Miracle-dz/UniFAR-FTUs huggingface dataset:
UATTA-EB: Uncertainty-Aware Test-Time Augmented Ensemble of BERTs for Classifying Common Mental Illnesses on Social Media Posts

Given the current state of the world, because of existing situations around the world, millions of people suffering from mental illnesses feel isolated and unable to receive help in person. Psychological studies have shown that our state of mind can manifest itself in the linguistic features we use to communicate. People have increasingly turned to online platforms to express themselves and seek help with their conditions. Deep learning methods have been commonly used to identify and analyze mental health conditions from various sources of information, including social media. Still, they face challenges, including a lack of reliability and overconfidence in predictions resulting in the poor calibration of the models. To solve these issues, We propose UATTA-EB: Uncertainty-Aware Test-Time Augmented Ensembling of BERTs for producing reliable and well-calibrated predictions to classify six possible types of mental illnesses- None, Depression, Anxiety, Bipolar Disorder, ADHD, and PTSD by analyzing unstructured user data on Reddit.
Split, Merge, and Refine: Fitting Tight Bounding Boxes via Learned Over-Segmentation and Iterative Search

We present a novel framework for ﬁnding a set of tight bounding boxes of a 3D shape via neural-network-based over-segmentation and iterative merging and reﬁnement. Achieving tight bounding boxes of a shape while guaranteeing the complete boundness is an essential task for efﬁcient geometric operations and unsupervised semantic part detection, but previous methods fail to achieve both full coverage and tightness. Neural-network-based methods are not suitable for these goals due to the non-differentiability of the objective, and also classic iterative search methods suffer from their sensitivity to the initialization. We demonstrate that the best integration of the learning-based and iterative search methods can achieve the bounding boxes with both properties. We employ an existing unsupervised segmentation network to split the shape and obtain over-segmentation. Then, we apply hierarchical merging with our novel tightness-aware merging and stopping criteria. To overcome the sensitivity to the initialization, we also re-ﬁne the bounding box parameters in a game setup with a soft reward function promoting a wider exploration. Lastly, we further improve the bounding boxes with a MCTS-based multi-action space exploration. Our experimental results demonstrate the full coverage, tightness, and the adequate number of bounding boxes of our method.
JWST and ALMA Imaging of Dust-obscured, Massive Substructures in a Typical z ∼ 3 Star-forming Disk Galaxy

We present an identification of dust-attenuated star-forming galactic-disk substructures in a typical star-forming galaxy (SFG), UDF2, at z = 2.696. To date, substructures containing significant buildup of stellar mass and actively forming stars have yet to be found in typical (i.e., main-sequence) SFGs at z > 2. This is due to the strong dust attenuation common in massive galaxies at the epoch and the scarcity of high-resolution, high-sensitivity extinction-independent imaging. To search for disk substructures, we subtracted the central stellar-mass disk from the JWST/NIRCam rest-frame 1.2 μm image (0.″13 resolution) and subtracted, in the visibility plane, the central starburst disk from Atacama Large Millimeter/submillimeter Array (ALMA) rest-frame 240 μm observations (0.″03 resolution). The residual images revealed substructures at rest-frame 1.2 μm colocated with those found at rest-frame 240 μm, ≃2 kpc away from the galactic center. The largest substructure contains ≃20% of the total stellar mass and ≃5% of the total star formation rate of the galaxy. While UDF2 exhibits a kinematically ordered velocity field of molecular gas consistent with a secularly evolving disk, more sensitive observations are required to characterize the nature and the origin of this substructure (spiral arms, minor merger, or other types of disk instabilities). UDF2 resides in an overdense region (N ≥ 4 massive galaxies within 70 kpc projected distance at z = 2.690–2.697) and the substructures may be associated with interaction-induced instabilities. Importantly, a statistical sample of such substructures identified with JWST and ALMA could play a key role in bridging the gap between the bulge-forming starburst and the rest of the galaxy.
Equality cases in monotonicity of quasi-entropies, Lieb’s concavity and Ando’s convexity

