Miracle-dz/UniFAR-FTUs huggingface dataset:
Veterinarians show resilience during COVID-19: challenges faced and successful coping strategies.

OBJECTIVE To identify challenges veterinarians faced during the COVID-19 pandemic, explore how they coped, identify coping strategies associated with greater resilience, and determine incentives and barriers to performing healthy coping behaviors. SAMPLES 266 surveys completed by veterinarians in the Potomac region. PROCEDURES A cross-sectional survey was distributed electronically through veterinary medical boards and professional associations between June and September 2021. RESULTS Most survey responses came from veterinarians working in Maryland (128/266 [48%]) and Virginia (63/266 [24%]) who were predominantly white (186/266 [70%]), female (162/266 [61%]), and working in small-animal clinical practice (185/266 [70%]). The greatest workplace challenges experienced were increased workloads (195/266 [73%]) and reevaluating existing workflows (189/266 [71%]). Separation from loved ones (161/266 [61%]) was the greatest personal challenge. Of the veterinarians who completed the 10-point Connor-Davidson Resilience Scale (n = 219), which measures resilience on a scale from 0 (no resilience) to 40 (greatest resilience), the mean score was 29.6 (SD, 6.9), with a median of 30 (IQR = 10). Intrinsic factors most strongly associated with greater resilience were increasing age (P = .01) and later career stage (P = .002). Job satisfaction, autonomy, good work-life balance, and approach-focused coping strategies were positively associated with resilience. Overwhelmingly, the primary reported barrier to performing healthy coping behaviors was limited time to devote to self-care (177/266 [67%]). CLINICAL RELEVANCE A combination of individual approach-focused coping strategies and organizational interventions are crucial to support a resilient veterinary workforce.
Advanced MR Techniques for Preoperative Glioma Characterization: Part 2

Preoperative clinical MRI protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this second part, we review magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), susceptibility‐weighted imaging (SWI), MRI‐PET, MR elastography (MRE), and MR‐based radiomics applications. The first part of this review addresses dynamic susceptibility contrast (DSC) and dynamic contrast‐enhanced (DCE) MRI, arterial spin labeling (ASL), diffusion‐weighted MRI, vessel imaging, and magnetic resonance fingerprinting (MRF).
The relationship between auditory behaviours and speech and language development in children with cleft lip and palate

Purpose of review Children born with a cleft lip and palate (CLP) are at risk of speech problems. There is a high prevalence of conductive hearing loss due to otitis media with effusion and recent research has highlighted broader difficulties with language and educational attainment. There has been increased interest in the impact of auditory processing on a child's development, but little study into links between auditory skills and speech and language development. Recent findings Children with CLP consistently struggle with auditory tasks. Difficulties processing words in a noisy background, auditory attention and temporal processing are frequently reported. A recent study in the United Kingdom investigated the auditory behaviours of 95 children aged 5–8 years with nonsyndromic CLP and compared these with speech and language profiles. Auditory difficulties correlated with speech development and most auditory behaviours correlated with language outcomes. There was also a correlation of language with speech outcomes, suggesting a more complex and interdependent relationship. Summary Children with CLP present with high levels of difficulties with auditory skills. The potential impact of this on broader development needs further research, but clinicians should ensure that auditory skills beyond the hearing test are monitored, and advice given to parents to minimize the impact.
Minimum Local Emissivity Variance Retrieval of Cloud Altitude and Effective Spectral Emissivity—Simulation and Initial Verification

Abstract This paper describes the theory and application of the minimum local emissivity variance (MLEV) technique for simultaneous retrieval of cloud pressure level and effective spectral emissivity from high-spectral-resolution radiances, for the case of single-layer clouds. This technique, which has become feasible only with the recent development of high-spectral-resolution satellite and airborne instruments, is shown to provide reliable cloud spectral emissivity and pressure level under a wide range of atmospheric conditions. The MLEV algorithm uses a physical approach in which the local variances of spectral cloud emissivity are calculated for a number of assumed or first-guess cloud pressure levels. The optimal solution for the single-layer cloud emissivity spectrum is that having the “minimum local emissivity variance” among the retrieved emissivity spectra associated with different first-guess cloud pressure levels. This is due to the fact that the absorption, reflection, and scattering processes.
Targeting endoplasmic reticulum associated degradation pathway combined with radiotherapy enhances the immunogenicity of esophageal cancer cells

