Miracle-dz/UniFAR-FTUs huggingface dataset:
Necroptosis and Alzheimer’s Disease: Pathogenic Mechanisms and Therapeutic Opportunities

Alzheimer’s disease (AD) is considered to be the most common neurodegenerative disease, with clinical symptoms encompassing progressive memory loss and cognitive impairment. Necroptosis is a form of programmed necrosis that promotes cell death and neuroinflammation, which further mediates the pathogenesis of several neurodegenerative diseases, especially AD. Current evidence has strongly suggested that necroptosis is activated in AD brains, resulting in neuronal death and cognitive impairment. We searched the PubMed database, screening all articles published before September 28, 2022 related to necroptosis in the context of AD pathology. The keywords in the search included: “necroptosis”, “Alzheimer’s disease”, “signaling pathways”, “Aβ”, Aβo”, “Tau”, “p-Tau”, “neuronal death”, “BBB damage”, “neuroinflammation”, “microglia”, “mitochondrial dysfunction”, “granulovacuolar degeneration”, “synaptic loss”, “axonal degeneration”, “Nec-1”, “Nec-1s”, “GSK872”, “NSA”, “OGA”, “RIPK1”, “RIPK3”, and “MLKL”. Results show that necroptosis has been involved in multiple pathological processes of AD, including amyloid-β aggregation, Tau accumulation, neuronal death, and blood-brain barrier damage, etc. More importantly, existing research on AD necroptosis interventions, including drug intervention and potential gene targets, as well as its current clinical development status, was discussed. Finally, the issues pertaining to necroptosis in AD were presented. Accordingly, this review may provide further insight into clinical perspectives and challenges for the future treatment of AD by targeting the necroptosis pathway.
Psychosocial impacts of the COVID‐19 pandemic on women with trauma histories: Study of Women's Health Across the Nation (SWAN)

Abstract Older adults, particularly those with trauma histories, may be vulnerable to adverse psychosocial outcomes during the COVID‐19 pandemic. We tested associations between prepandemic childhood abuse or intimate partner violence (IPV) and elevated depressive, anxiety, conflict, and sleep symptoms during the pandemic among aging women. Women (N = 582, age: 65–77 years) from three U.S. sites (Pittsburgh, Boston, Newark) of the longitudinal Study of Women's Health Across the Nation (SWAN) reported pandemic‐related psychosocial impacts from June 2020–March 2021. Prepandemic childhood abuse; physical/emotional IPV; social functioning; physical comorbidities; and depressive, anxiety, and sleep symptoms were drawn from SWAN assessments between 2009 and 2017. There were no measures of prepandemic conflict. In total, 47.7% and 35.3% of women, respectively, reported childhood abuse or IPV. Using logistic regression models adjusted for age; race/ethnicity; education; site; prepandemic social functioning and physical comorbidities; and, in respective models, prepandemic depressive, anxiety, or sleep symptoms, childhood abuse predicted elevated anxiety symptoms, OR = 1.67, 95% CI [1.10, 2.54]; household conflict, OR = 2.19, 95% CI [1.32, 3.61]; and nonhousehold family conflict, OR = 2.14, 95% CI [1.29, 3.55]. IPV predicted elevated sleep problems, OR = 1.63, 95% CI [1.07, 2.46], and household conflict, OR = 1.96, 95% CI [1.20, 3.21]. No associations emerged for depressive symptoms after adjusting for prepandemic depression. Aging women with interpersonal trauma histories reported worse anxiety, sleep, and conflict during the COVID‐19 pandemic than those without. Women's trauma histories and prepandemic symptoms are critical to understanding the psychosocial impacts of the pandemic.
Newbornscreening SMA – From Pilot Project to Nationwide Screening in Germany

Now that targeted therapies for spinal muscular atrophy are available, attempts are being made worldwide to include screening for spinal muscular atrophy in general newborn screening. In Germany, after pilot projects from 2018–2021, it was included in the general newborn screening from October 2021. To ensure a smooth transition, criteria for follow-up were developed together with key stakeholders. At the beginning of the transition to nationwide screening, false positive findings were reported in 3 patients. After optimization of the screening method in the laboratories concerned, all findings have been subsequently confirmed. On average, the first presentation to a neuromuscular center occurred on day 12 of life, and in patients with 2 or 3 SMN2 copies, therapy started on day 26 of life. Compared with the pilot project, there was no significant delay in timing.
Mild Cognitive Impairment, Reversion Rates, and Associated Factors: Comparison of Two Diagnostic Approaches

