Miracle-dz/UniFAR-FTUs huggingface dataset:
Who Is Alyx? A new Behavioral Biometric Dataset for User Identification in XR

Introduction: This paper addresses the need for reliable user identification in Extended Reality (XR), focusing on the scarcity of public datasets in this area.Methods: We present a new dataset collected from 71 users who played the game “Half-Life: Alyx” on an HTC Vive Pro for 45 min across two separate sessions. The dataset includes motion and eye-tracking data, along with physiological data from a subset of 31 users. Benchmark performance is established using two state-of-the-art deep learning architectures, Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). Results: The best model achieved a mean accuracy of 95% for user identification within 2 min when trained on the first session and tested on the second.Discussion: The dataset is freely available and serves as a resource for future research in XR user identification, thereby addressing a significant gap in the field. Its release aims to facilitate advancements in user identification methods and promote reproducibility in XR research.
Signatures of Massive Black Hole Merger Host Galaxies from Cosmological Simulations. I. Unique Galaxy Morphologies in Imaging

Low-frequency gravitational-wave experiments such as the Laser Interferometer Space Antenna and pulsar timing arrays are expected to detect individual massive black hole (MBH) binaries and mergers. However, secure methods of identifying the exact host galaxy of each MBH merger among the large number of galaxies in the gravitational-wave localization region are currently lacking. We investigate the distinct morphological signatures of MBH merger host galaxies, using the Romulus25 cosmological simulation. We produce mock telescope images of 201 simulated galaxies in Romulus25 hosting recent MBH mergers through stellar population synthesis and dust radiative transfer. Based on comparisons to mass- and redshift-matched control samples, we show that combining multiple morphological statistics via a linear discriminant analysis enables identification of the host galaxies of MBH mergers, with accuracies that increase with chirp mass and mass ratio. For mergers with high chirp masses (≳108.2 M ⊙) and high mass ratios (≳0.5), the accuracy of this approach reaches ≳80%, and does not decline for at least ∼1 Gyr after numerical merger. We argue that these trends arise because the most distinctive morphological characteristics of MBH merger and binary host galaxies are prominent classical bulges, rather than relatively short-lived morphological disturbances from their preceding galaxy mergers. Since these bulges are formed though major mergers of massive galaxies, they lead to (and become permanent signposts for) MBH binaries and mergers that have high chirp masses and mass ratios. Our results suggest that galaxy morphology can aid in identifying the host galaxies of future MBH binaries and mergers.
Continual Pre-Training of Large Language Models: How to (re)warm your model?

Large language models (LLMs) are routinely pre-trained on billions of tokens, only to restart the process over again once new data becomes available. A much cheaper and more efficient solution would be to enable the continual pre-training of these models, i.e. updating pre-trained models with new data instead of re-training them from scratch. However, the distribution shift induced by novel data typically results in degraded performance on past data. Taking a step towards efficient continual pre-training, in this work, we examine the effect of different warm-up strategies. Our hypothesis is that the learning rate must be re-increased to improve compute efficiency when training on a new dataset. We study the warmup phase of models pre-trained on the Pile (upstream data, 300B tokens) as we continue to pre-train on SlimPajama (downstream data, 297B tokens), following a linear warmup and cosine decay schedule. We conduct all experiments on the Pythia 410M language model architecture and evaluate performance through validation perplexity. We experiment with different pre-training checkpoints, various maximum learning rates, and various warmup lengths. Our results show that while rewarming models first increases the loss on upstream and downstream data, in the longer run it improves the downstream performance, outperforming models trained from scratch$\unicode{x2013}$even for a large downstream dataset.
Pb10−x Cu x (PO4)6O: a Mott or charge transfer insulator in need of further doping for (super)conductivity

