Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationships between improvement in physical function, pain interference, and mental health in musculoskeletal patients

IMPORTANCE: Among patients seeking care for musculoskeletal conditions, there is mixed evidence regarding whether traditional, structure-based care is associated with improvement in patients' mental health. OBJECTIVE: To determine whether improvements in physical function and pain interference are associated with meaningful improvements in anxiety and depression symptoms among patients seeking musculoskeletal care. DESIGN: Retrospective cohort study from June 22, 2015 to February 9, 2022. SETTING: Orthopedic department of a tertiary care US academic medical center. PARTICIPANTS: Consecutive sample of adult patients who presented to the musculoskeletal clinic 4 to 6 times during the study period and completed Patient-Reported Outcomes Measurement Information System (PROMIS) measures as standard care at each visit. EXPOSURE: PROMIS Physical Function and Pain Interference scores. MAIN OUTCOMES AND MEASURES: Linear mixed effects models were used to determine whether: 1) PROMIS Anxiety and 2) PROMIS Depression scores improved as a function of improved PROMIS Physical Function or Pain Interference scores, after controlling for age, gender, race, and PROMIS Depression (for the Anxiety model) and PROMIS Anxiety (for the Depression model). Clinically meaningful improvement was defined as [≥]3.0 points for PROMIS Anxiety and [≥]3.2 points for PROMIS Depression. RESULTS: Among 11,236 patients (mean [SD] age 57 [16] years), 9,706 (86%) were White, and 7,218 (64%) were women. Improvements in physical function ({beta}=-0.14 [95% CI -0.15-- -0.13], p<0.001) and pain interference ({beta}=0.26 [0.25-0.26], p<0.001) were each associated with improved anxiety symptoms. To reach a clinically meaningful improvement in anxiety symptoms, an improvement of [≥]21 [20-23] PROMIS points on Physical Function or [≥]12 [12-12] points on Pain Interference would be required. Improvements in physical function ({beta}=-0.05 [-0.06-- -0.04], p<0.001) and pain interference ({beta}=0.04 [0.04-0.05], p<0.001) were not associated with meaningfully improved depression symptoms. CONCLUSIONS AND RELEVANCE: In this cohort study, substantial improvements in physical function and pain interference were required for association with any clinically meaningful improvement in anxiety symptoms and were not associated with any meaningful improvement in depression symptoms. Among patients seeking musculoskeletal care, musculoskeletal clinicians and patients cannot assume that addressing physical health will result in improved symptoms of depression or potentially even sufficiently improved symptoms of anxiety.
High-redshift JWST Observations and Primordial Non-Gaussianity

Several bright and massive galaxy candidates at high redshifts have been recently observed by the James Webb Space Telescope. Such early massive galaxies seem difficult to reconcile with standard Λ cold dark matter model predictions. We discuss under which circumstances such observed massive galaxy candidates can be explained by introducing primordial non-Gaussianity in the initial conditions of cosmological perturbations.
Rapid-Motion-Track: Markerless Tracking of Fast Human Motion with Deeper Learning

Objective The coordination of human movement directly reflects function of the central nervous system. Small deficits in movement are often the first sign of an underlying neurological problem. The objective of this research is to develop a new end-to-end, deep learning-based system, Rapid-Motion-Track (RMT) that can track the fastest human movement accurately when webcams or laptop cameras are used. Materials and Methods We applied RMT to finger tapping, a well-validated test of motor control that is one of the most challenging human motions to track with computer vision due to the small keypoints of digits and the high velocities that are generated. We recorded 160 finger tapping assessments simultaneously with a standard 2D laptop camera (30 frames/sec) and a high-speed wearable sensor-based 3D motion tracking system (250 frames/sec). RMT and a range of DLC models were applied to the video data with tapping frequencies up to 8Hz to extract movement features. Results The movement features (e.g. speed, rhythm, variance) identified with the new RMT system exhibited very high concurrent validity with the gold-standard measurements (97.3\% of RMT measures were within +/-0.5Hz of the Optotrak measures), and outperformed DLC and other advanced computer vision tools (around 88.2\% of DLC measures were within +/-0.5Hz of the Optotrak measures). RMT also accurately tracked a range of other rapid human movements such as foot tapping, head turning and sit-to -stand movements. Conclusion: With the ubiquity of video technology in smart devices, the RMT method holds potential to transform access and accuracy of human movement assessment.
Introduction to Presentation Attacks in Signature Biometrics and Recent Advances

