Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance of an auto-adjusting prosthetic socket during walking with intermittent socket release

Introduction A challenge in the engineering of auto-adjusting prosthetic sockets is to maintain stable operation of the control system while users change their bodily position and activity. The purpose of this study was to test the stability of a socket that automatically adjusted socket size to maintain fit. Socket release during sitting was conducted between bouts of walking. Methods Adjustable sockets with sensors that monitored distance between the liner and socket were fabricated. Motor-driven panels and a microprocessor-based control system adjusted socket size during walking to maintain a target sensed distance. Limb fluid volume was recorded continuously. During eight sit/walk cycles, the socket panels were released upon sitting and then returned to position for walking, either the size at the end of the prior bout or a size 1.0% larger in volume. Results In six transtibial prosthesis users, the control system maintained stable operation and did not saturate (move to and remain at the end of the actuator’s range) during 98% of the walking bouts. Limb fluid volume changes generally matched the panel position changes executed by the control system. Conclusions Stable operation of the control system suggests that the auto-adjusting socket is ready for testing in users’ at-home settings.
Greek teachers' resilience levels during the COVID‐19 pandemic lockdown and its association with attitudes towards emergency remote teaching and perceived stress

Abstract The mandatory closure of schools due to the Covid‐19 pandemic affected the mental health, and, by extension, the personal and professional life of teachers. This study explored their level of resilience during the pandemic and its association with attitudes towards emergency remote teaching and perceived stress. Data were collected through the administration of three self‐report instruments: (a) the “Teachers' Professional Attitudes and Behaviors Scale”, (b) the “Teachers' Resilience Scale”, and (c) the “Perceived Stress Scale”. The sample consisted of 1415 general teachers from central Greece. Teachers were found to possess neutral attitudes towards emergency remote teaching, moderate levels of resilience, and experienced slightly increased levels of stress. The examined constructs were largely influenced by some of the teachers' demographic and professional characteristics. Teachers' perceptions of resilience predicted their attitudes to emergency remote teaching and stress levels during the pandemic. The study has reaffirmed the importance of holding sufficient levels of resilience to cope with adverse circumstances and has offered clear directions about which groups of teachers are in most need for counselling and professional support so that their resilience is strengthened.
Comparison of Fluid Analysis and Cytologic Findings of Cerebrospinal Fluid Between Three Collection Sites in Adult Equids With Neurological Disease

Cerebrospinal fluid (CSF) is routinely collected from three sites in the horse, the atlanto-occipital (AO), atlantoaxial (AA), and lumbosacral (LS) space. A comparison between fluid analysis parameters [total protein, total nucleated cell count (TNCC), red blood cell (RBC) count, and morphologic analysis] from samples obtained at each of the three sites has not previously been performed. A retrospective analysis was performed to evaluate the differences in fluid analysis of CSF between the AO, AA, and LS sites in equids presented to a referral service for evaluation of suspected neurological disease. A total of 113 equids aged ≥1 year that underwent CSF collection between 2008 and 2020 were included. Total nucleated cell count, RBC concentration, total protein (TP), and morphologic evaluation between CSF samples obtained from the three sites were compared. When comparing all samples, LS centesis was associated with higher RBC compared to other sites (p < 0.05); TP was lower in the AA group than in the LS group (p < 0.05). Within a subset of cytologically unremarkable samples, RBC concentration was highest in LS samples (p < 0.01); TP was higher in LS samples compared to AA samples (p < 0.05) and TNCC was higher (p < 0.01) in AA and LS groups compared to the AO. In cytologically abnormal samples, there were no significant differences between sites in any parameter. Abnormal cytology was correlated with non-survival (p = 0.0002). Non-survival was associated with higher TNCC (p < 0.01). The receiver operating characteristic (ROC) curve for TNCC had an area under the curve of 0.67 (95% CI, 0.55–0.79) and indicated that a cutoff value of 24 cells/μL maximized specificity (72%) and sensitivity (54%) to predict non-survival in all horses. Positive predictive value was 45%; negative predictive value was 78%. The concentration of RBC was higher in samples from the LS site. This has clinical implications due to the importance of comparative diagnostics and its potential impact on cytologic evaluation. There were minimal differences in multiple other parameters between sites, which are likely clinically insignificant.
Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modeling

We examine the extent to which, in principle, different syntactic and semantic graph representations can complement and improve neural language modeling. Specifically, by conditioning on a subgraph encapsulating the locally relevant sentence history, can a model make better next-word predictions than a pretrained sequential language model alone? With an ensemble setup consisting of GPT-2 and ground-truth graphs from one of 7 different formalisms, we find that the graph information indeed improves perplexity and other metrics. Moreover, this architecture provides a new way to compare different frameworks of linguistic representation. In our oracle graph setup, training and evaluating on English WSJ, semantic constituency structures prove most useful to language modeling performance—outpacing syntactic constituency structures as well as syntactic and semantic dependency structures.
Multimodal Transformer for Accelerated MR Imaging

Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution for fast MR imaging, providing superior performance in restoring the target modality from its undersampled counterpart with guidance from an auxiliary modality. However, existing works simply combine the auxiliary modality as prior information, lacking in-depth investigations on the potential mechanisms for fusing different modalities. Further, they usually rely on the convolutional neural networks (CNNs), which is limited by the intrinsic locality in capturing the long-distance dependency. To this end, we propose a multi-modal transformer (MTrans), which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging. To capture deep multi-modal information, our MTrans utilizes an improved multi-head attention mechanism, named cross attention module, which absorbs features from the auxiliary modality that contribute to the target modality. Our framework provides three appealing benefits: (i) Our MTrans use an improved transformers for multi-modal MR imaging, affording more global information compared with existing CNN-based methods. (ii) A new cross attention module is proposed to exploit the useful information in each modality at different scales. The small patch in the target modality aims to keep more fine details, the large patch in the auxiliary modality aims to obtain high-level context features from the larger region and supplement the target modality effectively. (iii) We evaluate MTrans with various accelerated multi-modal MR imaging tasks, e.g., MR image reconstruction and super-resolution, where MTrans outperforms state-of-the-art methods on fastMRI and real-world clinical datasets.
User Guide for KOTE: Korean Online Comments Emotions Dataset

Sentiment analysis that classifies data into positive or negative has been dominantly used to recognize emotional aspects of texts, despite the deficit of thorough examination of emotional meanings. Recently, corpora labeled with more than just valence are built to exceed this limit. However, most Korean emotion corpora are small in the number of instances and cover a limited range of emotions. We introduce KOTE dataset. KOTE contains 50k (250k cases) Korean online comments, each of which is manually labeled for 43 emotion labels or one special label (NO EMOTION) by crowdsourcing (Ps = 3,048). The emotion taxonomy of the 43 emotions is systematically established by cluster analysis of Korean emotion concepts expressed on word embedding space. After explaining how KOTE is developed, we also discuss the results of finetuning and analysis for social discrimination in the corpus.
An integrated debris environment assessment model

Launch behaviors are a key determinant of the orbital environment. Physical and economic forces such as fragmentations and changing launch costs, or policies like post-mission disposal (PMD) compliance requirements, will alter the relative attractiveness of different orbits and lead operators to adjust their launch behaviors. However, integrating models of adaptive launch behavior with models of the debris environment remains an open challenge. We present a statistical framework for integrating theoretically-grounded models of launch behavior with evolutionary models of the low-Earth orbit (LEO) environment. We implement this framework using data on satellite launches, the orbital environment, launch vehicle prices, sectoral revenues, and government budgets over 2007-2020. The data are combined with a multi-shell and multi-species Particle-in-a-Box (PIB) model of the debris environment and a two-stage budgeting model of commercial, civil government, and defense decisions to allocate new launches across orbital shells. We demonstrate the framework's capabilities in three counterfactual scenarios: unexpected fragmentation events in highly-used regions, a sharp decrease in the cost of accessing lower parts of LEO, and increasing compliance with 25-year PMD guidelines. Substitution across orbits based on their evolving characteristics and the behavior of other operators induces notable changes in the debris environment relative to models without behavioral channels.
Dimers, webs, and local systems

For a planar bipartite graph $\mathcal G$ equipped with a $\mathrm{SL}_n$-local system, we show that the determinant of the associated Kasteleyn matrix counts"$n$-multiwebs"(generalizations of $n$-webs) in $\mathcal G$, weighted by their web-traces. We use this fact to study random $n$-multiwebs in graphs on some simple surfaces.
Recurrent Encoder–Decoder Networks for Vessel Trajectory Prediction With Uncertainty Estimation

Recent deep learning methods for vessel trajectory prediction are able to learn complex maritime patterns from historical automatic identification system (AIS) data and accurately predict sequences of future vessel positions with a prediction horizon of several hours. However, in maritime surveillance applications, reliably quantifying the prediction uncertainty can be as important as obtaining high accuracy. This article extends deep learning frameworks for trajectory prediction tasks by exploring how recurrent encoder–decoder neural networks can be tasked not only to predict but also to yield a corresponding prediction uncertainty via Bayesian modeling of aleatoric and epistemic uncertainties. We compare the prediction performance of two different models based on labeled or unlabeled input data to highlight how uncertainty quantification and accuracy can be improved by using, if available, additional information on the intention of the ship (e.g., its planned destination).
Numerical method for approximately optimal solutions of two-stage distributionally robust optimization with marginal constraints