ABSTRACT Immunogenic cell death (ICD) is essential for the activation of immune system against cancer. We aimed to investigate the efficacy of endoplasmic reticulum (ER)-associated protein degradation (ERAD) inhibitors (EerI and NMS-873) in enhancing radiation-induced ICD in esophageal cancer (EC). EC cells were administered with ERAD inhibitors, radiation therapy (RT), and the combination treatment. ICD hallmarks including calreticulin (CALR), adenosine triphosphate (ATP), and high mobility group protein B1 (HMGB1) were detected. The efficacy of ERAD inhibitors combined with RT in stimulating ICD was analyzed. Additionally, the role of ICD hallmarks in immune cell infiltration and patient survival was investigated. Inhibiting ERAD pathways was able to stimulate ICD component emission from dying EC cells in a dose-dependent pattern. Radiation-induced ICD was significantly increased after high doses RT (≥10 Gy). ERAD inhibitor combined with moderate dose RT (≥6 Gy) was capable of stimulating increased ICD in EC cells. Dual therapy could elicit the antitumor immune response by enhancing dendritic cells maturation and phagocytosis. Further investigation revealed a significant correlation between CALR and tumor-infiltrating immune cells. Low expression of ATP and HMGB1 and high expression of CALR were associated with favorable survival in patients with EC. The immunogenicityof EC can be enhanced by ERAD inhibitors combined with moderate doses of RT. ICD hallmark genes, especially CALR, are correlated to immune cell infiltration and clinical outcomes in EC. The present results demonstrated an important method to improve the immunogenicity of EC cells for enhanced antitumor immune response.
Hypogammaglobulinemia after front-line immunochemotherapy in patients with follicular lymphoma

Introduction Hypogammaglobulinemia after front-line immunochemotherapy for follicular lymphoma is a poorly studied adverse event that could be related to the appearance of severe and/or recurrent non-neutropenic infections which could affect the quality of life of the patients, even motivating a need of long-term replacement therapy with human immunoglobulins. Methods Observational, retrospective study aiming to estimate the incidence of hypogammaglobulinemia, as well as its severity and clinical consequences, and to explore possible predictive factors for its development. Specific immunoglobulin deficiencies were also studied. Results 76.5% of patients had hypogammaglobulinemia during or after front-line treatment, mostly grade 1-2; with 38.8% patients who developed clinically relevant infections and 20% patients requiring human immunoglobulins replacement therapy. A high-risk FLIPI score was identified as a risk factor for hypogammaglobulinemia (ods ratio: 4.51; 95% confidence interval: 1.29–15.68; p < 0.001) and basal gamma globulin level as a protective factor (odds ratio: 0.92; 95% confidence interval: 0.988–0.996; p = 0.018). Any type of immunochemotherapy regimen was associated with different risks of hypogammaglobulinemia in our study. Conclusions Hypogammaglobulinemia appears in a high percentage of patients with follicular lymphoma in a real-world population, identifying a high-risk FLIPI score as a risk factor for its development and basal gamma globulins as a protective factor.
Model-tuning Via Prompts Makes NLP Models Adversarially Robust

In recent years, NLP practitioners have converged on the following practice: (i) import an off-the-shelf pretrained (masked) language model; (ii) append a multilayer perceptron atop the CLS token's hidden representation (with randomly initialized weights); and (iii) fine-tune the entire model on a downstream task (MLP-FT). This procedure has produced massive gains on standard NLP benchmarks, but these models remain brittle, even to mild adversarial perturbations. In this work, we demonstrate surprising gains in adversarial robustness enjoyed by Model-tuning Via Prompts (MVP), an alternative method of adapting to downstream tasks. Rather than appending an MLP head to make output prediction, MVP appends a prompt template to the input, and makes prediction via text infilling/completion. Across 5 NLP datasets, 4 adversarial attacks, and 3 different models, MVP improves performance against adversarial substitutions by an average of 8% over standard methods and even outperforms adversarial training-based state-of-art defenses by 3.5%. By combining MVP with adversarial training, we achieve further improvements in adversarial robustness while maintaining performance on unperturbed examples. Finally, we conduct ablations to investigate the mechanism underlying these gains. Notably, we find that the main causes of vulnerability of MLP-FT can be attributed to the misalignment between pre-training and fine-tuning tasks, and the randomly initialized MLP parameters.
OverlapNetVLAD: A Coarse-to-Fine Framework for LiDAR-based Place Recognition

Place recognition is a challenging yet crucial task in robotics. Existing 3D LiDAR place recognition methods suffer from limited feature representation capability and long search times. To address these challenges, we propose a novel coarse-to-fine framework for 3D LiDAR place recognition that combines Birds' Eye View (BEV) feature extraction, coarse-grained matching, and fine-grained verification. In the coarse stage, our framework leverages the rich contextual information contained in BEV features to produce global descriptors. Then the top-\textit{K} most similar candidates are identified via descriptor matching, which is fast but coarse-grained. In the fine stage, our overlap estimation network reuses the corresponding BEV features to predict the overlap region, enabling meticulous and precise matching. Experimental results on the KITTI odometry benchmark demonstrate that our framework achieves leading performance compared to state-of-the-art methods. Our code is available at: \url{https://github.com/fcchit/OverlapNetVLAD}.
Multi PILOT: Learned Feasible Multiple Acquisition Trajectories for Dynamic MRI

