Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Estimation and Optimization of Directed Information Over Continuous Spaces

This work develops a new method for estimating and optimizing the directed information rate between two jointly stationary and ergodic stochastic processes. Building upon recent advances in machine learning, we propose a recurrent neural network (RNN)-based estimator which is optimized via gradient ascent over the RNN parameters. The estimator does not require prior knowledge of the underlying joint/marginal distributions and can be easily optimized over continuous input processes realized by a deep generative model. We prove consistency of the proposed estimation and optimization methods and combine them to obtain end-to-end performance guarantees. Applications for channel capacity estimation of continuous channels with memory are explored, and empirical results demonstrating the scalability and accuracy of our method are provided. When the channel is memoryless, we investigate the mapping learned by the optimized input generator.
Harmonising Quality to the Lowest Clinical Diagnostic Standard?: The Case against Regulation of 'in vitro' Diagnostics (IVDs) for Use in Clinical Diagnostic Laboratories

A revised framework for the regulation of in vitro diagnostic devices (IVDs) will come into force on July 1, 2010 that aims to 'ensure that public and personal health are adequately protected', but which instead may lead to adverse outcomes in clinical diagnosis and management. The regulatory process aims to regulate all IVDs, including those used by clinical diagnostic laboratories, which are already subject to scrutiny as part of the current laboratory accreditation process. The intended aim of the IVDs regulatory process also appears to imply that the current procedure for regulation of clinical laboratories and their use of current IVDs is somehow inadequate and is placing the public at risk, although the evidence clearly indicates the opposing view, showing ongoing improvements over time in clinical diagnostic practice, driven by current accreditation requirements. In contrast, existing evidence related to regulation of IVDs in the USA, for example, highlights several failings in this process that leads to ongoing use of inappropriate test panels or methodologies in clinical diagnostics. This is largely due to direct and indirect costs associated with, and the effort needed to fulfil, regulatory requirements. The end result is that manufacturers of IVDs will conform to existing regulatory standards ahead of clinical best practice standards, and that future improvements to clinical best practice standards may not be translated into clinical practice because the regulatory burden may act as an impediment to manufacturers to undertake any future improvements in existing IVDs. It is therefore difficult to identify how the new framework will achieve its goal of 'ensuring public and personal health' with respect to the regulation of IVDs used in clinical diagnostic laboratories, since 'public and personal health' is best served by the timely introduction of appropriate diagnostics. Thus, the alternative outcome, that public and personal health will suffer following its implementation is the clear danger.
Impact of 
in situ
 controlled disorder screening on fractional quantum Hall effects and composite fermion transport

We examine the impact of random potential due to remote impurites (RIs) and its in-situ controlled screening on fractional quantum Hall effects (FQHEs) around Landau-level filling factor $\nu = 1/2$. The experiment is made possible by using a dual-gate GaAs quantum well (QW) that allows for the independent control of the density $n_{e}$ of the two-dimensional electron system in the QW and that ($n_\text{SL}$) of excess electrons in the modulation-doping superlattice. As the screening is reduced by decreasing $n_\text{SL}$ at a fixed $n_{e}$, we observe a decrease in the apparent energy gap of the FQHEs deduced from thermal activation, which signifies a corresponding increase in the disorder broadening $\Gamma$ of composite fermions (CFs). Interestingly, the increase in $\Gamma$ is accompanied by a noticeable increase in the longitudinal resistivity at $\nu = 1/2$ ($\rho_{1/2}$), with a much stronger correlation with $\Gamma$ than electron mobility $\mu$ has. The in-situ control of RI screening enables us to disentangle the contributions of RIs and background impurities (BIs) to $\rho_{1/2}$, with the latter in good agreement with the CF theory. We construct a scaling plot that helps in estimating the BI contribution to $\rho_{1/2}$ for a given set of $n_{e}$ and $\mu$.
ANNA”:" Enhanced Language Representation for Question Answering

