Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Regression-based Event Study Analysis Using a Topological Machine-learning Method

This paper introduces a new correction scheme to a conventional regression-based event study method: a topological machine-learning approach with a self-organizing map (SOM). We use this new scheme to analyze a major market event in Japan and find that the factors of abnormal stock returns can be easily can be easily identified and the event-cluster can be depicted.We also find that a conventional event study method involves an empirical analysis mechanism that tends to derive bias due to its mechanism, typically in an event-clustered market situation. We explain our new correction scheme and apply it to an event in the Japanese market --- the holding disclosure of the Government Pension Investment Fund (GPIF) on July 31, 2015.
Tracing cultural diachronic semantic shifts in Russian using word embeddings: test sets and baselines

The paper introduces manually annotated test sets for the task of tracing diachronic (temporal) semantic shifts in Russian. The two test sets are complementary in that the first one covers comparatively strong semantic changes occurring to nouns and adjectives from pre-Soviet to Soviet times, while the second one covers comparatively subtle socially and culturally determined shifts occurring in years from 2000 to 2014. Additionally, the second test set offers more granular classification of shifts degree, but is limited to only adjectives. The introduction of the test sets allowed us to evaluate several well-established algorithms of semantic shifts detection (posing this as a classification problem), most of which have never been tested on Russian material. All of these algorithms use distributional word embedding models trained on the corresponding in-domain corpora. The resulting scores provide solid comparison baselines for future studies tackling similar tasks. We publish the datasets, code and the trained models in order to facilitate further research in automatically detecting temporal semantic shifts for Russian words, with time periods of different granularities.
TraceWalk: Semantic-based Process Graph Embedding for Consistency Checking

Process consistency checking (PCC), an interdiscipline of natural language processing (NLP) and business process management (BPM), aims to quantify the degree of (in)consistencies between graphical and textual descriptions of a process. However, previous studies heavily depend on a great deal of complex expert-defined knowledge such as alignment rules and assessment metrics, thus suffer from the problems of low accuracy and poor adaptability when applied in open-domain scenarios. To address the above issues, this paper makes the first attempt that uses deep learning to perform PCC. Specifically, we proposed TraceWalk, using semantic information of process graphs to learn latent node representations, and integrates it into a convolutional neural network (CNN) based model called TraceNet to predict consistencies. The theoretical proof formally provides the PCC's lower limit and experimental results demonstrate that our approach performs more accurately than state-of-the-art baselines.
Dynamics of Quadrotor UAVs for Aerial Networks: An Energy Perspective

In this letter, we present a model for quadrotor unmanned aerial vehicles (UAVs), including control, communication, and wireless charging. In so doing, we investigate associated energy and battery dynamics. Indeed, energy and battery expressions are derived by leveraging motors' and battery electrical models. Through an experiment, their performances are evaluated for different parameters. The objective is to provide a simple yet practical model of quadrotor UAV consumed/harvested energy and battery dynamics for researchers conducting work on energy-efficient aerial networks.
A Temporary Epoch of Stalled Spin-down for Low-mass Stars: Insights from NGC 6811 with Gaia and Kepler

Stellar rotation was proposed as a potential age diagnostic that is precise, simple, and applicable to a broad range of low-mass stars ( 1 ). Unfortunately, rotation period ( ) measurements of low-mass members of open clusters have undermined the idea that stars spin down with a common age dependence (i.e., ): K dwarfs appear to spin down more slowly than F and G dwarfs. Agüeros et al. interpreted data for the ≈1.4 Gyr-old cluster NGC 752 differently, proposing that after having converged onto a slow-rotating sequence in their first 600–700 Myr (by the age of Praesepe), K dwarf stall on that sequence for an extended period of time. We use data from Gaia DR2 to identify likely single-star members of the ≈1 Gyr-old cluster NGC 6811 with Kepler light curves. We measure for 171 members, more than doubling the sample relative to the existing catalog and extending the mass limit from to ≈0.6 . We then apply a gyrochronology formula calibrated with Praesepe and the Sun to 27 single G dwarfs in NGC 6811 to derive a precise gyrochronological age for the cluster of 1.04 ± 0.07 Gyr. However, when our new low-mass rotators are included, NGC 6811's color– sequence deviates away from the naive 1 Gyr projection down to K (K5V, 0.7 ), where it clearly overlaps with Praesepe’s. Combining these data with for other clusters, we conclude that the assumption that mass and age are separable dependencies is invalid. Furthermore, the cluster data show definitively that stars experience a temporary epoch of reduced braking efficiency where stall, and that the duration of this epoch lasts longer for lower-mass stars.
TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning

Handling previously unseen tasks after given only a few training examples continues to be a tough challenge in machine learning. We propose TapNets, neural networks augmented with task-adaptive projection for improved few-shot learning. Here, employing a meta-learning strategy with episode-based training, a network and a set of per-class reference vectors are learned across widely varying tasks. At the same time, for every episode, features in the embedding space are linearly projected into a new space as a form of quick task-specific conditioning. The training loss is obtained based on a distance metric between the query and the reference vectors in the projection space. Excellent generalization results in this way. When tested on the Omniglot, miniImageNet and tieredImageNet datasets, we obtain state of the art classification accuracies under various few-shot scenarios.
Better Security Bug Report Classification via Hyperparameter Optimization

When security bugs are detected, they should be (a)~discussed privately by security software engineers; and (b)~not mentioned to the general public until security patches are available. Software engineers usually report bugs to bug tracking system, and label them as security bug reports (SBRs) or not-security bug reports (NSBRs), while SBRs have a higher priority to be fixed before exploited by attackers than NSBRs. Yet suspected security bug reports are often publicly disclosed because the mislabelling issues ( i.e., mislabel security bug reports as not-security bug report). The goal of this paper is to aid software developers to better classify bug reports that identify security vulnerabilities as security bug reports through parameter tuning of learners and data pre-processor. Previous work has applied text analytics and machine learning learners to classify which reported bugs are security related. We improve on that work, as shown by our analysis of five open source projects. We apply hyperparameter optimization to (a)~the control parameters of a learner; and (b)~the data pre-processing methods that handle the case where the target class is a small fraction of all the data. We show that optimizing the pre-processor is more useful than optimizing the learners. We also show that improvements gained from our approach can be very large. For example, using the same data sets as recently analyzed by our baseline approach, we show that adjusting the data pre-processing results in improvements to classification recall of 35% to 65% (median to max) with moderate increment of false positive rate.
AlgoNet: $C^\infty$ Smooth Algorithmic Neural Networks

Artificial neural networks revolutionized many areas of computer science in recent years since they provide solutions to a number of previously unsolved problems. On the other hand, for many problems, classic algorithms exist, which typically exceed the accuracy and stability of neural networks. To combine these two concepts, we present a new kind of neural networks$-$algorithmic neural networks (AlgoNets). These networks integrate smooth versions of classic algorithms into the topology of neural networks. A forward AlgoNet includes algorithmic layers into existing architectures while a backward AlgoNet can solve inverse problems without or with only weak supervision. In addition, we present the $\texttt{algonet}$ package, a PyTorch based library that includes, inter alia, a smoothly evaluated programming language, a smooth 3D mesh renderer, and smooth sorting algorithms.
Ethically Aligned Design: An Empirical Evaluation of the RESOLVEDD-Strategy in Software and Systems Development Context

Use of artificial intelligence (AI) in human contexts calls for ethical considerations for the design and development of AI-based systems. However, little knowledge currently exists on how to provide useful and tangible tools that could help software developers and designers implement ethical considerations into practice. In this paper, we empirically evaluate a method that enables ethically aligned design in a decision-making process. Though this method, titled the RESOLVEDD strategy, originates from the field of business ethics, it is being applied in other fields as well. We tested the RESOLVEDD strategy in a multiple case study of five student projects where the use of ethical tools was given as one of the design requirements. A key finding from the study indicates that simply the presence of an ethical tool has an effect on ethical consideration, creating more responsibility even in instances where the use of the tool is not intrinsically motivated.
Dynamic Neural Network Channel Execution for Efficient Training

Existing methods for reducing the computational burden of neural networks at run-time, such as parameter pruning or dynamic computational path selection, focus solely on improving computational efficiency during inference. On the other hand, in this work, we propose a novel method which reduces the memory footprint and number of computing operations required for training and inference. Our framework efficiently integrates pruning as part of the training procedure by exploring and tracking the relative importance of convolutional channels. At each training step, we select only a subset of highly salient channels to execute according to the combinatorial upper confidence bound algorithm, and run a forward and backward pass only on these activated channels, hence learning their parameters. Consequently, we enable the efficient discovery of compact models. We validate our approach empirically on state-of-the-art CNNs - VGGNet, ResNet and DenseNet, and on several image classification datasets. Results demonstrate our framework for dynamic channel execution reduces computational cost up to 4x and parameter count up to 9x, thus reducing the memory and computational demands for discovering and training compact neural network models.
Probing quantum optical excitations with fast electrons

