Data Mining Integrated environment (DMIE) to recommend relevant interface while they seek for a type conversion situation. We have a mined repository of respective adapter classes and related APIs from where developer, search their query and get their result using the relevant transformer classes. The system that recommends developers titled automated objective ensembles (AOE plugin).From the investigation as we have ever made, we can see that our approach much better than some of the existing approaches.

Spiking neural networks (SNNs) are considered as a potential candidate to overcome current challenges such as the high-power consumption encountered by artificial neural networks (ANNs), however there is still a gap between them with respect to the recognition accuracy on practical tasks. A conversion strategy was thus introduced recently to bridge this gap by mapping a trained ANN to an SNN. However, it is still unclear that to what extent this obtained SNN can benefit both the accuracy advantage from ANN and high efficiency from the spike-based paradigm of computation. In this paper, we propose two new conversion methods, namely TerMapping and AugMapping. The TerMapping is a straightforward extension of a typical threshold-balancing method with a double-threshold scheme, while the AugMapping additionally incorporates a new scheme of augmented spike that employs a spike coefficient to carry the number of typical all-or-nothing spikes occurring at a time step. We examine the performance of our methods based on MNIST, Fashion-MNIST and CIFAR10 datasets. The results show that the proposed double-threshold scheme can effectively improve accuracies of the converted SNNs. More importantly, the proposed AugMapping is more advantageous for constructing accurate, fast and efficient deep SNNs as compared to other state-of-the-art approaches. Our study therefore provides new approaches for further integration of advanced techniques in ANNs to improve the performance of SNNs, which could be of great merit to applied developments with spike-based neuromorphic computing.

Assistive robots enable people with disabilities to conduct everyday tasks on their own. However, these tasks can be complex, containing both coarse reaching motions and fine-grained manipulation. For example, when eating, not only does one need to move to the correct food item, but they must also precisely manipulate the food in different ways (e.g., cutting, stabbing, scooping). Shared autonomy methods make robot teleoperation safer and more precise by arbitrating user inputs with robot controls. However, these works have focused mainly on the high-level task of reaching a goal from a discrete set, while largely ignoring manipulation of objects at that goal. Meanwhile, dimensionality reduction techniques for teleoperation map useful high-dimensional robot actions into an intuitive low-dimensional controller, but it is unclear if these methods can achieve the requisite precision for tasks like eating. Our insight is that---by combining intuitive embeddings from learned latent actions with robotic assistance from shared autonomy---we can enable precise assistive manipulation. In this work, we adopt learned latent actions for shared autonomy by proposing a new model structure that changes the meaning of the human's input based on the robot's confidence of the goal. We show convergence bounds on the robot's distance to the most likely goal, and develop a training procedure to learn a controller that is able to move between goals even in the presence of shared autonomy. We evaluate our method in simulations and an eating user study.

Convolutional neural networks (CNN) have shown promising results for end-to-end speech recognition, albeit still behind other state-of-the-art methods in performance. In this paper, we study how to bridge this gap and go beyond with a novel CNN-RNN-transducer architecture, which we call ContextNet. ContextNet features a fully convolutional encoder that incorporates global context information into convolution layers by adding squeeze-and-excitation modules. In addition, we propose a simple scaling method that scales the widths of ContextNet that achieves good trade-off between computation and accuracy. We demonstrate that on the widely used LibriSpeech benchmark, ContextNet achieves a word error rate (WER) of 2.1\%/4.6\% without external language model (LM), 1.9\%/4.1\% with LM and 2.9\%/7.0\% with only 10M parameters on the clean/noisy LibriSpeech test sets. This compares to the previous best published system of 2.0\%/4.6\% with LM and 3.9\%/11.3\% with 20M parameters. The superiority of the proposed ContextNet model is also verified on a much larger internal dataset.

We propose an augmented Lagrangian preconditioner for a three-field stress-velocity-pressure discretization of stationary non-Newtonian incompressible flow with an implicit constitutive relation of power-law type. The discretization employed makes use of the divergence-free Scott-Vogelius pair for the velocity and pressure. The preconditioner builds on the work [P. E. Farrell, L. Mitchell, and F. Wechsung, SIAM J. Sci. Comput., 41 (2019), pp. A3073-A3096], where a Reynolds-robust preconditioner for the three-dimensional Newtonian system was introduced. The preconditioner employs a specialized multigrid method for the stress-velocity block that involves a divergence-capturing space decomposition and a custom prolongation operator. The solver exhibits excellent robustness with respect to the parameters arising in the constitutive relation, allowing for the simulation of a wide range of materials.

