Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Cryogenic Fully Differential Gain Boosting-OTA by the $g_{m}/I_{d}$ methodology used for a 14 bit Pipelined-SAR ADC

—Quantum computing (QC) requires cryogenic elec- tronic circuits as control and readout sub-systems of quantum chips to meet the qubit scale-up challenges.At this temper- ature,MOSFETs transistors exhibition many changes such as higher threshold voltage,higher mobility,and steeper substhresh- old slope.We present a cryogenic fully differential gain boosting-OTA used for a 14 bit Pipelined-SAR ADC operating at 4.2K as the readout circuit for semiconductor-based quantum computing system.Using g m /I d methodology to get pre-computed lookup tables based on the cryogenic 110nm BSIM4 model.The proposed OTA achieves very high unity-gain frequency@1.23GHz and open-loop low frequency gain@101dB.The total power consumption is 2.66mW at 4.2K,and a setting accuracy better than 0.01% with f − 3 dB of 37MHz in a closed-loop application.
A Survey of Video-based Action Quality Assessment

Human action recognition and analysis have great demand and important application significance in video surveillance, video retrieval, and human-computer interaction. The task of human action quality evaluation requires the intelligent system to automatically and objectively evaluate the action completed by the human. The action quality assessment model can reduce the human and material resources spent in action evaluation and reduce subjectivity. In this paper, we provide a comprehensive survey of existing papers on video-based action quality assessment. Different from human action recognition, the application scenario of action quality assessment is relatively narrow. Most of the existing work focuses on sports and medical care. We first introduce the definition and challenges of human action quality assessment. Then we present the existing datasets and evaluation metrics. In addition, we summarized the methods of sports and medical care according to the model categories and publishing institutions according to the characteristics of the two fields. At the end, combined with recent work, the promising development direction in action quality assessment is discussed.
Extra boson mix with Z boson explaining the mass of W boson

We explore the possibility of explaining the W mass with an extra gauge boson mixing with the Z boson at tree level. Extra boson mixing with Z boson will change the expression of Z boson mass, thus altering the W boson mass. We explore two models in this work. We ﬁnd that in the Derivative Portal Dark Matter model, there are parameters space which can give the observed W boson mass, as well as the observed Dark Matter relic density. In the U(1) extension model, the kinetic mixing between extra boson and B boson can also give the observed W boson mass. However, to fulﬁll electroweak oblique parameters ﬁt the kinetic mixing in the U(1) model can only contribute about 27 MeV extra mass to the Standard Model W boson mass. Both model indicate the extra vector boson with best ﬁt mass around 120 GeV.
Per-run Algorithm Selection with Warm-starting using Trajectory-based Features

Per-instance algorithm selection seeks to recommend, for a given problem instance and a given performance criterion, one or several suitable algorithms that are expected to perform well for the particular setting. The selection is classically done offline, using openly available information about the problem instance or features that are extracted from the instance during a dedicated feature extraction step. This ignores valuable information that the algorithms accumulate during the optimization process. In this work, we propose an alternative, online algorithm selection scheme which we coin per-run algorithm selection. In our approach, we start the optimization with a default algorithm, and, after a certain number of iterations, extract instance features from the observed trajectory of this initial optimizer to determine whether to switch to another optimizer. We test this approach using the CMA-ES as the default solver, and a portfolio of six different optimizers as potential algorithms to switch to. In contrast to other recent work on online per-run algorithm selection, we warm-start the second optimizer using information accumulated during the first optimization phase. We show that our approach outperforms static per-instance algorithm selection. We also compare two different feature extraction principles, based on exploratory landscape analysis and time series analysis of the internal state variables of the CMA-ES, respectively. We show that a combination of both feature sets provides the most accurate recommendations for our test cases, taken from the BBOB function suite from the COCO platform and the YABBOB suite from the Nevergrad platform.
GIMO: Gaze-Informed Human Motion Prediction in Context

