Miracle-dz/UniFAR-FTUs huggingface dataset:
"It's a Match!" - A Benchmark of Task Affinity Scores for Joint Learning

While the promises of Multi-Task Learning (MTL) are attractive, characterizing the conditions of its success is still an open problem in Deep Learning. Some tasks may benefit from being learned together while others may be detrimental to one another. From a task perspective, grouping cooperative tasks while separating competing tasks is paramount to reap the benefits of MTL, i.e., reducing training and inference costs. Therefore, estimating task affinity for joint learning is a key endeavor. Recent work suggests that the training conditions themselves have a significant impact on the outcomes of MTL. Yet, the literature is lacking of a benchmark to assess the effectiveness of tasks affinity estimation techniques and their relation with actual MTL performance. In this paper, we take a first step in recovering this gap by (i) defining a set of affinity scores by both revisiting contributions from previous literature as well presenting new ones and (ii) benchmarking them on the Taskonomy dataset. Our empirical campaign reveals how, even in a small-scale scenario, task affinity scoring does not correlate well with actual MTL performance. Yet, some metrics can be more indicative than others.
The impact of neglected confounding and interactions in mixed-effects meta-regression

Analysts seldom include interaction terms in meta-regression model, what can introduce bias if an interaction is present. We illustrate this in the current paper by re-analyzing an example from research on acute heart failure, where neglecting an interaction might have led to erroneous inference and conclusions. Moreover, we perform a brief simulation study based on this example highlighting the eﬀects caused by omitting or unnecessarily including interaction terms. Based on our results, we recommend to always include interaction terms in mixed-eﬀects meta-regression models, when such interactions are plausible.
A Survey of Learning-based Automated Program Repair

Automated program repair (APR) aims to fix software bugs automatically and plays a crucial role in software development and maintenance. With the recent advances in deep learning (DL), an increasing number of APR techniques have been proposed to leverage neural networks to learn bug-fixing patterns from massive open-source code repositories. Such learning-based techniques usually treat APR as a neural machine translation (NMT) task, where buggy code snippets (i.e., source language) are translated into fixed code snippets (i.e., target language) automatically. Benefiting from the powerful capability of DL to learn hidden relationships from previous bug-fixing datasets, learning-based APR techniques have achieved remarkable performance. In this paper, we provide a systematic survey to summarize the current state-of-the-art research in the learning-based APR community. We illustrate the general workflow of learning-based APR techniques and detail the crucial components, including fault localization, patch generation, patch ranking, patch validation, and patch correctness phases. We then discuss the widely adopted datasets and evaluation metrics and outline existing empirical studies. We discuss several critical aspects of learning-based APR techniques, such as repair domains, industrial deployment, and the open science issue. We highlight several practical guidelines on applying DL techniques for future APR studies, such as exploring explainable patch generation and utilizing code features. Overall, our paper can help researchers gain a comprehensive understanding about the achievements of the existing learning-based APR techniques and promote the practical application of these techniques. Our artifacts are publicly available at the repository: https://github.com/iSEngLab/AwesomeLearningAPR.
Sublinear Time Algorithms for Several Geometric Optimization (With Outliers) Problems In Machine Learning

In this paper, we study several important geometric optimization problems arising in machine learning. First, we revisit the Minimum Enclosing Ball (MEB) problem in Euclidean space $\mathbb{R}^d$. The problem has been extensively studied before, but real-world machine learning tasks often need to handle large-scale datasets so that we cannot even afford linear time algorithms. Motivated by the recent studies on {\em beyond worst-case analysis}, we introduce the notion of stability for MEB, which is natural and easy to understand. Roughly speaking, an instance of MEB is stable, if the radius of the resulting ball cannot be significantly reduced by removing a small fraction of the input points. Under the stability assumption, we present two sampling algorithms for computing radius-approximate MEB with sample complexities independent of the number of input points $n$. In particular, the second algorithm has the sample complexity even independent of the dimensionality $d$. We also consider the general case without the stability assumption. We present a hybrid algorithm that can output either a radius-approximate MEB or a covering-approximate MEB. Our algorithm improves the running time and the number of passes for the previous sublinear MEB algorithms. Our method relies on two novel techniques, the Uniform-Adaptive Sampling method and Sandwich Lemma. Furthermore, we observe that these two techniques can be generalized to design sublinear time algorithms for a broader range of geometric optimization problems with outliers in high dimensions, including MEB with outliers, one-class and two-class linear SVMs with outliers, $k$-center clustering with outliers, and flat fitting with outliers. Our proposed algorithms also work fine for kernels.
A family of elliptic curves with non-trivial Shafarevich-Tate group