We briefly review the status quo of research on the putative superconductor Pb9Cu(PO4)6O also known as LK-99. Further, we provide ab initio derived tight-binding parameters for a two- and five-band model, and solve these in dynamical-mean-field theory. The interaction-to-bandwidth ratio makes LK-99 a Mott or charge transfer insulator. Electron or hole doping (which is different from substituting Pb by Cu and thus differs from LK-99) is required to make it metallic and potentially superconducting.
Safe Control Synthesis for Multicopter via Control Barrier Function Backstepping

A safe controller for multicopter is proposed using control barrier function. Multicopter dynamics are reformulated to deal with mixed-relative-degree and non-strict-feedback-form dynamics, and a time-varying safe backstepping controller is designed. Despite the time-varying variation, it is proven that the control input can be obtained by solving quadratic programming with affine inequality constraints. The proposed controller does not utilize a cascade control system design, unlike existing studies on the safe control of multicopter. Various safety constraints on angular velocity, total thrust direction, velocity, and position can be considered. Numerical simulation results support that the proposed safe controller does not violate all safety constraints including low-and high-level dynamics.
Comparative Analysis of the wav2vec 2.0 Feature Extractor

Automatic speech recognition (ASR) systems typically use handcrafted feature extraction pipelines. To avoid their inherent information loss and to achieve more consistent modeling from speech to transcribed text, neural raw waveform feature extractors (FEs) are an appealing approach. Also the wav2vec 2.0 model, which has recently gained large popularity, uses a convolutional FE which operates directly on the speech waveform. However, it is not yet studied extensively in the literature. In this work, we study its capability to replace the standard feature extraction methods in a connectionist temporal classification (CTC) ASR model and compare it to an alternative neural FE. We show that both are competitive with traditional FEs on the LibriSpeech benchmark and analyze the effect of the individual components. Furthermore, we analyze the learned filters and show that the most important information for the ASR system is obtained by a set of bandpass filters.
V-DETR: DETR with Vertex Relative Position Encoding for 3D Object Detection

We introduce a highly performant 3D object detector for point clouds using the DETR framework. The prior attempts all end up with suboptimal results because they fail to learn accurate inductive biases from the limited scale of training data. In particular, the queries often attend to points that are far away from the target objects, violating the locality principle in object detection. To address the limitation, we introduce a novel 3D Vertex Relative Position Encoding (3DV-RPE) method which computes position encoding for each point based on its relative position to the 3D boxes predicted by the queries in each decoder layer, thus providing clear information to guide the model to focus on points near the objects, in accordance with the principle of locality. In addition, we systematically improve the pipeline from various aspects such as data normalization based on our understanding of the task. We show exceptional results on the challenging ScanNetV2 benchmark, achieving significant improvements over the previous 3DETR in $\rm{AP}_{25}$/$\rm{AP}_{50}$ from 65.0\%/47.0\% to 77.8\%/66.0\%, respectively. In addition, our method sets a new record on ScanNetV2 and SUN RGB-D datasets.Code will be released at http://github.com/yichaoshen-MS/V-DETR.
Sub-Exponential Lower Bounds for Branch-and-Bound with General Disjunctions via Interpolation

This paper investigates linear programming based branch-and-bound using general disjunctions, also known as stabbing planes, for solving integer programs. We derive the first sub-exponential lower bound (in the encoding length $L$ of the integer program) for the size of a general branch-and-bound tree for a particular class of (compact) integer programs, namely $\smash{2^{\Omega(L^{1/12 -\epsilon})}}$ for every $\epsilon>0$. This is achieved by showing that general branch-and-bound admits quasi-feasible monotone real interpolation, which allows us to utilize sub-exponential lower-bounds for monotone real circuits separating the so-called clique-coloring pair. Moreover, this also implies that refuting $\Theta(\log(n))$-CNFs requires size $2^{n^{\Omega(1)}}$ branch-and-bound trees with high probability by considering the closely related notion of infeasibility certificates introduced by Hrubes and Pudl\'ak. One important ingredient of the proof of our interpolation result is that for every general branch-and-bound tree proving integer-freeness of a product $P\times Q$ of two polytopes $P$ and $Q$, there exists a closely related branch-and-bound tree for showing integer-freeness of $P$ or one showing integer-freeness of $Q$. Moreover, we prove that monotone real circuits can perform binary search efficiently.
Set-based value operators for non-stationary Markovian environments