Applications based on biometric authentication have received a lot of interest in the last years due to the breathtaking results obtained using personal traits such as face or fingerprint. However, it is important not to forget that these biometric systems have to withstand different types of possible attacks. This chapter carries out an analysis of different Presentation Attack (PA) scenarios for on-line handwritten signature verification. The main contributions of this chapter are: i) an updated overview of representative methods for Presentation Attack Detection (PAD) in signature biometrics; ii) a description of the different levels of PAs existing in on-line signature verification regarding the amount of information available to the impostor, as well as the training, effort, and ability to perform the forgeries; and iii) an evaluation of the system performance in signature biometrics under different scenarios considering recent publicly available signature databases, DeepSignDB and SVC2021_EvalDB. This work is in line with recent efforts in the Common Criteria standardization community towards security evaluation of biometric systems.
Learning to Dexterously Pick or Separate Tangled-Prone Objects for Industrial Bin Picking

Industrial bin picking for tangled-prone objects requires the robot to either pick up untangled objects or perform separation manipulation when the bin contains no isolated objects. The robot must be able to flexibly perform appropriate actions based on the current observation. It is challenging due to high occlusion in the clutter, elusive entanglement phenomena, and the need for skilled manipulation planning. In this letter, we propose an autonomous, effective and general approach for picking up tangled-prone objects for industrial bin picking. First, we learn PickNet - a network that maps the visual observation to pixel-wise possibilities of picking isolated objects or separating tangled objects and infers the corresponding grasp. Then, we propose two effective separation strategies: Dropping the entangled objects into a buffer bin to reduce the degree of entanglement; pulling to separate the entangled objects in the buffer bin planned by PullNet - a network that predicts position and direction for pulling from visual input. To efficiently collect data for training PickNet and PullNet, we embrace the self-supervised learning paradigm using an algorithmic supervisor in a physics simulator. Real-world experiments show that our policy can dexterously pick up tangled-prone objects with success rates of 90%. We further demonstrate the generalization of our policy by picking a set of unseen objects.
Energy Efficient Operation of Adaptive Massive MIMO 5G HetNets

For energy efficient operation of the massive multiple-input multiple-output (MIMO) networks, various aspects of energy efficiency maximization have been addressed, where a careful selection of number of active antennas has shown significant gains. Moreover, switching-off physical resource blocks (PRBs) and carrier shutdown saves energy in low load scenarios. However, the joint optimization of spectral PRB allocation and spatial layering in a heterogeneous network has not been completely solved yet. Therefore, we study a power consumption model for multi-cell multi-user massive MIMO 5G network, capturing the joint effects of both dimensions. We characterize the optimal resource allocation under practical constraints, i.e., limited number of available antennas, PRBs, base stations (BSs), and frequency bands. We observe a single spatial layer achieving lowest energy consumption in very low load scenarios, whereas, spatial layering is required in high load scenarios. Finally, we derive novel algorithms for energy efficient user to BS assignment and propose an adaptive algorithm for PRB assignment and power control. All results are illustrated by numerical system-level simulations, describing a realistic metropolis scenario. The results show that a higher frequency band should be used to support users with large rate requirements via spatial multiplexing and assigning each user maximum available PRBs.
LiQuiD-MIMO Radar: Distributed MIMO Radar with Low-Bit Quantization

Distributed MIMO radar is known to achieve superior sensing performance by employing widely separated antennas. However, it is challenging to implement a low-complexity distributed MIMO radar due to the complex operations at both the receivers and the fusion center. This work proposes a low-bit quantized distributed MIMO (LiQuiD-MIMO) radar to significantly reduce the burden of signal acquisition and data transmission. In the LiQuiD-MIMO radar, the widely-separated receivers are restricted to operating with low-resolution ADCs and deliver the low-bit quantized data to the fusion center. At the fusion center, the induced quantization distortion is explicitly compensated via digital processing. By exploiting the inherent structure of our problem, a quantized version of the robust principal component analysis (RPCA) problem is formulated to simultaneously recover the low-rank target information matrices as well as the sparse data transmission errors. The least squares-based method is then employed to estimate the targets’ positions and velocities from the recovered target information matrices. Numerical experiments demonstrate that the proposed LiQuiD-MIMO radar, configured with the developed algorithm, can achieve accurate target parameter estimation.
An Efficient Framework for Execution of Smart Contracts in Hyperledger Sawtooth

Blockchain technology is a distributed, decentralized, and immutable ledger system. It is the platform of choice for managing smart contract transactions (SCTs). Smart contracts are self-executing codes of agreement between interested parties commonly implemented using blockchains. A block contains a set of transactions representing changes to the system and a hash of the previous block. The SCTs are executed multiple times during the block production and validation phases across the network. The execution is sequential in most blockchain technologies. In this work, we incorporate a direct acyclic graph (DAG) based parallel scheduler framework for concurrent execution of SCTs. The dependencies among a block's transactions are represented through a concurrent DAG data structure that assists in throughput optimization. We have created a DAG scheduler module that can be incorporated into blockchain platforms for concurrent execution with ease. We have also formally established the safety and liveness properties of the DAG scheduler. For evaluation, our framework is implemented in Hyperledger Sawtooth V1.2. 6. The performance across multiple smart contract applications is measured for various scheduler types. Experimental analysis shows that the proposed framework achieves notable performance improvements over the parallel SCT execution frameworks.
Classical uncertainty relations and entropy production in non-equilibrium statistical mechanics

