Miracle-dz/UniFAR-FTUs huggingface dataset:
XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization

Much recent progress in applications of machine learning models to NLP has been driven by benchmarks that evaluate models across a wide variety of tasks. However, these broad-coverage benchmarks have been mostly limited to English, and despite an increasing interest in multilingual models, a benchmark that enables the comprehensive evaluation of such methods on a diverse range of languages and tasks is still missing. To this end, we introduce the Cross-lingual TRansfer Evaluation of Multilingual Encoders XTREME benchmark, a multi-task benchmark for evaluating the cross-lingual generalization capabilities of multilingual representations across 40 languages and 9 tasks. We demonstrate that while models tested on English reach human performance on many tasks, there is still a sizable gap in the performance of cross-lingually transferred models, particularly on syntactic and sentence retrieval tasks. There is also a wide spread of results across languages. We release the benchmark to encourage research on cross-lingual learning methods that transfer linguistic knowledge across a diverse and representative set of languages and tasks.
Large-scale network analysis reveals cheating spreads through victimization and observation

Antisocial behavior such as negative gossip, cheating, or bullying can be contagious, spreading from individual to individual and rippling through social networks. Previous experimental research has suggested that individuals who either experience or observe antisocial behavior become more likely to behave antisocially. Here, we distinguish between victimization and observation using an observational study. We apply temporal network analysis on large-scale digital trace data to study the spread of cheating in online gaming. We analyze 1,146,941 matches of the multiplayer online game PlayerUnknown's Battlegrounds, in which up to 100 players compete individually or in teams against strangers. We identify temporal motifs in which a player who is killed by or observes cheaters starts cheating, and evaluate the extent to which these motifs would appear if we preserve the team and interaction structure but assume an alternative sequence of events. The results suggest that social contagion is only likely to exist for those who both experience and observe cheating multiple times. The findings point to strategies for targeted interventions to stem the spread of cheating and antisocial behavior in online communities, schools, organizations, and sports.
Charged black hole mergers: orbit circularisation and chirp mass bias

We consider the inspiral of black holes carrying U(1) charge that is not electromagnetic, but corresponds to some dark sector. In the weak-field, low-velocity regime, the components follow Keplerian orbits. We investigate how the orbital parameters evolve for dipole-dominated emission and find that the orbit quickly circularises, though not as efficiently as for a gravitationally dominated emission. We then regard circular orbits, and look for modifications in the gravitational waveform from the components carrying small charges. Taking this into account we populate the waveform with simplified LIGO noise and put it through a matched filtering procedure where the template bank only consists of uncharged templates, focusing on the charges’ effect on the chirp mass estimation. We find a consistent overestimation of the ‘generalised’ chirp mass, and a possible over- and underestimation of the actual chirp mass. Finally, we briefly consider the effect of such charges on hyperbolic encounters, finding again a bias arising from interpreting the generalised chirp mass as the actual chirp mass.
Auto-Ensemble: An Adaptive Learning Rate Scheduling Based Deep Learning Model Ensembling

Ensembling deep learning models is a shortcut to promote its implementation in new scenarios, which can avoid tuning neural networks, losses and training algorithms from scratch. However, it is difficult to collect sufficient accurate and diverse models through once training. This paper proposes Auto-Ensemble (AE) to collect checkpoints of deep learning model and ensemble them automatically by adaptive learning rate scheduling algorithm. The advantage of this method is to make the model converge to various local optima by scheduling the learning rate in once training. When the number of local optimal solutions tends to be saturated, all the collected checkpoints are used for ensemble. Our method is universal, it can be applied to various scenarios. Experiment results on multiple datasets and neural networks demonstrate it is effective and competitive, especially on few-shot learning. Besides, we proposed a method to measure the distance among models. Then we can ensure the accuracy and diversity of collected models.
Predictive coding underlies adaptation in the subcortical sensory pathway.

