Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal transport meets noisy label robust loss and MixUp regularization for domain adaptation

It is common in computer vision to be confronted with domain shift: images which have the same class but different acquisition conditions. In domain adaptation (DA), one wants to classify unlabeled target images using source labeled images. Unfortunately, deep neural networks trained on a source training set perform poorly on target images which do not belong to the training domain. One strategy to improve these performances is to align the source and target image distributions in an embedded space using optimal transport (OT). However OT can cause negative transfer, i.e. aligning samples with different labels, which leads to overfitting especially in the presence of label shift between domains. In this work, we mitigate negative alignment by explaining it as a noisy label assignment to target images. We then mitigate its effect by appropriate regularization. We propose to couple the MixUp regularization \citep{zhang2018mixup} with a loss that is robust to noisy labels in order to improve domain adaptation performance. We show in an extensive ablation study that a combination of the two techniques is critical to achieve improved performance. Finally, we evaluate our method, called \textsc{mixunbot}, on several benchmarks and real-world DA problems.
RECOLLEMENTS OF DERIVED CATEGORIES OF GRADED GENTLE ALGEBRAS AND SURFACE CUTS

In this paper we study recollements of the derived categories of graded quadratic monomial algebras induced by idempotents. In particular, we show that in such recollements, all three terms are given by graded quadratic monomial algebras which we explicitly determine. We then apply these results to graded gentle algebras and their associated surface model, in which case, all three terms are derived categories of graded gentle algebras. We show that these recolle- ments correspond to cutting the corresponding surfaces along simple curves connecting boundary components. For homologically smooth and proper graded gentle algebras, these recollements restrict to the bounded derived categories and induce recollements of the corresponding partially wrapped Fukaya categories. We then apply our results to show that silting reductions and reductions of simple-minded collections of graded gentle algebras correspond to cutting the associated surfaces. Furthermore, we determine for which homologically smooth and proper graded gentle algebras the perfect derived category admits full exceptional sequences. We also show that almost all homologically smooth and proper graded gentle algebras admit silting objects or, equivalently, simple-minded collections. We further conjecture that our classiﬁcation of graded gentle algebras admitting silting objects is complete and that, in particular, there is an explicitly determined inﬁnite family of graded gentle algebras which do not admit silting objects.
Social insects and beyond: The physics of soft, dense invertebrate aggregations

Aggregation is a common behavior by which groups of organisms arrange into cohesive groups. Whether suspended in the air (like honey bee clusters), built on the ground (such as army ant bridges), or immersed in water (such as sludge worm blobs), these collectives serve a multitude of biological functions, from protection against predation to the ability to maintain a relatively desirable local environment despite a variable ambient environment. In this review, we survey dense aggregations of a variety of insects, other arthropods, and worms from a soft matter standpoint. An aggregation can be orders of magnitude larger than its individual organisms, consisting of tens to hundreds of thousands of individuals, and yet functions as a coherent entity. Understanding how aggregating organisms coordinate with one another to form a superorganism requires an interdisciplinary approach. We discuss how considering the physics of an aggregation can yield additional insights to those gained from ecological and physiological considerations, given that the aggregating individuals exchange information, energy, and matter continually with the environment and one another. While the connection between animal aggregations and the physics of non-living materials has been proposed since the early 1900s, the recent advent of physics of behavior studies provides new insights into social interactions governed by physical principles. Current efforts focus on eusocial insects; however, we show that these may just be the tip of an iceberg of superorganisms that take advantage of physical interactions and simple behavioral rules to adapt to changing environments. By bringing attention to a wide range of invertebrate aggregations, we wish to inspire a new generation of scientists to explore collective dynamics and bring a deeper understanding of the physics of dense living aggregations.
Supermodular f-divergences and bounds on lossy compression and generalization error with mutual f-information

In this paper, we introduce super-modular f -divergences and provide three applications for them: (i) we introduce Sanov’s upper bound on the tail probability of sum of independent random variables based on super-modular f -divergence and show that our generalized Sanov’s bound strictly improves over ordinary one, (ii) we consider the lossy compression problem which studies the set of achievable rates for a given distortion and code length. We extend the rate-distortion function using mutual f -information and provide new and strictly better bounds on achievable rates in the ﬁnite blocklength regime using super-modular f -divergences, and (iii) we provide a connection between the generalization error of algorithms with bounded input/output mutual f -information and a generalized rate-distortion problem. This connection allows us to bound the generalization error of learning algorithms using lower bounds on the rate-distortion function. Our bound is based on a new lower bound on the rate distortion function that (for some examples) strictly improves over previously best-known bounds. Moreover, super-modular f -divergences are utilized to reduce the dimension of the problem and obtain single-letter bounds.
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning

We propose derivative-informed neural operators (DINOs), a general family of neural networks to approximate operators as infinite-dimensional mappings from input function spaces to output function spaces or quantities of interest. After discretizations both inputs and outputs are high-dimensional. We aim to approximate not only the operators with improved accuracy but also their derivatives (Jacobians) with respect to the input function-valued parameter to empower derivative-based algorithms in many applications, e.g., Bayesian inverse problems, optimization under parameter uncertainty, and optimal experimental design. The major difficulties include the computational cost of generating derivative training data and the high dimensionality of the problem leading to large training cost. To address these challenges, we exploit the intrinsic low-dimensionality of the derivatives and develop algorithms for compressing derivative information and efficiently imposing it in neural operator training yielding derivative-informed neural operators. We demonstrate that these advances can significantly reduce the costs of both data generation and training for large classes of problems (e.g., nonlinear steady state parametric PDE maps), making the costs marginal or comparable to the costs without using derivatives, and in particular independent of the discretization dimension of the input and output functions. Moreover, we show that the proposed DINO achieves significantly higher accuracy than neural operators trained without derivative information, for both function approximation and derivative approximation (e.g., Gauss-Newton Hessian), especially when the training data are limited.
Comparing Formulaic Language in Human and Machine Translation: Insight from a Parliamentary Corpus

A recent study has shown that, compared to human translations, neural machine translations contain more strongly-associated formulaic sequences made of relatively high-frequency words, but far less strongly-associated formulaic sequences made of relatively rare words. These results were obtained on the basis of translations of quality newspaper articles in which human translations can be thought to be not very literal. The present study attempts to replicate this research using a parliamentary corpus. The results confirm the observations on the news corpus, but the differences are less strong. They suggest that the use of text genres that usually result in more literal translations, such as parliamentary corpora, might be preferable when comparing human and machine translations.
Exploring the Effectiveness of Self-supervised Learning and Classifier Chains in Emotion Recognition of Nonverbal Vocalizations

We present an emotion recognition system for nonverbal vocalizations (NVs) submitted to the ExVo Few-Shot track of the ICML Expressive Vocalizations Competition 2022. The proposed method uses self-supervised learning (SSL) models to extract features from NVs and uses a classiﬁer chain to model the label dependency between emotions. Experimental results demon-strate that the proposed method can signiﬁcantly improve the performance of this task compared to several baseline methods. Our proposed method obtained a mean concordance correlation coefﬁ-cient (CCC) of 0 . 725 in the validation set and 0 . 739 in the test set, while the best baseline method only obtained 0 . 554 in the validation set. We publicate our code at https://github. com/Aria-K-Alethia/ExVo to help others to reproduce our experimental results.
DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation

Recently, one critical issue looms large in the field of recommender systems – there are no effective benchmarks for rigorous evaluation – which consequently leads to unreproducible evaluation and unfair comparison. We, therefore, conduct studies from the perspectives of practical theory and experiments, aiming at benchmarking recommendation for rigorous evaluation. Regarding the theoretical study, a series of hyper-factors affecting recommendation performance throughout the whole evaluation chain are systematically summarized and analyzed via an exhaustive review on 141 papers published at eight top-tier conferences within 2017-2020. We then classify them into model-independent and model-dependent hyper-factors, and different modes of rigorous evaluation are defined and discussed in-depth accordingly. For the experimental study, we release DaisyRec 2.0 library by integrating these hyper-factors to perform rigorous evaluation, whereby a holistic empirical study is conducted to unveil the impacts of different hyper-factors on recommendation performance. Supported by the theoretical and experimental studies, we finally create benchmarks for rigorous evaluation by proposing standardized procedures and providing performance of ten state-of-the-arts across six evaluation metrics on six datasets as a reference for later study. Overall, our work sheds light on the issues in recommendation evaluation, provides potential solutions for rigorous evaluation, and lays foundation for further investigation.
The Problem of Semantic Shift in Longitudinal Monitoring of Social Media: A Case Study on Mental Health During the COVID-19 Pandemic