Dynamic Magnetic Resonance Imaging (MRI) is known to be a powerful and reliable technique for the dynamic imaging of internal organs and tissues, making it a leading diagnostic tool. A major difficulty in using MRI in this setting is the relatively long acquisition time (and, hence, increased cost) required for imaging in high spatio-temporal resolution, leading to the appearance of related motion artifacts and decrease in resolution. Compressed Sensing (CS) techniques have become a common tool to reduce MRI acquisition time by subsampling images in the k-space according to some acquisition trajectory. Several studies have particularly focused on applying deep learning techniques to learn these acquisition trajectories in order to attain better image reconstruction, rather than using some predefined set of trajectories. To the best of our knowledge, learning acquisition trajectories has been only explored in the context of static MRI. In this study, we consider acquisition trajectory learning in the dynamic imaging setting. We design an end-to-end pipeline for the joint optimization of multiple per-frame acquisition trajectories along with a reconstruction neural network, and demonstrate improved image reconstruction quality in shorter acquisition times. The code for reproducing all experiments is accessible at https://github.com/tamirshor7/MultiPILOT.
FAMILIES OF INVARIANT MANIFOLDS CORRESPONDING TO NONZERO CHARACTERISTIC EXPONENTS

A theorem on conditional stability is proved for a family of mappings of class C1 + , satisfying a condition more general than Ljapunov regularity. Using this theorem, families of invariant manifolds are constructed for a diffeomorphism of a smooth manifold onto a set where at least one Ljapunov characteristic exponent is nonzero. The property of absolute continuity is proved for these families.Bibliography: 10 titles.
On Model Compression for Neural Networks: Framework, Algorithm, and Convergence Guarantee

Model compression is a crucial part of deploying neural networks (NNs), especially when the memory and storage of computing devices are limited in many applications. This paper focuses on two model compression techniques: low-rank approximation and weight pruning in neural networks, which are very popular nowadays. However, training NN with low-rank approximation and weight pruning always suffers significant accuracy loss and convergence issues. In this paper, a holistic framework is proposed for model compression from a novel perspective of nonconvex optimization by designing an appropriate objective function. Then, we introduce NN-BCD, a block coordinate descent (BCD) algorithm to solve the nonconvex optimization. One advantage of our algorithm is that an efficient iteration scheme can be derived with closed-form, which is gradient-free. Therefore, our algorithm will not suffer from vanishing/exploding gradient problems. Furthermore, with the Kurdyka-{\L}ojasiewicz (K{\L}) property of our objective function, we show that our algorithm globally converges to a critical point at the rate of O(1/k), where k denotes the number of iterations. Lastly, extensive experiments with tensor train decomposition and weight pruning demonstrate the efficiency and superior performance of the proposed framework. Our code implementation is available at https://github.com/ChenyangLi-97/NN-BCD
Reducing Non-IID Effects in Federated Autonomous Driving with Contrastive Divergence Loss

Federated learning has been widely applied in autonomous driving since it enables training a learning model among vehicles without sharing users' data. However, data from autonomous vehicles usually suffer from the non-independent-and-identically-distributed (non-IID) problem, which may cause negative effects on the convergence of the learning process. In this paper, we propose a new contrastive divergence loss to address the non-IID problem in autonomous driving by reducing the impact of divergence factors from transmitted models during the local learning process of each silo. We also analyze the effects of contrastive divergence in various autonomous driving scenarios, under multiple network infrastructures, and with different centralized/distributed learning schemes. Our intensive experiments on three datasets demonstrate that our proposed contrastive divergence loss significantly improves the performance over current state-of-the-art approaches.
A Differential Effect Approach to Partial Identification of Treatment Effects

We consider identification and inference for the average treatment effect and heterogeneous treatment effect conditional on observable covariates in the presence of unmeasured confounding. Since point identification of these treatment effects is not achievable without strong assumptions, we obtain bounds on these treatment effects by leveraging differential effects, a tool that allows for using a second treatment to learn the effect of the first treatment. The differential effect is the effect of using one treatment in lieu of the other. We provide conditions under which differential treatment effects can be used to point identify or partially identify treatment effects. Under these conditions, we develop a flexible and easy-to-implement semi-parametric framework to estimate bounds and leverage a two-stage approach to conduct statistical inference on effects of interest. The proposed method is examined through a simulation study and a case study that investigates the effect of smoking on the blood level of cadmium using the National Health and Nutrition Examination Survey.
Explainable AI for Time Series via Virtual Inspection Layers