We analyze Fürth’s 1933 classical uncertainty relations in the modern language of stochastic differential equations. Our interest is motivated by their application to non-equilibrium classical statistical mechanics. We show that Fürth’s uncertainty relations are a property inherent in martingales within the framework of a diffusion process. This result implies a lower bound on the fluctuations in current velocities of entropic quantifiers associated with transitions in stochastic thermodynamics. In cases of particular interest, we recover a well-known inequality for optimal mass transport relating the mean kinetic energy of the current velocity and the squared quadratic Wasserstein distance between the probability distributions of the entropy. We take advantage in particular of an unpublished suggestion by Krzysztof Gawȩdzki to derive a lower bound to the entropy production by a transition described by a Langevin–Kramers process in terms of the squared quadratic Wasserstein distance between the initial and final states of the transition. Finally, we illustrate how Fürth’s relations admit a straightforward extension to piecewise deterministic processes. We show that the results presented in this paper pertain to the characteristics exhibited by general Markov processes.
Counterfactual Reasoning for Bias Evaluation and Detection in a Fairness under Unawareness setting

Current AI regulations require discarding sensitive features (e.g., gender, race, religion) in the algorithm's decision-making process to prevent unfair outcomes. However, even without sensitive features in the training set, algorithms can persist in discrimination. Indeed, when sensitive features are omitted (fairness under unawareness), they could be inferred through non-linear relations with the so called proxy features. In this work, we propose a way to reveal the potential hidden bias of a machine learning model that can persist even when sensitive features are discarded. This study shows that it is possible to unveil whether the black-box predictor is still biased by exploiting counterfactual reasoning. In detail, when the predictor provides a negative classification outcome, our approach first builds counterfactual examples for a discriminated user category to obtain a positive outcome. Then, the same counterfactual samples feed an external classifier (that targets a sensitive feature) that reveals whether the modifications to the user characteristics needed for a positive outcome moved the individual to the non-discriminated group. When this occurs, it could be a warning sign for discriminatory behavior in the decision process. Furthermore, we leverage the deviation of counterfactuals from the original sample to determine which features are proxies of specific sensitive information. Our experiments show that, even if the model is trained without sensitive features, it often suffers discriminatory biases.
Temporal Entanglement in Chaotic Quantum Circuits

The concept of space-evolution (or space-time duality) has emerged as a promising approach for studying quantum dynamics. The basic idea involves exchanging the roles of space and time, evolving the system using a space transfer matrix rather than the time evolution operator. The infinite-volume limit is then described by the fixed points of the latter transfer matrix, also known as influence matrices. To establish the potential of this method as a bona fide computational scheme, it is important to understand whether the influence matrices can be efficiently encoded in a classical computer. Here we begin this quest by presenting a systematic characterisation of their entanglement -- dubbed temporal entanglement -- in chaotic quantum systems. We consider the most general form of space-evolution, i.e., evolution in a generic space-like direction, and present two fundamental results. First, we show that temporal entanglement always follows a volume law in time. Second, we identify two marginal cases -- (i) pure space evolution in generic chaotic systems (ii) any space-like evolution in dual-unitary circuits -- where R\'enyi entropies with index larger than one are sub-linear in time while the von Neumann entanglement entropy grows linearly. We attribute this behaviour to the existence of a product state with large overlap with the influence matrices. This unexpected structure in the temporal entanglement spectrum might be the key to an efficient computational implementation of the space evolution.
Continuous Remote Sensing Image Super-Resolution Based on Context Interaction in Implicit Function Space

Despite its fruitful applications in remote sensing, image super-resolution (SR) is troublesome to train and deploy as it handles different resolution magnifications with separate models. Accordingly, we propose a highly applicable SR framework called FunSR, which settles different magnifications with a unified model by exploiting context interaction within implicit function space. FunSR composes a functional representor, a functional interactor, and a functional parser. Specifically, the representor transforms the low-resolution image from Euclidean space to multiscale pixelwise function maps; the interactor enables pixelwise function expression with global dependencies; and the parser, which is parameterized by the interactor’s output, converts the discrete coordinates with additional attributes to RGB values. Extensive experimental results demonstrate that FunSR reports the state-of-the-art performance on both fixed- and continuous-magnification settings; meanwhile, it provides many friendly applications due to its unified nature. Our code is available at https://github.com/KyanChen/FunSR.
Local vs. translationally-invariant slowest operators in quantum Ising spin chains