Smart meters enable remote and automatic electricity, water and gas consumption reading and are being widely deployed in developed countries. Nonetheless, there is still a huge number of non-smart meters in operation. Image-based Automatic Meter Reading (AMR) focuses on dealing with this type of meter readings. We estimate that the Energy Company of Paran'a (Copel), in Brazil, performs more than 850,000 readings of dial meters per month. Those meters are the focus of this work. Our main contributions are: (i) a public real-world dial meter dataset (shared upon request) called UFPR-ADMR; (ii) a deep learning-based recognition baseline on the proposed dataset; and (iii) a detailed error analysis of the main issues present in AMR for dial meters. To the best of our knowledge, this is the first work to introduce deep learning approaches to multi-dial meter reading, and perform experiments on unconstrained images. We achieved a 100.0% F1-score on the dial detection stage with both Faster R-CNN and YOLO, while the recognition rates reached 93.6% for dials and 75.25% for meters using Faster R-CNN (ResNext-101).

In this paper, we continue our prior work on using imitation learning (IL) and model free reinforcement learning (RL) to learn driving policies for autonomous driving in urban scenarios, by introducing a model based RL method to drive the autonomous vehicle in the Carla urban driving simulator. Although IL and model free RL methods have been proved to be capable of solving lots of challenging tasks, including playing video games, robots, and, in our prior work, urban driving, the low sample efficiency of such methods greatly limits their applications on actual autonomous driving. In this work, we developed a model based RL algorithm of guided policy search (GPS) for urban driving tasks. The algorithm iteratively learns a parameterized dynamic model to approximate the complex and interactive driving task, and optimizes the driving policy under the nonlinear approximate dynamic model. As a model based RL approach, when applied in urban autonomous driving, the GPS has the advantages of higher sample efficiency, better interpretability, and greater stability. We provide extensive experiments validating the effectiveness of the proposed method to learn robust driving policy for urban driving in Carla. We also compare the proposed method with other policy search and model free RL baselines, showing 100x better sample efficiency of the GPS based RL method, and also that the GPS based method can learn policies for harder tasks that the baseline methods can hardly learn.

In this paper, we present a novel MaxSAT-based technique to compute Maximum Probability Minimal Cut Sets (MPMCSs) in fault trees. We model the MPMCS problem as a Weighted Partial MaxSAT problem and solve it using a parallel SAT-solving architecture. The results obtained with our open source tool indicate that the approach is effective and efficient.

A new study suggests that vulnerability to CTE is not limited to professional athletes.

[IMAGE-1]After having their school year totally disrupted by the coronavirus pandemic, a return to some semblance of normalcy come summer is all many school-age kids and their families are looking forward to. For many, this anticipation includes annual summer camp traditions, from sleep-away adventures on the lake to fun-filled day camps for arts, learning or team sports.…

Agencies, local authorities and national governments do not agree on who should be quarantined or what that should actually look like. Here’s what we do know. By Maya Miller, Caroline Chen and Joshua Kaplan ProPublica People who have been exposed to the coronavirus are being given incomplete or misleading information about whether they should quarantine themselves, exposing major gaps in the public health response to the pandemic and illuminating disagreement among officials about how useful the tactic even is at this point in the disease’s spread.…

[IMAGE-1] I started sobbing from the opening moments of A Beautiful Day in the Neighborhood, and I didn't stop crying for two hours. And then after I left the cinema and ran into a fellow film critic who had also just seen it, I literally could not manage a word of discussion without bursting into tears again.…

[IMAGE-1] Noah Baumbach's Marriage Story begins as its central marriage is coming to an end. Our two protagonists are fiercely independent, articulate, opinionated creative types: Charlie (Adam Driver) is the director of an avant-garde theater troupe in New York City; Nicole (Scarlett Johansson) is an actress and one of his primary collaborators.…

