nils-herrmann/clean_scirepeval huggingface dataset:
Learning Python Code Suggestion with a Sparse Pointer Network

To enhance developer productivity, all modern integrated development environments (IDEs) include code suggestion functionality that proposes likely next tokens at the cursor. While current IDEs work well for statically-typed languages, their reliance on type annotations means that they do not provide the same level of support for dynamic programming languages as for statically-typed languages. Moreover, suggestion engines in modern IDEs do not propose expressions or multi-statement idiomatic code. Recent work has shown that language models can improve code suggestion systems by learning from software repositories. This paper introduces a neural language model with a sparse pointer network aimed at capturing very longrange dependencies. We release a large-scale code suggestion corpus of 41M lines of Python code crawled from GitHub. On this corpus, we found standard neural language models to perform well at suggesting local phenomena, but struggle to refer to identifiers that are introduced many tokens in the past. By augmenting a neural language model with a pointer network specialized in referring to predefined classes of identifiers, we obtain a much lower perplexity and a 5 percentage points increase in accuracy for code suggestion compared to an LSTM baseline. In fact, this increase in code suggestion accuracy is due to a 13 times more accurate prediction of identifiers. Furthermore, a qualitative analysis shows this model indeed captures interesting long-range dependencies, like referring to a class member defined over 60 tokens in the past.
Tracking deformable objects with point clouds

We introduce an algorithm for tracking deformable objects from a sequence of point clouds. The proposed tracking algorithm is based on a probabilistic generative model that incorporates observations of the point cloud and the physical properties of the tracked object and its environment. We propose a modified expectation maximization algorithm to perform maximum a posteriori estimation to update the state estimate at each time step. Our modification makes it practical to perform the inference through calls to a physics simulation engine. This is significant because (i) it allows for the use of highly optimized physics simulation engines for the core computations of our tracking algorithm, and (ii) it makes it possible to naturally, and efficiently, account for physical constraints imposed by collisions, grasping actions, and material properties in the observation updates. Even in the presence of the relatively large occlusions that occur during manipulation tasks, our algorithm is able to robustly track a variety of types of deformable objects, including ones that are one-dimensional, such as ropes; two-dimensional, such as cloth; and three-dimensional, such as sponges. Our implementation can track these objects in real time.
Gradient Diversity: a Key Ingredient for Scalable Distributed Learning

It has been experimentally observed that distributed implementations of mini-batch stochastic gradient descent (SGD) algorithms exhibit speedup saturation and decaying generalization ability beyond a particular batch-size. In this work, we present an analysis hinting that high similarity between concurrently processed gradients may be a cause of this performance degradation. We introduce the notion of gradient diversity that measures the dissimilarity between concurrent gradient updates, and show its key role in the performance of mini-batch SGD. We prove that on problems with high gradient diversity, mini-batch SGD is amenable to better speedups, while maintaining the generalization performance of serial (one sample) SGD. We further establish lower bounds on convergence where mini-batch SGD slows down beyond a particular batch-size, solely due to the lack of gradient diversity. We provide experimental evidence indicating the key role of gradient diversity in distributed learning, and discuss how heuristics like dropout, Langevin dynamics, and quantization can improve it.
Learning Goal-Oriented Hierarchical Tasks from Situated Interactive Instruction

Our research aims at building interactive robots and agents that can expand their knowledge by interacting with human users. In this paper, we focus on learning goal-oriented tasks from situated interactive instructions. Learning the structure of novel tasks and how to execute them is a challenging computational problem requiring the agent to acquire a variety of knowledge including goal definitions and hierarchical control information. We frame acquisition of novel tasks as an explanation-based learning (EBL) problem and propose an interactive learning variant of EBL for a robotic agent. We show that our approach can exploit information in situated instructions along with the domain knowledge to demonstrate fast generalization on several tasks. The knowledge acquired transfers across structurally similar tasks. Finally, we show that our approach seamlessly combines agent-driven exploration with instructions for mixed-initiative learning.
Empirical comparison of fast partitioning-based clustering algorithms for large data sets