In this paper we study one-dimensional quantum Ising spin chains in external magnetic field close to an integrable point. We concentrate on the dynamics of the slowest operator, that plays a key role at the final period of thermalization. We introduce two independent definitions of the slowest operator: local and translationally-invariant ones. We construct both operators numerically using tensor networks and extensively compare their physical properties. We find that the local operator has a significant overlap with energy flux, it does not correspond to an integral of motion, and, as one goes away from the integrable point, its revivals get suppressed and the rate of delocalization changes from extremely slow to slower than diffusion. The translationally-invariant operator corresponds to an integral of motion; as the system becomes less integrable, at some point this operator changes its nature: from no overlap with any magnetization and fast rate of delocalization, to non-zero overlap with magnetizations $\sigma_{x}$ and $\sigma_{z}$ and slow rate of delocalization.
A RIS-Based Vehicle DOA Estimation Method With Integrated Sensing and Communication System

With the development of intelligent transportation, growing attention has been received to integrated sensing and communication (ISAC) systems. In this paper, we formulate a novel passive sensing technique to obtain information on the vehicle's direction of arrival (DOA) using reconfigurable intelligent surfaces (RIS). A novel estimation method is proposed in the scenario with a receiver using only one full-functional channel, where multiple measurements for the DOA estimation are achieved by controlling the reflection matrix (measurement matrix) in the RIS. Moreover, different from the existing estimation methods, we also consider the interference signals introduced by wireless communication in the ISAC system. Then, we propose a novel atomic norm-based method to remove the interference signals and reconstruct the sparse signal. Additionally, a novel Hankel-based multiple signal classification (MUSIC) method is formulated to obtain the DOA information after the interference removal. To reduce the interference signals more efficiently and improve the performance of the sparse reconstruction, we optimize the measurement matrix to improve the signal-to-interference-plus-noise ratio (SINR). Finally, the theoretical Cram'{e}r-Rao lower bound (CRLB) is derived for the ISAC system on the vehicle DOA estimation. Simulation results show that the proposed method can achieve better performance in the DOA estimation, and the corresponding CRLB with different distributions of the sensing nodes are shown. The code for the proposed method is available online https://github.com/chenpengseu/PassiveDOA-ISAC-RIS.git.
GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow

The Gaussian Process (GP) based Chance-Constrained Optimal Power Flow (CC-OPF) is an open-source Python code developed for solving economic dispatch (ED) problem in modern power grids. In recent years, integrating a significant amount of renewables into a power grid causes high fluctuations and thus brings a lot of uncertainty to power grid operations. This fact makes the conventional model-based CC-OPF problem non-convex and computationally complex to solve. The developed tool presents a novel data-driven approach based on the GP regression model for solving the CC-OPF problem with a trade-off between complexity and accuracy. The proposed approach and developed software can help system operators to effectively perform ED optimization in the presence of large uncertainties in the power grid.
A Survey on Event-Based News Narrative Extraction

Narratives are fundamental to our understanding of the world, providing us with a natural structure for knowledge representation over time. Computational narrative extraction is a subfield of artificial intelligence that makes heavy use of information retrieval and natural language processing techniques. Despite the importance of computational narrative extraction, relatively little scholarly work exists on synthesizing previous research and strategizing future research in the area. In particular, this article focuses on extracting news narratives from an event-centric perspective. Extracting narratives from news data has multiple applications in understanding the evolving information landscape. This survey presents an extensive study of research in the area of event-based news narrative extraction. In particular, we screened more than 900 articles, which yielded 54 relevant articles. These articles are synthesized and organized by representation model, extraction criteria, and evaluation approaches. Based on the reviewed studies, we identify recent trends, open challenges, and potential research lines.
Personalized Audio Quality Preference Prediction

This paper proposes to use both audio input and subject information to predict the personalized preference of two audio segments with the same content in different qualities. A siamese network is used to compare two inputs and predict the preference. Several different structures for each side of the siamese network are investigated. The baseline structure which uses only audio information involves using a pretrained audio encoder followed by fully connected layers. In several different proposed structures, the approach of concatenating subject information with audio embedding before feeding it into fully connected layers outperforms the baseline model the most, resulting in an increase in overall accuracy from 77.56% to 78.04%. Experimental results also demonstrate that utilizing the complete set of subject information, which includes age, gender, and headphone/earphone specifications such as impedance, frequency response range, and sensitivity, is more effective than using a subset of this information.