Miracle-dz/UniFAR-FTUs huggingface dataset:
Soft thermodynamics of gravitational shock wave

The gravitational shock waves have provided crucial insights into entanglement structures of black holes in the AdS/CFT correspondence. Recent progress on the soft hair physics suggests that these developments from holography may also be applicable to geometries beyond negatively curved spacetime. In this work, we derive a remarkably simple thermodynamic relation which relates the gravitational shock wave to a microscopic area deformation. Our treatment is based on the covariant phase space formalism and is applicable to any Killing horizon in generic static spacetime which is governed by arbitrary covariant theory of gravity. The central idea is to probe the gravitational shock wave, which shifts the horizon in the $u$ direction, by the Noether charge constructed from a vector field which shifts the horizon in the $v$ direction. As an application, we illustrate its use for the Gauss-Bonnet gravity. We also derive a simplified form of the gravitational scattering unitary matrix and show that its leading-order contribution is nothing but the exponential of the horizon area: $\mathcal{U}=\exp(i \text{Area})$.
Leveraging Community and Author Context to Explain the Performance and Bias of Text-Based Deception Detection Models

Deceptive news posts shared in online communities can be detected with NLP models, and much recent research has focused on the development of such models. In this work, we use characteristics of online communities and authors — the context of how and where content is posted — to explain the performance of a neural network deception detection model and identify sub-populations who are disproportionately affected by model accuracy or failure. We examine who is posting the content, and where the content is posted to. We find that while author characteristics are better predictors of deceptive content than community characteristics, both characteristics are strongly correlated with model performance. Traditional performance metrics such as F1 score may fail to capture poor model performance on isolated sub-populations such as specific authors, and as such, more nuanced evaluation of deception detection models is critical.
S 5: The Destruction of a Bright Dwarf Galaxy as Revealed by the Chemistry of the Indus Stellar Stream

The recently discovered Indus stellar stream exhibits a diverse chemical signature compared to what is found for most other streams due to the abundances of two outlier stars, Indus_0 and Indus_13. Indus_13 exhibits an extreme enhancement in rapid neutron-capture (r-)process elements with [Eu/Fe] = + 1.81. It thus provides direct evidence of the accreted nature of r-process-enhanced stars. In this paper we present a detailed chemical analysis of the neutron-capture elements in Indus_13, revealing the star to be slightly actinide poor. The other outlier, Indus_0, displays a globular cluster-like signature with high N, Na, and Al abundances, while the rest of the Indus stars show abundances compatible with a dwarf galaxy origin. Hence, Indus_0 provides the first chemical evidence of a fully disrupted dwarf containing a globular cluster. We use the chemical signature of the Indus stars to discuss the nature of the stream progenitor which was likely a chemically evolved system, with a mass somewhere in the range from Ursa Minor to Fornax.
Hypersuccinct Trees - New universal tree source codes for optimal compressed tree data structures

We present a new universal source code for distributions of unlabeled binary and ordinal trees that achieves optimal compression to within lower order terms for all tree sources covered by existing universal codes. At the same time, it supports answering many navigational queries on the compressed representation in constant time on the word-RAM; this is not known to be possible for any existing tree compression method. The resulting data structures, “hypersuccinct trees”, hence combine the compression achieved by the best known universal codes with the operation support of the best succinct tree data structures. We apply hypersuccinct trees to obtain a universal compressed data structure for range-minimum queries. It has constant query time and the optimal worst-case space usage of 2n + o(n) bits, but the space drops to 1.736n + o(n) bits on average for random permutations of n elements, and 2 lg ( n r ) + o(n) for arrays with r increasing runs, respectively. Both results are optimal; the former answers an open problem of Davoodi et al. (2014) and Golin et al. (2016). Compared to prior work on succinct data structures, we do not have to tailor our data structure to specific applications; hypersuccinct trees automatically adapt to the trees at hand. We show that they simultaneously achieve the optimal space usage to within lower order terms for a wide range of distributions over tree shapes, including: binary search trees (BSTs) generated by insertions in random order / Cartesian trees of random arrays, random fringe-balanced BSTs, binary trees with a given number of binary/unary/leaf nodes, random binary tries generated from memoryless sources, full binary trees, unary paths, as well as uniformly chosen weight-balanced BSTs, AVL trees, and left-leaning red-black trees. 2012 ACM Subject Classification Theory of computation → Data compression; Theory of computation → Data structures design and analysis
Smoothing Proximal Gradient Methods for Nonsmooth Sparsity Constrained Optimization: Optimality Conditions and Global Convergence