[IMAGE-1] Before smartphones and Instagram, there were influencers, and they could be as shallow, overconfident and pejorative as they are today. This new adaptation of Jane Austen's Emma — the feature debuts of photographer and music-video director Autumn de Wilde and Man Booker Prize-winning novelist turned screenwriter Eleanor Catton — brings that sort of modern frisson to its retelling of the tale of a very rich young woman who amuses herself by interfering in the romantic lives of those around her.…

Earlier this morning, Sleater-Kinney announced on Twitter that they're hitting the road on a co-headlining tour with Wilco this summer. Great news!…

When it comes to the music scene in the midst of the coronavirus pandemic, the math is pretty simple: No shows equals no revenue.…

You might not know it from the fancy attire they wear on stage at the Fox Theater, but for the musicians in the Spokane Symphony, it's a part-time gig. It's a prestigious gig, to be sure, but like most artists, for the musicians, it's just one piece of a puzzle full of hustle they have to solve to make a living.…

The Spokane Comedy Club might be quiet right now, but there are still laughs to be had on Zoom, and not just from watching your co-workers try to navigate the online meeting platform. Saturday night, and again next Saturday, the comedy club is hosting Comedians Doing Comedy: A Virtual Comedy Show.…

At this point in our locked-down lives, it’s entirely possible many of us have exhausted our Netflix queue, completed every puzzle in our houses and perfected our sourdough loaves. OK, probably not.…

Warm summer nights are on the way.…

As soon as state Rep. Marcus Riccelli returned home from Olympia, he jumpstarted a community-wide effort to feed Spokane constituents deeply affected by the COVID-19 crisis.…

A computer implemented method for automatic chemical assay classification, the method comprising steps the computer is programmed to perform, the steps comprising: receiving a plurality of sets of parameters, each one of the received sets of parameters characterizing a respective assay of a chemical reaction, calculating a space enhancing proximity among points representative of assays of qualitatively identical chemical reactions, and representing each one of at least two of the received sets of parameters as a respective point in the calculated space, and dividing the points in the calculated space into a number of groups, according to proximity among the points in the calculated space, each group pertaining to a respective chemical reaction, thereby classifying the assays.

Heterocyclic compounds of formula (I) useful as imaging probes of Tau pathology in Alzheimer's disease are described. Compositions and methods of making such compounds are also described.

A caulk composition having a first color upon application and a second color upon formation of a skin, wherein said skin is suitable for application of a surface treatment.

An embodiment of the present invention relates to a method for treating spent caustic soda generated from an oil refinery process, a petrochemical process, etc. through a process in which a series of treatment steps are integrated, wherein the method can constitute a process under mild conditions excluding high temperature and/or high pressure conditions and can be advantageous to a post treatment process since the amount of by-products is small.

A method for computer-implemented unit-conversion method, the method comprising identifying a first numerical value in a first system of units displayed on a computing device, converting the first numerical value in the first system of units into a second numerical value, and displaying the second numerical value and the second system of units on the computing device.

Various embodiments of the present invention provide systems and methods for data processing. As an example, a data processing circuit is disclosed that includes a data detector circuit. The data detector circuit includes an anti-causal noise predictive filter circuit and a data detection circuit. In some cases, the anti-causal noise predictive filter circuit is operable to apply noise predictive filtering to a detector input to yield a filtered output, and the data detection circuit is operable to apply a data detection algorithm to the filtered output derived from the anti-causal noise predictive filter circuit.

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.

A system and method for reconfiguring a computing environment comprising a consumption analysis server, a placement server, an infrastructure management client and a data warehouse in communication with a set of data collection agents and a database. The consumption analysis server operates on measured resource utilization data to yield a set of resource consumptions in regularized time blocks, collects host and virtual machine configurations from the computing environment and determines available capacity for a set of target hosts. The placement server assigns a set of target virtual machines to the target set of hosts in a new placement. In one mode of operation the new placement is nearly optimal. In another mode of operation, the new placement is “good enough” to achieve a threshold score based on an objective function of resource capacity headroom. The new placement is implemented in the computing environment.

An electronic device for using pause to manage resources is described. The electronic device includes a processor and instructions stored in memory. The electronic device monitors a pause duration and determines whether to perform a resource management operation based on the pause duration. The electronic device performs the resource management operation based on the pause duration.