The field of eXplainable Artificial Intelligence (XAI) has greatly advanced in recent years, but progress has mainly been made in computer vision and natural language processing. For time series, where the input is often not interpretable, only limited research on XAI is available. In this work, we put forward a virtual inspection layer, that transforms the time series to an interpretable representation and allows to propagate relevance attributions to this representation via local XAI methods like layer-wise relevance propagation (LRP). In this way, we extend the applicability of a family of XAI methods to domains (e.g. speech) where the input is only interpretable after a transformation. Here, we focus on the Fourier transformation which is prominently applied in the interpretation of time series and LRP and refer to our method as DFT-LRP. We demonstrate the usefulness of DFT-LRP in various time series classification settings like audio and electronic health records. We showcase how DFT-LRP reveals differences in the classification strategies of models trained in different domains (e.g., time vs. frequency domain) or helps to discover how models act on spurious correlations in the data.
Periodic Handover Skipping in Cellular Networks: Spatially Stochastic Modeling and Analysis

Handover (HO) management is one of the most crucial tasks in dense cellular networks with mobile users. A problem in the HO management is to deal with increasing HOs due to network densification in the 5G evolution and various HO skipping techniques have so far been studied in the literature to suppress excessive HOs. In this paper, we propose yet another HO skipping scheme, called periodic HO skipping. The proposed scheme prohibits the HOs of a mobile user equipment (UE) for a certain period of time, referred to as skipping period, thereby enabling flexible operation of the HO skipping by adjusting the length of the skipping period. We investigate the performance of the proposed scheme on the basis of stochastic geometry. Specifically, we derive analytical expressions of two performance metrics—the HO rate and the expected downlink data rate—when a UE adopts the periodic HO skipping. Numerical results based on the analysis demonstrate that the periodic HO skipping scenario can outperform the scenario without any HO skipping in terms of a certain utility metric representing the trade-off between the HO rate and the expected downlink data rate, in particular when the UE moves fast. Furthermore, we numerically show that there can exist an optimal length of the skipping period, which locally maximizes the utility metric, and approximately provide the optimal skipping period in a simple form. Numerical comparison with some other HO skipping techniques is also conducted.
Impact of perceived recognition by physics instructors on women’s self-efficacy and interest

Students' self-efficacy, interest, and perceived recognition from others have been shown to be very important for the development of their identity in a given field, which is a critical predictor of students' career decisions. Prior research suggests that students' self-efficacy and interest play an important role in their performance and persistence in STEM fields. However, very little has been investigated about the role of perceived recognition and validation by instructors on students' self-efficacy and interest. Moreover, prior quantitative studies show that women often report a lower level of physics perceived recognition, self-efficacy and interest. In this study, we analyzed data from individual interviews with 38 female students to investigate their learning experiences in physics courses in order to obtain a qualitative understanding of the factors that shape their self-efficacy and interest. We find that female students' negative and positive perceived recognition from instructors and teaching assistants (TAs) greatly influenced their self-efficacy and interest and even impacted their desire to persist in STEM majors. We categorize different types of perceived recognition that women reported in our interviews and how they influenced them. For example, many women reported that they felt belittled for their questions or efforts in physics courses, which often negatively influenced their self-efficacy. These findings can help physics educators develop better ways to interact with students in order to provide positive recognition and validation, such as acknowledging students' efforts and questions, expressing faith in students' ability to excel, and being careful not to give unintended messages to students. Our research also suggests that it is important for instructors/TAs to note that it is not their intentions that matter but the impact they are having on their students.
Maximum Mean Discrepancy on Exponential Windows for Online Change Detection

Detecting changes is of fundamental importance when analyzing data streams and has many applications, e.g., predictive maintenance, fraud detection, or medicine. A principled approach to detect changes is to compare the distributions of observations within the stream to each other via hypothesis testing. Maximum mean discrepancy (MMD; also called energy distance) is a well-known (semi-)metric on the space of probability distributions. MMD gives rise to powerful non-parametric two-sample tests on kernel-enriched domains under mild conditions, which makes its deployment for change detection desirable. However, the classic MMD estimators suffer quadratic complexity, which prohibits their application in the online change detection setting. We propose a general-purpose change detection algorithm, Maximum Mean Discrepancy on Exponential Windows (MMDEW), which leverages the MMD two-sample test, facilitates its efficient online computation on any kernel-enriched domain, and is able to detect any disparity between distributions. Our experiments and analysis show that (1) MMDEW achieves better detection quality than state-of-the-art competitors and that (2) the algorithm has polylogarithmic runtime and logarithmic memory requirements, which allow its deployment to the streaming setting.