Rank computation of an elliptic curve is one of the most important problems in number theory due to its importance in various open questions in number theory. A common way to understand this rank is via understanding the Selmer group and the Shafarevich-Tate group of an elliptic curve. We construct a family of Heronian elliptic curves of rank $0$ and $2$-Selmer rank $2$. This, in turn, produces a family of elliptic curves such that the Shafarevich-Tate group is isomorphic to the Klein four-group.
Multiphase Gas Interactions on Subarcsec Scales in the Shocked Intergalactic Medium of Stephan’s Quintet with JWST and ALMA

We combine James Webb Space Telescope (JWST) and Hubble Space Telescope imaging with Atacama Large Millimeter Array CO(2–1) spectroscopy to study the highly turbulent multiphase intergalactic medium (IGM) in Stephan’s Quintet on 25–150 pc scales. Previous Spitzer observations revealed luminous H2 line cooling across a 45 kpc-long filament, created by a giant shock wave, following the collision with an intruder galaxy, NGC 7318b. We demonstrate that the Mid-Infrared Instrument/F1000W/F770W filters are dominated by 0–0 S(3) H2 and a combination of polycyclic aromatic hydrocarbon and 0–0 S(5) H2 emission. These observations reveal the dissipation of kinetic energy as massive clouds experience collisions, interactions, and likely destruction/recycling within different phases of the IGM. In 1 kpc-scaled structure, warm H2 was seen to form a triangular-shaped head and tail of compressed and stripped gas behind a narrow shell of cold H2. In another region, two cold molecular clumps with very different velocities are connected by an arrow-shaped stream of warm, probably shocked, H2 suggesting a cloud–cloud collision is occurring. In both regions, a high warm-to-cold molecular gas fraction indicates that the cold clouds are being disrupted and converted into warm gas. We also map gas associated with an apparently forming dwarf galaxy. We suggest that the primary mechanism for exciting strong mid-IR H2 lines throughout Stephan’s Quintet is through a fog of warm gas created by the shattering of denser cold molecular clouds and mixing/recycling in the post-shocked gas. A full picture of the diverse kinematics and excitation of the warm H2 will require future JWST mid-IR spectroscopy. The current observations reveal the rich variety of ways that different gas phases can interact with one another.
Fully H(gradcurl)-nonconforming Finite Element Method for The Singularly Perturbed Quad-curl Problem on Cubical Meshes

In this paper, we develop two fully nonconforming (both H(grad curl)-nonconforming and H(curl)-nonconforming) finite elements on cubical meshes which can fit into the Stokes complex. The newly proposed elements have 24 and 36 degrees of freedom, respectively. Different from the fully H(grad curl)-nonconforming tetrahedral finite elements in [9], the elements in this paper lead to a robust finite element method to solve the singularly perturbed quad-curl problem. To confirm this, we prove the optimal convergence of order $O(h)$ for a fixed parameter $\epsilon$ and the uniform convergence of order $O(h^{1/2})$ for any value of $\epsilon$. Some numerical examples are used to verify the correctness of the theoretical analysis.
A Framework for Large Scale Particle Filters Validated with Data Assimilation for Weather Simulation