Background: As mild cognitive impairment (MCI) is typically used to identify prodromal stages of dementia, it is essential to identify MCI criteria with high diagnostic stability and prediction of dementia. Moreover, further investigation into pinpointing key factors for reversion is required to foresee future prognosis of MCI patients accurately. Objective: To explore disparities in diagnostic stability by examining reversion rates produced by two operationalizations of the MCI definition: the widely applied Petersen criteria and a version of the Neuropsychological (NP) criteria and to identify cognitive, lifestyle, and health related factors for reversion. Methods: MCI was retrospectively classified in a sample from the Swedish community-based study Good Aging in Skåne with the Petersen criteria (n = 744, median follow-up = 7.0 years) and the NP criteria (n = 375, median follow-up, 6.7 years), respectively. Poisson regression models estimated the effect of various factors on the likelihood of incident reversion. Results: Reversion rates were 323/744 (43.4%, 95% confidence intervals (CI): 39.8; 47.0) and 181/375 (48.3% 95% CI: 43.2; 53.5) for the Petersen criteria and NP criteria, respectively. Participants with impairment in a single cognitive domain, regular alcohol consumption, living with someone, older age, and lower body mass index had a higher likelihood of reverting to normal. Conclusion: Reversion rates were similar for Petersen and NP criteria indicating that one definition is not superior to the other regarding diagnostic stability. Additionally, the results highlight important aspects such as multiple domain MCI, cohabitation, and the role of alcohol on predicting the trajectory of those diagnosed with MCI.
An Information-Theoretic Analysis of Compute-Optimal Neural Scaling Laws

We study the compute-optimal trade-off between model and training data set sizes for large neural networks. Our result suggests a linear relation similar to that supported by the empirical analysis of chinchilla. While that work studies transformer-based large language models trained on the MassiveText corpus gopher, as a starting point for development of a mathematical theory, we focus on a simpler learning model and data generating process, each based on a neural network with a sigmoidal output unit and single hidden layer of ReLU activation units. We introduce general error upper bounds for a class of algorithms which incrementally update a statistic (for example gradient descent). For a particular learning model inspired by barron 1993, we establish an upper bound on the minimal information-theoretically achievable expected error as a function of model and data set sizes. We then derive allocations of computation that minimize this bound. We present empirical results which suggest that this approximation correctly identifies an asymptotic linear compute-optimal scaling. This approximation also generates new insights. Among other things, it suggests that, as the input dimension or latent space complexity grows, as might be the case for example if a longer history of tokens is taken as input to a language model, a larger fraction of the compute budget should be allocated to growing the learning model rather than training data.
Compressive Instabilities Enable Cell-Induced Extreme Densification Patterns in the Fibrous Extracellular Matrix: Discrete Model Predictions

Through modelling and simulations we show that material instabilities play a dominant role in the mechanical behavior of the fibrous collagen Extracellular Matrix (ECM), as observed when the ECM is deformed by contractile biological cells. We compare two families of fiber network models, Family 1 with stable and Family 2 with unstable force-stretch response of individual fibers in compression. The latter is a characteristic of post-buckling of beams with hierarchical structure. Our simulations reveal different compression instabilities at play in each family, namely, fiber collapse (buckling) in Family 2 and fiber element collapse (snap-through) in Family 1. These result in highly localized densification zones consisting of strongly aligned fibers emanating from individual contractile cells or joining neighbouring cells, as observed in experiments. Despite substantial differences in the response of the two families, our work underscores the importance of buckling/compression instabilities in the behavior of fibrous biological tissues, with implications in cancer invasion and metastasis.
3D-LDM: Neural Implicit 3D Shape Generation with Latent Diffusion Models

Diffusion models have shown great promise for image generation, beating GANs in terms of generation diversity, with comparable image quality. However, their application to 3D shapes has been limited to point or voxel representations that can in practice not accurately represent a 3D surface. We propose a diffusion model for neural implicit representations of 3D shapes that operates in the latent space of an auto-decoder. This allows us to generate diverse and high quality 3D surfaces. We additionally show that we can condition our model on images or text to enable image-to-3D generation and text-to-3D generation using CLIP embeddings. Furthermore, adding noise to the latent codes of existing shapes allows us to explore shape variations.
Investigating Deep Learning Model Calibration for Classification Problems in Mechanics

Recently, there has been a growing interest in applying machine learning methods to problems in engineering mechanics. In particular, there has been significant interest in applying deep learning techniques to predicting the mechanical behavior of heterogeneous materials and structures. Researchers have shown that deep learning methods are able to effectively predict mechanical behavior with low error for systems ranging from engineered composites, to geometrically complex metamaterials, to heterogeneous biological tissue. However, there has been comparatively little attention paid to deep learning model calibration, i.e., the match between predicted probabilities of outcomes and the true probabilities of outcomes. In this work, we perform a comprehensive investigation into ML model calibration across seven open access engineering mechanics datasets that cover three distinct types of mechanical problems. Specifically, we evaluate both model and model calibration error for multiple machine learning methods, and investigate the influence of ensemble averaging and post hoc model calibration via temperature scaling. Overall, we find that ensemble averaging of deep neural networks is both an effective and consistent tool for improving model calibration, while temperature scaling has comparatively limited benefits. Looking forward, we anticipate that this investigation will lay the foundation for future work in developing mechanics specific approaches to deep learning model calibration.
The medial axis of closed bounded sets is Lipschitz stable with respect to the Hausdorff distance under ambient diffeomorphisms

