Miracle-dz/UniFAR-FTUs huggingface dataset:
ShapeNet: Shape Constraint for Galaxy Image Deconvolution

Deep Learning (DL) has shown remarkable results in solving inverse problems in various domains. In particular, the Tikhonet approach is very powerful to deconvolve optical astronomical images (Sureau et al. 2020). Yet, this approach only uses the $\ell_2$ loss, which does not guarantee the preservation of physical information (e.g. flux and shape) of the object reconstructed in the image. In Nammour et al. (2021), a new loss function was proposed in the framework of sparse deconvolution, which better preserves the shape of galaxies and reduces the pixel error. In this paper, we extend Tikhonet to take into account this shape constraint, and apply our new DL method, called ShapeNet, to optical and radio-interferometry simulated data set. The originality of the paper relies on i) the shape constraint we use in the neural network framework, ii) the application of deep learning to radio-interferometry image deconvolution for the first time, and iii) the generation of a simulated radio data set that we make available for the community. A range of examples illustrates the results.
Data Smells in Public Datasets

The adoption of Artificial Intelligence (AI) in high-stakes domains such as healthcare, wildlife preservation, autonomous driving and criminal justice system calls for a data-centric approach to AI. Data scientists spend the majority of their time studying and wrangling the data, yet tools to aid them with data analysis are lacking. This study identifies the recurrent data quality issues in public datasets. Analogous to code smells, we introduce a novel catalogue of data smells that can be used to indicate early signs of problems or technical debt in machine learning systems. To understand the prevalence of data quality issues in datasets, we analyse 25 public datasets and identify 14 data smells.
Modular and Parameter-Efficient Multimodal Fusion with Prompting

Recent research has made impressive progress in large-scale multimodal pre-training. In the context of the rapid growth of model size, it is necessary to seek efficient and flexible methods other than finetuning. In this paper, we propose to use prompt vectors to align the modalities. Our method achieves comparable performance to several other multimodal fusion methods in low-resource settings. We further show that our method is modular and parameter-efficient for processing tasks involving two or more data modalities.
Approaching Massive MIMO Performance with Reconfigurable Intelligent Surfaces: We Do Not Need Many Antennas

This paper considers an antenna structure where a (non-large) array of radiating elements is placed at short distance in front of a reconfigurable intelligent surface (RIS). This structure is analyzed as a possible emulator of a traditional MIMO antenna with a large number of active antenna elements and RF chains. Focusing on both the cases of active and passive RIS, we tackle the issues of channel estimation, downlink signal processing, power control, and RIS configuration optimization. With regard to the last point, an optimization problem is formulated and solved, both for the cases of active and passive RIS, aimed at minimizing the channel signatures cross-correlations and thereby reducing the interference. Downlink spectral efficiency (SE) formulas are also derived by using the popular hardening lower-bound. Numerical results, represented with reference to max-fairness power control, show that the proposed structure is capable of outperforming conventional non-RIS aided MIMO systems even when the MIMO system has a considerably larger number of antennas and RF chains. The proposed antenna structure is thus shown to be able to approach massive MIMO performance levels in a cost-effective way with reduced hardware resources.
ReACC: A Retrieval-Augmented Code Completion Framework

Code completion, which aims to predict the following code token(s) according to the code context, can improve the productivity of software development. Recent work has proved that statistical language modeling with transformers can greatly improve the performance in the code completion task via learning from large-scale source code datasets. However, current approaches focus only on code context within the file or project, i.e. internal context. Our distinction is utilizing ”external” context, inspired by human behaviors of copying from the related code snippets when writing code. Specifically, we propose a retrieval-augmented code completion framework, leveraging both lexical copying and referring to code with similar semantics by retrieval. We adopt a stage-wise training approach that combines a source code retriever and an auto-regressive language model for programming language. We evaluate our approach in the code completion task in Python and Java programming languages, achieving a state-of-the-art performance on CodeXGLUE benchmark.
Mu2e-II: Muon to electron conversion with PIP-II

An observation of Charged Lepton Flavor Violation (CLFV) would be unambiguous evidence for physics beyond the Standard Model. The Mu2e and COMET experiments, under construction, are designed to push the sensitivity to CLFV in the mu to e conversion process to unprecedented levels. Whether conversion is observed or not, there is a strong case to be made for further improving sensitivity, or for examining the process on additional target materials. Mu2e-II is a proposed upgrade to Mu2e, with at least an additional order of magnitude in sensitivity to the conversion rate over Mu2e. The approach and challenges for this proposal are summarized. Mu2e-II may be regarded as the next logical step in a continued high-intensity muon program at FNAL.
Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers

Training very deep neural networks is still an extremely challenging task. The common solution is to use shortcut connections and normalization layers, which are both crucial ingredients in the popular ResNet architecture. However, there is strong evidence to suggest that ResNets behave more like ensembles of shallower networks than truly deep ones. Recently, it was shown that deep vanilla networks (i.e. networks without normalization layers or shortcut connections) can be trained as fast as ResNets by applying certain transformations to their activation functions. However, this method (called Deep Kernel Shaping) isn't fully compatible with ReLUs, and produces networks that overfit significantly more than ResNets on ImageNet. In this work, we rectify this situation by developing a new type of transformation that is fully compatible with a variant of ReLUs -- Leaky ReLUs. We show in experiments that our method, which introduces negligible extra computational cost, achieves validation accuracies with deep vanilla networks that are competitive with ResNets (of the same width/depth), and significantly higher than those obtained with the Edge of Chaos (EOC) method. And unlike with EOC, the validation accuracies we obtain do not get worse with depth.
Research Findings Concerning the Effect of Axle Shapes on the Performance of Cyclone Separators

The authors have studied a kind of high-concentration salt-mist separating device-cyclone separator,and numerically simulated under rated operating conditions three models of cyclone separators featuring three axle shapes,namely,1.hollow cylinder shape,2.slim solid truncated cone shape and 3.podgy solid circular truncated cone shape.The drag force characteristics of the corresponding models and their separation-efficiency characteristics at different particle diameters have been given.Meanwhile,on a specialized wind tunnel test rig,drag force and separating efficiency characteristics of the three models were also tested.The test results show that model No.2 has a relatively stable separating efficiency and a comparatively small drag-force loss with its overall performance being better than that of the other two models.When comparing the test results with the numerical simulation ones,one can find that they are in good agreement,thus providing reference data for further investigations.
Synergistic Scheduling of Learning and Allocation of Tasks in Human-Robot Teams

We consider the problem of completing a set of $n$ tasks with a human-robot team using minimum effort. In many domains, teaching a robot to be fully autonomous can be counterproductive if there are finitely many tasks to be done. Rather, the optimal strategy is to weigh the cost of teaching a robot and its benefit- how many new tasks it allows the robot to solve autonomously. We formulate this as a planning problem where the goal is to decide what tasks the robot should do autonomously (act), what tasks should be delegated to a human (delegate) and what tasks the robot should be taught (learn) so as to complete all the given tasks with minimum effort. This planning problem results in a search tree that grows expo-nentially with $n$ - making standard graph search algorithms intractable. We address this by converting the problem into a mixed integer program that can be solved efficiently using off-the-shelf solvers with bounds on solution quality. To predict the benefit of learning, we use an approximate simulation model of the tasks to train a precondition model that is parameterized by the training task. Finally, we evaluate our approach on peg insertion and Lego stacking tasks- both in simulation and real-world, showing substantial savings in human effort.
Algorithm of distance protection based on transfer power flow sensibility

The protective chain-trip of zone 3 induced by non-fault overcurrent is the main cause of widespread blackout,which can be avoided by blocking out the protective action for non -self faults.An algorithm based on transfer power flow sensibility is presented for distance protection.The measured power flow is compared with that calculated from transfer power flow sensibility and the power flow before fault to distinguish the faulty overcurrent from transfer overcurrent for non-fault overcurrent blocking.Simulation of 6-bus test system is carried out under Matlab/Simulink,showing its better correctness and practicability.
Dynamical Modeling and Control of Soft Robots with Non-constant Curvature Deformation

The Piecewise Constant Curvature (PCC) model is the most widely used soft robotic modeling and control. However, the PCC fails to accurately describe the deformation of the soft robots when executing dynamic tasks or interacting with the environment. This paper presents a simple threedimensional (3D) modeling method for a multi-segment soft robotic manipulator with non-constant curvature deformation. We devise kinematic and dynamical models for soft manipulators by modeling each segment of the manipulator as two stretchable links connected by a universal joint. Based on that, we present two controllers for dynamic trajectory tracking in confguration space and pose control in task space, respectively. Model accuracy is demonstrated with simulations and experimental data. The controllers are implemented on a four-segment soft robotic manipulator and validated in dynamic motions and pose control with unknown loads. The experimental results show that the dynamic controller enables a stable reference trajectory tracking at speeds up to 7m/s.
Data Storage for HEP Experiments in the Era of High-Performance Computing