We revisit and improve joint concavity/convexity and monotonicity properties of quasi-entropies due to Petz in a new fashion. Then we characterize equality cases in the monotonicity inequalities (the data-processing inequalities) of quasi-entropies in several ways as follows: Let Φ:B(H)→B(K) be a trace-preserving map such that Φ* is a Schwarz map. When f is an operator monotone or operator convex function on [0, ∞), we present several equivalent conditions for the equality SfK(Φ(ρ)‖Φ(σ))=SfΦ*(K)(ρ‖σ) to hold for given positive operators ρ, σ on H and K∈B(K). The conditions include equality cases in the monotonicity versions of Lieb’s concavity and Ando’s convexity theorems. Specializing the map Φ we have equivalent conditions for equality cases in Lieb’s concavity and Ando’s convexity. Similar equality conditions are discussed also for monotone metrics and χ2-divergences. We further consider some types of linear preserver problems for those quantum information quantities.
Coherently excited nonlocal quantum features using polarization-frequency correlation between quantum erasers

Photon indistinguishability is an essential concept to understanding mysterious quantum features from the viewpoint of the wave-particle duality in quantum mechanics. The physics of indistinguishability lies in the manipulation of quantum superposition between orthonormal bases of a single photon such as in a quantum eraser. Here, a pure coherence approach is applied for the nonlocal correlation based on the polarization-frequency correlation of Poisson-distributed coherent photon pairs to investigate the role of measurements. For this, a gated heterodyne-detection technique is adopted for coincidence measurements between space-like separated delayed-choice quantum erasers, resulting in an inseparable basis product between them. For this coherently induced inseparable basis product, polarization-frequency correlated photon pairs are selectively measured through a dc-cut ac-pass filter to eliminate unwanted group of polarization-product bases. Finally, the Bell inequality violation is numerically confirmed for the coherence solutions of the nonlocal correlation.
RescueSNN: enabling reliable executions on spiking neural network accelerators under permanent faults

To maximize the performance and energy efficiency of Spiking Neural Network (SNN) processing on resource-constrained embedded systems, specialized hardware accelerators/chips are employed. However, these SNN chips may suffer from permanent faults which can affect the functionality of weight memory and neuron behavior, thereby causing potentially significant accuracy degradation and system malfunctioning. Such permanent faults may come from manufacturing defects during the fabrication process, and/or from device/transistor damages (e.g., due to wear out) during the run-time operation. However, the impact of permanent faults in SNN chips and the respective mitigation techniques have not been thoroughly investigated yet. Toward this, we propose RescueSNN, a novel methodology to mitigate permanent faults in the compute engine of SNN chips without requiring additional retraining, thereby significantly cutting down the design time and retraining costs, while maintaining the throughput and quality. The key ideas of our RescueSNN methodology are (1) analyzing the characteristics of SNN under permanent faults; (2) leveraging this analysis to improve the SNN fault-tolerance through effective fault-aware mapping (FAM); and (3) devising lightweight hardware enhancements to support FAM. Our FAM technique leverages the fault map of SNN compute engine for (i) minimizing weight corruption when mapping weight bits on the faulty memory cells, and (ii) selectively employing faulty neurons that do not cause significant accuracy degradation to maintain accuracy and throughput, while considering the SNN operations and processing dataflow. The experimental results show that our RescueSNN improves accuracy by up to 80% while maintaining the throughput reduction below 25% in high fault rate (e.g., 0.5 of the potential fault locations), as compared to running SNNs on the faulty chip without mitigation. In this manner, the embedded systems that employ RescueSNN-enhanced chips can efficiently ensure reliable executions against permanent faults during their operational lifetime.
Graphene and Related Materials for the Internet of Bio-Nano Things