We prove that the medial axis of closed sets is Hausdorff stable in the following sense: Let $\mathcal{S} \subseteq \mathbb{R}^d$ be (fixed) closed set (that contains a bounding sphere). Consider the space of $C^{1,1}$ diffeomorphisms of $\mathbb{R}^d$ to itself, which keep the bounding sphere invariant. The map from this space of diffeomorphisms (endowed with some Banach norm) to the space of closed subsets of $\mathbb{R}^d$ (endowed with the Hausdorff distance), mapping a diffeomorphism $F$ to the closure of the medial axis of $F(\mathcal{S})$, is Lipschitz. This extends a previous stability result of Chazal and Soufflet on the stability of the medial axis of $C^2$ manifolds under $C^2$ ambient diffeomorphisms.
Information Retrieval from the Digitized Books

Extracting the relevant information out of a large number of documents is a challenging and tedious task. The quality of results generated by the traditionally available full-text search engine and text-based image retrieval systems is not optimal. Information retrieval (IR) tasks become more challenging with the nontraditional language scripts, as in the case of Indic scripts. The authors have developed OCR (Optical Character Recognition) Search Engine to make an Information Retrieval&Extraction (IRE) system that replicates the current state-of-the-art methods using the IRE and Natural Language Processing (NLP) techniques. Here we have presented the study of the methods used for performing search and retrieval tasks. The details of this system, along with the statistics of the dataset (source: National Digital Library of India or NDLI), is also presented. Additionally, the ideas to further explore and add value to research in IRE are also discussed.
Numerical Simulation of Atmospheric Pressure Plasma Jet Using Lattice Boltzmann Method

A model for calculating the velocity and temperature field of plasma jet by Lattice Boltzmann (LB) method was established in this paper. The numerical simulation of plasma jet was derived by selecting two opportune equilibrium distribution functions, and the results obtained are compared with those of the experiment and in literature. It is found that the LB method is simpler and more efficient than the traditional finite difference method.
Slowly Varying Jump and Transition Phenomena Associated with Algebraic Bifurcation Problems

Parameter-dependent equilibrium solutions are analyzed as the parameter slowly varies through critical values corresponding to a bifurcation or to a jump phenomena. At these critical times, interior nonlinear transition layers are necessary. Depending on the particular situation, local scaling analysis yields the first and a second Painleve transcendent among other generic equations. In specific cases the resulting boundary layer solutions either increase algebraically or explode (via a singularity). The algebraic growth corresponds to a smooth transition to a bifurcated equilibrium. When a jump phenomena is expected, an explosion can occur. In this case, the solution of first-order differential equations approaches the equilibrium, describing the slow evolution through such a jump. However, second-order differential equations have finite amplitude oscillations around the new equilibrium.
Observed Rate Variations in Superflaring G-type Stars

Flare occurrence on the Sun is highly variable, exhibiting both short-term variation due to the emergence and evolution of active regions, and long-term variation from the solar cycle. On solar-like stars, more energetic stellar flares (superflares) have been observed, and it is of interest to determine whether the observed rates of superflare occurrence exhibit similar variability to solar flares. We analyze 274 G-type stars using data from the Transiting Exoplanet Survey Satellite and identify seven stars which exhibit statistically significant changes in the rate of superflare occurrence by fitting a piecewise constant-rate model with the Bayesian blocks algorithm. We investigate the properties of these stars and their flaring rates, and discuss the possible reasons for the low number of stars with detectable rate variation.
Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures

One of the common traits of past and present approaches for Semantic Role Labeling (SRL) is that they rely upon discrete labels drawn from a predefined linguistic inventory to classify predicate senses and their arguments. However, we argue this need not be the case. In this paper, we present an approach that leverages Definition Modeling to introduce a generalized formulation of SRL as the task of describing predicate-argument structures using natural language definitions instead of discrete labels. Our novel formulation takes a first step towards placing interpretability and flexibility foremost, and yet our experiments and analyses on PropBank-style and FrameNet-style, dependency-based and span-based SRL also demonstrate that a flexible model with an interpretable output does not necessarily come at the expense of performance. We release our software for research purposes at https://github.com/SapienzaNLP/dsrl.
On-device Training: A First Overview on Existing Systems