Particle filters are a group of algorithms to solve inverse problems through statistical Bayesian methods when the model does not comply with the linear and Gaussian hypothesis. Particle filters are used in domains like data assimilation, probabilistic programming, neural networkoptimization, localization and navigation. Particle filters estimate the probabilitydistribution of model states by running a large number of model instances, the so called particles. The ability to handle a very large number of particles is critical for high dimensional models.This paper proposes a novel paradigm to run very large ensembles of parallel model instances on supercomputers. The approach combines an elastic and fault tolerant runner/server model minimizing data movementswhile enabling dynamic load balancing. Particle weights are computed locally on each runner andtransmitted when available to a server that normalizes them, resamples new particles based on their weight, and redistributes dynamically the work torunners to react to load imbalance. Our approach relies on a an asynchronously manageddistributed particle cache permitting particles to move from one runner to another inthe background while particle propagation goes on. This also enables the number ofrunners to vary during the execution either in reaction to failures and restarts, orto adapt to changing resource availability dictated by external decision processes.The approach is experimented with the Weather Research and Forecasting (WRF) model, toassess its performance for probabilistic weather forecasting. Up to 2555particles on 20442 compute cores are used to assimilate cloud cover observations into short--range weather forecasts over Europe.
Parallel Reasoning Network for Human-Object Interaction Detection

Human-Object Interaction (HOI) detection aims to learn how human interacts with surrounding objects. Previous HOI detection frameworks simultaneously detect human, objects and their corresponding interactions by using a predictor. Using only one shared predictor cannot differentiate the attentive field of instance-level prediction and relation-level prediction. To solve this problem, we propose a new transformer-based method named Parallel Reasoning Network(PR-Net), which constructs two independent predictors for instance-level localization and relation-level understanding. The former predictor concentrates on instance-level localization by perceiving instances' extremity regions. The latter broadens the scope of relation region to reach a better relation-level semantic understanding. Extensive experiments and analysis on HICO-DET benchmark exhibit that our PR-Net effectively alleviated this problem. Our PR-Net has achieved competitive results on HICO-DET and V-COCO benchmarks.
Crafting with a Robot Assistant: Use Social Cues to Inform Adaptive Handovers in Human-Robot Collaboration

We study human-robot handovers in a naturalistic collaboration scenario, where a mobile manipulator robot assists a person during a crafting session by providing and retrieving objects used for wooden piece assembly (functional activities) and painting (creative activities). We collect quantitative and qualitative data from 20 participants in a Wizard-of-Oz study, generating the Functional And Creative Tasks Human-Robot Collaboration dataset (the FACT HRC dataset), available to the research community. This work illustrates how social cues and task context inform the temporal-spatial coordination in human-robot handovers, and how human-robot collaboration is shaped by and in turn influences people's functional and creative activities.
Safer Together: Machine Learning Models Trained on Shared Accident Datasets Predict Construction Injuries Better than Company-Specific Models

In this study, we capitalized on a collective dataset repository of 57k accidents from 9 companies belonging to 3 domains and tested whether models trained on multiple datasets (generic models) predicted safety outcomes better than the company-specific models. We experimented with full generic models (trained on all data), per-domain generic models (construction, electric T&D, oil&gas), and with ensembles of generic and specific models. Results are very positive, with generic models outperforming the company-specific models in most cases while also generating finer-grained, hence more useful, forecasts. Successful generic models remove the needs for training company-specific models, saving a lot of time and resources, and give small companies, whose accident datasets are too limited to train their own models, access to safety outcome predictions. It may still however be advantageous to train specific models to get an extra boost in performance through ensembling with the generic models. Overall, by learning lessons from a pool of datasets whose accumulated experience far exceeds that of any single company, and making these lessons easily accessible in the form of simple forecasts, generic models tackle the holy grail of safety cross-organizational learning and dissemination in the construction industry.
STARS-ISAC: How Many Sensors Do We Need?