The Internet of Bio-Nano Things (IoBNT) is a transformative communication framework characterized by heterogeneous networks comprising both biological entities and artificial micro/nano-scale devices, so-called Bio-Nano Things (BNTs), interfaced with conventional communication networks for enabling innovative biomedical and environmental applications. Realizing the potential of IoBNT requires the development of new and unconventional communication technologies, such as molecular communications, as well as the corresponding transceivers, bio-cyber interfacing technologies connecting the biochemical domain of IoBNT to the electromagnetic domain of conventional networks, and miniaturized energy harvesting and storage components for the continuous power supply to BNTs. Graphene and related materials (GRMs) exhibit exceptional electrical, optical, biochemical, and mechanical properties, rendering them ideal candidates for addressing the challenges posed by IoBNT. This perspective article highlights recent advancements in GRM-based device technologies that are promising for implementing the core components of IoBNT. By identifying the unique opportunities afforded by GRMs and aligning them with the practical challenges associated with IoBNT, particularly in the materials domain, our aim is to accelerate the transition of envisaged IoBNT applications from theoretical concepts to practical implementations while also uncovering new application areas for GRMs.
Spectral representation of Matsubara n-point functions: Exact kernel functions and applications

In the field of quantum many-body physics, the spectral (or Lehmann) representation simplifies the calculation of Matsubara nn-point correlation functions if the eigensystem of a Hamiltonian is known. It is expressed via a universal kernel function and a system- and correlator-specific product of matrix elements. Here we provide the kernel functions in full generality, for arbitrary nn, arbitrary combinations of bosonic or fermionic operators and an arbitrary number of anomalous terms. As an application, we consider bosonic 3- and 4-point correlation functions for the fermionic Hubbard atom and a free spin of length SS, respectively.
Biological Sequence Kernels with Guaranteed Flexibility

Applying machine learning to biological sequences - DNA, RNA and protein - has enormous potential to advance human health, environmental sustainability, and fundamental biological understanding. However, many existing machine learning methods are ineffective or unreliable in this problem domain. We study these challenges theoretically, through the lens of kernels. Methods based on kernels are ubiquitous: they are used to predict molecular phenotypes, design novel proteins, compare sequence distributions, and more. Many methods that do not use kernels explicitly still rely on them implicitly, including a wide variety of both deep learning and physics-based techniques. While kernels for other types of data are well-studied theoretically, the structure of biological sequence space (discrete, variable length sequences), as well as biological notions of sequence similarity, present unique mathematical challenges. We formally analyze how well kernels for biological sequences can approximate arbitrary functions on sequence space and how well they can distinguish different sequence distributions. In particular, we establish conditions under which biological sequence kernels are universal, characteristic and metrize the space of distributions. We show that a large number of existing kernel-based machine learning methods for biological sequences fail to meet our conditions and can as a consequence fail severely. We develop straightforward and computationally tractable ways of modifying existing kernels to satisfy our conditions, imbuing them with strong guarantees on accuracy and reliability. Our proof techniques build on and extend the theory of kernels with discrete masses. We illustrate our theoretical results in simulation and on real biological data sets.
A Reinforcement Learning-assisted Genetic Programming Algorithm for Team Formation Problem Considering Person-Job Matching

An efficient team is essential for the company to successfully complete new projects. To solve the team formation problem considering person-job matching (TFP-PJM), a 0-1 integer programming model is constructed, which considers both person-job matching and team members' willingness to communicate on team efficiency, with the person-job matching score calculated using intuitionistic fuzzy numbers. Then, a reinforcement learning-assisted genetic programming algorithm (RL-GP) is proposed to enhance the quality of solutions. The RL-GP adopts the ensemble population strategies. Before the population evolution at each generation, the agent selects one from four population search modes according to the information obtained, thus realizing a sound balance of exploration and exploitation. In addition, surrogate models are used in the algorithm to evaluate the formation plans generated by individuals, which speeds up the algorithm learning process. Afterward, a series of comparison experiments are conducted to verify the overall performance of RL-GP and the effectiveness of the improved strategies within the algorithm. The hyper-heuristic rules obtained through efficient learning can be utilized as decision-making aids when forming project teams. This study reveals the advantages of reinforcement learning methods, ensemble strategies, and the surrogate model applied to the GP framework. The diversity and intelligent selection of search patterns along with fast adaptation evaluation, are distinct features that enable RL-GP to be deployed in real-world enterprise environments.
Identity-Guided Collaborative Learning for Cloth-Changing Person Reidentification