The recent breakthroughs in machine learning (ML) and deep learning (DL) have catalyzed the design and development of various intelligent systems over wide application domains. While most existing machine learning models require large memory and computing power, efforts have been made to deploy some models on resource-constrained devices as well. A majority of the early application systems focused on exploiting the inference capabilities of ML and DL models, where data captured from different mobile and embedded sensing components are processed through these models for application goals such as classification and segmentation. More recently, the concept of exploiting the mobile and embedded computing resources for ML/DL model training has gained attention, as such capabilities allow (i) the training of models via local data without the need to share data over wireless links, thus enabling privacy-preserving computation by design, (ii) model personalization and environment adaptation, and (ii) deployment of accurate models in remote and hardly accessible locations without stable internet connectivity. This work targets to summarize and analyze state-of-the-art systems research that allows such on-device model training capabilities and provide a survey of on-device training from a systems perspective.
DeclutterCam: A Photographic Assistant System with Clutter Detection and Removal

Photographs convey the stories of photographers to the audience. However, this story-telling aspect of photography is easily distracted by visual clutter. Informed by a pilot study, we identified the kinds of clutter that amateurs frequently include in their photos. We were thus inspired to develop DeclutterCam, a photographic assistant system that incorporates novel user interactions and AI algorithms for photographic decluttering. Clutter elements are detected by an aesthetic quality evaluation algorithm and are highlighted so that users can interactively identify distracting elements. A GAN-based iterative clutter removal tool enables users to test their photographic ideas in real-time. User studies with 32 photography beginners demonstrate that our system provides flexible interfaces, accurate algorithms, and immediate feedback that allow users to avoid clutter and explore more photographic ideas. Evaluations by photography experts show that users can take higher-quality photos that better convey the intended story using our system.
Optical bistability and X-shaped hysteresis in laser diode amplifiers

Laser diode amplifiers exhibit optical bistability when biased just below lasing threshold, and have applications as optical memory elements and optical switches, due to their very low switching energy (fJ) and presence of optical gain. While LDAs operated in transmission have received much attention, the reflection mode of operation has been largely neglected. In this paper we present a detailed experimental and numerical investigation of Fabry Perot LDA (FP-LDA) behavior under light injection when operated in reflection. We observe transitions from anticlockwise to clockwise hysteresis with changing bias level or incident wavelength detuning. Moreover, we also report the first experimental observation of X-shaped (or butterfly hysteresis) in this system. Our results are in very good agreement with calculation. <<ETX>>
First-Order Methods for Nonsmooth Nonconvex Functional Constrained Optimization with or without Slater Points

Constrained optimization problems where both the objective and constraints may be nonsmooth and nonconvex arise across many learning and data science settings. In this paper, we show for any Lipschitz, weakly convex objectives and constraints, a simple first-order method finds a feasible, $\epsilon$-stationary point at a convergence rate of $O(\epsilon^{-4})$ without relying on compactness or Constraint Qualification (CQ). When CQ holds, this convergence is measured by approximately satisfying the Karush-Kuhn-Tucker conditions. When CQ fails, we guarantee the attainment of weaker Fritz-John conditions. As an illustrative example, our method stably converges on piecewise quadratic SCAD regularized problems despite frequent violations of constraint qualification. The considered algorithm is similar to those of"Quadratically regularized subgradient methods for weakly convex optimization with weakly convex constraints"by Ma et al. and"Stochastic first-order methods for convex and nonconvex functional constrained optimization"by Boob et al. (whose guarantees further assume compactness and CQ), iteratively taking inexact proximal steps, computed via an inner loop applying a switching subgradient method to a strongly convex constrained subproblem. Our non-Lipschitz analysis of the switching subgradient method appears to be new and may be of independent interest.
Task-Oriented Image Semantic Communication Based on Rate-Distortion Theory

Task-oriented image semantic communication is a new communication paradigm, which aims to transmit semantics for artificial intelligent (AI) tasks while ignoring the reconstruction quality of the images. However, in some applications, such as autonomous driving, both image reconstruction quality and the performance of the followed AI tasks must be simultaneously considered. To tackle this challenge, this paper proposes a task-oriented semantic communication scheme with semantic reconstruction (TOSC-SR). Its main goal is to simultaneously minimize pixel-level and task-relevant semantic-level distortion during communications under a certain rate, which formulates a new rate-distortion optimization problem. To successfully measure the loss at the semantic level, a new form of semantic distortion measured by the mutual information between the semantic-reconstructed images and the task labels is proposed. Then, we derive an analytical solution for the formulated problem, where the self-consistent equations of the problem are obtained to determine the optimal mapping of the source and the semantic-reconstructed images. To implement TOSC-SR, we further obtain an extended form of rate-distortion form based on the variational approximation of mutual information, which is applicable to multiple AI tasks. Experimental results show that the proposed approach outperforms the traditional JPEG, JPEG2000, BPG, VVC-based image communication systems and deep learning based benchmarks in terms of image reconstruction quality, AI task performance, and multi-task generalization ability.