Predicting human motion is critical for assistive robots and AR/VR applications, where the interaction with humans needs to be safe and comfortable. Meanwhile, an accurate prediction depends on understanding both the scene context and human intentions. Even though many works study scene-aware human motion prediction, the latter is largely underexplored due to the lack of ego-centric views that disclose human intent and the limited diversity in motion and scenes. To reduce the gap, we propose a large-scale human motion dataset that delivers high-quality body pose sequences, scene scans, as well as ego-centric views with the eye gaze that serves as a surrogate for inferring human intent. By employing inertial sensors for motion capture, our data collection is not tied to specific scenes, which further boosts the motion dynamics observed from our subjects. We perform an extensive study of the benefits of leveraging the eye gaze for ego-centric human motion prediction with various state-of-the-art architectures. Moreover, to realize the full potential of the gaze, we propose a novel network architecture that enables bidirectional communication between the gaze and motion branches. Our network achieves the top performance in human motion prediction on the proposed dataset, thanks to the intent information from eye gaze and the denoised gaze feature modulated by the motion. Code and data can be found at https://github.com/y-zheng18/GIMO.
Estimating Density Models with Truncation Boundaries using Score Matching

Truncated densities are probability density functions defined on truncated domains. They share the same parametric form with their non-truncated counterparts up to a normalizing constant. Since the computation of their normalizing constants is usually infeasible, Maximum Likelihood Estimation cannot be easily applied to estimate truncated density models. Score Matching (SM) is a powerful tool for fitting parameters using only unnormalized models. However, it cannot be directly applied here as boundary conditions used to derive a tractable SM objective are not satisfied by truncated densities. In this paper, we study parameter estimation for truncated probability densities using SM. The estimator minimizes a weighted Fisher divergence. The weight function is simply the shortest distance from a data point to the boundary of the domain. We show this choice of weight function naturally arises from minimizing the Stein discrepancy as well as upperbounding the finite-sample estimation error. The usefulness of our method is demonstrated by numerical experiments and a study on the Chicago crime data set. We also show that the proposed density estimation can correct the outlier-trimming bias caused by aggressive outlier detection methods.
Triggering micronovae through magnetically confined accretion flows in accreting white dwarfs

Rapid bursts at optical wavelengths have been reported for several accreting white dwarfs, where the optical luminosity can increase by up to a factor 30 in less than an hour fading on timescales of several hours, and where the energy release can reach ≈ 10 39 erg (“micronovae”). Several systems have also shown these bursts to be semi-recurrent on timescales of days to months and the temporal profiles of these bursts strongly resemble those observed in Type-I X-ray bursts in accreting neutron stars. It has been suggested that the observed micronovae may be the result of localised thermonuclear runaways on the surface layers of accreting white dwarfs. Here we propose a model where magnetic confinement of the accretion stream on to accreting magnetic white dwarfs may trigger localised thermonuclear runaways. The proposed model to trigger micronovae appears to favour magnetic systems with both high white dwarf masses and high mass-transfer rates.
Radiology Text Analysis System (RadText): Architecture and Evaluation

Analyzing radiology reports is a time-consuming and error-prone task, which raises the need for an efficient automated radiology report analysis system to alleviate the workloads of radiologists and encourage precise diagnosis. In this work, we present RadText, an open-source radiology text analysis system developed by Python. RadText offers an easy-to-use text analysis pipeline, including de-identification, section segmentation, sentence split and word tokenization, named entity recognition, parsing, and negation detection. RadText features a flexible modular design, provides a hybrid text processing schema, and supports raw text processing and local processing, which enables better usability and improved data privacy. Rad-Text adopts BioC as the unified interface, and also standardizes the input / output into a structured representation compatible with Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). This allows for a more systematic approach to observational research across multiple, disparate data sources. We evaluated RadText on the MIMIC-CXR dataset, with five new disease labels we annotated for this work. RadText demonstrates highly accurate classification performances, with an average precision of, a recall of 0.94, and an F-1 score of 0.92. We have made our code, documentation, examples, and the test set available at https://github.com/bionlplab/radtext.
RangeUDF: Semantic Surface Reconstruction from 3D Point Clouds

