nils-herrmann/clean_scirepeval huggingface dataset:
Improving long short-term memory networks using maxout units for large vocabulary speech recognition

Long short-tem memory (LSTM) recurrent neural networks have been shown to give state-of-the-art performance on many speech recognition tasks. To achieve a further performance improvement, in this paper, maxout units are proposed to be integrated with the LSTM cells, considering those units have brought significant improvements to deep feed-forward neural networks. A novel architecture was constructed by replacing the input activation units (generally tanh) in the LSTM networks with maxout units. We implemented the LSTM network training on multi-GPU devices with truncated BPTT, and empirically evaluated the proposed designs on a large vocabulary Mandarin conversational telephone speech recognition task. The experimental results support our claim that the performance of LSTM based acoustic models can be further improved using the maxout units.
Relative entropy and free energy dualities: Connections to Path Integral and KL control

This paper integrates recent work on Path Integral (PI) and Kullback Leibler (KL) divergence stochastic optimal control theory with earlier work on risk sensitivity and the fundamental dualities between free energy and relative entropy. We derive the path integral optimal control framework and its iterative version based on the aforemetioned dualities. The resulting formulation of iterative path integral control is valid for general feedback policies and in contrast to previous work, it does not rely on pre-specified policy parameterizations. The derivation is based on successive applications of Girsanov's theorem and the use of Radon-Nikodým derivative as applied to diffusion processes due to the change of measure in the stochastic dynamics. We compare the PI control derived based on Dynamic Programming with PI based on the duality between free energy and relative entropy. Moreover we extend our analysis on the applicability of the relationship between free energy and relative entropy to optimal control of markov jump diffusions processes. Furthermore, we present the links between KL stochastic optimal control and the aforementioned dualities and discuss its generalizability.
Illumination for Computer Generated Pictures

The quality of computer generated images of three-dimensional scenes depends on the shading technique used to paint the objects on the cathode-ray tube screen. The shading algorithm itself depends in part on the method for modeling the object, which also determines the hidden surface algorithm. The various methods of object modeling, shading, and hidden surface removal are thus strongly interconnected. Several shading techniques corresponding to different methods of object modeling and the related hidden surface algorithms are presented here. Human visual perception and the fundamental laws of optics are considered in the development of a shading rule that provides better quality and increased realism in generated images.
Sensorless control of permanent magnet synchronous motor using extended Kalman filter

This paper presents the modeling, analysis, design and experimental validation of a robust sensorless control method for permanent magnet synchronous motor (PMSM) based on extended Kalman filter (EKF). A real-time PMSM and its EKF models in the MATLAB/Simulink simulation environment are developed. The position/speed sensorless control scheme along with the power electronic circuitry is modeled. The performance of the proposed control is assessed and verified for different types of dynamic and static torque loads. The robustness of the sensorless method is demonstrated by starting the motor with different rotor initial positions. The proposed EKF speed/position estimation method is also proved insensitive to the PMSM parameter variations. Proper operations of this EKF based sensorless control method for a high-speed permanent magnet synchronous machine are verified experimentally in the research laboratory at Honeywell.
A Logical Language for Expressing Authorizations

A major drawback of existing access control systems is that they have all been developed with a specific access control policy in mind. This means that all protection requirements (i.e., accesses to be allowed or denied) must be specified in terms of the policy enforced by the system. While this may be trivial for some requirements, specification of other requirements may become quite complex or even impossible. The reason for this is that a single policy simply cannot capture different protection requirements users ma y need to enforce on different data. In this paper we take a first step towards a model able to support different access control policies. We propose a logical language for the specification of authorizations on which such a model can be based. The language allows users to specify, together with the authorizations, the pol icy according to which access control decisions are to be made. Policies are expressed by means of rules which enforce derivation of authorizations, conflict resolution, access co ntrol, and integrity constraint checking. We illustrate the power of our languageby showing how different constraints that are sometimes required, but very seldom supported by existing access control systems, can be represented in our
A homotopy-based sparse representation for fast and accurate shape prior modeling in liver surgical planning