Pre-trained language models have brought significant improvements in performance in a variety of natural language processing tasks. Most existing models performing state-of-the-art results have shown their approaches in the separate perspectives of data processing, pre-training tasks, neural network modeling, or fine-tuning. In this paper, we demonstrate how the approaches affect performance individually, and that the language model performs the best results on a specific question answering task when those approaches are jointly considered in pre-training models. In particular, we propose an extended pre-training task, and a new neighbor-aware mechanism that attends neighboring tokens more to capture the richness of context for pre-training language modeling. Our best model achieves new state-of-the-art results of 95.7% F1 and 90.6% EM on SQuAD 1.1 and also outperforms existing pre-trained language models such as RoBERTa, ALBERT, ELECTRA, and XLNet on the SQuAD 2.0 benchmark.
Direct Numerical Simulation of High Prandtl Number Fluid Flow in the Downcomer of an Advanced Reactor

Abstract The design of advanced nuclear reactors [Generation IV (Gen IV)] involves an array of challenging fluid-flow issues that affect its safety and performance. Given that Gen IV designs have improved passive safety features, the downcomer plays a crucial role in loss-of-power scenarios. Fluid-flow behavior in the downcomer can involve forced to mixed to natural convection, and characterizing the heat transfer for these changing regimes is a daunting challenge. The creation of a high-resolution heat transfer numerical database can potentially support the development of precise and affordable reduced-resolution heat transfer models. These models can be designed based on a multiscale hierarchy developed as part of the recently U.S. Department of Energy–funded Center of Excellence for Thermal Fluids Applications in Nuclear Energy, which can help address industrial-driven issues associated with the heat transfer behavior of advanced reactors. In this paper, the downcomer is simplified to heated parallel plates, and high Prandtl number fluid (FLiBe) is considered for all simulations. The calculations are performed for a wide range of Richardson numbers from 0 to 400 at two different FLiBe Prandtl numbers (12 and 24), which result in 40 simulated cases in total. Time-averaged and time series statistics, as well as Nusselt number correlations, are investigated to illuminate mixed convection behavior. The calculated database will be instrumental in understanding flow behavior in the downcomer. Ultimately, we aim to evaluate existing heat transfer correlations, and some modifications are proposed.
Derived superequivalences for spin symmetric groups and odd sl(2)-categorifications

We show that actions of the odd categorification of sl(2) induce derived superequivalences analogous to those introduced by Chuang and Rouquier. Using Kang, Kashiwara, and Oh's action of the odd 2-category on blocks of the cyclotomic affine Hecke-Clifford algebra, our equivalences imply that blocks related by a certain affine Weyl group action are derived equivalent. By recent results of Kleshchev and Livesey, we show this implies Broue's abelian defect conjecture for the modular representations of the spin symmetric group.
Metaphors in Pre-Trained Language Models: Probing and Generalization Across Datasets and Languages

Human languages are full of metaphorical expressions. Metaphors help people understand the world by connecting new concepts and domains to more familiar ones. Large pre-trained language models (PLMs) are therefore assumed to encode metaphorical knowledge useful for NLP systems. In this paper, we investigate this hypothesis for PLMs, by probing metaphoricity information in their encodings, and by measuring the cross-lingual and cross-dataset generalization of this information. We present studies in multiple metaphor detection datasets and in four languages (i.e., English, Spanish, Russian, and Farsi). Our extensive experiments suggest that contextual representations in PLMs do encode metaphorical knowledge, and mostly in their middle layers. The knowledge is transferable between languages and datasets, especially when the annotation is consistent across training and testing sets. Our findings give helpful insights for both cognitive and NLP scientists.
Job satisfaction among staffs of three radio stations in Kuala Lumpur