Several fast algorithms for clustering very large data sets have been proposed in the literature, including CLARA, CLARANS, GAC-R, and GAC-RARw. CLARA is a combination of a sampling procedure and the classical PAM algorithm, while CLARANS adopts a serial randomized search strategy to find the optimal set of medoids. GAC-R and GAC-RARw exploit genetic search heuristics for solving clustering problems. In this research, we conducted an empirical comparison of these four clustering algorithms over a wide range of data characteristics described by data size, number of clusters, cluster distinctness, cluster asymmetry, and data randomness. According to the experimental results, CLARANS outperforms its counterparts both in clustering quality and execution time when the number of clusters increases, clusters are more closely related, more asymmetric clusters are present, or more random objects exist in the data set. With a specific number of clusters, CLARA can efficiently achieve satisfactory clustering quality when the data size is larger, whereas GAC-R and GACRARw can achieve satisfactory clustering quality and efficiency when the data size is small, the number of clusters is small, and clusters are more distinct and symmetric. q 2003
Where to put the Image in an Image Caption Generator

When a neural language model is used for caption generation, the image information can be fed to the neural network either by directly incorporating it in a recurrent neural network – conditioning the language model by injecting image features – or in a layer following the recurrent neural network – conditioning the language model by merging the image features. While merging implies that visual features are bound at the end of the caption generation process, injecting can bind the visual features at a variety stages. In this paper we empirically show that late binding is superior to early binding in terms of different evaluation metrics. This suggests that the different modalities (visual and linguistic) for caption generation should not be jointly encoded by the RNN; rather, the multimodal integration should be delayed to a subsequent stage. Furthermore, this suggests that recurrent neural networks should not be viewed as actually generating text, but only as encoding it for prediction in a subsequent
Perceived effectiveness of text vs. multimedia Location-Based Advertising messaging

The emergence of mobile communication and positioning technologies has presented advertisers and marketers with a radically innovative advertising channel: Location-Based Advertising (LBA). Despite the growing attention given to LBA, little is understood about the differential effects of text and multimedia advertising formats on the mobile consumer perceptions and behaviours. This exploratory study empirically examines the effects of multimedia advertisements vis-à-vis text-based advertisements on consumer perceptions and behaviours in a simulated LBA environment. A structural model was formulated to test their effects on consumer perceptions of entertainment, informativeness and irritation. Results show that multimedia LBA messages lead to more favourable attitude, increase the intention to use the LBA application, and have significant impact on purchase intention. Furthermore, this study indicates the role of multimedia as a double-edged sword: on the one hand, it suggests that multimedia impose a higher level of irritation; on the other hand, it suggests that multimedia enhance the informativeness and entertainment value of LBA. Implications for theory and practice are discussed. Perceived effectiveness of text vs. multimedia LBA messaging 155
Data stream mining in ubiquitous environments: state-of-the-art and current directions

In this article, we review the state-of-the-art techniques in mining data streams for mobile and ubiquitous environments. We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. In a wide range of applications, data streams are required to be processed on small ubiquitous devices like smartphones and sensor devices. Mobile and ubiquitous data mining target these applications with tailored techniques and approaches addressing scarcity of resources and mobility issues. Two categories can be identified for mobile and ubiquitous mining of streaming data: single-node and distributed. This survey will cover both categories. Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. We identify the key characteristics of these algorithms and present illustrative applications. Distributed data stream mining in the mobile environment is then discussed, presenting the Pocket Data Mining framework. Mobility of users stimulates the adoption of context-awareness in this area of research. Context-awareness and collaboration are discussed in the Collaborative Data Stream Mining, where agents share knowledge to learn adaptive accurate models.
The attentional cost of receiving a cell phone notification.

It is well documented that interacting with a mobile phone is associated with poorer performance on concurrently performed tasks because limited attentional resources must be shared between tasks. However, mobile phones generate auditory or tactile notifications to alert users of incoming calls and messages. Although these notifications are generally short in duration, they can prompt task-irrelevant thoughts, or mind wandering, which has been shown to damage task performance. We found that cellular phone notifications alone significantly disrupted performance on an attention-demanding task, even when participants did not directly interact with a mobile device during the task. The magnitude of observed distraction effects was comparable in magnitude to those seen when users actively used a mobile phone, either for voice calls or text messaging.
Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression

Although fully generative models have been successfully used to model the contents of text documents, they are often awkward to apply to combinations of text data and document metadata. In this paper we propose a Dirichlet-multinomial regression (DMR) topic model that includes a log-linear prior on document-topic distributions that is a function of observed features of the document, such as author, publication venue, references, and dates. We show that by selecting appropriate features, DMR topic models can meet or exceed the performance of several previously published topic models designed for specific data.
Discriminant analysis in correlation similarity measure space