Shape prior plays an important role in accurate and robust liver segmentation. However, liver shapes have complex variations and accurate modeling of liver shapes is challenging. Using large-scale training data can improve the accuracy but it limits the computational efficiency. In order to obtain accurate liver shape priors without sacrificing the efficiency when dealing with large-scale training data, we investigate effective and scalable shape prior modeling method that is more applicable in clinical liver surgical planning system. We employed the Sparse Shape Composition (SSC) to represent liver shapes by an optimized sparse combination of shapes in the repository, without any assumptions on parametric distributions of liver shapes. To leverage large-scale training data and improve the computational efficiency of SSC, we also introduced a homotopy-based method to quickly solve the L1-norm optimization problem in SSC. This method takes advantage of the sparsity of shape modeling, and solves the original optimization problem in SSC by continuously transforming it into a series of simplified problems whose solution is fast to compute. When new training shapes arrive gradually, the homotopy strategy updates the optimal solution on the fly and avoids re-computing it from scratch. Experiments showed that SSC had a high accuracy and efficiency in dealing with complex liver shape variations, excluding gross errors and preserving local details on the input liver shape. The homotopy-based SSC had a high computational efficiency, and its runtime increased very slowly when repository's capacity and vertex number rose to a large degree. When repository's capacity was 10,000, with 2000 vertices on each shape, homotopy method cost merely about 11.29 s to solve the optimization problem in SSC, nearly 2000 times faster than interior point method. The dice similarity coefficient (DSC), average symmetric surface distance (ASD), and maximum symmetric surface distance measurement was 94.31 ± 3.04%, 1.12 ± 0.69 mm and 3.65 ± 1.40 mm respectively.
A 78mW 11.8Gb/s serial link transceiver with adaptive RX equalization and baud-rate CDR in 32nm CMOS

The last few years have witnessed a rapid increase in serial IO data rates as well as number of IO ports in microprocessors. This trend, poses significant challenges to the serial IO design because of area and power budget limitations but, above all, by the need to achieve stringent bit error rates in the presence of interconnect constraints such as channel loss, impedance discontinuities induced by package and connectors, and crosstalk effects due to routing limitations in the package and on the motherboard. To effectively meet these challenges, the serial IO architecture has evolved to include more complex equalization schemes. Building on the previous work on a 4-tap DFE , this paper presents the design of a complete serial IO capable of operating up to 11.8Gb/s, and in particular, focuses on the integration of an adaptive equalizer and baud-rate CDR in the receiver of the serial IO.
Vaccination and the theory of games.

Voluntary vaccination policies for childhood diseases present parents with a subtle challenge: if a sufficient proportion of the population is already immune, either naturally or by vaccination, then even the slightest risk associated with vaccination will outweigh the risk from infection. As a result, individual self-interest might preclude complete eradication of a vaccine-preventable disease. We show that a formal game theoretical analysis of this problem leads to new insights that help to explain human decision-making with respect to vaccination. Increases in perceived vaccine risk will tend to induce larger declines in vaccine uptake for pathogens that cause more secondary infections (such as measles and pertussis). After a vaccine scare, even if perceived vaccine risk is greatly reduced, it will be relatively difficult to restore prescare vaccine coverage levels.
A Reference-Less Single-Loop Half-Rate Binary CDR

This paper proposes a half-rate single-loop reference-less binary CDR that operates from 8.5 Gb/s to 12.1 Gb/s (36% capture range). The high capture range is made possible by adding a novel frequency detection mechanism which limits the magnitude of the phase error between the input data and the VCO clock. The proposed frequency detector produces three phases of the data, and feeds into the phase detector the data phase that minimizes the CDR phase error. This frequency detector, implemented within a 10 Gb/s CDR in Fujitsu's 65 nm CMOS, consumes 11 mW and improves the capture range by up to 6 × when it is activated.
Neural underpinnings of gesture discrimination in patients with limb apraxia.

Limb apraxia (LA), is a neuropsychological syndrome characterized by difficulty in performing gestures and may therefore be an ideal model for investigating whether action execution deficits are causatively linked to deficits in action understanding. We tested 33 left brain-damaged patients and 8 right brain-damaged patients for the presence of the LA. Importantly, we also tested all the patients in an ad hoc developed gesture recognition task wherein an actor performs, either correctly or incorrectly, transitive (using objects) or intransitive (without objects) meaningful conventional limb gestures. Patients were instructed to judge whether the observed gesture was correct or incorrect. Lesion analysis enabled us to evaluate the relationship between specific brain regions and behavioral performance in gesture execution and gesture comprehension. We found that LA was present in 21 left brain-damaged patients and it was linked to frontal and parietal lesions. Moreover, we found that recognition of correct execution of familiar gestures performed by others was more impaired in patients with LA than in nonapraxic patients. Crucially, the gesture comprehension deficit correlated with damage to the opercular and triangularis portions of the inferior frontal gyrus, two regions that are involved in complex aspects of action-related processing. In contrast, no such relationship was observed with lesions centered on the inferior parietal cortex. The present findings suggest that lesions to left frontal regions that are involved in planning and performing actions are causatively associated with deficits in the recognition of the correct execution of meaningful gestures.
Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding

Current systems of fine-grained entity typing use distant supervision in conjunction with existing knowledge bases to assign categories (type labels) to entity mentions. However, the type labels so obtained from knowledge bases are often noisy (i.e., incorrect for the entity mention's local context). We define a new task, Label Noise Reduction in Entity Typing (LNR), to be the automatic identification of correct type labels (type-paths) for training examples, given the set of candidate type labels obtained by distant supervision with a given type hierarchy. The unknown type labels for individual entity mentions and the semantic similarity between entity types pose unique challenges for solving the LNR task. We propose a general framework, called PLE, to jointly embed entity mentions, text features and entity types into the same low-dimensional space where, in that space, objects whose types are semantically close have similar representations. Then we estimate the type-path for each training example in a top-down manner using the learned embeddings. We formulate a global objective for learning the embeddings from text corpora and knowledge bases, which adopts a novel margin-based loss that is robust to noisy labels and faithfully models type correlation derived from knowledge bases. Our experiments on three public typing datasets demonstrate the effectiveness and robustness of PLE, with an average of 25% improvement in accuracy compared to next best method.
Intelligent tutors as teachers' aides: exploring teacher needs for real-time analytics in blended classrooms

Intelligent tutoring systems (ITSs) are commonly designed to enhance student learning. However, they are not typically designed to meet the needs of teachers who use them in their classrooms. ITSs generate a wealth of analytics about student learning and behavior, opening a rich design space for real-time teacher support tools such as dashboards. Whereas real-time dashboards for teachers have become popular with many learning technologies, we are not aware of projects that have designed dashboards for ITSs based on a broad investigation of teachers' needs. We conducted design interviews with ten middle school math teachers to explore their needs for on-the-spot support during blended class sessions, as a first step in a user-centered design process of a real-time dashboard. Based on multi-methods analyses of this interview data, we identify several opportunities for ITSs to better support teachers' needs, noting that the analytics commonly generated by existing teacher support tools do not strongly align with the analytics teachers expect to be most useful. We highlight key tensions and tradeoffs in the design of such real-time supports for teachers, as revealed by "Speed Dating" possible futures with teachers. This paper has implications for our ongoing co-design of a real-time dashboard for ITSs, as well as broader implications for the design of ITSs that can effectively collaborate with teachers in classroom settings.
Automatic Behaviour-based Analysis and Classification System for Malware Detection

Malware is any kind of program explicitly designed to harm, such as viruses, trojan horses or worms. Since the amount of malware is growing exponentially, it already poses a serious security threat. Therefore, every incoming code must be analysed in order to classify it as malware or benign software. These tests commonly combine static and dynamic analysis techniques in order to extract the major amount of information from distrustful files. Moreover, the increment of the number of attacks hinders manually testing the thousands of suspicious archives that every day reach antivirus laboratories. Against this background, we address here an automatised system for malware behaviour analysis based on emulation and simulation techniques. Hence, creating a secure and reliable sandbox environment allows us to test the suspicious code retrieved without risk. In this way, we can also generate evidences and classify the samples with several machine-learning algorithms. We have developed the proposed solution, testing it with real malware. Finally, we have evaluated it in terms of reliability and time performance, two of the main aspects for such a system to work.
A Ripple-Based Constant On-Time Control With Virtual Inductor Current and Offset Cancellation for DC Power Converters

In recent years, there has been a growing trend of mandating high-power conversion efficiency, for not only the heavy-load but also the light-load conditions. To achieve this purpose, a ripple-based constant on-time (RBCOT) control for dc-dc converters has received wide attentions because of its natural characteristic of switching frequency reduction under the light-load condition. However, a RBCOT control suffers from an output-voltage offset problem and a subharmonic instability problem. In this paper, a modified RBCOT buck converter circuit is proposed to solve both problems. The circuit uses the concept of virtual inductor current to stabilize the feedback, and an offset-cancellation circuit to eliminate the output dc offset. The modified circuit can be fabricated into an integrated circuit (IC) without adding any pin compared to conventional circuits. A control model based on describing function is developed for the modified converter. The small-signal characteristics and design criteria to meet stability are derived. From the model, it is also found out that it is much easier to accomplish adaptive voltage positioning using the proposed modified RBCOT scheme compared to a conventional constant-frequency controller. Simulation and experimental results are given to verify the proposed scheme.
Accelerating Neural Transformer via an Average Attention Network

With parallelizable attention networks, the neural Transformer is very fast to train. However, due to the auto-regressive architecture and self-attention in the decoder, the decoding procedure becomes slow. To alleviate this issue, we propose an average attention network as an alternative to the self-attention network in the decoder of the neural Transformer. The average attention network consists of two layers, with an average layer that models dependencies on previous positions and a gating layer that is stacked over the average layer to enhance the expressiveness of the proposed attention network. We apply this network on the decoder part of the neural Transformer to replace the original target-side self-attention model. With masking tricks and dynamic programming, our model enables the neural Transformer to decode sentences over four times faster than its original version with almost no loss in training time and translation performance. We conduct a series of experiments on WMT17 translation tasks, where on 6 different language pairs, we obtain robust and consistent speed-ups in
Convolutional Neural Net and Bearing Fault Analysis

