Miracle-dz/UniFAR-FTUs huggingface dataset:
BanditLinQ: A Scalable Link Scheduling for Dense D2D Networks with One-Bit Feedback

This paper addresses cooperative link scheduling problems for base station (BS) aided device-to-device (D2D) communications using limited channel state information (CSI) at BS. We first derive the analytical form of ergodic sum-spectral efficiency as a function of network parameters, assuming statistical CSI at the BS. However, the optimal link scheduling, which maximizes the ergodic sum-spectral efficiency, becomes computationally infeasible when network density increases. To overcome this challenge, we present a low-complexity link scheduling algorithm that divides the D2D network into sub-networks and identifies the optimal link scheduling strategy per sub-network. Furthermore, we consider the scenario when the statistical CSI is not available to the BS. In such cases, we propose a quasi-optimal scalable link scheduling algorithm that utilizes one-bit feedback information from D2D receivers. The algorithm clusters the links and applies the UCB algorithm per cluster using the collected one-bit feedback information. We highlight that even with reduced scheduling complexity, the proposed algorithm identifies a link scheduling action that ensures optimality within a constant throughput gap. We also demonstrate through simulations that the proposed algorithm achieves higher sum-spectral efficiency than the existing link scheduling algorithms, even without explicit CSI or network parameters knowledge.
Room dimensions and absorption inference from room transfer function via machine learning

The inference of the absorption configuration of an existing room solely using acoustic signals can be challenging. This research presents two methods for estimating the room dimensions and frequency-dependent absorption coefficients using room transfer functions. The first method, a knowledge-based approach, calculates the room dimensions through damped resonant frequencies of the room. The second method, a machine learning approach, employs multi-task convolutional neural networks for inferring the room dimensions and frequency-dependent absorption coefficients of each surface. The study shows that accurate wave-based simulation data can be used to train neural networks for real-world measurements and demonstrates a potential for this algorithm to be used to estimate the boundary input data for room acoustic simulations. The proposed methods can be a valuable tool for room acoustic simulations during acoustic renovation or intervention projects, as they enable to infer the room geometry and absorption conditions with reasonably small data requirements.
Using Intent Estimation and Decision Theory to Support Lifting Motions with a Quasi-Passive Hip Exoskeleton

This paper compares three controllers for quasi-passive exoskeletons. The Utility Maximizing Controller (UMC) uses intent estimation to recognize user motions and decision theory to activate the support mechanism. The intent estimation algorithm requires demonstrations for each motion to be recognized. Depending on what motion is recognized, different control signals are sent to the exoskeleton. The Extended UMC (E-UMC) adds a calibration step and a velocity module to trigger the UMC. As a benchmark, and to compare the behavior of the controllers irrespective of the hardware, a Passive Exoskeleton Controller (PEC) is developed as well. The controllers were implemented on a hip exoskeleton and evaluated in a user study consisting of two phases. First, demonstrations of three motions were recorded: squat, stoop left and stoop right. Afterwards, the controllers were evaluated. The E-UMC combines benefits from the UMC and the PEC, confirming the need for the two extensions. The E-UMC discriminates between the three motions and does not generate false positives for previously unseen motions such as stair walking. The proposed methods can also be applied to support other motions.
On the Use of Singular Value Decomposition as a Clutter Filter for Ultrasound Flow Imaging

Filtering based on Singular Value Decomposition (SVD) provides substantial separation of clutter, flow and noise in high frame rate ultrasound flow imaging. The use of SVD as a clutter filter has greatly improved techniques such as vector flow imaging, functional ultrasound and super-resolution ultrasound localization microscopy. The removal of clutter and noise relies on the assumption that tissue, flow and noise are each represented by different subsets of singular values, so that their signals are uncorrelated and lay on orthogonal sub-spaces. This assumption fails in the presence of tissue motion, for near-wall or microvascular flow, and can be influenced by an incorrect choice of singular value thresholds. Consequently, separation of flow, clutter and noise is imperfect, which can lead to image artefacts not present in the original data. Temporal and spatial fluctuation in intensity are the commonest artefacts, which vary in appearance and strengths. Ghosting and splitting artefacts are observed in the microvasculature where the flow signal is sparsely distributed. Singular value threshold selection, tissue motion, frame rate, flow signal amplitude and acquisition length affect the prevalence of these artefacts. Understanding what causes artefacts due to SVD clutter and noise removal is necessary for their interpretation.
Global wellposedness and blowup of solutions to a nonlocal evolutionproblem with singular kernels