Cloth-changing person reidentification (ReID) is a newly emerging research topic aimed at addressing the issues of large feature variations due to cloth-changing and pedestrian view/pose changes. Although significant progress has been achieved by introducing extra information (e.g., human contour sketching information, human body keypoints, and 3D human information), cloth-changing person ReID remains challenging because pedestrian appearance representations can change at any time. Moreover, human semantic information and pedestrian identity information are not fully explored. To solve these issues, we propose a novel identity-guided collaborative learning scheme (IGCL) for cloth-changing person ReID, where the human semantic is effectively utilized and the identity is unchangeable to guide collaborative learning. First, we design a novel clothing attention degradation stream to reasonably reduce the interference caused by clothing information where clothing attention and mid-level collaborative learning are employed. Second, we propose a human semantic attention and body jigsaw stream to highlight the human semantic information and simulate different poses of the same identity. In this way, the extraction features not only focus on human semantic information that is unrelated to the background but are also suitable for pedestrian pose variations. Moreover, a pedestrian identity enhancement stream is proposed to enhance the identity importance and extract more favorable identity robust features. Most importantly, all these streams are jointly explored in an end-to-end unified framework, and the identity is utilized to guide the optimization. Extensive experiments on six public clothing person ReID datasets (LaST, LTCC, PRCC, NKUP, Celeb-reID-light, and VC-Clothes) demonstrate the superiority of the IGCL method. It outperforms existing methods on multiple datasets, and the extracted features have stronger representation and discrimination ability and are weakly correlated with clothing.
Use the Detection Transformer as a Data Augmenter

Detection Transformer (DETR) is a Transformer architecture based object detection model. In this paper, we demonstrate that it can also be used as a data augmenter. We term our approach as DETR assisted CutMix, or DeMix for short. DeMix builds on CutMix, a simple yet highly effective data augmentation technique that has gained popularity in recent years. CutMix improves model performance by cutting and pasting a patch from one image onto another, yielding a new image. The corresponding label for this new example is specified as the weighted average of the original labels, where the weight is proportional to the area of the patch. CutMix selects a random patch to be cut. In contrast, DeMix elaborately selects a semantically rich patch, located by a pre-trained DETR. The label of the new image is specified in the same way as in CutMix. Experimental results on benchmark datasets for image classification demonstrate that DeMix significantly outperforms prior art data augmentation methods including CutMix. Oue code is available at https://github.com/ZJLAB-AMMI/DeMix.
Portability and Scalability of OpenMP Offloading on State-of-the-art Accelerators

Over the last decade, most of the increase in computing power has been gained by advances in accelerated many-core architectures, mainly in the form of GPGPUs. While accelerators achieve phenomenal performances in various computing tasks, their utilization requires code adaptations and transformations. Thus, OpenMP, the most common standard for multi-threading in scientific computing applications, introduced offloading capabilities between host (CPUs) and accelerators since v4.0, with increasing support in the successive v4.5, v5.0, v5.1, and the latest v5.2 versions. Recently, two state-of-the-art GPUs -- the Intel Ponte Vecchio Max 1100 and the NVIDIA A100 GPUs -- were released to the market, with the oneAPI and NVHPC compilers for offloading, correspondingly. In this work, we present early performance results of OpenMP offloading capabilities to these devices while specifically analyzing the portability of advanced directives (using SOLLVE's OMPVV test suite) and the scalability of the hardware in representative scientific mini-app (the LULESH benchmark). Our results show that the coverage for version 4.5 is nearly complete in both latest NVHPC and oneAPI tools. However, we observed a lack of support in versions 5.0, 5.1, and 5.2, which is particularly noticeable when using NVHPC. From the performance perspective, we found that the PVC1100 and A100 are relatively comparable on the LULESH benchmark. While the A100 is slightly better due to faster memory bandwidth, the PVC1100 reaches the next problem size (400^3) scalably due to the larger memory size.
Effect of Food Security on Health in Developing Countries