There has been immense success on the application of Convolutional Neural Nets (CNN) to image and acoustic data analysis. In this paper, rather than preprocessing vibration signals to denoise or extract features, we investigate the usage of CNNs on raw signals; in particular, we test the accuracy of CNNs as classifiers on bearing fault data, by varying the configurations of the CNN from one-layer up to a deep three-layer model. We inspect the convolution filters learned by the CNN, and show that the filters detect unique features of every classification category. We also study the effectiveness of the various CNN models when the input signals are corrupted with noise.
Deep Voice 2: Multi-Speaker Neural Text-to-Speech

We introduce a technique for augmenting neural text-to-speech (TTS) with lowdimensional trainable speaker embeddings to generate different voices from a single model. As a starting point, we show improvements over the two state-ofthe-art approaches for single-speaker neural TTS: Deep Voice 1 and Tacotron. We introduce Deep Voice 2, which is based on a similar pipeline with Deep Voice 1, but constructed with higher performance building blocks and demonstrates a significant audio quality improvement over Deep Voice 1. We improve Tacotron by introducing a post-processing neural vocoder, and demonstrate a significant audio quality improvement. We then demonstrate our technique for multi-speaker speech synthesis for both Deep Voice 2 and Tacotron on two multi-speaker TTS datasets. We show that a single neural TTS system can learn hundreds of unique voices from less than half an hour of data per speaker, while achieving high audio quality synthesis and preserving the speaker identities almost perfectly.
A Distributed Cache for Hadoop Distributed File System in Real-Time Cloud Services

The improvement of file access performance is a great challenge in real-time cloud services. In this paper, we analyze preconditions of dealing with this problem considering the aspects of requirements, hardware, software, and network environments in the cloud. Then we describe the design and implementation of a novel distributed layered cache system built on the top of the Hadoop Distributed File System which is named HDFS-based Distributed Cache System (HDCache). The cache system consists of a client library and multiple cache services. The cache services are designed with three access layers an in-memory cache, a snapshot of the local disk, and the actual disk view as provided by HDFS. The files loading from HDFS are cached in the shared memory which can be directly accessed by a client library. Multiple applications integrated with a client library can access a cache service simultaneously. Cache services are organized in the P2P style using a distributed hash table. Every file cached has three replicas in different cache service nodes in order to improve robustness and alleviates the workload. Experimental results show that the novel cache system can store files with a wide range in their sizes and has the access performance in a millisecond level in highly concurrent environments.
Learning 3D Object Categories by Looking Around Them

Traditional approaches for learning 3D object categories use either synthetic data or manual supervision. In this paper, we propose a method which does not require manual annotations and is instead cued by observing objects from a moving vantage point. Our system builds on two innovations: a Siamese viewpoint factorization network that robustly aligns different videos together without explicitly comparing 3D shapes; and a 3D shape completion network that can extract the full shape of an object from partial observations. We also demonstrate the benefits of configuring networks to perform probabilistic predictions as well as of geometry-aware data augmentation schemes. We obtain state-of-the-art results on publicly-available benchmarks.
User acceptance of mobile Internet: Implication for convergence technologies

Using the Technology Acceptance Model as a conceptual framework and a method of structural equation modeling, this study analyzes the consumer attitude toward Wi-Bro drawing data from 515 consumers. Individuals’ responses to questions about whether they use/accept Wi-Bro were collected and combined with various factors modified from the Technology Acceptance Model. The result of this study show that users’ perceptions are significantly associated with their motivation to use Wi-Bro. Specifically, perceived quality and perceived availability are found to have significant effect on users’ extrinsic and intrinsic motivation. These new factors are found to be Wi-Bro-specific factors, playing as enhancing factors to attitudes and intention.
Character-Based Parsing with Convolutional Neural Network

We describe a novel convolutional neural network architecture with k-max pooling layer that is able to successfully recover the structure of Chinese sentences. This network can capture active features for unseen segments of a sentence to measure how likely the segments are merged to be the constituents. Given an input sentence, after all the scores of possible segments are computed, an efficient dynamic programming parsing algorithm is used to find the globally optimal parse tree. A similar network is then applied to predict syntactic categories for every node in the parse tree. Our networks archived competitive performance to existing benchmark parsers on the CTB-5 dataset without any task-specific feature engineering.