Probing optical excitations with nanometer resolution is important for understanding their dynamics and interactions down to the atomic scale. Electron microscopes currently offer the unparalleled ability of rendering spatially-resolved electron spectra with combined meV and sub-nm resolution, while the use of ultrafast optical pulses enables fs temporal resolution and exposure of the electrons to ultraintense confined optical fields. Here, we theoretically investigate fundamental aspects of the interaction of fast electrons with localized optical modes that are made possible by these advances. We use a quantum-optics description of the optical field to predict that the resulting electron spectra strongly depend on the statistics of the sample excitations (bosonic or fermionic) and their population (Fock, coherent, or thermal), whose autocorrelation functions are directly retrieved from the ratios of electron gain intensities. We further explore feasible experimental scenarios to probe the quantum characteristics of the sampled excitations and their populations.
Derived Codebooks for High-Accuracy Nearest Neighbor Search

High-dimensional Nearest Neighbor (NN) search is central in multimedia search systems. Product Quantization (PQ) is a widespread NN search technique which has a high performance and good scalability. PQ compresses high-dimensional vectors into compact codes thanks to a combination of quantizers. Large databases can, therefore, be stored entirely in RAM, enabling fast responses to NN queries. In almost all cases, PQ uses 8-bit quantizers as they offer low response times. In this paper, we advocate the use of 16-bit quantizers. Compared to 8-bit quantizers, 16-bit quantizers boost accuracy but they increase response time by a factor of 3 to 10. We propose a novel approach that allows 16-bit quantizers to offer the same response time as 8-bit quantizers, while still providing a boost of accuracy. Our approach builds on two key ideas: (i) the construction of derived codebooks that allow a fast and approximate distance evaluation, and (ii) a two-pass NN search procedure which builds a candidate set using the derived codebooks, and then refines it using 16-bit quantizers. On 1 billion SIFT vectors, with an inverted index, our approach offers a Recall@100 of 0.85 in 5.2 ms. By contrast, 16-bit quantizers alone offer a Recall@100 of 0.85 in 39 ms, and 8-bit quantizers a Recall@100 of 0.82 in 3.8 ms.
A Method for Stereoscopic Images Generation in Virtual Reality Systems

This paper introduced a novel method for stereoscopic image creation in virtual reality system. In our method, the left and right images are created in a single camera space, and the image disparities are produced by a pair of matrices. Based on the analysis on the relations among disparity, perceived depth and deformation of perceived objects in a single camera space, disparity and perceived deformation was modeled as 3D mesh warping, consequently the disparity and the perceived deformation could be controlled flexible by deforming the model mesh. Finally, we proposed a nonlinear depth mapping method using model warping, which could reduce perceived object distortion and avoid the image shift caused by perceived depth mapping in stereoscopic head-tracked display system when the viewer moved his head left and right.
Underlying Pathology of Stroke of Unknown Cause (Cryptogenic Stroke)

Underlying pathologies of stroke of unknown cause include atherosclerosis in brain arteries, either intracranial (e.g. moderate middle cerebral artery stenosis), extracranial (e.g. vertebral artery origin stenosis) or proximal (e.g. thick plaques in the aortic arch), thought to be unrelated to the stroke; cardiac findings of undetermined causal link (e.g. patent foramen ovale); incomplete or late workup responsible for true, undiagnosed cause (e.g. arterial dissection, mobile intracardiac/luminal thrombus disappeared at the time of examination). In the next years, new diagnostic tools may hopefully determine which of these pathologies are or are not causally related to stroke.
Inpatient Volume and Quality of Mental Health Care Among Patients With Unipolar Depression.

OBJECTIVE The relationship between inpatient volume and the quality of mental health care remains unclear. This study examined the association between inpatient volume in psychiatric hospital wards and quality of mental health care among patients with depression admitted to wards in Denmark. METHODS In a nationwide, population-based cohort study, 17,971 patients (N=21,120 admissions) admitted to psychiatric hospital wards between 2011 and 2016 were identified from the Danish Depression Database. Inpatient volume was categorized into quartiles according to the individual ward's average caseload volume per year during the study period: low volume (quartile 1, <102 inpatients per year), medium volume (quartile 2, 102-172 inpatients per year), high volume (quartile 3, 173-227 inpatients per year) and very high volume (quartile 4, >227 inpatients per year). Quality of mental health care was assessed by receipt of process performance measures reflecting national clinical guidelines for care of depression. RESULTS Compared with patients admitted to low-volume psychiatric hospital wards, patients admitted to very-high-volume wards were more likely to receive a high overall quality of mental health care (≥80% of the recommended process performance measures) (adjusted relative risk [ARR]=1.78, 95% confidence interval [CI]=1.02-3.09) as well as individual processes of care, including a somatic examination (ARR=1.35, CI=1.03-1.78). CONCLUSIONS Admission to very-high-volume psychiatric hospital wards was associated with a greater chance of receiving guideline-recommended process performance measures for care of depression.
Supporting the development of inclusive products: the effects of everyday ambient illumination levels and contrast on older adults' near visual acuity