A simultaneously transmitting and reflecting surface (STARS) enabled two-phase integrated sensing and communications (ISAC) framework is proposed, where a novel bi-directional sensing-STARS architecture is devised to facilitate the full-space communication and sensing in a time-switching manner. Based on the proposed framework, a joint optimization problem is formulated, where the Cram $\acute {\text {e}}\text{r}$ -Rao bound (CRB) for estimating the 2-dimension direction-of-arrival of the sensing target is minimized. Two cases are considered for sensing performance enhancement. 1) For the two-user case with the fixed number of sensors, an alternating optimization algorithm is proposed. In particular, the maximum number of deployable sensors is obtained in the closed-form expressions, where the maximum number of sensors is revealed to be only relevant to the QoS requirements of communications. 2) For the multi-user case with the variable number of sensors, an extended CRB (ECRB) metric is proposed to characterize the impact of the number of sensors on the sensing performance. A generic decoupling approach is proposed to convexify the non-convex ECRB expression. Based on this, a novel penalty-based double-loop (PDL) algorithm is proposed. Simulation results reveal that 1) the proposed PDL algorithm achieves a near-optimal performance with consideration of sensor deployment; 2) it is preferable to deploy more passive elements than sensors in terms of achieving optimal sensing performance.
Real-Time Feedback Based Online Aggregate EV Power Flexibility Characterization

As an essential measure to combat global warming, electric vehicles (EVs) have witnessed rapid growth. Flexible EVs can enhance power systems' ability to handle renewable generation uncertainties. How EV flexibility can be utilized in power grid operation has captured great attention. However, the direct control of individual EVs is challenging due to their small capacity and large number. Hence, it is the aggregator that interacts with the grid on behalf of the EVs by characterizing their aggregate flexibility. In this article, we focus on the aggregate EV power flexibility characterization problem. First, an offline model is built to obtain the lower and upper bounds of the aggregate EV power flexibility region. It ensures that any trajectory within the region is feasible. Then, considering that parameters such as real-time electricity prices and EV arrival/departure times are not known in advance, an online algorithm is developed based on Lyapunov optimization techniques. We provide a theoretical bound for the maximum charging delay under the proposed online algorithm. Furthermore, real-time feedback is designed and integrated into the proposed online algorithm to better unlock EV power flexibility. Comprehensive performance comparisons are carried out to demonstrate the advantages of the proposed method.
Premerger Sky Localization of Gravitational Waves from Binary Neutron Star Mergers Using Deep Learning

The simultaneous observation of gravitational waves (GW) and prompt electromagnetic counterparts from the merger of two neutron stars can help reveal the properties of extreme matter and gravity during and immediately after the final plunge. Rapid sky localization of these sources is crucial to facilitate such multimessenger observations. As GWs from binary neutron star (BNS) mergers can spend up to 10–15 minutes in the frequency bands of the detectors at design sensitivity, early-warning alerts and premerger sky localization can be achieved for sufficiently bright sources, as demonstrated in recent studies. In this work, we present premerger BNS sky localization results using GW-SkyLocator, a deep-learning model capable of inferring sky location posterior distributions of GW sources at orders of magnitude faster speeds than standard Markov Chain Monte Carlo methods. We test our model’s performance on a catalog of simulated injections from Sachdev, recovered at 0–60 s before the merger, and obtain comparable sky localization areas to the rapid localization tool BAYESTAR. These results show the feasibility of our model for premerger sky localization and the possibility of follow-up observations for precursor emissions from BNS mergers.
Bidirectional Learning for Offline Model-based Biological Sequence Design