Correlation is one of the most widely used similarity measures in machine learning like Euclidean and Mahalanobis distances. However, compared with proposed numerous discriminant learning algorithms in distance metric space, only a very little work has been conducted on this topic using correlation similarity measure. In this paper, we propose a novel discriminant learning algorithm in correlation measure space, Correlation Discriminant Analysis (CDA). In this framework, based on the definitions of within-class correlation and between-class correlation, the optimum transformation can be sought for to maximize the difference between them, which is in accordance with good classification performance empirically. Under different cases of the transformation, different implementations of the algorithm are given. Extensive empirical evaluations of CDA demonstrate its advantage over alternative methods.
Wideband Circularly Polarized Aperture-Fed Rotated Stacked Patch Antenna

A novel aperture stacked patch (ASP) antenna with circular polarization is proposed. The antenna consists of four parasitic patches, each one being rotated by an angle of 30° relative to its adjacent patches. The proposed antenna has achieved a simultaneous axial ratio <;3 dB and voltage standing wave ratio (VSWR) <;2 bandwidth of 33.6% (7.2-10.11 GHz) in the single element and 36.15% (7.1-10.2 GHz) in a 2 × 1-element array configuration. The antenna behavior is explained by a thorough parameter study together with fabrication and measurement.
Regularizing Neural Networks by Penalizing Confident Output Distributions

We systematically explore regularizing neural networks by penalizing low entropy output distributions. We show that penalizing low entropy output distributions, which has been shown to improve exploration in reinforcement learning, acts as a strong regularizer in supervised learning. Furthermore, we connect a maximum entropy based confidence penalty to label smoothing through the direction of the KL divergence. We exhaustively evaluate the proposed confidence penalty and label smoothing on 6 common benchmarks: image classification (MNIST and Cifar-10), language modeling (Penn Treebank), machine translation (WMT’14 English-to-German), and speech recognition (TIMIT and WSJ). We find that both label smoothing and the confidence penalty improve state-of-the-art models across benchmarks without modifying existing hyperparameters, suggesting the wide applicability of these regularizers.
Dreaming and the brain: toward a cognitive neuroscience of conscious states.

Sleep researchers in different disciplines disagree about how fully dreaming can be explained in terms of brain physiology. Debate has focused on whether REM sleep dreaming is qualitatively different from nonREM (NREM) sleep and waking. A review of psychophysiological studies shows clear quantitative differences between REM and NREM mentation and between REM and waking mentation. Recent neuroimaging and neurophysiological studies also differentiate REM, NREM, and waking in features with phenomenological implications. Both evidence and theory suggest that there are isomorphisms between the phenomenology and the physiology of dreams. We present a three-dimensional model with specific examples from normally and abnormally changing conscious states.
SmartRoad: Smartphone-Based Crowd Sensing for Traffic Regulator Detection and Identification

In this article we present SmartRoad, a crowd-sourced road sensing system that detects and identifies traffic regulators, traffic lights, and stop signs, in particular. As an alternative to expensive road surveys, SmartRoad works on participatory sensing data collected from GPS sensors from in-vehicle smartphones. The resulting traffic regulator information can be used for many assisted-driving or navigation systems. In order to achieve accurate detection and identification under realistic and practical settings, SmartRoad automatically adapts to different application requirements by (i) intelligently choosing the most appropriate information representation and transmission schemes, and (ii) dynamically evolving its core detection and identification engines to effectively take advantage of any external ground truth information or manual label opportunity. We implemented SmartRoad on a vehicular smartphone test bed, and deployed it on 35 external volunteer users’ vehicles for two months. Experiment results show that SmartRoad can robustly, effectively, and efficiently carry out the detection and identification tasks.
The Kuramoto model : A simple paradigm for synchronization phenomena

Synchronization phenomena in large populations of interacting elements are the subject of intense research efforts in physical, biological, chemical, and social systems. A successful approach to the problem of synchronization consists of modeling each member of the population as a phase oscillator. In this review, synchronization is analyzed in one of the most representative models of coupled phase oscillators, the Kuramoto model. A rigorous mathematical treatment, specific numerical methods, and many variations and extensions of the original model that have appeared in the last few years are presented. Relevant applications of the model in different contexts are also included.
Impedance Control of a Rotary Series Elastic Actuator for Knee Rehabilitation

This paper deals with impedance control of a rotary Series Elastic Actuator designed to assist in flexion/extension of the knee joint during physical therapy. The device includes a DC motor, a worm gear and a customized torsion spring. Since the elastic element is the most important component in the actuator design, a finite element analysis is used to meet the specific requirements of knee assistance. The proposed impedance control scheme comprises three cascade controllers: an inner-loop PI velocity controller, a PI torque controller, and an outer-loop PD position controller. The impedance controller’s performance is evaluated through the Frequency Response Function analysis, and experimental results demonstrate the ability of the system to reproduce impedance behavior within an amplitude and frequency range compatible for gait assistance.
Natural language processing : a Paninian perspective