The subcortical sensory pathways are the fundamental channels for mapping the outside world to our minds. Sensory pathways efficiently transmit information by adapting neural responses to the local statistics of the sensory input. The longstanding mechanistic explanation for this adaptive behaviour is that neuronal habituation scales activity to the local statistics of the stimuli. An alternative account is that neural coding is directly driven by expectations of the sensory input. Here we used abstract rules to manipulate expectations independently of local stimulus statistics. The ultra-high-field functional-MRI data show that expectations, and not habituation, are the main driver of the response amplitude to tones in the human auditory pathway. These results provide first unambiguous evidence of predictive coding and abstract processing in a subcortical sensory pathway, indicating that the brain only holds subjective representations of the outside world even at initial points of the processing hierarchy.
Volumization as a Natural Generalization of Weight Decay

We propose a novel regularization method, called \textit{volumization}, for neural networks. Inspired by physics, we define a physical volume for the weight parameters in neural networks, and we show that this method is an effective way of regularizing neural networks. Intuitively, this method interpolates between an $L_2$ and $L_\infty$ regularization. Therefore, weight decay and weight clipping become special cases of the proposed algorithm. We prove, on a toy example, that the essence of this method is a regularization technique to control bias-variance tradeoff. The method is shown to do well in the categories where the standard weight decay method is shown to work well, including improving the generalization of networks and preventing memorization. Moreover, we show that the volumization might lead to a simple method for training a neural network whose weight is binary or ternary.
Cylindrical Convolutional Networks for Joint Object Detection and Viewpoint Estimation

Existing techniques to encode spatial invariance within deep convolutional neural networks only model 2D transformation fields. This does not account for the fact that objects in a 2D space are a projection of 3D ones, and thus they have limited ability to severe object viewpoint changes. To overcome this limitation, we introduce a learnable module, cylindrical convolutional networks (CCNs), that exploit cylindrical representation of a convolutional kernel defined in the 3D space. CCNs extract a view-specific feature through a view-specific convolutional kernel to predict object category scores at each viewpoint. With the view-specific feature, we simultaneously determine objective category and viewpoints using the proposed sinusoidal soft-argmax module. Our experiments demonstrate the effectiveness of the cylindrical convolutional networks on joint object detection and viewpoint estimation.
Exocomets: A spectroscopic survey

Context. While exoplanets are now routinely detected, the detection of small bodies in extrasolar systems remains challenging. Since the discovery of sporadic events, which are interpreted to be exocomets (falling evaporating bodies) around β Pic in the early 1980s, only ∼20 stars have been reported to host exocomet-like events. Aims. We aim to expand the sample of known exocomet-host stars, as well as to monitor the hot-gas environment around stars with previously known exocometary activity. Methods. We have obtained high-resolution optical spectra of a heterogeneous sample of 117 main-sequence stars in the spectral type range from B8 to G8. The data were collected in 14 observing campaigns over the course of two years from both hemispheres. We analysed the Ca II K&H and Na I D lines in order to search for non-photospheric absorptions that originated in the circumstellar environment and for variable events that could be caused by the outgassing of exocomet-like bodies. Results. We detected non-photospheric absorptions towards 50% of the sample, thus attributing a circumstellar origin to half of the detections (i.e. 26% of the sample). Hot circumstellar gas was detected in the metallic lines inspected via narrow stable absorptions and/or variable blue- and red-shifted absorption events. Such variable events were found in 18 stars in the Ca II and/or Na I lines; six of them are reported in the context of this work for the first time. In some cases, the variations we report in the Ca II K line are similar to those observed in β Pic. While we do not find a significant trend in the age or location of the stars, we do find that the probability of finding CS gas in stars with larger v sin i is higher. We also find a weak trend with the presence of near-infrared excess and with anomalous (λ Boo-like) abundances, but this would require confirmation by expanding the sample.
Tight Algorithms for the Submodular Multiple Knapsack Problem

