Miracle-dz/UniFAR-FTUs huggingface dataset:
AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis

Compressed sensing (CS) has been playing a key role in accelerating the magnetic resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence, deep neural networks and CS algorithms are being integrated to redefine the state of the art of fast MRI. The past several years have witnessed substantial growth in the complexity, diversity, and performance of deep-learning-based CS techniques that are dedicated to fast MRI. In this meta-analysis, we systematically review the deep-learning-based CS techniques for fast MRI, describe key model designs, highlight breakthroughs, and discuss promising directions. We have also introduced a comprehensive analysis framework and a classification system to assess the pivotal role of deep learning in CS-based acceleration for MRI.
TraceLab: Reproducing Empirical Software Engineering Research

The ability to reproduce experiments in software engineering research is a hidden issue in validating and improving on new approaches. The lack of tool support, data availability, implementation-specific details, and even minute environment differences all contribute to problems for researchers attempting to investigate new ideas. In this thesis, I present examples of how unpublished details of an approach can drastically change the results. To address this issue, I promote the use of TraceLab, a research instrument designed to perform and share software engineering experiments in their entirety with accurate results. I leverage TraceLab’s ability to extend its framework with new tools and functionality to create a new Component Library (CL) and Component Development Kit (CDK) designed to provide researchers with all of the tools necessary to evaluate and improve new techniques. To discover which tools to include, I perform a systematic mapping study of publications from a subset of top international software engineering conferences in the past 10 years. Based on these results, I implemented the most popular tools and techniques in the CL and CDK. I show that by using the CL and CDK in TraceLab, 37% of the approaches identified in the mapping study can be completely recreated, with an additional 37% of approaches missing only 1 technique. Lastly, I reproduce examples of existing software engineering approaches that provides a working body of knowledge in order to drive new research.
Multimodal Personality Recognition using Cross-Attention Transformer and Behaviour Encoding

Personality computing and affective computing have gained recent interest in many research areas. The datasets for the task generally have multiple modalities like video, audio, language and bio-signals. In this paper, we propose a flexible model for the task which exploits all available data. The task involves complex relations and to avoid using a large model for video processing specifically, we propose the use of behaviour encoding which boosts performance with minimal change to the model. Cross-attention using transformers has become popular in recent times and is utilised for fusion of different modalities. Since long term relations may exist, breaking the input into chunks is not desirable, thus the proposed model processes the entire input together. Our experiments show the importance of each of the above contributions
Generalization capabilities of neural networks in lattice applications

In recent years, the use of machine learning has become increasingly popular in the context of lattice field theories. An essential element of such theories is represented by symmetries, whose inclusion in the neural network properties can lead to high reward in terms of performance and generalizability. A fundamental symmetry that usually characterizes physical systems on a lattice with periodic boundary conditions is equivariance under spacetime translations. Here we investigate the advantages of adopting translationally equivariant neural networks in favor of non-equivariant ones. The system we consider is a complex scalar field with quartic interaction on a two-dimensional lattice in the flux representation, on which the networks carry out various regression and classification tasks. Promising equivariant and non-equivariant architectures are identified with a systematic search. We demonstrate that in most of these tasks our best equivariant architectures can perform and generalize significantly better than their non-equivariant counterparts, which applies not only to physical parameters beyond those represented in the training set, but also to different lattice sizes.
BANMo: Building Animatable 3D Neural Models from Many Casual Videos

Prior work for articulated 3D shape reconstruction often relies on specialized multi-view and depth sensors or pre-built deformable 3D models. Such methods do not scale to diverse sets of objects in the wild. We present a method that requires neither of them. It aims to create high-fidelity, articulated 3D models from many casual RGB videos in a differentiable rendering framework. Our key in-sight is to merge three schools of thought: (1) classic deformable shape models that make use of articulated bones and blend skinning, (2) canonical embeddings that establish correspondences between pixels and a canonical 3D model, and (3) volumetric neural radiance fields (NeRFs) that are amenable to gradient-based optimization. We introduce neural blend skinning models that allow for differentiable and invertible articulated deformations. When combined with canonical embeddings, such models allow us to establish dense correspondences across videos that can be self-supervised with cycle consistency. On real and synthetic datasets, our method shows higher-fidelity 3D reconstructions than prior works for humans and animals, with the ability to render realistic images from novel viewpoints. Project page: https://banmo-www.github. io/.
Higher-form subsystem symmetry breaking: Subdimensional criticality and fracton phase transitions