Social media allows researchers to track societal and cultural changes over time based on language analysis tools. Many of these tools rely on statistical algorithms which need to be tuned to specific types of language. Recent studies have shown the absence of appropriate tuning, specifically in the presence of semantic shift, can hinder robustness of the underlying methods. However, little is known about the practical effect this sensitivity may have on downstream longitudinal analyses. We explore this gap in the literature through a timely case study: understanding shifts in depression during the course of the COVID-19 pandemic. We find that inclusion of only a small number of semantically-unstable features can promote significant changes in longitudinal estimates of our target outcome. At the same time, we demonstrate that a recently-introduced method for measuring semantic shift may be used to proactively identify failure points of language-based models and, in turn, improve predictive generalization.
40 Years of Designing Code Comprehension Experiments: A Systematic Mapping Study

The relevance of code comprehension in a developer’s daily work was recognized more than 40 years ago. Consequently, many experiments were conducted to find out how developers could be supported during code comprehension and which code characteristics contribute to better comprehension. Today, such studies are more common than ever. While this is great for advancing the field, the number of publications makes it difficult to keep an overview. Additionally, designing rigorous code comprehension experiments with human participants is a challenging task, and the multitude of design options can make it difficult for researchers, especially newcomers to the field, to select a suitable design. We therefore conducted a systematic mapping study of 95 source code comprehension experiments published between 1979 and 2019. By structuring the design characteristics of code comprehension studies, we provide a basis for subsequent discussion of the huge diversity of design options in the face of a lack of basic research on their consequences and comparability. We describe what topics have been studied, as well as how these studies have been designed, conducted, and reported. Frequently chosen design options and deficiencies are pointed out to support researchers of all levels of domain expertise in designing their own studies.
Predicting Team Performance with Spatial Temporal Graph Convolutional Networks

This paper presents a new approach for predicting team performance from the behavioral traces of a set of agents. This spatiotemporal forecasting problem is very relevant to sports analytics challenges such as coaching and opponent modeling. We demonstrate that our proposed model, Spatial Temporal Graph Convolutional Networks (ST-GCN), outperforms other classification techniques at predicting game score from a short segment of player movement and game features. Our proposed architecture uses a graph convolutional network to capture the spatial relationships between team members and Gated Recurrent Units to analyze dynamic motion information. An ablative evaluation was performed to demonstrate the contributions of different aspects of our architecture.
The Structure of Gamma Ray Burst Jets

Due to relativistic bulk motion, the structure and orientation of gamma-ray burst (GRB) jets have a fundamental role in determining how they appear. The recent discovery of the GW170817 binary neutron star merger and the associated GRB boosted the interest in the modeling and search for signatures of the presence of a (possibly quasi-universal) jet structure in long and short GRBs. In this review, following a pedagogical approach, we summarize the history of GRB jet structure research over the last two decades, from the inception of the idea of a universal jet structure to the current understanding of the complex processes that shape the structure, which involves the central engine that powers the jet and the interaction of the latter with the progenitor vestige. We put some emphasis on the observable imprints of jet structure on prompt and afterglow emission and on the luminosity function, favoring intuitive reasoning over technical explanations.
Langevin Monte Carlo for Contextual Bandits

We study the efficiency of Thompson sampling for contextual bandits. Existing Thompson sampling-based algorithms need to construct a Laplace approximation (i.e., a Gaussian distribution) of the posterior distribution, which is inefficient to sample in high dimensional applications for general covariance matrices. Moreover, the Gaussian approximation may not be a good surrogate for the posterior distribution for general reward generating functions. We propose an efficient posterior sampling algorithm, viz., Langevin Monte Carlo Thompson Sampling (LMC-TS), that uses Markov Chain Monte Carlo (MCMC) methods to directly sample from the posterior distribution in contextual bandits. Our method is computationally efficient since it only needs to perform noisy gradient descent updates without constructing the Laplace approximation of the posterior distribution. We prove that the proposed algorithm achieves the same sublinear regret bound as the best Thompson sampling algorithms for a special case of contextual bandits, viz., linear contextual bandits. We conduct experiments on both synthetic data and real-world datasets on different contextual bandit models, which demonstrates that directly sampling from the posterior is both computationally efficient and competitive in performance.
Voltage-amplified heat rectification in SIS junctions