We consider a general class of two-stage distributionally robust optimization (DRO) problems which includes prominent instances such as task scheduling, the assemble-to-order system, and supply chain network design. The ambiguity set is constrained by ﬁxed marginal distributions that are not necessarily discrete. We develop a numerical algorithm for computing approximately optimal solutions of such problems. Through replacing the marginal constraints by a ﬁnite collection of linear constraints, we derive a relaxation of the DRO problem which serves as its upper bound. We can control the relaxation error to be arbitrarily close to 0. We develop duality results and transform the inf-sup problem into an inf-inf problem. This leads to a numerical algorithm for two-stage DRO problems with marginal constraints which solves a linear semi-inﬁnite optimization problem. Besides an approximately optimal solution, the algorithm computes both an upper bound and a lower bound for the optimal value of the problem. The diﬀerence between the computed bounds provides a direct sub-optimality estimate of the computed solution. Most importantly, one can choose the inputs of the algorithm such that the sub-optimality is controlled to be arbitrarily small. In our numerical examples, we apply the proposed algorithm to task scheduling, the assemble-to-order system, and supply chain network design. The ambiguity sets in these problems involve a large number of marginals, which include both discrete and continuous distributions. The numerical results showcase that the proposed algorithm computes high-quality robust decisions along with their corresponding sub-optimality estimates with practically reasonable magnitudes that are not over-conservative.
Absolutely 
k
-incoherent quantum states and spectral inequalities for the factor width of a matrix

We investigate the set of quantum states that can be shown to be k -incoherent based only on their eigenvalues (equivalently, we explore which Hermitian matrices can be shown to have small factor width based only on their eigenvalues). In analogy with the absolute separability problem in quantum resource theory, we call these states “absolutely k -incoherent”, and we derive several necessary and sufﬁcient conditions for membership in this set. We obtain many of our results by making use of recent results concerning hyperbolicity cones associated with elementary symmetric polynomials.
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Few-shot in-context learning (ICL) enables pre-trained language models to perform a previously-unseen task without any gradient-based training by feeding a small number of training examples as part of the input. ICL incurs substantial computational, memory, and storage costs because it involves processing all of the training examples every time a prediction is made. Parameter-efficient fine-tuning (PEFT) (e.g. adapter modules, prompt tuning, sparse update methods, etc.) offers an alternative paradigm where a small set of parameters are trained to enable a model to perform the new task. In this paper, we rigorously compare few-shot ICL and PEFT and demonstrate that the latter offers better accuracy as well as dramatically lower computational costs. Along the way, we introduce a new PEFT method called (IA)$^3$ that scales activations by learned vectors, attaining stronger performance while only introducing a relatively tiny amount of new parameters. We also propose a simple recipe based on the T0 model called T-Few that can be applied to new tasks without task-specific tuning or modifications. We validate the effectiveness of T-Few on completely unseen tasks by applying it to the RAFT benchmark, attaining super-human performance for the first time and outperforming the state-of-the-art by 6% absolute. All of the code used in our experiments is publicly available.
Deep Depth Completion: A Survey

—Depth completion aims at predicting dense pixel-wise depth from a sparse map captured from a depth sensor. It plays an essential role in various applications such as autonomous driving, 3D reconstruction, augmented reality, and robot navigation. Recent successes on the task have been demonstrated and dominated by deep learning based solutions. In this article, for the ﬁrst time, we provide a comprehensive literature review that helps readers better grasp the research trends and clearly understand the current advances. We investigate the related studies from the design aspects of network architectures, loss functions, benchmark datasets, and learning strategies with a proposal of a novel taxonomy that categorizes existing methods. Besides, we present a quantitative comparison of model performance on two widely used benchmark datasets, including an indoor and an outdoor dataset. Finally, we discuss the challenges of prior works and provide readers with some insights for future research directions.
Black-hole ringdown as a probe of higher-curvature gravity theories