A leading thread and a trailing thread are executed in parallel. Assuming that no transient fault occurs in each section, a system is speculatively executed in the section, with the leading thread and the trailing thread preferably being assigned to two different cores. At this time, the leading thread and the trailing thread are simultaneously executed, performing a buffering operation on a thread local area without performing a write operation on a shared memory. When the respective execution results of the two threads match each other, the content buffered to the thread local area is committed and written to the shared memory. When the respective execution results of the two threads do not match each other, the leading thread and the trailing thread are rolled back to a preceding commit point and re-executed.

A method is disclosed which may include analyzing communication requests in a business process between a client and a server offering a service application to be tested. The method may further include identifying a call to a web service in the analyzed communication. The method may also include determining a location of a Web Service Description Language (WSDL) file relating to the web service on a remote server and downloading the WSDL file from the determined location. A computer readable medium having stored thereon instructions for performing the method and a computer system are also disclosed.

The disclosure generally describes computer-implemented methods, software, and systems for creating and using two-tier mobile applications. A computer-implemented method includes identifying at least a portion of a database to be associated with a mobile application, retrieving a set of metadata associated with the at least a portion of the identified database, automatically generating a set of mobile application source code for directly accessing the at least a portion of the database based on the set of retrieved metadata, and compiling the set of mobile application source code into a distributable mobile application, the distributable mobile application configured to directly access the identified database associated with the mobile application. In some instances, the identifying, retrieving, generating, and compiling operations are performed at design time, while at runtime, the mobile application is executable by a mobile device and, during runtime execution, can request database-related information directly from the identified database.

Disclosed is a novel computer implemented system, on demand service, computer program product and a method that leverages combined concrete and symbolic execution and several fault-localization techniques to automatically detects failures and localizes faults in PHP Hypertext Preprocessor (“PHP”) Web applications.

A method of automating the deployment of a number of enterprise applications on one or more computer data processing systems. Each enterprise application or update is stored in a dynamic distribution directory and is provided with identifying indicia, such as stage information, target information, and settings information. When automated enterprise deployment is invoked, computer instructions in a computer readable medium provide for initializing deployment, performing deployment, and finalizing deployment of the enterprise applications or updates.

Disclosed is a feature for a vehicle that enables taking precautionary actions in response to conditions on the road network around or ahead of the vehicle, in particular, a blind intersection along a section of road. A database that represents the road network is used to determine locations where a blind intersection is located along a section of road. Then, precautionary action data is added to the database to indicate a location at which a precautionary action is to be taken about the blind intersection located along the section of road. A precautionary action system installed in a vehicle uses this database, or a database derived therefrom, in combination with a positioning system to determine when the vehicle is at a location that corresponds to the location of a precautionary action. When the vehicle is at such a location, a precautionary action is taken by a vehicle system as the vehicle is approaching a blind intersection.

A method of adjusting a speed of a mobile machine is provided. Image data of a location is collected where currently generated sensor data and previously generated sensor data indicate a discontinuity in sensor data. The image data is analyzed to determine if a non-motion blur score for the image data is above a threshold value. Then, a speed of the mobile machine is adjusted based on a determination that the non-motion blur score is above the threshold value.

Traffic information readings corresponding to a vehicle are received, the readings including at least a location. The traffic information readings are compared to information already within a map database, and are used to derive additional map information augmenting or correcting that already within the database, the additional map information subsequently being stored in the database. Additional information that is derived includes the presence of stop signs and traffic lights at intersections, the legality of turns at certain times of day, and the connectedness or non-connectedness of road segments.

A system, method, and computer program product for automatically generating equivalent assertions in different forms for different verification tools, which may be analog or digital. A user submits a set of logic assertions that, if unclocked, are converted to clocked assertions by generating and skewing clocks to ensure simulator uniformity. A stimulus is generated, perhaps at random, or input. A test bench is either input or synthesized. For each verification tool, the test bench is simulated and simulation results are captured. An assertion status difference engine evaluates result differences between the verification tools, and identifies and outputs differences indicating a significant inconsistency. Errors in verification tool implementation and user assertion coding can be detected. The simulators used may include SPICE and Verilog, or any other simulators that differ in type, simulation algorithm, input format, or vendor implementation.