Offline model-based optimization aims to maximize a black-box objective function with a static dataset of designs and their scores. In this paper, we focus on biological sequence design to maximize some sequence score. A recent approach employs bidirectional learning, combining a forward mapping for exploitation and a backward mapping for constraint, and it relies on the neural tangent kernel (NTK) of an infinitely wide network to build a proxy model. Though effective, the NTK cannot learn features because of its parametrization, and its use prevents the incorporation of powerful pre-trained Language Models (LMs) that can capture the rich biophysical information in millions of biological sequences. We adopt an alternative proxy model, adding a linear head to a pre-trained LM, and propose a linearization scheme. This yields a closed-form loss and also takes into account the biophysical information in the pre-trained LM. In addition, the forward mapping and the backward mapping play different roles and thus deserve different weights during sequence optimization. To achieve this, we train an auxiliary model and leverage its weak supervision signal via a bi-level optimization framework to effectively learn how to balance the two mappings. Further, by extending the framework, we develop the first learning rate adaptation module \textit{Adaptive}-$\eta$, which is compatible with all gradient-based algorithms for offline model-based optimization. Experimental results on DNA/protein sequence design tasks verify the effectiveness of our algorithm. Our code is available~\href{https://anonymous.4open. science/r/BIB-ICLR2023-Submission/README.md}{here. }
Numerical approximation of a thermodynamically complete rate-type model for the elastic-perfectly plastic response

We analyse a numerical scheme for a system arising from a novel description of the standard elastic–perfectly plastic response. The elastic–perfectly plastic response is described via rate‐type equations that do not make use of the standard elastic‐plastic decomposition, and the model does not require the use of variational inequalities. Furthermore, the model naturally includes the evolution equation for temperature. We present a low order discretisation based on the finite element method. Under certain restrictions on the mesh we subsequently prove the existence of discrete solutions, and we discuss the stability properties of the numerical scheme. The analysis is supplemented with computational examples.
Cutoff of transverse waves through the solar transition region

Context. Transverse oscillations are ubiquitously observed in the solar corona, both in coronal loops and open magnetic flux tubes. Numerical simulations suggest that their dissipation could heat coronal loops, counterbalancing radiative losses. These models rely on a continuous driver at the footpoint of the loops. However, analytical works predict that transverse waves are subject to a cut-off in the transition region. It is thus unclear whether they can reach the corona, and indeed heat coronal loops. Aims. Our aims are to determine how the cut-off of kink waves affects their propagation into the corona, and to characterize the variation of the cut-off frequency with altitude. Methods. Using 3D magnetohydrodynamic simulations, we modelled the propagation of kink waves in a magnetic flux tube, embedded in a realistic atmosphere with thermal conduction, that starts in the chromosphere and extends into the corona. We drove kink waves at four different frequencies, and determined whether they experienced a cut-off. We then calculated the altitude at which the waves were cut-off, and compared it to the prediction of several analytical models. Results. We show that kink waves indeed experience a cut-off in the transition region, and we identified the analytical model that gives the best predictions. In addition, we show that waves with periods shorter than approximately 500 s can still reach the corona by tunnelling through the transition region, with little to no attenuation of their amplitude. This means that such waves can still propagate from the footpoints of loop, and result in heating in the corona.
Transceiver Cooperative Learning-aided Semantic Communications Against Mismatched Background Knowledge Bases

Semantic communications learned on background knowledge bases (KBs) have been identified as a promising technology for communications between intelligent agents. Existing works assume that transceivers of semantic communications share the same KB. However, intelligent transceivers may suffer from the communication burden or worry about privacy leakage to exchange data in KBs. Besides, the transceivers may independently learn from the environment and dynamically update their KBs, leading to timely sharing of the KBs infeasible. All these cause the mismatch between the KBs, which may result in a semantic-level misunderstanding on the receiver side. To address this issue, we propose a transceiver cooperative learning-assisted semantic communication (TCL-SC) scheme against mismatched KBs. In TCL-SC, the transceivers cooperatively train semantic encoder and decoder neuron networks (NNs) of the same structure based on their own KBs. They periodically share the parameters of NNs. To reduce the communication overhead of parameter sharing, parameter quantization is adopted. Moreover, we discuss the impacts of the number of communication rounds on the performance of semantic communication systems. Experiments on real-world data demonstrate that our proposed TCL-SC can reduce the semantic-level misunderstanding on the receiver side caused by the mismatch between the KBs, especially at the low signal-to-noise (SNR) ratio regime.