Subsystem symmetry has emerged as a powerful organizing principle for unconventional quantum phases of matter, most prominently fracton topological orders. Here, we focus on a special subclass of such symmetries, known as higher-form subsystem symmetries, which allow us to adapt tools from the study of conventional topological phases to the fracton setting. We demonstrate that certain transitions out of familiar fracton phases, including the X-cube model, can be understood in terms of the spontaneous breaking of higher-form subsystem symmetries. We find simple pictures for these seemingly complicated fracton topological phase transitions by relating them in an exact manner, via gauging, to spontaneous higher-form subsystem symmetry breaking phase transitions of decoupled stacks of lower-dimensional models. We harness this perspective to construct a sequence of unconventional subdimensional critical points in two and three spatial dimensions based on the stacking and gauging of canonical models with higher-form symmetry. Through numerous examples, we illustrate the ubiquity of coupled layer constructions in theories with higher-form subsystem symmetries.
A Shared Incumbent Environment for the Minimum Power Broadcasting Problem in Wireless Networks

Abstract. In this study, we consider the minimum power broadcasting problem in wireless actuator networks. We attack the problem with a method - the shared incumbent environment - that executes two algorithms in parallel: a mathematical programming approach and a simulated annealing approach. According to the shared incumbent environment paradigm, when an incumbent solution is found by one method, the other method is notified and profits from the information received. Experimental results show that the shared incumbent environment lead to results which are better than those of the two algorithms combined in it taken singularly. Keywords: Shared incumbent environment, mixed integer linear programming, simulated annealing, matheuristics, minimum power broadcast, wireless networks. 1. Introduction Wireless actuator networks establish communication by using devices called terminals that use omni-directional antennea to send and receive radio signals. The same data can be sent to multiple terminals at the same time, as long as they are within the coverage area of the sender terminal (this is called wireless multicast advantage [1]). Terminals which are outside the coverage area of the sender terminal can still receive the data with the help of intermediate terminals acting as routers [1, 2]. Among the applications of wireless networks, an interesting one is that consisting in commanding from remote the actuators, which are at locations difficult to be reached by people [3]. In such applications, the command is generated in a source terminal and sent to the wireless terminals attached to the actuators. The Minimum Power Broadcast Problem (MPBP) is faced because of the fact that the terminals usually depend on small mobile batteries. This means, there is limited power available for the network. As the coverage area of a terminal increases, the power usage also increases. Therefore, finding a topology where the coverage areas are minimized would decrease the power usage and ensure a longer life span for the network. Thus, MPBP is the problem of finding a topology in which all terminals can receive data from the source terminal, with the total transmission power is minimized [1]. Many different approaches were previously proposed for solving MPBP. Among them are a polynomial-time heuristic called broadcast incremental power algorithm [1] and different approaches based on mixed integer linear programming (MILP) [4, 5]. A
Partial O-state Clamping PWM Method for Three-Level NPC Inverter with a SiC Clamp Diode

This paper presents the reverse recovery characteristic according to the change of switching states when Si diode and SiC diode are used as clamp diode and proposes a method to minimize the switching loss containing the reverse recovery loss in the neutral-point-clamped inverter at low modulation index. The previous papers introduce many multiple circuits replacing Si diode with SiC diode to reduce the switching loss. In the neutral-point-clamped inverter, the switching loss can be also reduced by replacing device in the clamp diode. However, the switching loss in IGBT is large and the reduced switching loss cannot be still neglected. It is expected that the reverse recovery effect can be infrequent and the switching loss can be considerably reduced by the proposed method. Therefore, it is also possible to operate the inverter at the higher frequency with the better system efficiency and reduce the volume, weight and cost of filters and heatsink. The effectiveness of the proposed method is verified by numerical analysis and experiment results.
Density Evolution Analysis of the Iterative Joint Ordered-Statistics Decoding for NOMA

In this paper, we develop a density evolution (DE) framework for analyzing the iterative joint decoding (JD) for non-orthogonal multiple access (NOMA) systems, where the ordered-statistics decoding (OSD) is applied to decode short block codes. We first investigate the density-transform feature of the soft-output OSD (SOSD), by deriving the density of the extrinsic log-likelihood ratio (LLR) with known densities of the priori LLR. Then, we represent the OSD-based JD by bipartite graphs (BGs), and develop the DE framework by characterizing the density-transform features of nodes over the BG under the binary phase shift keying (BPSK) transmission. Numerical examples show that the proposed DE framework accurately tracks the evolution of LLRs during the iterative decoding, especially at moderate-to-high SNRs. Based on the DE framework, we further analyze the BER performance of the OSD-based JD, and the convergence points of the two-user and equal-power systems.
Entropy-Regularized Partially Observed Markov Decision Processes

In this article, we investigate partially observed Markov decision processes (POMDPs) with cost functions regularized by entropy terms describing state, observation, and control uncertainty. Standard POMDP techniques are shown to offer bounded-error solutions to these entropy-regularized POMDPs, with exact solutions possible when the regularization involves the joint entropy of the state, observation, and control trajectories. Our joint-entropy result is particularly surprising since it constitutes a novel, tractable formulation of active state estimation.
Do Multi-Lingual Pre-trained Language Models Reveal Consistent Token Attributions in Different Languages?