A method of creating a datasheet includes obtaining integrated circuit data from at least one data source, creating a data structure including the integrated circuit data obtained from the at least one data source, and creating a datasheet using data contained in the data structure. The datasheet is created in a human-readable format.

Aspects of the invention relate to techniques of defect injection for transistor-level fault simulation. A circuit element in a circuit netlist of a circuit is first selected for defect injection. Next, a defect is determined based on whether the selected circuit element is a design-intent circuit element or a parasitic circuit element. After the defect is determined, the defect is injected into the circuit netlist and then the circuit is simulated.

Fault Current Limiters (FCL) provide protection for upstream and/or downstream devices in electric power grids. Conventional FCL require the use of expensive conductors and liquid or gas cryogen handling. Disclosed embodiments describe FCL systems and devices that use lower cost superconductors, require no liquid cryogen, and are fast cycling. These improved FCL can sustain many sequential faults and require less time to clear faults while avoiding the use of liquid cryogen. Disclosed embodiments describe a FCL with a superconductor and cladding cooled to cryogenic temperatures; these are connected in parallel with a second resistor across two nodes in a circuit. According to disclosed embodiments, the resistance of the superconducting components and its sheath in the fault mode are sufficiently high to minimize energy deposition within the cryogenic system, minimizing recovery time. A scheme for intermediate heat storage also is described which allows a useful compromise between conductor length enabled energy minimization and allowable number of sequential faults to enable an overall system design which is affordable, and yet allows conduction cooled (cryogen free) systems which have fast recovery and allows for multiple sequential faults.

An inductive fault current limiter (1), has a normally conducting primary coil assembly (2) with a multiplicity of turns (3), and a superconducting, short-circuited secondary coil assembly (4). The primary coil assembly (2) and the secondary coil assembly (4) are disposed at least substantially coaxially with respect to each other and at least partially interleaved in each other. The secondary coil assembly (4) has a first coil section (4a) disposed radially inside the turns (3) of the primary coil assembly (2) and a second coil section (4b) disposed radially outside the turns (3) of the primary coil assembly (2). The fault current limiter has an increased inductance ratio.

A method of detecting pre-determined phrases to determine compliance quality is provided. The method includes determining whether at least one of an event or a precursor event has occurred based on a comparison between pre-determined phrases and a communication between a sender and a recipient in a communications network, and rating the recipient based on the presence of the pre-determined phrases associated with the event or the presence of the pre-determined phrases associated with the precursor event in the communication.

Methods, systems, and computer program products are provided for email administration for rendering email on a digital audio player. Embodiments include retrieving an email message; extracting text from the email message; creating a media file; and storing the extracted text of the email message as metadata associated with the media file. Embodiments may also include storing the media file on a digital audio player and displaying the metadata describing the media file, the metadata containing the extracted text of the email message.

An apparatus includes a plurality of applications and an integrator having a voice recognition module configured to identify at least one voice command from a user. The integrator is configured to integrate information from a remote source into at least one of the plurality of applications based on the identified voice command. A method includes analyzing speech from a first user of a first mobile device having a plurality of applications, identifying a voice command based on the analyzed speech using a voice recognition module, and incorporating information from the remote source into at least one of a plurality of applications based on the identified voice command.

Low bit rate audio coding such as BWE algorithm often encounters conflict goal of achieving high time resolution and high frequency resolution at the same time. In order to achieve best possible quality, input signal can be first classified into fast signal and slow signal. This invention focuses on classifying signal into fast signal and slow signal, based on at least one of the following parameters or a combination of the following parameters: spectral sharpness, temporal sharpness, pitch correlation (pitch gain), and/or spectral envelope variation. This classification information can help to choose different BWE algorithms, different coding algorithms, and different postprocessing algorithms respectively for fast signal and slow signal.

An apparatus for processing an audio signal and method thereof are disclosed. The present invention includes receiving a downmix signal and side information; extracting control restriction information from the side information; receiving control information for controlling gain or panning at least one object signal; generating at least one of first multi-channel information and first downmix processing information based on the control information and object information, without using the control restriction information; and, generating an output signal by applying the at least one of the first multichannel information and the first downmix processing information to the downmix signal, wherein the control restriction information relates to a parameter indicating limiting degree of the control information.