We consider a nonlocal evolution equation in $R^2$: $\partial_t u + \nabla \cdot (u K*u )= 0$, where $K(x) = \mu \frac x {|x|^\alpha}$, $\mu=\pm 1$ and $1 < \alpha < 2 $. We study wellposedness, continuation/blowup criteria and smoothness of solutions in Sobolev spaces. In the repulsive case ($\mu=1$), by using the sharp blowup criteria, we prove global wellposedness for any positive large initial data. In the attractive case ($\mu=-1$), by using a novel free energy inequality together with a mass localization technique, we construct finite time blowups for a large class of smooth initial data.
Black hole hairs in scalar-tensor gravity and the lack thereof

Scalar-tensor theories are a natural alternative to general relativity, as they may provide an effective dark energy phenomenology on cosmological scales while passing local tests, but their black hole solutions are still poorly understood. Here, we generalize existing no-hair theorems for spherical black holes and specific theories in the scalar-tensor class. We show that shift symmetry prevents the appearance of scalar hairs in rotating (asymptotically flat, stationary and axisymmetric) black holes for all theories in the Horndeski/beyond Horndeski/DHOST classes, but for those with a coupling between the scalar and the Gauss--Bonnet invariant. Our proof also applies to higher dimensions. We also compute the values of the scalar hair charges if shift symmetry and asymptotic flatness are violated by a time growth of the scalar field at infinity, under suitable regularity conditions at the event horizon.
How an era of kination impacts substructure and the dark matter annihilation rate

An era of kination occurs when the Universe's energy density is dominated by a fast-rolling scalar field. Dark matter that is thermally produced during an era of kination requires larger-than-canonical annihilation cross sections to generate the observed dark matter relic abundance. Furthermore, dark matter density perturbations that enter the horizon during an era of kination grow linearly with the scale factor prior to radiation domination. We show how the resulting enhancement to the small-scale matter power spectrum increases the microhalo abundance and boosts the dark matter annihilation rate. We then use gamma-ray observations to constrain thermal dark matter production during kination. The annihilation boost factor depends on the minimum halo mass, which is determined by the small-scale cutoff in the matter power spectrum. Therefore, observational limits on the dark matter annihilation rate imply a minimum cutoff scale for a given dark matter particle mass and kination scenario. For dark matter that was once in thermal equilibrium with the Standard Model, this constraint establishes a maximum allowed kinetic decoupling temperature for the dark matter. This bound on the decoupling temperature implies that the growth of perturbations during kination cannot appreciably boost the dark matter annihilation rate if dark matter was once in thermal equilibrium with the Standard Model.
Fulfilling Formal Specifications ASAP by Model-free Reinforcement Learning

We propose a model-free reinforcement learning solution, namely the ASAP-Phi framework, to encourage an agent to fulfill a formal specification ASAP. The framework leverages a piece-wise reward function that assigns quantitative semantic reward to traces not satisfying the specification, and a high constant reward to the remaining. Then, it trains an agent with an actor-critic-based algorithm, such as soft actor-critic (SAC), or deep deterministic policy gradient (DDPG). Moreover, we prove that ASAP-Phi produces policies that prioritize fulfilling a specification ASAP. Extensive experiments are run, including ablation studies, on state-of-the-art benchmarks. Results show that our framework succeeds in finding sufficiently fast trajectories for up to 97\% test cases and defeats baselines.
Understanding and Predicting Human Label Variation in Natural Language Inference through Explanation

Human label variation (Plank 2022), or annotation disagreement, exists in many natural language processing (NLP) tasks. To be robust and trusted, NLP models need to identify such variation and be able to explain it. To this end, we created the first ecologically valid explanation dataset with diverse reasoning, LiveNLI. LiveNLI contains annotators' highlights and free-text explanations for the label(s) of their choice for 122 English Natural Language Inference items, each with at least 10 annotations. We used its explanations for chain-of-thought prompting, and found there is still room for improvement in GPT-3's ability to predict label distribution with in-context learning.
A generalized fractional Laplacian

In this article we show that the fractional Laplacian in $R^{2}$ can be factored into a product of the divergence operator, a Riesz potential operator, and the gradient operator. Using this factored form we introduce a generalization of the fractional Laplacian, involving a matrix $K(x)$, suitable when the fractional Laplacian is applied in a non homogeneous medium. For the case of $K(x)$ a constant, symmetric positive definite matrix we show that the fractional Poisson equation is well posed, and determine the regularity of the solution in terms of the regularity of the right hand side function.
On-chip synchronous pumped $\chi^{(3)}$ optical parametric oscillator on thin-film lithium niobate