As particle physics experiments push their limits on both the energy and the intensity frontiers, the amount and complexity of the produced data are also expected to increase accordingly. With such large data volumes, next-generation efforts like the HL-LHC and DUNE will rely even more on both high-throughput (HTC) and high-performance (HPC) computing clusters. Full utilization of HPC resources requires scalable and efficient data-handling and I/O. For the last few decades, ROOT has been used by most HEP experiments to store data. However, other storage technologies like HDF5 may perform better in HPC environments. Initial explorations with HDF5 have begun using ATLAS, CMS and DUNE data; the DUNE experiment has also adopted HDF5 for its data-acquisition system. This paper presents the future outlook of the HEP computing and the role of HPC, and a summary of ongoing and future works to use HDF5 as a possible data storage technology for the HEP experiments to use in HPC environments.
Recent Progress and Next Steps for the MATHUSLA LLP Detector

We report on recent progress and next steps in the design of the proposed MATHUSLA Long Lived Particle (LLP) detector for the HL-LHC as part of the Snowmass 2021 process. Our understanding of backgrounds has greatly improved, aided by detailed simulation studies, and significant R&D has been performed on designing the scintillator detectors and understanding their performance. The collaboration is on track to complete a Technical Design Report, and there are many opportunities for interested new members to contribute towards the goal of designing and constructing MATHUSLA in time for HL-LHC collisions, which would increase the sensitivity to a large variety of highly motivated LLP signals by orders of magnitude.
Warped Compactifications in Particle Physics, Cosmology and Quantum Gravity

Particle physics has evolved in the past decade through evaluating the consequences of experimental measurements as well as exploiting theoretical tools that permit exploration of new model building and cosmological possibilities. Particularly due to insights from the AdS/CFT correspondence, higher-dimensional warped compactifications, in particular, have played a big role in recent developments by allowing a study of regimes of parameters that would otherwise be intractable. Similarly, theoretical developments in quantum gravity benefit from the bigger range of possibilities that can be explored using warped geometry, allowing for constructions of string vacua with positive cosmological constant and for the exploration of entanglement and information transfer in arbitrary dimensions. Puzzles remain in both more phenomenologically oriented and more theoretically oriented contexts which form the basis for a rich research program in the future as well.
Regenerative Particle Thompson Sampling

This paper proposes regenerative particle Thompson sampling (RPTS), a flexible variation of Thompson sampling. Thompson sampling itself is a Bayesian heuristic for solving stochastic bandit problems, but it is hard to implement in practice due to the intractability of maintaining a continuous posterior distribution. Particle Thompson sampling (PTS) is an approximation of Thompson sampling obtained by simply replacing the continuous distribution by a discrete distribution supported at a set of weighted static particles. We observe that in PTS, the weights of all but a few fit particles converge to zero. RPTS is based on the heuristic: delete the decaying unfit particles and regenerate new particles in the vicinity of fit surviving particles. Empirical evidence shows uniform improvement from PTS to RPTS and flexibility and efficacy of RPTS across a set of representative bandit problems, including an application to 5G network slicing.
Characterizing Reddit Participation of Users Who Engage in the QAnon Conspiracy Theories

Widespread conspiracy theories may significantly impact our society. This paper focuses on the QAnon conspiracy theory, a consequential conspiracy theory that started on and disseminated successfully through social media. Our work characterizes how Reddit users who have participated in QAnon-focused subreddits engage in activities on the platform, especially outside their own communities. Using a large-scale Reddit moderation action against QAnon-related activities in 2018 as the source, we identified 13,000 users active in the early QAnon communities. We collected the 2.1 million submissions and 10.8 million comments posted by these users across all of Reddit from October 2016 to January 2021. The majority of these users were only active after the emergence of the QAnon conspiracy theory and decreased in activity after Reddit's 2018 QAnon ban. A qualitative analysis of a sample of 915 subreddits where the "QAnon-enthusiastic" users were especially active shows that they participated in a diverse range of subreddits, often of unrelated topics to QAnon. However, most of the users' submissions were concentrated in subreddits that have sympathetic attitudes towards the conspiracy theory, characterized by discussions that were pro-Trump, or emphasized unconstricted behavior (often anti-establishment and anti-interventionist). Further study of a sample of 1,571 of these submissions indicates that most consist of links from low-quality sources, bringing potential harm to the broader Reddit community. These results point to the likelihood that the activities of early QAnon users on Reddit were dedicated and committed to the conspiracy, providing implications on both platform moderation design and future research.
From 2D to 3D: Re-thinking Benchmarking of Monocular Depth Prediction