During the past several years, a surge of multi-lingual Pre-trained Language Models (PLMs) has been proposed to achieve state-of-the-art performance in many cross-lingual downstream tasks. However, the understanding of why multi-lingual PLMs perform well is still an open domain. For example, it is unclear whether multi-Lingual PLMs reveal consistent token attributions in different languages. To address this, in this paper, we propose a Cross-lingual Consistency of Token Attributions (CCTA) evaluation framework. Extensive experiments in three downstream tasks demonstrate that multi-lingual PLMs assign significantly different attributions to multi-lingual synonyms. Moreover, we have the following observations: 1) the Spanish achieves the most consistent token attributions in different languages when it is used for training PLMs; 2) the consistency of token attributions strongly correlates with performance in downstream tasks.
Normal forms of parabolic logarithmic transseries

We give formal normal forms for parabolic logarithmic transseries f = z+ · · · , with respect to parabolic logarithmic normalizations. Normalizations are given algorithmically, using fixed point theorems, as limits of Picard’s sequences in appropriate complete metric spaces, in contrast to transfinite term-by-term eliminations described in former works. Furthermore, we give the explicit formula for the residual coefficient in the normal form and show that, in the larger logarithmic class, we can even eliminate the residual term from the normal form.
Simulator Adaptation Syndrome (SAS) and the Vestibular System: An Exploratory Study

Driving simulators are increasingly being used for the evaluation and rehabilitation of fitness-to-drive. However, difficulties with Simulator Adaptation Syndrome (SAS) are commonly reported in the literature. In this study, the authors investigated the relationship between SAS and incongruences between vestibular and visual inputs. Fifteen individuals (age = 61±13 years, 9 females) who developed SAS (SAS+) and 10 individuals (age = 58±13 years, 4 females) who did not develop SAS (SAS-) during two recently completed simulator-based driving studies participated in this study. Participants were each administered the six conditions of the Sensory Organization Test (SOT), made to rest for 30 minutes, and then drove four Wii®-based programs. Performance on the six SOT conditions and on the four the Wii®-based driving programs by SAS+ participants were compared to those of the SAS- participants. No participant reported feeling sick during the SOT. Participants in the SAS+ group performed better (p = 0.0053) than those in the SAS- group only on the SOT fifth condition, which required reliance on vestibular input for maintaining balance control. Four participants in the SAS+ versus one in the SAS- group experienced SAS during the Wii®-based driving (chi-square = 4.63, p = .0314). The results of the study suggest that individuals who developed SAS in the driving simulator probably relied more heavily on their vestibular system than individuals who did not develop SAS, and thus performed better when in situations that required significant integration of vestibular input for maintenance of equilibrium. The independent effect of the driving simulator versus the Wii driving game also suggests that specific characteristics of simulator environment immersion also likely played a role in eliciting SAS. Individuals who rely heavily on their vestibular system for balance control may be more prone to developing SAS in virtual environments that provide only minimal vestibular feedback. More studies are needed to better understand SAS and develop more effective strategies to mitigate it.
Biases in human mobility data impact epidemic modeling

Large-scale human mobility data is a key resource in data-driven policy making and across many scientific fields. Most recently, mobility data was extensively used during the COVID-19 pandemic to study the effects of governmental policies and to inform epidemic models. Large-scale mobility is often measured using digital tools such as mobile phones. However, it remains an open question how truthfully these digital proxies represent the actual travel behavior of the general population. Here, we examine mobility datasets from multiple countries and identify two fundamentally different types of bias caused by unequal access to, and unequal usage of mobile phones. We introduce the concept of data generation bias, a previously overlooked type of bias, which is present when the amount of data that an individual produces influences their representation in the dataset. We find evidence for data generation bias in all examined datasets in that high-wealth individuals are overrepresented, with the richest 20% contributing over 50% of all recorded trips, substantially skewing the datasets. This inequality is consequential, as we find mobility patterns of different wealth groups to be structurally different, where the mobility networks of high-wealth users are denser and contain more long-range connections. To mitigate the skew, we present a framework to debias data and show how simple techniques can be used to increase representativeness. Using our approach we show how biases can severely impact outcomes of dynamic processes such as epidemic simulations, where biased data incorrectly estimates the severity and speed of disease transmission. Overall, we show that a failure to account for biases can have detrimental effects on the results of studies and urge researchers and practitioners to account for data-fairness in all future studies of human mobility.
Exterior-point Optimization for Sparse and Low-rank Optimization