The purpose of the study is to identify the job satisfaction among staff of three radio stations in Kuala Lumpur. In this study, the job satisfaction among staff in broadcasting was affected by several factors, namely work environment, promotion, recognition, reward, and salary. This study presented the results from a sample of 200 respondents of three radio stations in Kuala Lumpur, which consisted of Ultra 101.3 Fm, IM4U Fm, and KL Fm. The questionnaires were distributed to 250 respondents, but only 200 complete sets were retrieved. Correlation test was used to measure the significant level of all the variables used in the study. Other than that, the Herzberg Two-factor Theory also helped to support this study towards job satisfaction. The results of all independents variables showed a strong relationship and the variables were significant; (recognition at r = 0.959, promotion at r = 0.956, salary at r = 0.946, work environment at r = 0.919 and reward at r = 0.878). It was concluded that job satisfaction was affected by various factors, namely, work environment, promotion, recognition, reward, and salary within the organization. All of the hypotheses were accepted and this study is important as it helps managers to enhance job satisfaction among staff, including those attached to the radio stations.
REGTR: End-to-end Point Cloud Correspondences with Transformers

Despite recent success in incorporating learning into point cloud registration, many works focus on learning feature descriptors and continue to rely on nearest-neighbor feature matching and outlier filtering through RANSAC to obtain the final set of correspondences for pose estimation. In this work, we conjecture that attention mechanisms can replace the role of explicit feature matching and RANSAC, and thus propose an end-to-end framework to directly predict the final set of correspondences. We use a network architecture consisting primarily of transformer layers containing self and cross attentions, and train it to predict the probability each point lies in the overlapping region and its corresponding position in the other point cloud. The required rigid transformation can then be estimated directly from the predicted correspondences without further post-processing. Despite its simplicity, our approach achieves state-of-the-art performance on 3DMatch and ModelNet benchmarks. Our source code can be found at https://github.com/yewzijian/RegTR.
On the Galois-Gauss sums of weakly ramified characters

Bley, Burns and Hahn used relative algebraic $K$-theory methods to formulate a precise conjectural link between the (second Adams-operator twisted) Galois-Gauss sums of weakly ramified Artin characters and the square root of the inverse different of finite, odd degree, Galois extensions of number fields. We provide concrete new evidence for this conjecture in the setting of extensions of odd prime-power degree by using a refined version of a well-known result of Ullom.
Ion alfvén velocity fluctuations and implications for the diffusion of streaming cosmic rays

The interstellar medium (ISM) of star-forming galaxies is magnetized and turbulent. Cosmic rays (CRs) propagate through it, and those with energies from ∼ GeV − TeV are likely subject to the streaming instability, whereby the wave damping processes balances excitation of resonant ionic Alfvén waves by the CRs, reaching an equilibrium in which the propagation speed of the CRs is very close to the local ion Alfvén velocity. The transport of streaming CRs is therefore sensitive to ionic Alfvén velocity fluctuations. In this paper we systematically study these fluctuations using a large ensemble of compressible MHD turbulence simulations. We show that for sub-Alfvénic turbulence, as applies for a strongly magnetized ISM, the ionic Alfvén velocity probability density function (PDF) is determined solely by the density fluctuations from shocked gas forming parallel to the magnetic field, and we develop analytical models for the ionic Alfvén velocity PDF up to second moments. For super-Alfvénic turbulence, magnetic and density fluctuations are correlated in complex ways, and these correlations as well as contributions from the magnetic fluctuations sets the ionic Alfvén velocity PDF. We discuss the implications of these findings for underlying “macroscopic” diffusion mechanisms in CRs undergoing the streaming instability, including modeling the macroscopic diffusion coefficient for the parallel transport in sub-Alfvénic plasmas. We also describe how, for highly-magnetized turbulent gas, the gas density PDF, and hence column density PDF, can be used to access information about ionic Alfvén velocity structure from observations of the magnetized ISM.
MQDD – Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain

This work proposes a new pipeline for leveraging data collected on the Stack Overﬂow web-site for pre-training a multimodal model for searching duplicates on question answering websites. Our multimodal model is trained on question descriptions and source codes in multiple programming languages. We design two new learning objectives to improve duplicate detection capabilities. The result of this work is a mature, ﬁne-tuned Multimodal Question Duplicity Detection (MQDD) model, ready to be integrated into a Stack Overﬂow search sys-tem, where it can help users ﬁnd answers for already answered questions. Alongside the MQDD model, we release two datasets re-lated to the software engineering domain. The ﬁrst Stack Overﬂow Dataset (SOD) represents a massive corpus of paired questions and answers. The second Stack Overﬂow Duplicity Dataset (SODD) contains data for training duplicate detection models.
Non-Thermal Fixed Points in Bose Gas Experiments

One of the most challenging tasks in physics has been understanding the route an out-of-equilibrium system takes to its thermalized state. This problem can be particularly overwhelming when one considers a many-body quantum system. However, several recent theoretical and experimental studies have indicated that some far-from-equilibrium systems display universal dynamics when close to a so-called non-thermal fixed point (NTFP), following a rescaling of both space and time. This opens up the possibility of a general framework for studying and categorizing out-of-equilibrium phenomena into well-defined universality classes. This paper reviews the recent advances in observing NTFPs in experiments involving Bose gases. We provide a brief introduction to the theory behind this universal scaling, focusing on experimental observations of NTFPs. We present the benefits of NTFP universality classes by analogy with renormalization group theory in equilibrium critical phenomena.
MutexMatch: Semi-supervised Learning with Mutex-based Consistency Regularization

The core issue in semi-supervised learning (SSL) lies in how to effectively leverage unlabeled data, whereas most existing methods tend to put a great emphasis on the utilization of high-confidence samples yet seldom fully explore the usage of low-confidence samples. In this article, we aim to utilize low-confidence samples in a novel way with our proposed mutex-based consistency regularization, namely MutexMatch. Specifically, the high-confidence samples are required to exactly predict "what it is" by the conventional true-positive classifier (TPC), while low-confidence samples are employed to achieve a simpler goal-to predict with ease "what it is not" by the true-negative classifier (TNC). In this sense, we not only mitigate the pseudo-labeling errors but also make full use of the low-confidence unlabeled data by the consistency of dissimilarity degree. MutexMatch achieves superior performance on multiple benchmark datasets, i.e., Canadian Institute for Advanced Research (CIFAR)-10, CIFAR-100, street view house numbers (SVHN), self-taught learning 10 (STL-10), and mini-ImageNet. More importantly, our method further shows superiority when the amount of labeled data is scarce, e.g., 92.23% accuracy with only 20 labeled data on CIFAR-10. Code has been released at https://github.com/NJUyued/MutexMatch4SSL.
A Joint Cross-Attention Model for Audio-Visual Fusion in Dimensional Emotion Recognition

Multimodal emotion recognition has recently gained much attention since it can leverage diverse and complementary modalities, such as audio, visual, and biosignals. However, most state-of-the- art audio-visual (A-V) fusion methods rely on recurrent networks or conventional attention mechanisms that do not effectively leverage the complementary nature of A-V modalities. This paper focuses on dimensional emotion recognition based on the fusion of facial and vocal modalities extracted from videos. We propose a joint cross-attention fusion model that can effectively exploit the complementary inter-modal relationships, allowing for an accurate prediction of valence and arousal. In particular, this model computes cross-attention weights based on the correlation between joint feature representations and individual modalities. By deploying a joint A-V feature representation into the cross-attention module, the performance of our fusion model improves significantly over the vanilla cross-attention module. Experimental results1 on the AffWild2 dataset highlight the robustness of our proposed A-V fusion model. It has achieved a concordance correlation coefficient (CCC) of 0.374 (0.663) and 0.363 (0.584) for valence and arousal, respectively, on the test set (validation set). This represents a significant improvement over the baseline for the third challenge of Affective Behavior Analysis in-the-Wild 2022 (ABAW3) competition, with a CCC of 0.180 (0.310) and 0.170 (0.170).
Stability threshold for 2D shear flows near Couette of the Navier-Stokes equation