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Probabilistic topic modeling in multilingual settings: An overview of its methodology and applications

Probabilistic topic models are unsupervised generative models that model document content as a two-step generation process, i.e., documents are observed as mixtures of latent topics, while topics are probability distributions over vocabulary words. Recently, a significant research effort has been invested into transferring the probabilistic topic modeling concept from monolingual to multilingual settings. Novel topic models have been designed to work with parallel and comparable texts. We define the concept of multilingual probabilistic topic modeling and present a short high-level overview of the current research and methodology. As a representative example, we thoroughly describe a multilingual probabilistic topic model called bilingual LDA (BiLDA) trained on comparable data in the appendix. In the paper we provide a short overview of cross-lingual applications for which we utilized the model in our research so far.1
Mining text and social streams: a review

The large amount of text data which are continuously produced over time in a variety of large scale applications such as social networks results in massive streams of data. Typically massive text streams are created by very large scale interactions of individuals, or by structured creations of particular kinds of content by dedicated organizations. An example in the latter category would be the massive text streams created by news-wire services. Such text streams provide unprecedented challenges to data mining algorithms from an efficiency perspective. In this paper, we review text stream mining algorithms for a wide variety of problems in data mining such as clustering, classification and topic modeling. A recent challenge arises in the context of social streams, which are generated by large social networks such as Twitter. We also discuss a number of future challenges in this area of research.
Labeling images with a computer game

We introduce a new interactive system: a game that is fun and can be used to create valuable output. When people play the game they help determine the contents of images by providing meaningful labels for them. If the game is played as much as popular online games, we estimate that most images on the Web can be labeled in a few months. Having proper labels associated with each image on the Web would allow for more accurate image search, improve the accessibility of sites (by providing descriptions of images to visually impaired individuals), and help users block inappropriate images. Our system makes a significant contribution because of its valuable output and because of the way it addresses the image-labeling problem. Rather than using computer vision techniques, which don't work well enough, we encourage people to do the work by taking advantage of their desire to be entertained.
Via-less microwave crossover using microstrip-CPW transitions in slotline propagation mode

This paper presents the design of a microstrip-CPW transition where the CPW line propagates close to slotline mode. This design allows the solution to be determined entirely though analytical techniques. In addition, a planar via-less microwave crossover using this technique is proposed. The experimental results at 5 GHz show that the crossover has a minimum isolation of 32 dB. It also has low in-band insertion loss and return loss of 1.2 dB and 18 dB respectively over more than 44 % of bandwidth.
Enhancing Security and Privacy of Tor's Ecosystem by Using Trusted Execution Environments

With Tor being a popular anonymity network, many attacks have been proposed to break its anonymity or leak information of a private communication on Tor. However, guaranteeing complete privacy in the face of an adversary on Tor is especially difficult because Tor relays are under complete control of world-wide volunteers. Currently, one can gain private information, such as circuit identifiers and hidden service identifiers, by running Tor relays and can even modify their behaviors with malicious intent. This paper presents a practical approach to effectively enhancing the security and privacy of Tor by utilizing Intel SGX, a commodity trusted execution environment. We present a design and implementation of Tor, called SGX-Tor, that prevents code modification and limits the information exposed to untrusted parties. We demonstrate that our approach is practical and effectively reduces the power of an adversary to a traditional network-level adversary. Finally, SGX-Tor incurs moderate performance overhead; the end-to-end latency and throughput overheads for HTTP connections are 3.9% and 11.9%, respectively.
Noise reduction using an undecimated discrete wavelet transform

A new nonlinear noise reduction method is presented that uses the discrete wavelet transform. Similar to Donoho (1995) and Donohoe and Johnstone (1994, 1995), the authors employ thresholding in the wavelet transform domain but, following a suggestion by Coifman, they use an undecimated, shift-invariant, nonorthogonal wavelet transform instead of the usual orthogonal one. This new approach can be interpreted as a repeated application of the original Donoho and Johnstone method for different shifts. The main feature of the new algorithm is a significantly improved noise reduction compared to the original wavelet based approach. This holds for a large class of signals, both visually and in the l/sub 2/ sense, and is shown theoretically as well as by experimental results.