Optical parametric oscillation (OPO) has widely been utilized as a means of generating light with wide spectral coverage from a single pump laser. These oscillators can be driven using either continuous-wave (CW) light, which only requires lining up of the pump frequency with OPO resonance, or pulsed light, which also mandates that the repetition rate of the pulse and free spectral range of the OPO cavity are carefully tuned to match each other. Advancements in nanophotonics have ignited interest in chip-scale OPOs, which enable low-footprint and high-efficiency solutions to broadband light generation. CW-pumped integrated OPO has been demonstrated using both $\chi^{(2)}$ and $\chi^{(3)}$ parametric oscillation. However, realizing pulse-driven on-chip OPO remains challenging, as microresonator cavities have limited tuning range in the FSR and resonance frequency compared to traditional bulk cavities. Here, we overcome this limitation and demonstrate a $\chi^{(3)}$ pulse-driven OPO by using a tunable on-chip femtosecond pulse generator to synchronously pump the oscillator. The output frequency comb generated by our OPO has 30-GHz repetition rate, spans 2/5 of an octave and consists of over 1400 comb lines with a pump-to-comb conversion efficiency of 10%.
A Preliminary Evaluation of ChatGPT in Requirements Information Retrieval

Context : Recently, many illustrative examples have shown ChatGPT’s impressive ability to perform programming tasks and answer general domain questions. Objective : We empirically evaluate how ChatGPT performs on requirements analysis tasks to derive insights into how generative large language model, represented by ChatGPT, inﬂuence the research and practice of natural language processing for requirements engineering. Method : We design an evaluation pipeline including two common requirements information retrieval tasks, four public datasets involving two typical requirements artifacts, querying ChatGPT with ﬁxed task prompts, and quantitative and qualitative results analysis. Results : Quantitative results show that ChatGPT achieves comparable or better Fβ values in all datasets under a zero-shot setting. Qualitative analysis further illustrates ChatGPT’s powerful natural language processing ability and limited requirements engineering domain knowledge. Conclusion : The evaluation results demonstrate ChatGPT’ impressive ability to retrieve requirements information from diﬀerent types artifacts involving multiple languages under a zero-shot setting. It is worthy for the research and industry communities to study generative large language model based requirements retrieval models and to develop corresponding tools.
Quantum-secured single-pixel imaging with enhanced security

In this paper, we propose a novel quantum-secured single-pixel imaging method that utilizes non-classical correlations of a photon pair. Our method can detect any attempts to deceive it by exploiting a non-classical correlation of photon pairs while rejecting strong chaotic light illumination through photon heralding. A security analysis based on polarization-correlation has been conducted, demonstrating that our method has improved security compared to existing quantum-secured imaging. More specifically, a partial deceiving attack, which sends a mixture of a true and a false signal, can be detected with our proposed analysis, while currently employed methods cannot. We also provide proof-of-principle demonstrations of our method and trustworthy images reconstructed using our security analysis. Our method can be developed using matured techniques used in quantum secure communication, thus offering a promising direction for practical applications in secure imaging.
The mixability of elliptical distributions and log-elliptical distributions

The concept of $\phi$-complete mixability and $\phi$-joint mixability was first introduced in Bignozzi and Puccetti (2015), which is an extension of complete and joint mixability. Following Bignozzi and Puccetti (2015), we consider two more general cases of $\phi$ and investigate the $\phi$-joint mixability for elliptical distributions and logarithmic elliptical distributions. Some sufficient conditions for the $\phi$-joint mixability of some distributions are investigated. In addition, a conjecture on the uniqueness of the center of $\phi$-joint mixability and the forms of the densities for some elliptical distributions are given.
Adaptive Weighted Multi-View Clustering

Learning multi-view data is an emerging problem in machine learning research, and nonnegative matrix factorization (NMF) is a popular dimensionality-reduction method for integrating information from multiple views. These views often provide not only consensus but also complementary information. However, most multi-view NMF algorithms assign equal weight to each view or tune the weight via line search empirically, which can be infeasible without any prior knowledge of the views or computationally expensive. In this paper, we propose a weighted multi-view NMF (WM-NMF) algorithm. In particular, we aim to address the critical technical gap, which is to learn both view-specific weight and observation-specific reconstruction weight to quantify each view's information content. The introduced weighting scheme can alleviate unnecessary views' adverse effects and enlarge the positive effects of the important views by assigning smaller and larger weights, respectively. Experimental results confirm the effectiveness and advantages of the proposed algorithm in terms of achieving better clustering performance and dealing with the noisy data compared to the existing algorithms.
On black hole uniqueness theorems

We obtain a classification of stationary, appropriately regular, nondegenerate and analytic electro-vacuum space-times in terms of Weinstein solutions. In particular, for connected horizons, we prove uniqueness of the Kerr-Newman black holes. This is done by means of a new and explicit definition of regularity (I–regularity) which allows us to overcome a considerable amount of technical gaps existing in the previous literature on the subject. We also prove an upper bound for angular-momentum and charge in terms of the mass for electro-vacuum asymptotically flat axisymmetric initial data sets with simply connected orbit space.
Development of high coherence high power 193nm laser