Submodular function maximization has been a central topic in the theoretical computer science community over the last decade. A plenty of well-performed approximation algorithms have been designed for the maximization of monotone/non-monotone submodular functions over a variety of constraints. In this paper, we consider the submodular multiple knapsack problem (SMKP), which is the submodular version of the well-studied multiple knapsack problem (MKP). Roughly speaking, the problem asks to maximize a monotone submodular function over multiple bins (knapsacks). Despite lots of known results in the field of submodular maximization, surprisingly, it remains unknown whether or not this problem enjoys the well-known tight $(1 - 1 / e)$-approximation. In this paper, we answer this question affirmatively by proposing tight $(1 - 1 / e - \epsilon)$-approximation algorithms for this problem in most cases. We first considered the case when the number of bins is a constant. Previously a randomized approximation algorithm can obtain approximation ratio $(1 - 1 / e-\epsilon)$ based on the involved continuous greedy technique. Here we provide a simple combinatorial deterministic algorithm with ratio $(1-1/e)$ by directly applying the greedy technique. We then generalized the result to arbitrary number of bins. When the capacity of bins are identical, we design a combinatorial and deterministic algorithm which can achieve the tight approximation ratio $(1 - 1 / e-\epsilon)$. When the ratio between the maximum capacity and the minimum capacity of the bins is bounded by a constant, we provide a $(1/2-\epsilon)$-approximation algorithm which is also combinatorial and deterministic. We can further boost the approximation ratio to $(1 - 1 / e - \epsilon)$ with the help of continuous greedy technique, which gives a tight randomized approximation algorithm for this case.
Management strategies in a SEIR model of COVID 19 community spread

The 2019 Novel Corona virus infection (COVID-19) is an ongoing public health emergency of international focus. Significant gaps persist in our knowledge of COVID-19 epidemiology, transmission dynamics, investigation tools and management, despite (or possibly because of) the fact that the outbreak is an unprecedented global threat. On the positive side, enough is currently known about the epidemic process to permit the construction of mathematical predictive models. We construct and analyze here one first step: that of adapting a traditional SEIR epidemic model to the specific dynamic compartments and epidemic parameters of COVID-19, as it spreads in an age-heterogeneous community. We analyze the current management strategy of the epidemic course (travel bans, service closures and interruptions, social distancing). We generate predictions, and assess the efficiency of these control measures, in the context in which their sustainability is currently being questioned.
Keyword-Attentive Deep Semantic Matching

Deep Semantic Matching is a crucial component in various natural language processing applications such as question and answering (QA), where an input query is compared to each candidate question in a QA corpus in terms of relevance. Measuring similarities between a query-question pair in an open domain scenario can be challenging due to diverse word tokens in the queryquestion pair. We propose a keyword-attentive approach to improve deep semantic matching. We first leverage domain tags from a large corpus to generate a domain-enhanced keyword dictionary. Built upon BERT, we stack a keyword-attentive transformer layer to highlight the importance of keywords in the query-question pair. During model training, we propose a new negative sampling approach based on keyword coverage between the input pair. We evaluate our approach on a Chinese QA corpus using various metrics, including precision of retrieval candidates and accuracy of semantic matching. Experiments show that our approach outperforms existing strong baselines. Our approach is general and can be applied to other text matching tasks with little adaptation.
Driver Modeling Through Deep Reinforcement Learning and Behavioral Game Theory

In this work, a synergistic combination of deep reinforcement learning and hierarchical game theory is proposed as a modeling framework for behavioral predictions of drivers in highway driving scenarios. The modeling framework presented in this work can be used in a high-fidelity traffic simulator consisting of multiple human decision-makers. This simulator can reduce the time and effort spent for testing autonomous vehicles by allowing safe and quick assessment of self-driving control algorithms. To demonstrate the fidelity of the proposed modeling framework, game-theoretical driver models are compared with real human driver behavior patterns extracted from two different sets of traffic data.
Gender differences in wage expectations