—This paper analyzes ﬁnite state Markov Decision Processes (MDPs) with uncertain parameters in compact sets and re-examines results from robust MDP via set-based ﬁxed point theory. We generalize the Bellman and policy evaluation operators to operators that contract on the space of value functions and denote them as value operators . We generalize these value operators to act on the space of value function sets and denote them as set-based value operators . We prove that these set-based value operators are contractions in the space of compact value function sets. Leveraging insights from set theory, we generalize the rectangularity condition for the Bellman operator from classic robust MDP literature to a containment condition for a generic value operator, which is weaker and can be applied to a larger set of parameter-uncertain MDPs and contractive operators in dynamic programming and reinforcement learning. We prove that both the rectangu-larity condition and the containment condition sufﬁciently ensure that the set-based value operator’s ﬁxed point set contains its own supremum and inﬁmum elements. For convex and compact sets of uncertain MDP parameters, we show equivalence between the classic robust value function and the supremum of the ﬁxed point set of the set-based Bellman operator. Under dynamically changing MDP parameters in compact sets, we prove a set convergence result for value iteration, which otherwise may not converge to a single value function.
Development and repair of blood vessels in the zebrafish spinal cord

The vascular system is inefficiently repaired after spinal cord injury (SCI) in mammals, resulting in secondary tissue damage and immune deregulation that contribute to the limited functional recovery. Unlike mammals, zebrafish can repair the spinal cord (SC) and restore motility, but the vascular response to injury has not been investigated. Here, we describe the zebrafish SC blood vasculature, starting in development with the initial vessel ingression in a body size-dependent manner, the acquisition of perivascular support and the establishment of ventral to dorsal blood circulation. The vascular organization grows in complexity and displays multiple barrier specializations in adulthood. After injury, vessels rapidly regrow into the lesion, preceding the glial bridge and axons. Vascular repair involves an early burst of angiogenesis that creates dysmorphic and leaky vessels. Dysfunctional vessels are later removed, as pericytes are recruited and the blood–SC barrier is re-established. This study demonstrates that zebrafish can successfully re-vascularize the spinal tissue, reinforcing the value of this organism as a regenerative model for SCI.
Self-assembly of a barnacle cement protein into intertwined amyloid fibres and determination of their adhesive and viscoelastic properties

The stalked barnacle Pollicipes pollicipes uses a multi-protein cement to adhere to highly varied substrates in marine environments. We investigated the morphology and adhesiveness of a component 19 kDa protein in barnacle cement gland- and seawater-like conditions, using transmission electron microscopy and state-of-the art scanning probe techniques. The protein formed amyloid fibres after 5 days in gland-like but not seawater conditions. After 7–11 days, the fibres self-assembled under gland-like conditions into large intertwined fibrils of up to 10 µm in length and 200 nm in height, with a distinctive twisting of fibrils evident after 11 days. Atomic force microscopy (AFM)-nanodynamic mechanical analysis of the protein in wet conditions determined E′ (elasticity), E′′ (viscosity) and tan δ values of 2.8 MPa, 1.2 MPa and 0.37, respectively, indicating that the protein is a soft and viscoelastic material, while the adhesiveness of the unassembled protein and assembled fibres, measured using peak force quantitative nanomechanical mapping, was comparable to that of the commercial adhesive Cell-Tak™. The study provides a comprehensive insight into the nanomechanical and viscoelastic properties of the barnacle cement protein and its self-assembled fibres under native-like conditions and may have application in the design of amyloid fibril-based biomaterials or bioadhesives.
Does the primate face cue personality?