In this paper we present the nonconvex exterior-point optimization solver (NExOS) -- a first-order algorithm tailored to constrained nonconvex optimization problems. We consider the problem of minimizing a convex function over nonconvex constraints, where the projection onto the constraint set is single-valued around local minima. A wide range of nonconvex optimization problems have this structure including (but not limited to) sparse and low-rank optimization problems. By exploiting the underlying geometry of the constraint set, NExOS finds a locally optimal point by solving a sequence of penalized problems with strictly decreasing penalty parameters. NExOS solves each penalized problem by applying a first-order algorithm, which converges linearly to a local minimum of the corresponding penalized formulation under regularity conditions. Furthermore, the local minima of the penalized problems converge to a local minimum of the original problem as the penalty parameter goes to zero. We then implement and test NExOS on many instances from a wide variety of sparse and low-rank optimization problems. We demonstrate that our algorithm, outperforms specialized methods on several examples of well-known nonconvex optimization problems involving sparse and low-rank optimization.
Adaptive Evolution in TRIF Leads to Discordance between Human and Mouse Innate Immune Signaling

Abstract The TIR domain-containing adapter inducing IFN-β (TRIF) protein is an innate immune system protein that mediates the MyD88-independent toll-like receptor response pathway in mice and humans. Previously, we identified positive selection at seven distinct residues in mouse TRIF (mTRIF), as compared with human and other mammalian orthologs, thus predicting protein functional shift in mTRIF. We reconstructed TRIF for the most recent common ancestor of mouse and human, and mutated this at the seven sites to their extant mouse/human states. We overexpressed these TRIF mutants in immortalized human and mouse cell lines and monitored TRIF-dependent cytokine production and gene expression induction. We show that optimal TRIF function in human and mouse is dependent on the identity of the positively selected sites. These data provide us with molecular data relating observed differences in response between mouse and human MyD88-independent signaling in the innate immune system with protein functional change.
Establishing Key Integration Factors for Integrating Engineering and Sustainability

This study established the key integration factors (KIFs) to help participants to scrutinize the key issues during incorporating sustainability issues into engineering development projects. Firstly, Sustainability disputes in one case project were reviewed to understand their evolvement and to analyze the dispute. The key factors were identified by comparing other two types of integration cases. Secondly, focused interviews were conducted with practitioners to scrutinize the eight KIFs by analysing the interviewed opinions to ensure its applicability. The study results show that the eight KIFs were consistent with the key issues in the integration practice. These factors can remind the integration team to check the data quality, choose the appropriate assessment techniques, communicate with stakeholders, invite qualified experts , and then achieve consensus in the integration process.
The impact of positive and negative testimony on children’s attitudes toward others

Children can identify who is benevolent or malevolent not only through first-hand experiences and observations but also from the testimony of others. In this study, we investigated whether 5- and 7-year-olds (N = 128) would form their attitudes toward others after hearing testimony about that person’s past moral behavior and whether the valence of testimony would differently influence the children. In the positive condition, half of the participants gained information about three puppets: puppet A’s prosocial behavior by their own first-hand observation, testimony about puppet B’s past prosocial behavior, and testimony about puppet C’s past neutral behavior. In the negative condition, the other half also learned information about the three puppets: puppet A’s antisocial behavior by their own first-hand observation, testimony about puppet B’s past antisocial behavior, and testimony about puppet C’s past neutral behavior. Then they engaged in tasks that measured their behavioral attitudes toward the puppets and evaluated the goodness of each puppet to assess their attitudes at a cognitive level. Our results concluded that the children form their behavioral attitudes toward others based on testimony starting at the age of 7, and attitude formation at the cognitive level based on testimony is seen at age 5. Negative testimony, rather than positive testimony, influences the children’s attitudes toward others. In addition, the 7-year-olds’ use of testimony differs depending whether they are the allocators or the receivers of rewards. Our findings deepen understanding of how children rely on the verbal information around themselves when they navigate interactions with others.
Impact of stock health on fish prices: Evaluation and implications for food accessibility

The continuous rise of the world’s population has made food security a major point of the global agenda, with fisheries providing a key source of nutrition, especially in developing countries. Ensuring their health is key to maintain the availability of the resource, but its effect over accessibility is yet unclear. In this paper, we discuss the relevance of stock health for ensuring the price accessibility of the resource. A Least Square Dummy Variable panel model is proposed for bluefin tuna prices, with a biological explanatory component, and dummy variables reflecting changes in fishing trends. Both have proven to be significant to explain annual price variations, with improvements in stock health achieving price reductions.