We present RangeUDF, a new implicit representation based framework to recover the geometry and semantics of continuous 3D scene surfaces from point clouds. Unlike occupancy fields or signed distance fields which can only model closed 3D surfaces, our approach is not restricted to any type of topology. Being different from the existing unsigned distance fields, our framework does not suffer from any surface ambiguity. In addition, our RangeUDF can jointly estimate precise semantics for continuous surfaces. The key to our approach is a range-aware unsigned distance function together with a surface-oriented semantic segmentation module. Extensive experiments show that RangeUDF clearly surpasses state-of-the-art approaches for surface reconstruction on four point cloud datasets. Moreover, RangeUDF demonstrates superior generalization capability across multiple unseen datasets, which is nearly impossible for all existing approaches.
Numerical solution to the time-independent Gross-Pitaevskii equation

We solve the time-independent Gross-Pitaevskii equation modeling the Bose-Einstein condensate trapped in an anistropic harmonic potential using a pseudospectral method. Numerically obtained values for an energy and a chemical potential for the condensate with positive and negative scattering length have been compared with those from the literature. The results show that they are in good agreement when an atomic interaction is not too strong.
Curvilinear parallelogram identity and mean-value property for a semilinear hyperbolic equation of second-order

In this paper, we discuss some of the important qualitative properties of solutions of second-order hyperbolic equations, whose coefficients of the terms involving the second-order derivatives are independent of the desired function and its derivatives. Solutions of these equations have a special property called the curvilinear parallelogram identity (or the mean-value property), which can be used to solve some initial-boundary value problems.
A double-peaked Lyman-α emitter with a stronger blue peak multiply imaged by the galaxy cluster RXC J0018.5+1626

We report the discovery of a double-peaked Lyman-α (Lyα) emitter (LAE) at z = 3.2177 ± 0.0001 in VLT/MUSE data. The galaxy is strongly lensed by the galaxy cluster RXC J0018.5+1626 recently observed in the RELICS survey, and the double-peaked Lyα emission is clearly detected in the two counter images in the MUSE field-of-view. We measure a relatively high Lyα rest-frame equivalent width (EW) of EWLyα, 0 = (63 ± 2) Å. Additional spectroscopy with Gemini/GNIRS in the near-infrared (NIR) allows us to measure the Hβ, [O iii]λ4959Å and [O iii]λ5007Å emission lines, which show moderate rest-frame EWs of the order of a few ∼10 − 100 Å, an [O iii]λ5007Å/Hβ ratio of 4.8 ± 0.7, and a lower limit on the [O iii]/[O ii] ratio of >9.3. The galaxy has very blue UV-continuum slopes of βFUV = −2.23 ± 0.06 and βNUV = −3.0 ± 0.2, and is magnified by factors μ ∼ 7 − 10 in each of the two images, thus enabling a view into a low-mass (M⋆ ≃ 107.5 M⊙) high-redshift galaxy analog. Notably, the blue peak of the Lyα profile is significantly stronger than the red peak, which suggests an inflow of matter and possibly very low H i column densities in its circumgalactic gas. To the best of our knowledge, this is the first detection of such a Lyα profile. Combined with the high lensing magnification and image multiplicity, these properties make this galaxy a prime candidate for follow-up observations to search for LyC emission and constrain the LyC photon escape fraction.
Simulation of machine learning-based 6G systems in virtual worlds