Using an own survey on wage expectations among students at two Swiss institutions of higher education, we examine the wage expectations of our respondents along two main lines. First, we investigate the rationality of wage expectations by comparing average expected wages from our sample with those of similar graduates; further, we examine how our respondents revise their expectations when provided information about actual wages. Second, using causal mediation analysis, we test whether the consideration of a rich set of personal and professional controls, inclusive of preferences on family formation and number of children in addition to professional preferences, accounts for the difference in wage expectations across genders. Results suggest that both males and females overestimate their wages compared to actual ones and that males respond in an overconfident manner to information about realized wages. Personal mediators alone cannot explain the indirect effect of gender on wage expectations; however, when combined with professional mediators, this results in a quantitatively large reduction in the unexplained effect of gender on wage expectations. Nonetheless, a non-negligible and statistically significant direct (or unexplained) effect of gender on wage expectations remains in several, but not all specifications.
Joint lattice QCD–dispersion theory analysis confirms the quark-mixing top-row unitarity deficit

Recently, the first ever lattice computation of the $\gamma W$-box radiative correction to the rate of the semileptonic pion decay allowed for a reduction of the theory uncertainty of that rate by a factor of $\sim3$. A recent dispersion evaluation of the $\gamma W$-box correction on the neutron also led to a significant reduction of the theory uncertainty, but shifted the value of $V_{ud}$ extracted from the neutron and superallowed nuclear $\beta$ decay, resulting in a deficit of the CKM unitarity in the top row. A direct lattice computation of the $\gamma W$-box correction for the neutron decay would provide an independent cross-check for this result but is very challenging. Before those challenges are overcome, we propose a hybrid analysis, converting the lattice calculation on the pion to that on the neutron by a combination of dispersion theory and phenomenological input. The new prediction for the universal radiative correction to free and bound neutron $\beta$-decay reads $\Delta_R^V=0.02477(24)$, in excellent agreement with the dispersion theory result $\Delta_R^V=0.02467(22)$. Combining with other relevant information, the top-row CKM unitarity deficit persists.
Joint Deep Cross-Domain Transfer Learning for Emotion Recognition

Deep learning has been applied to achieve significant progress in emotion recognition. Despite such substantial progress, existing approaches are still hindered by insufficient training data, and the resulting models do not generalize well under mismatched conditions. To address this challenge, we propose a learning strategy which jointly transfers the knowledge learned from rich datasets to source-poor datasets. Our method is also able to learn cross-domain features which lead to improved recognition performance. To demonstrate the robustness of our proposed framework, we conducted experiments on three benchmark emotion datasets including eNTERFACE, SAVEE, and EMODB. Experimental results show that the proposed method surpassed state-of-the-art transfer learning schemes by a significant margin.
The Method of Conditional Expectations for PAPR and Cubic Metric Reduction

The OFDM waveform exhibits high fluctuation in the signal envelope which causes distortion in the nonlinear power amplifier of the transmitter. Peak-to-Average Power Ratio (PAPR) and Cubic Metric (CM) are the common metrics to quantify the phenomenon. A promising approach for PAPR or CM reduction is Sign Selection which is based on altering the signs of the data symbols. In this paper, the Method of Conditional Expectations (CE Method) is proposed to obtain a competing suboptimal solution to the Sign Selection problem. For PAPR reduction, a surrogate metric is introduced which allows for an efficient application of the CE Method. For CM reduction, the tractability of the definition of CM is exploited to this end. The algorithm is analyzed to obtain an upper bound on the worst-case reduced metric value. A noticeable characteristic is the persistent reduction capability for a wide range of subcarrier numbers. In particular, simulations show a reduction of the so-called "effective PAPR" to about 6.5 dB from 10.5 dB and 11.7 dB respectively for 64 to 1024 subcarriers. A similar steady reduction of 3 dB is observed for CM. In addition, the CE Method leads to a pruned version of Sign Selection which halves the rate loss.
Machine learning for quantum matter