There have been numerous recently proposed methods for monocular depth prediction (MDP) coupled with the equally rapid evolution of benchmarking tools. However, we argue that MDP is currently witnessing benchmark over-fitting and relying on metrics that are only partially helpful to gauge the usefulness of the predictions for 3D applications. This limits the design and development of novel methods that are truly aware of - and improving towards estimating - the 3D structure of the scene rather than optimizing 2D-based distances. In this work, we aim to bring structural awareness to MDP, an inherently 3D task, by exhibiting the limits of evaluation metrics towards assessing the quality of the 3D geometry. We propose a set of metrics well suited to evaluate the 3D geometry of MDP approaches and a novel indoor benchmark, RIO-D3D, crucial for the proposed evaluation methodology. Our benchmark is based on a real-world dataset featuring high-quality rendered depth maps obtained from RGB-D reconstructions. We further demonstrate this to help benchmark the closely-tied task of 3D scene completion.
The Unexplored Treasure Trove of Phabricator Code Reviews

Phabricator is a modern code collaboration tool used by popular projects like FreeBSD and Mozilla. However, unlike the other well-known code review environments, such as Gerrit or GitHub, there is no readily accessible public code review dataset for Phabricator. This paper describes our experience mining code reviews from five different projects that use Phabricator (Blender, FreeBSD, KDE, LLVM, and Mozilla). We discuss the challenges associated with the data retrieval process and our solutions, resulting in a dataset with details regarding 317,476 Phabricator code reviews. Our dataset11https://doi.org/10.6084/m9.figshare. 17139245 is available in both JSON and MySQL database dump formats. The dataset enables analyses of the history of code reviews at a more granular level than other platforms. In addition, given that the projects we mined are publicly accessible via the Conduit API [18], our dataset can be used as a foundation to fetch additional details and insights.
MOBDrone: a Drone Video Dataset for Man OverBoard Rescue

Modern Unmanned Aerial Vehicles (UAV) equipped with cameras can play an essential role in speeding up the identification and rescue of people who have fallen overboard, i.e., man overboard (MOB). To this end, Artificial Intelligence techniques can be leveraged for the automatic understanding of visual data acquired from drones. However, detecting people at sea in aerial imagery is challenging primarily due to the lack of specialized annotated datasets for training and testing detectors for this task. To fill this gap, we introduce and publicly release the MOBDrone benchmark, a collection of more than 125K drone-view images in a marine environment under several conditions, such as different altitudes, camera shooting angles, and illumination. We manually annotated more than 180K objects, of which about 113K man overboard, precisely localizing them with bounding boxes. Moreover, we conduct a thorough performance analysis of several state-of-the-art object detectors on the MOBDrone data, serving as baselines for further research.
Bayesian Filtering for Bluetooth RSS-based Indoor Tracking

With the technical advances of wireless sensor networking and smart mobile device, the demand for position information of pedestrians (especially in the indoor environment) has increased remarkably. In this paper, we proposed an indoor localization approach based on received signal strength (RSS) and Bayesian filter. In the following sections, we describe our virtual modeling method of environment and the way we take object's movement sequences as history conditions in Bayesian filter. The experiment results show that our solution provides accurate tracking results (within 80 centimeters for moving object). The contribution of this research is that it provides a general implementation utilizing Bayesian filter which is able to estimate location precisely with off-the-shelf hardware. And Bluetooth Low Energy (BLE) is employed which reduces power consumption considerably. Meanwhile the accuracy is sufficient for pedestrian tracking in real application scenarios where BLE devices can be easily deployed. Simply connecting static points will distort the real trajectory. One solution for this problem is filter technique. Filter technique is not only can be used in signal measurements, but also on general position estimations. In this paper we propose a Bluetooth indoor pedestrian tracking system which employs RSS as scene profiling basis. During the tracking process, our system keeps iterating a recursion of prediction and update in which Bayes filter (4) is the mathematical fundamental. Other than the classic procedure of filtering the measurement data only, our filter integrates context information and history record as conditions for the estimation. Our evaluation shows that our combination of current measurements with history records increases both the accuracy and efficiency. The rest of this paper is organized as follows: Section II overviews fundamental and essential related works. Then Section III describes detailed implementation. After that in Section IV we present an evaluation of the proposed algorithm by comparing it with existing RSS-based fingerprint approaches. Finally, Section VI summarizes the main conclusions and gives directions for future work.
Insights into Jatropha Projects Worldwide - Key Facts & Figures from a Global Survey

The objective of this survey of oilseed production from Jatropha and other oilseed-bearing perennials is to contribute by establishing an up-to-date overview based on interviews with producers and industry experts in 2011. This survey of 154 projects yielded a comprehensive database that covers critical aspects such as agronomic settings, agricultural practices, yields, business activities and financing as well as sustainability management. This summary of key facts and figures provides an overview of the 111 commercial Jatropha plantations found to be operational at the time of the survey in mid-2011. It presents key agronomic and economic data points from this sample. The report targets a wide range of stakeholders in governmental and non-governmental organisations, academia, as well as businesses and investors.