In this paper, we consider the stability threshold of the 2D shear ﬂow ( U ( y ) , 0) ⊤ of the Navier-Stokes equation at high Reynolds number Re . When the shear ﬂow is near in Sobolev norm to the Couette ﬂow ( y, 0) ⊤ in some sense, we prove that if the initial data u 0 satisﬁes k u 0 − ( U ( y ) , 0) ⊤ k ≤ ǫRe − 1 / 3 , then the solution of the 2D Navier-Stokes equation approaches to some shear ﬂow which is also close to the Couette ﬂow for t ≫ Re 1 / 3 , as t → ∞ .
An Empirical Study and Comparison of Recent Few-Shot Object Detection Algorithms

—The generic object detection (GOD) task has been successfully tackled by recent deep neural networks, trained by an avalanche of annotated training samples from some common classes. However, it is still non-trivial to generalize these object detectors to the novel long-tailed object classes, which has only few labeled training samples. To this end, the Few-Shot Object Detection (FSOD) has been topical recently, as it mimics the humans’ ability of learning to learn , and intelligently transfers the learnt generic object knowledge from the common heavy-tailed, to the novel long-tailed object classes. Especially, the research in this emerging ﬁeld has been ﬂourish in the recent years with various benchmarks, backbones, and methodologies proposed. To review these FSOD works, there are several insightful FSOD survey articles [1]–[4] that systematically study and compare them as the groups of ﬁne-tuning/transfer learning, and meta-learning methods. In contrast, we compare these FSOD algorithms from the new perspective and taxonomy of their contributions, i.e., data-oriented, model-oriented, and algorithm oriented ones. Thus, an empirical study and comparison has been conducted on the recent achievements of FSOD. Furthermore, we also analyze the technical challenges, the merits and demerits of these methods, and envision the future directions of FSOD. Speciﬁcally, we give an overview of FSOD, including the problem deﬁnition, common datasets, and evaluation protocols. A new taxonomy is then proposed based on the role of prior knowledge during object detection of novel classes. Following this taxonomy, we provide a systematic review of the advances in FSOD. Finally, further discussions on performance, challenges, and future directions are presented.
Determining the number of factors in a large-dimensional generalised factor model

: This paper proposes new estimators of the number of factors for a generalised factor model with more relaxed assumptions than the strict factor model. Under the framework of large cross-sections N and large time dimensions T , we ﬁrst derive the bias-corrected estimator ˆ σ 2 ∗ of the noise variance in a generalised factor model by random matrix theory. Then we construct three information criteria based on ˆ σ 2 ∗ , further propose the consistent estimators of the number of factors. Finally, simulations and real data analysis illustrate that our proposed estimations are more accurate and avoid the overestimation in some existing works.
Uni6D: A Unified CNN Framework without Projection Breakdown for 6D Pose Estimation

As RGB-D sensors become more affordable, using RGB- D images to obtain high-accuracy 6D pose estimation results becomes a better option. State-of-the-art approaches typically use different backbones to extract features for RGB and depth images. They use a 2D CNN for RGB images and a perpixel point cloud network for depth data, as well as a fusion network for feature fusion. We find that the essential reason for using two independent backbones is the “projection breakdown” problem. In the depth image plane, the projected 3D structure of the physical world is preserved by the 1D depth value and its built-in 2D pixel coordinate (UV). Any spatial transformation that modifies UV, such as resize, flip, crop, or pooling operations in the CNN pipeline, breaks the binding between the pixel value and UV coordinate. As a consequence, the 3D structure is no longer preserved by a modified depth image or feature. To address this issue, we propose a simple yet effective method denoted as Uni6D that explicitly takes the extra UV data along with RGB-D images as input. Our method has a Unified CNN framework for 6D pose estimation with a single CNN backbone. In particular, the architecture of our method is based on Mask R-CNN with two extra heads, one named RT head for directly predicting 6D pose and the other named abc head for guiding the network to map the visible points to their coordinates in the 3D model as an auxiliary module. This end-to-end approach balances simplicity and accuracy, achieving comparable accuracy with state of the arts and 7.2x faster inference speed on the YCB-Video dataset.