ABSTRACT Quantum matter, the research field studying phases of matter whose properties are intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter physics, materials science, statistical mechanics, quantum information, quantum gravity, and large-scale numerical simulations. Recently, researchers interested in quantum matter and strongly correlated quantum systems have turned their attention to the algorithms underlying modern machine learning with an eye on making progress in their fields. Here we provide a short review on the recent development and adaptation of machine learning ideas for the purpose advancing research in quantum matter, including ideas ranging from algorithms that recognize conventional and topological states of matter in synthetic experimental data, to representations of quantum states in terms of neural networks and their applications to the simulation and control of quantum systems. We discuss the outlook for future developments in areas at the intersection between machine learning and quantum many-body physics. Graphical abstract
NEEDS ANALYSIS: ESP SYLLABUS DESIGN FOR INDONESIAN EFL NURSING STUDENTS

Language in the field of English for Specific Purposes (ESP) is learnt to equip the learners for entry into a more specific linguistic environment, thus the most prominent feature in ESP course design is that the syllabus is based on an analysis of the students’ needs (Basturkmen 2006). The aims of this study are to explore the needs of nursing students, faculty members in the nursing department and professional nurses to design an ESP syllabus for Indonesian nursing students; also to discuss the implications for EFL teachers who teach English for Nurses. The needs analysis were conducted by distributing questionnaires to and interviewing the respondents. The answers from the respondents were analyzed based on comprehensive concept of need analysis proposed by Dudley-Evans and St. John. The findings showed that most students consider themselves as poor in vocabulary and weak in speaking, listening, writing and pronunciation. Hence, it is crucial to improve their skills to enable them communicating effectively with foreign patients and avoiding misunderstanding which may happen when interacting with foreign patients. The innovative ESP syllabus for Indonesian nurses was then developed based on these findings and the theory of material development proposed by Harding.
“In-Bore” MRI-Guided Prostate Biopsy for Prostate Cancer Diagnosis: Results from 140 Consecutive Patients

Objectives: Transrectal ultrasound-guided biopsy (TRUS-GB) is the current reference standard procedure for diagnosis of prostate cancer (PCa) but this procedure has limitations related to the low detection rate (DR) described in the literature. The aim of the study was to evaluate the DR efficiency, and complication rate in a pure “in-bore” magnetic resonance imaging-guided biopsy (MRI-GB) series according to the Prostate Imaging Reporting and Data System, version 2 (PI-RADS v2). Materials and Methods: From July 2015 to April 2018, a series of 142 consecutive patients undergoing MRI-GB were prospectively enrolled. According to the European Society of Urogenital Radiology guidelines, the presence of clinically significant PCa (csPCa) on multiparametric magnetic resonance imaging was defined as equivocal, likely, or highly likely according to a PI-RADS v2, score of 3, 4, or 5, respectively. Results: Of 142 patients, 76 (53.5%) were biopsy naive and 66 (46.5%) had ≤ 1 previous negative set of random TRUS-GB findings. The MRI-GB findings were positive in 75 of 142 patients with a DR of 52.8%. Of the 76 patients with ≤ 1 previous set of TRUS-GB, 43 had PCa found by MRI-GB, with a DR of 57.3%. The DR in the 66 biopsy-naive patients was 48% (32/66). Of the 75 patients with positive biopsy findings, 54 (80.5%) were found to have csPCa on histological examination. Of these 54 patients, 28 had an International Society of Urological Pathology grade 2; 5 had grade 3, 19 had grade 4, and 2 had grade 5. Considering the anatomic distribution of the index lesions using the PI-RADS v2 scheme, the probability of PCa was greater for lesions located in the peripheral zone (55 of 75, 73.3%) than for those in the central zone (20 of 75, 26.7%). Conclusions: Our study conducted on 142 patients confirmed the greater DR of csPCa by MRI-GB, with a very low number of cores needed and a negligible incidence of complications, especially in patients with a previous negative biopsy. MRI-GB is optimal for the diagnosis of anterior and central lesions.