Current older adult capability data-sets fail to account for the effects of everyday environmental conditions on capability. This article details a study that investigates the effects of everyday ambient illumination conditions (overcast, 6000 lx; in-house lighting, 150 lx and street lighting, 7.5 lx) and contrast (90%, 70%, 50% and 30%) on the near visual acuity (VA) of older adults (n = 38, 65–87 years). VA was measured at a 1-m viewing distance using logarithm of minimum angle of resolution (LogMAR) acuity charts. Results from the study showed that for all contrast levels tested, VA decreased by 0.2 log units between the overcast and street lighting conditions. On average, in overcast conditions, participants could detect detail around 1.6 times smaller on the LogMAR charts compared with street lighting. VA also significantly decreased when contrast was reduced from 70% to 50%, and from 50% to 30% in each of the ambient illumination conditions. Practitioner summary: This article presents an experimental study that investigates the impact of everyday ambient illumination levels and contrast on older adults' VA. Results show that both factors have a significant effect on their VA. Findings suggest that environmental conditions need to be accounted for in older adult capability data-sets/designs.
Inferring Concise Specifications of APIs

Modern software relies on libraries and uses them via application programming interfaces (APIs). Correct API usage as well as many software engineering tasks are enabled when APIs have formal specifications. In this work, we analyze the implementation of each method in an API to infer a formal postcondition. Conventional wisdom is that, if one has preconditions, then one can use the strongest postcondition predicate transformer (SP) to infer postconditions. However, SP yields postconditions that are exponentially large, which makes them difficult to use, either by humans or by tools. Our key idea is an algorithm that converts such exponentially large specifications into a form that is more concise and thus more usable. This is done by leveraging the structure of the specifications that result from the use of SP. We applied our technique to infer postconditions for over 2,300 methods in seven popular Java libraries. Our technique was able to infer specifications for 75.7% of these methods, each of which was verified using an Extended Static Checker. We also found that 84.6% of resulting specifications were less than 1/4 page (20 lines) in length. Our technique was able to reduce the length of SMT proofs needed for verifying implementations by 76.7% and reduced prover execution time by 26.7%.
The effect of individual differences in working memory in older adults on performance with different degrees of automated technology

Abstract A leading hypothesis to explain older adults’ overdependence on automation is age-related declines in working memory. However, it has not been empirically examined. The purpose of the current experiment was to examine how working memory affected performance with different degrees of automation in older adults. In contrast to the well-supported idea that higher degrees of automation, when the automation is correct, benefits performance but higher degrees of automation, when the automation fails, increasingly harms performance, older adults benefited from higher degrees of automation when the automation was correct but were not differentially harmed by automation failures. Surprisingly, working memory did not interact with degree of automation but did interact with automation correctness or failure. When automation was correct, older adults with higher working memory ability had better performance than those with lower abilities. But when automation was incorrect, all older adults, regardless of working memory ability, performed poorly. Practitioner Summary: The design of automation intended for older adults should focus on ways of making the correctness of the automation apparent to the older user and suggest ways of helping them recover when it is malfunctioning.
End-to-End Entity Resolution for Big Data: A Survey

One of the most important tasks for improving data quality and the reliability of data analytics results is Entity Resolution (ER). ER aims to identify different descriptions that refer to the same real-world entity, and remains a challenging problem. While previous works have studied specific aspects of ER (and mostly in traditional settings), in this survey, we provide for the first time an end-to-end view of modern ER workflows, and of the novel aspects of entity indexing and matching methods in order to cope with more than one of the Big Data characteristics simultaneously. We present the basic concepts, processing steps and execution strategies that have been proposed by different communities, i.e., database, semantic Web and machine learning, in order to cope with the loose structuredness, extreme diversity, high speed and large scale of entity descriptions used by real-world applications. Finally, we provide a synthetic discussion of the existing approaches, and conclude with a detailed presentation of open research directions.
Meta reinforcement learning as task inference

Humans achieve efficient learning by relying on prior knowledge about the structure of naturally occurring tasks. There is considerable interest in designing reinforcement learning (RL) algorithms with similar properties. This includes proposals to learn the learning algorithm itself, an idea also known as meta learning. One formal interpretation of this idea is as a partially observable multi-task RL problem in which task information is hidden from the agent. Such unknown task problems can be reduced to Markov decision processes (MDPs) by augmenting an agent's observations with an estimate of the belief about the task based on past experience. However estimating the belief state is intractable in most partially-observed MDPs. We propose a method that separately learns the policy and the task belief by taking advantage of various kinds of privileged information. Our approach can be very effective at solving standard meta-RL environments, as well as a complex continuous control environment with sparse rewards and requiring long-term memory.