Digital representations of the real world are being used in many applications, such as augmented reality. 6G systems will not only support use cases that rely on virtual worlds but also benefit from their rich contextual information to improve performance and reduce communication overhead. This paper focuses on the simulation of 6G systems that rely on a 3D representation of the environment, as captured by cameras and other sensors. We present new strategies for obtaining paired MIMO channels and multimodal data. We also discuss trade-offs between speed and accuracy when generating channels via ray tracing. We finally provide beam selection simulation results to assess the proposed methodology.
Perceiving the World: Question-guided Reinforcement Learning for Text-based Games

Text-based games provide an interactive way to study natural language processing. While deep reinforcement learning has shown effectiveness in developing the game playing agent, the low sample efficiency and the large action space remain to be the two major challenges that hinder the DRL from being applied in the real world. In this paper, we address the challenges by introducing world-perceiving modules, which automatically decompose tasks and prune actions by answering questions about the environment. We then propose a two-phase training framework to decouple language learning from reinforcement learning, which further improves the sample efficiency. The experimental results show that the proposed method significantly improves the performance and sample efficiency. Besides, it shows robustness against compound error and limited pre-training data.
K-LITE: Learning Transferable Visual Models with External Knowledge

The new generation of state-of-the-art computer vision systems are trained from natural language supervision, ranging from simple object category names to descriptive captions. This form of supervision ensures high generality and usability of the learned visual models, due to the broad concept coverage achieved via large-scale data collection process. Alternatively, we argue that learning with external knowledge is a promising way which leverages a much more structured source of supervision and offers sample efficiency. We propose K-LITE, a simple strategy to leverage external knowledge for building transferable visual systems: In training, it enriches entities in text with WordNet and Wiktionary knowledge, leading to an efficient and scalable approach to learning image representations that uses knowledge about the visual concepts. In evaluation, the text is also augmented with external knowledge and then used to reference learned visual concepts (or describe new ones) to enable zero-shot and few-shot transfer of the pre-trained models. We study the performance of K-LITE on two important computer vision problems, image classification and object detection, benchmarking on 20 and 13 different existing datasets, respectively. The proposed knowledge-augmented models show significant improvement in transfer learning performance over existing methods. Our code is available at https://github.com/microsoft/klite.
Small Promise CSPs that reduce to large CSPs

For relational structures A, B of the same signature, the Promise Constraint Satisfaction Problem PCSP(A,B) asks whether a given input structure maps homomorphically to A or does not even map to B. We are promised that the input satisfies exactly one of these two cases. If there exists a structure C with homomorphisms $A\to C\to B$, then PCSP(A,B) reduces naturally to CSP(C). To the best of our knowledge all known tractable PCSPs reduce to tractable CSPs in this way. However Barto showed that some PCSPs over finite structures A, B require solving CSPs over infinite C. We show that even when such a reduction to finite C is possible, this structure may become arbitrarily large. For every integer $n>1$ and every prime p we give A, B of size n with a single relation of arity $n^p$ such that PCSP(A, B) reduces via a chain of homomorphisms $ A\to C\to B$ to a tractable CSP over some C of size p but not over any smaller structure. In a second family of examples, for every prime $p\geq 7$ we construct A, B of size $p-1$ with a single ternary relation such that PCSP(A, B) reduces via $A\to C\to B$ to a tractable CSP over some C of size p but not over any smaller structure. In contrast we show that if A, B are graphs and PCSP(A,B) reduces to tractable CSP(C) for some finite digraph C, then already A or B has a tractable CSP. This extends results and answers a question of Deng et al.
People and Safety

Abstract This paper is based on work carried out while employed by BNFL at Sellafield (now Sellafield Ltd) and Vectra in its various guises (and now Arcadis/EC Harris) but the views and opinions expressed are solely those of the author. Its intent is based on the notion that “organisations don't have memories, only people have memories”, and as such is a contribution to “people's memories as an aid to organisations”.
50 Years of Sports Teams in Work Teams Research: Missed Opportunities and New Directions for Studying Team Processes