We have been developing a hybrid 193 nm ArF laser system that consists of a solid state seeding laser and an ArF excimer laser amplifier for power-boosting. The solid state laser consists of an Yb-fiber-solid hybrid laser system and an Er-fiber laser system as fundamentals, and one LBO and three CLBO crystals for frequency conversion. In an ArF power amplifier, the seed laser passes through the ArF gain media three times, and an average power of 110 W is obtained. As a demonstration of the potential applications of the laser, an interference exposure test is performed.
A Quality Improvement Approach to Violence Reduction

Background: The impact of aggressive and violent behaviors directed toward staff in psychiatric facilities has layers of implications tied to recruitment and retention, cost, quality and safety. Local Problem: Increased patient aggressive behaviors contributed to staff dissatisfaction and high turnover rates, triggering an analysis of current aggression management approaches. Methods: The Plan-Do-Study-Act quality improvement method was used for this project. Interventions: The Dynamic Appraisal of Situational Aggression (DASA) risk assessment tool was implemented. Results: Daily aggression risk identification increased by 69%, and aggressive incidents toward staff and patients decreased by 64% and 28%, respectively, when the tool was completed more consistently. Surveys revealed acceptance of the tool by nurses. Conclusions: Quality improvement statistical tools supported evidence-based strategies. A risk for aggression assessment laid the foundations for implementing strategies for reducing aggression and violence.
Burden of idiopathic inflammatory rheumatic diseases in occupational healthcare: increased absenteeism and healthcare resource utilization

Objectives Patients with idiopathic inflammatory rheumatic diseases (IIRD) often have decreased working capacity resulting in indirect costs. However, data on patients’ short-term sick leave has been limited. This retrospective cohort study evaluated the number and length of sick leave, including short-term leave, and occupational healthcare resource utilization (HCRU) of the working-aged patients with IIRD compared to controls. Methods The data on sick leave and occupational HCRU were gathered from the electronic medical records of the largest occupational healthcare provider in Finland from January 2012 to December 2019. Employed patients with an IIRD (including rheumatoid arthritis, spondyloarthritis, psoriatic and enteropathic arthritis, juvenile arthritis, and reactive arthritis) with at least a 12-months follow-up were identified and compared to age-, sex-, and follow-up matched controls without IIRD. Results Altogether 5405 patients with IIRD were identified and compared with an equal number of controls. The patients incurred approximately 2.5 times more sick leave than controls: 21.7 versus 8.5 days per patient year, respectively. Short-term sick leave was common: 83% of sickness absence periods of the patients lasted 1–9 days and represented 30% of the total absenteeism. Loss of productivity due to lost workdays was on average €4572 (95% confidence interval €4352–4804) per patient year. Occupational HCRU was approximately 1.8 times higher among IIRD patients than controls. Conclusions Workers with an IIRD incur considerably more sick leave and use more occupational healthcare services than controls. Short sick leave not registered in national insurance registers constitute a significant portion of days off work among patients with IIRD.
Volatolomics analysis of exhaled breath and gastric-endoluminal gas for distinguishing early upper gastrointestinal cancer from benign

Exhaled breath and gastric-endoluminal gas (volatile products of diseased tissues) contain a large number of volatile organic compounds, which are valuable for early diagnosis of upper gastrointestinal (UGI) cancer. In this study, exhaled breath and gastric-endoluminal gas of patients with UGI cancer and benign disease were analyzed by gas chromatography-mass spectrometry (GC-MS) and ultraviolet photoionization time-of-flight mass spectrometry (UVP-TOFMS) to construct UGI cancer diagnostic models. Breath samples of 116 UGI cancer and 77 benign disease subjects and gastric-endoluminal gas samples of 114 UGI cancer and 76 benign disease subjects were collected. Machine learning (ML) algorithms were used to construct UGI cancer diagnostic models. Classification models based on exhaled breath for distinguishing UGI cancer from the benign group have area under the curve (AUC) of receiver operating characteristic curve values of 0.959 and 0.994 corresponding to GC-MS and UVP-TOFMS analysis, respectively. The AUC values of models based on gastric-endoluminal gas for UGI cancer and benign group classification are 0.935 and 0.929 corresponding to GC-MS and UVP-TOFMS analysis, respectively. This work indicates that volatolomics analysis of exhaled breath and gastric-endoluminal diseased tissues have great potential in early screening of UGI cancer. Moreover, gastric-endoluminal gas can be a means of gas biopsy to provide auxiliary information for the examination of tissue lesions during gastroscopy.
Mean-shift Tracking Algorithm with Adjustive Bandwidth Based on Partition

The classical mean-shift tracking algorithm with the fixed bandwidth easily leads to some problems, such as the missing of the target and the mismatching of the tracking window and the target. A new mean-shift tracking method to adjust the tracking window based on partition is proposed. After the accurate location of the target centroid, the variation of the target's scale can be reflected by the variation of the distance between the target centroid and the block centroids, which can be used to adjust the tracking window. The proposed approach is illustrated by some tracking examples with valid results.