This study examines the impact of food security on health outcomes in 56 developing countries from 2011 to 2019, using a comprehensive measure of food security. Applying generalized methods of moments, the results provide supportive evidence that food security influences health in a positive way. The existence of positive effects suggests that food availability (i.e., more supply), accessibility (i.e., higher income), utilization (i.e., healthy foods), and stability (i.e., more certainty in production) for livelihoods sustain life and promote good health. As a result, this study justifies the need for governments to provide equal support to all four dimensions of food security to promote better nutrition and health.
Stress and coping in the face of COVID-19: a qualitative inquiry into early pandemic experiences and psychological well-being of health workers in Burkina Faso, Senegal and The Gambia

Abstract COVID-19 represented an unprecedented challenge for health workers around the world, resulting in strong concerns about impacts on their psychological well-being. To inform on-going support and future preparedness activities, this study documented health workers’ experiences, well-being and coping throughout the first wave of the pandemic, in Burkina Faso, Senegal and The Gambia. We collected data from 68 primarily clinical staff from the COVID-19 treatment, maternity and emergency departments in 13 purposely hospitals and laboratories across the three countries. Following in-depth interviews via Zoom (mid-May to September 2020), we regularly followed up via WhatsApp until the end of 2020. We used a mixed deductive and inductive coding approach and a framework matrix to organize and analyse the material. All respondents initially assessed the situation as stressful and threatening. Major emotional reactions included fear of own infection, fear of being a risk to loved ones, guilt, compassion, and anxiety regarding the future. Many suffered from feeling left alone with the emerging crisis and feeling unvalued and unappreciated, particularly by their governments and ministries of health. Conversely, health workers drew much strength from support and valuation by direct supervisors and team members and, in part, also by patients, friends and family. We observed important heterogeneity between places of work and individual backgrounds. Respondents coped with the situation in various ways, particularly with strategies to manage adverse emotions, to minimize infection risk, to fortify health and to find meaning in the adverse circumstances. Coping strategies were primarily grounded in own resources rather than institutional support. Over time, the situation normalized and fears diminished for most respondents. With a view towards emergency preparedness, our findings underline the value of participation and transparent communication, institutional support and routine training to foster health workers’ psychological preparedness, coping skill set and resilience more generally.
Abnormal expression and role of MicroRNA‐214‐3p/SLC8A1 in neonatal Hypoxic‐Ischaemic encephalopathy

Neonatal hypoxic‐ischaemic encephalopathy (HIE) refers to brain damage caused by intra‐uterine distress and asphyxia/hypoxia during the perinatal and neonatal periods. MicroRNA (MiR)‐214‐3p plays a critical role in cell growth and apoptosis. The aim of this study was to investigate the expression and role of miR‐214‐3p in neonatal HIE development, and to explore the underlying mechanisms. The expression of miR‐214‐3p was significantly down‐regulated, while that of Slc8a1, a direct target of miR‐214‐3p, was significantly up‐regulated, in the brain tissue of neonatal HIE rats. The over‐expression of miR‐214‐3p promoted the proliferation and inhibited the apoptosis of neurones, while its down‐regulation had the opposite effect. Our results indicate that miR‐214‐3p expression was down‐regulated in neonatal HIE rats, and the up‐regulation of miR‐214‐3p expression protected against HIE development by inhibiting neuronal apoptosis.