Abstract When looking at others, primates primarily focus on the face – detecting the face first and looking at it longer than other parts of the body. This is because primate faces, even without expression, convey trait information crucial for navigating social relationships. Recent studies on primates, including humans, have linked facial features, specifically facial width-to-height ratio (fWHR), to rank and Dominance-related personality traits, suggesting these links’ potential role in social decisions. However, studies on the association between dominance and fWHR report contradictory results in humans and variable patterns in nonhuman primates. It is also not clear whether and how nonhuman primates perceive different facial cues to personality traits and whether these may have evolved as social signals. This review summarises the variable facial-personality links, their underlying proximate and evolutionary mechanisms and their perception across primates. We emphasise the importance of employing comparative research, including various primate species and human populations, to disentangle phylogeny from socio-ecological drivers and to understand the selection pressures driving the facial-personality links in humans. Finally, we encourage researchers to move away from single facial measures and towards holistic measures and to complement perception studies using neuroscientific methods.
A design of near-infrared single photon counting integrated module based on InGaAs/InP NFAD

InGaAs/InP Negative Feedback Avalanche Diode (NFAD), with a quenching negative feedback resistor integrated, is a new type of high-sensitivity and all-solid-state semiconductor device based on Single Photon Avalanche Diode (SPAD) structure. This paper proposes a near-infrared free-running single photon counting integrated module based on InGaAs/InP NFAD. It contains an active quench and extraction circuit, sampling and processing circuit, upper computer software design and TEC etc., and is designed to serve a NFAD adopting absorption-attenuation-charge-multiplication (SAGCM) structure with InGaAs/InP materials and operating in Geiger mode. In this module, we specifically design a full differential amplifier and comparator to exploit the performance of NFAD by detecting and extracting the weak avalanche signal and converting it into TTL pulse. The avalanche detection discrimination threshold voltage is adjustable by external high-precision DAC, and a programmable dead time could be set by Field Programmable Gate Array (FPGA). The module system provides timing logic in order to avoid false counting caused by the coupling noise of the differential amplifier. By developing Graphical User Interface (GUI) program, we are able to setup detector working parameters configuration, to display real-time counting data and to further meet different application requirements. The fabricated module exhibits good NFAD performance with PDE of 7.9% and 15.8%, DCR of 1.37 kHz and 1.06 kHz and the after-pulse probability of 34.2% and 16.8% at 223 K, 1550 nm with dead time of 200 ns and 1 μs, respectively. It turns out that a near-infrared single photon counting system possessing fast detection speed, fast quenching time, flexible dead time adjustment, small size and high integration will be available in the near future to strongly support lidar and quantum information facilities.
Interpretation of the aeromagnetic anomalies of mainland Scotland using pseudogravimetric transformation and other methods

A procedure to upward continue magnetic anomalies observed on an irregular surface onto a horizontal plane has been developed and applied to the aeromagnetic map of Great Britain. Pseudogravimetric transformation was then carried out on this reduced anomaly and both data sets have been used for analysis and interpretation of several prominent anomalies in Scotland along the Great Glen fault and over the Midland Valley. A prominent linear positive magnetic anomaly occurring along the Great Glen fault has been modelled as due to a locally magnetized outward dipping body almost symmetrical about its apex beneath the fault line, together with a magnetized crustal slab to the northwest of the fault. The outward dipping body has its top lying within the upper crust, a magnetization of greater than about 1.0 A/m, a half-width of about 40 km at its base and a thickness of the order of 7-18 km. The origin of the outward dipping magnetized body may possibly be explained by metamorphism produced by frictional heating resulting from the transcurrent fault movement. Alternatively the metamorphism may be associated with some other fault related process such as crustal fluid flow. Thermal modelling has been used to demonstrate this. The magnetization contrast across the fault may be the direct result of blocks of differing magnetization on opposite side, juxtaposed as a result of transcurrent movement. The modelling along a profile over the Clyde Plateau (Midland Valley of Scotland) using a well-constrained lava body reveals the presence of a long wavelength anomaly component due to a deeper crustal source. The basement anomaly is conspicuous on the pseudogravimetric map but not on the aeromagnetic map. A near circular magnetic anomaly near Bathgate in the Midland Valley can be explained by an unexposed intrusive body superimposed on the deep crustal source as above.
Effects of Corporate Governance on Creative Accounting Practices: Evidence from NSE-listed Companies in India