Detecting gravitational waves from coalescing compact binaries allows us to explore the dynamical, nonlinear regime of general relativity and constrain modifications to it. Some of the gravitational-wave events observed by the LIGO-Virgo Collaboration have sufficiently high signal-to-noise ratio in the merger, allowing us to probe the relaxation of the remnant black hole to its final, stationary state - the so-called black-hole ringdown, which is characterized by a set of quasinormal modes. Can we use the ringdown to constrain deviations from general relativity, as predicted by several of its contenders? Here, we address this question by using an inspiral-merger-ringdown waveform model in the effective-one-body formalism, augmented with a parametrization of the ringdown based on an expansion in the final black hole's spin. We give a prescription on how to include in this waveform model, the quasinormal mode frequencies calculated on a theory-by-theory basis. In particular, we focus on theories that modify general relativity by higher-order curvature corrections, namely, Einstein-dilaton-Gauss-Bonnet (EdGB), dynamical Chern-Simons (dCS) theories, and cubic- and quartic-order effective-field-theories (EFT) of general relativity. We use this parametrized waveform model to measure the ringdown properties of the two loudest ringdown signals observed so far, GW150914 and GW200129. We find that while EdGB theory cannot be constrained with these events, we can place upper bounds on the fundamental lengthscale of cubic- ($\ell_{\rm cEFT} \leqslant 38.2$ km) and quartic-order ($\ell_{\rm qEFT} \leqslant 51.3$ km) EFTs of general relativity, and of dCS gravity ($\ell_{\rm dCS} \leqslant 38.7$ km). The latter result is a concrete example of a theory presently unconstrained by inspiral-only analyses which, however, can be constrained by merger-ringdown studies with current gravitational-wave data.
CONTINUOUS FLOW METERING (CFM) ALTERNATIVE SOLUTION TO ALLEVIATE CONGESTION ON INTERSTATE 70 (I-70) EISENHOWER JOHNSON MEMORIAL TUNNEL (EJMT)

Metering aims to improve traffic flow on the main lanes of a freeway by regulating the inflow to these lanes. While metering is most often implemented at on-ramps to a freeway, it can also be used to regulate the flow of traffic on the main lanes themselves. Although counter-intuitive that regulating flow on the main lanes can also improve overall traffic flow, this can happen in cases where downstream bottlenecks exist. Alternatively, upstream metering may simply be required to prevent excessive queuing at a downstream bottleneck. This paper evaluates two metering techniques applied to the eastbound main lanes of Interstate 70 at the Eisenhower Johnson Memorial Tunnel (EJMT) to potentially improve traffic flow, shorten the queues both east and west of the EJMT, and reduce the need to completely stop traffic at the EJMT. One strategy evaluated is traditional or manual metering in which operations staff stop and start traffic as seen necessary to reduce a downstream queue. The other strategy is called continuous flow metering (CFM), which automatically displays red and green lights at a given cycle length to smooth out the traffic stream as well as reduce downstream queuing. The study results show that CFM did reduce travel times and save person hours of travel in several trial time periods.
Consistency between the attached-eddy model and the inner–outer interaction model: a study of streamwise wall-shear stress fluctuations in a turbulent channel flow

Abstract The inner–outer interaction model (Marusic et al., Science, vol. 329, 2010, pp. 193–196) and the attached-eddy model (Townsend, Cambridge University Press, 1976) are two fundamental models describing the multiscale turbulence interactions and the organization of energy-containing motions in the logarithmic region of high-Reynolds-number wall-bounded turbulence, respectively. In this paper, by coupling the additive description with the attached-eddy model, the generation process of streamwise wall-shear fluctuations, resulting from wall-attached eddies, is portrayed. Then, by resorting to the inner–outer interaction model, the streamwise wall-shear stress fluctuations generated by attached eddies in a turbulent channel flow are isolated. Direct comparison between the statistics from these two models demonstrates that they are consistent with and complement each other. Meanwhile, we further show that the superpositions of attached eddies follow an additive process strictly by verifying the validity of the strong and extended self-similarity. Moreover, we propose a Gaussian model to characterize the instantaneous distribution of streamwise wall-shear stress, resulting from the attached-eddy superpositions. These findings are important for developing an advanced reduced-order wall model.
A Survey on Fairness for Machine Learning on Graphs

Nowadays, the analysis of complex phenomena modeled by graphs plays a crucial role in many real-world application domains where decisions can have a strong societal impact. However, numerous studies and papers have recently revealed that machine learning models could lead to potential disparate treatment between individuals and unfair outcomes. In that context, algorithmic contributions for graph mining are not spared by the problem of fairness and present some specific challenges related to the intrinsic nature of graphs: (1) graph data is non-IID, and this assumption may invalidate many existing studies in fair machine learning, (2) suited metric definitions to assess the different types of fairness with relational data and (3) algorithmic challenge on the difficulty of finding a good trade-off between model accuracy and fairness. This survey is the first one dedicated to fairness for relational data. It aims to present a comprehensive review of state-of-the-art techniques in fairness on graph mining and identify the open challenges and future trends. In particular, we start by presenting several sensible application domains and the associated graph mining tasks with a focus on edge prediction and node classification in the sequel. We also recall the different metrics proposed to evaluate potential bias at different levels of the graph mining process; then we provide a comprehensive overview of recent contributions in the domain of fair machine learning for graphs, that we classify into pre-processing, in-processing and post-processing models. We also propose to describe existing graph data, synthetic and real-world benchmarks. Finally, we present in detail five potential promising directions to advance research in studying algorithmic fairness on graphs.