The control of thermal fluxes -- magnitude and direction, in mesoscale and nanoscale electronic circuits can be achieved by means of heat rectification using thermal diodes in two-terminal systems. The rectification coefficient $\mathcal{R}$, given by the ratio of forward and backward heat fluxes, varies with the design of the diode and the working conditions under which the system operates. A value of $\mathcal{R}\ll 1$ or $\mathcal{R}\gg 1$ is a signature of high heat rectification performance but current solutions allowing such ranges, necessitate rather complex designs. Here, we propose a simple solution: the use of a superconductor-insulator-superconductor (SIS) junction under an applied fast oscillating (THz range) voltage as the control of the heat flow direction and magnitude can be done by tuning the initial value of the superconducting phase. Our theoretical model based on the Green functions formalism and coherent transport theory, shows a possible sharp rise of the heat rectification coefficient with values up to $\mathcal{R} \approx 500$ beyond the adiabatic regime. The influence of quantum coherent effects on heat rectification in the SIS junction is highlighted.
SSMI: How to Make Objects of Interest Disappear without Accessing Object Detectors?

Most black-box adversarial attack schemes for object detectors mainly face two shortcomings: requiring access to the target model and generating inefficient adversarial examples (failing to make objects disappear in large numbers). To overcome these shortcomings, we propose a black-box adversarial attack scheme based on semantic segmentation and model inversion (SSMI). We first locate the position of the target object using semantic segmentation techniques. Next, we design a neighborhood background pixel replacement to replace the target region pixels with background pixels to ensure that the pixel modifications are not easily detected by human vision. Finally, we reconstruct a machine-recognizable example and use the mask matrix to select pixels in the reconstructed example to modify the benign image to generate an adversarial example. Detailed experimental results show that SSMI can generate efficient adversarial examples to evade human-eye perception and make objects of interest disappear. And more importantly, SSMI outperforms existing same kinds of attacks. The maximum increase in new and disappearing labels is 16%, and the maximum decrease in mAP metrics for object detection is 36%.
MultiEarth 2022 Deforestation Challenge - ForestGump

The estimation of deforestation in the Amazon Forest is challenge task because of the vast size of the area and the difﬁculty of direct human access. However, it is a crucial problem in that deforestation results in serious environmental problems such as global climate change, reduced biodi-versity, etc. In order to effectively solve the problems, satellite imagery would be a good alternative to estimate the deforestation of the Amazon. With a combination of optical images and Synthetic aperture radar (SAR) images, observation of such a massive area regardless of weather condi-tions become possible. In this paper, we present an accu-rate deforestation estimation method with conventional U-Net and comprehensive data processing. The diverse channels of Sentinel-1, Sentinel-2 and Landsat 8 are carefully selected and utilized to train deep neural networks. With the proposed method, deforestation status for novel queries are successfully estimated with high accuracy.
Learning Distribution Grid Topologies: A Tutorial

Unveiling feeder topologies from data is of paramount importance to advance situational awareness and proper utilization of smart resources in power distribution grids. This tutorial summarizes, contrasts, and establishes useful links between recent works on topology identification and detection schemes that have been proposed for power distribution grids. The primary focus is to highlight methods that overcome the limited availability of measurement devices in distribution grids, while enhancing topology estimates using conservation laws of power-flow physics and structural properties of feeders. Grid data from phasor measurement units or smart meters can be collected either passively in the traditional way, or actively, upon actuating grid resources and measuring the feeder’s voltage response. Analytical claims on feeder identifiability and detectability are reviewed under disparate meter placement scenarios. Such topology learning claims can be attained exactly or approximately so via algorithmic solutions with various levels of computational complexity, ranging from least-squares fits to convex optimization problems, and from polynomial-time searches over graphs to mixed-integer programs. Although the emphasis is on radial single-phase feeders, extensions to meshed and/or multiphase circuits are sometimes possible and discussed. This tutorial aspires to provide researchers and engineers with knowledge of the current state-of-the-art in tractable distribution grid learning and insights into future directions of work.
Diffusion approximation of a network model of meme popularity

Models of meme propagation on social networks, in which memes compete for limited user attention, can successfully reproduce the heavy-tailed popularity distributions observed in online settings. While system-wide popularity distributions have been derived analytically, the dynamics of individual meme trajectories have thus far evaded description. To address this, we formulate the diffusion of a given meme as a one-dimensional stochastic process, whose fluctuations result from aggregating local network dynamics using classic and generalised central limit theorems, with the latter based on stable distribution theory. Ultimately, our approach decouples competing trajectories of meme popularities, allowing them to be simulated independently, and thus parallelised, and expressed in terms of Fokker-Planck equations.