For the last 50 years, sports team samples have played a significant role in mainstream management literature. Prior research has analyzed different kinds of data, such as archival, survey-based, and interviews/observations, from a wide range of sports teams. These teams differ greatly in terms of contexts (i.e., recreational to intercollegiate to professional) and types of interdependence. We explore this body of work and focus on what is germane for the work teams literature as we examine 255 relevant articles spanning the years 1972–2021 in major management, organizational behavior (OB), human resource management (HRM), and strategy journals. Using the input-process-outcome model in our coding process, we identify the relative absence of the study of team processes. We develop a conceptual framework linking team interdependence, team processes, and the initiation and maintenance of these processes. This framework is intended to help guide future research on team processes in the context of sports team samples and enhance the generalizability of this research to the work teams domain. Additionally, we identify an agenda for future research using sports team samples for work groups/teams researchers. Overall, we intend to spur thoughtful and creative future research in the work groups/teams area using the rich field environment that sports teams present.
How Does Analyst Coverage Affect Corporate Social Responsibility? Evidence from China

ABSTRACT Using archival data of Chinese A-share listed companies from 2009 to 2018, this article examines the influence of analyst coverage on corporate social responsibility (hereinafter refer as CSR) performance. Main results suggest that sell-side analysts can significantly promote Chinese listed companies’ CSR activities. Several robustness tests are employed and proved the solidity of main conclusions. Mechanism tests show that the investor recognition channel plays a dominant role in explaining the positive effect of analyst coverage. Additional analysis further illustrates that stock exchanges’ regulatory practices concerning firms’ CSR activities can strengthen the investor recognition channel.
Quantizing gravity and spacetime: Where do we stand ?

We review five possible solutions to the riddle posed by Quantum Gravity: (1) Gravity should stay as a classical theory (L. Rosenfeld); (2) Quantum Gravity requires a formalism which will take the human mind (or the intelligent observer) into account, resolving at the same time the riddle of the collapse of the wave function/state vector in Quantum Mechanics in general (Penrose); (3) Perturbative Quantization; (4) Hamiltonian Quantization (Dirac, Ashtekar); (5) String Theory. We also discuss the quantization of spacetime.
Persistent current in normal metals

We discuss the recent experiments on persistent current in metallic rings in the backdrop of low temperature decoherence. The observed size of the persistent current, typically on the order of the Thouless energy, e/τD, is much larger than the theoretical results, obtained with or without electron interaction. In considering the phenomenology of both decoherence and persistent current, usually observed in similar systems, we argue towards a dynamic role played by decoherence in the generation of a persistent current. A field‐induced phase shift from near‐equilibrium high‐frequency fluctuations– which otherwise gives rise to decoherence–under certain conditions of periodicity and asymmetry due to disorder, is argued to induce a steady state diffusion current on the order of e/τD, comparable to the observed persistent current.
Another piece of knowledge in the puzzle of procoagulant COAT platelets

Factor XIII (FXIII) is a transglutaminase circulating in human plasma as a tetramer consisting of two zymogen A subunits and two pro-tective/inhibitory B subunits. 1 Interestingly, FXIII is also present as dimer of its A- subunit in the cytosol of human platelets at a con-centration that is 150- fold greater than in plasma. 2 The existence of this pool of cytosolic FXIII (cFXIII) is intriguing because platelet activation by single agonists does not elicit its secretion 3 and its intracellular functions are not clearly elucidated yet. 1,4 Some light was recently shed by Mitchell et al., 5 who showed for the first time that cFXIII is externalized and retained on the membrane of platelets when they are simultaneously activated by collagen and thrombin. In this issue of the Journal of Thrombosis and Haemostasis , Somodi et al. 6 set out to explore the mechanisms underlying cFXIII externalization on platelets and platelet- derived microparticles. First, they showed that a prolonged high cytosolic free calcium ([Ca 2 + ] cyt ) level is necessary but not sufficient for translocating cFXIII to the surface of