This paper investigates the effects of corporate governance on creative accounting practice in Indian NSE-listed public companies. To get insights and analysis of how mitigating effects of governance issues can impact creative accounting practices, a specific set of factors is identified and the multistage technique design is employed to collect samples of 51 companies with 255 firm-year observations for the years 2017–2021. The value of discretionary accrual as estimated using the modified Jones model. The panel data are used for analysis and testing the hypothesis with the help of Stata 15 version statistical software. The results of this study demonstrated that only audit independence has a considerable negative influence on creative accounting practice, indicating that increasing the number of audit independence could reduce unethical accounting manipulation in financial reporting practice. The paper digs further and provides additional details that the board size, return on asset and firm growth significantly appears under a flag of positive creative accounting practices. In contrast, the board’s independence, audit committee, firm size and leverage have no such influence on creative accounting practice. The study also recommends that companies should have an independent audit committee, so as to minimise the accounting irregularities in financial reports.
Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation

Abstract In accordance with the inability of various hair artefacts subjected to dermoscopic medical images, undergoing illumination challenges that include chest-Xray featuring conditions of imaging acquisi-tion situations built with clinical segmentation. The study proposed a novel deep-convolutional neural network (CNN)-integrated methodology for applying medical image segmentation upon chest-Xray and dermoscopic clinical images. The study develops a novel technique of segmenting medical images merged with CNNs with an architectural comparison that incorporates neural networks of U-net and fully convolutional networks (FCN) schemas with loss functions associated with Jaccard distance and Binary-cross entropy under optimised stochastic gradient descent + Nesterov practices. Digital image over clinical approach significantly built the diagnosis and determination of the best treatment for a patient’s condition. Even though medical digital images are subjected to varied components clarified with the effect of noise, quality, disturbance, and precision depending on the enhanced version of images segmented with the optimised process. Ultimately, the threshold technique has been employed for the output reached under the pre- and post-processing stages to contrast the image technically being developed. The data source applied is well-known in PH2 Database for Melanoma lesion segmentation and chest X-ray images since it has variations in hair artefacts and illumination. Experiment outcomes outperform other U-net and FCN architectures of CNNs. The predictions produced from the model on test images were post-processed using the threshold technique to remove the blurry boundaries around the predicted lesions. Experimental results proved that the present model has better efficiency than the existing one, such as U-net and FCN, based on the image segmented in terms of sensitivity = 0.9913, accuracy = 0.9883, and dice coefficient = 0.0246.
An exploration of female healthcare workers’ experiences during the COVID-19 pandemic in Bindura, Zimbabwe

Abstract This study explores the experiences and coping strategies of female frontline healthcare workers (HCWs) fighting the COVID-19 pandemic in resource-constrained settings. Female frontline HCWs face an elevated risk of contracting the COVID-19 infection and infecting their loved ones because when they return from work. The study adopted a qualitative approach to explore the experiences of female frontline HCWs working in high-volume departments at Bindura provincial hospital in Zimbabwe. Using purposive sampling, 15 participants were recruited. HCWs were asked to share their views and perspectives on COVID-19 and their work environment. The study focused on HCWs’ experiences of stigma and discrimination once infected; access to treatment; understanding of transmission; and life experiences of living with COVID-19. The findings suggest that female HCWs working in high-volume departments experience a higher risk of exposure to infection, barriers to assessing personal protective equipment (PPE) and stigma, which impact their mental health. However, despite the complex interrelated challenges female frontline HCWs face in Zimbabwe, there are limited interventions to ameliorate this burden, a knowledge gap this research aims to illuminate.