Nonsmooth sparsity constrained optimization encompasses a broad spectrum of applications in machine learning. This problem is generally non-convex and NP-hard. Existing solutions to this problem exhibit several notable limitations, including their inability to address general nonsmooth problems, tendency to yield weaker optimality conditions, and lack of comprehensive convergence analysis. This paper considers Smoothing Proximal Gradient Methods (SPGM) as solutions to nonsmooth sparsity constrained optimization problems. Two specific variants of SPGM are explored: one based on Iterative Hard Thresholding (SPGM-IHT) and the other on Block Coordinate Decomposition (SPGM-BCD). It is shown that the SPGM-BCD algorithm finds stronger stationary points compared to previous methods. Additionally, novel theories for analyzing the convergence rates of both SPGM-IHT and SPGM-BCD algorithms are developed. Our theoretical bounds, capitalizing on the intrinsic sparsity of the optimization problem, are on par with the best-known error bounds available to date. Finally, numerical experiments reveal that SPGM-IHT performs comparably to current IHT-style methods, while SPGM-BCD consistently surpasses them. Keywords: Sparsity Recovery, Nonsmooth Optimization, Nonconvex Optimization, Proximal Gradient Method, Block Coordinate Decomposition, Iterative Hard Thresholding, Convergence Analysis
Social Distancing in Chronic Migraine during the COVID-19 Outbreak: Results from a Multicenter Observational Study

Background: The restrictions taken to control the rapid spread of COVID-19 resulted in a sudden, unprecedented change in people’s lifestyle, leading to negative consequences on general health. This study aimed to estimate the impact of such changes on migraine severity during 2020 March–May lockdown. Methods: Patients affected by migraine with or without aura, diagnosed by expert physicians, completed a detailed interview comprehensive of: assessment of migraine characteristics; measure of physical activity (PA) levels; measure of the intake frequency of main Italian foods; the Insomnia Severity Index (ISI) questionnaire investigating sleep disorders. Results: We included 261 patients with a mean age of 44.5 ± 12.3 years. During social distancing, 72 patients (28%) reported a headache worsening, 86 (33%) an improvement, and 103 (39%) a stable headache frequency. A significant decrease of the PA levels during COVID-19 quarantine in the whole study sample was observed (median total metabolic equivalent task (METs) decreased from 1170 to 510; p < 0.001). Additionally, a significant difference was reported on median ISI scores (from 7 to 8; p < 0.001), which were increased in patients who presented a stable or worsening headache. Conclusions: Our study confirmed that the restrictions taken during the pandemic have affected the practice of PA levels and sleep quality in migraine. Hence, PA and sleep quality should be assessed to find strategies for an improvement in quality of life.
An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities

The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on technologies for the assessment of gait characteristics, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigenanalysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics.
Drying-Time Study in Graphene Oxide

Graphene oxide (GO) exhibits different properties from those found in free-standing graphene, which mainly depend on the type of defects induced by the preparation method and post-processing. Although defects in graphene oxide are widely studied, we report the effect of drying time in GO and how this modifies the presence or absence of edge-, basal-, and sp3-type defects. The effect of drying time is evaluated by Raman spectroscopy, UV-visible spectroscopy, and transmission electron microscopy (TEM). The traditional D, G, and 2D peaks are observed together with other less intense peaks called the D’, D*, D**, D+G, and G+D. Remarkably, the D* peak is activated/deactivated as a direct consequence of drying time. Furthermore, the broad region of the 2D peak is discussed as a function of its deconvoluted 2D1A, 2D2A, and D+G bands. The main peak in UV-visible absorption spectra undergoes a redshift as drying time increases. Finally, TEM measurements demonstrate the stacking of exfoliated GO sheets as the intercalated (water) molecules are removed.
An Effective Interface Tracking Method for Simulating the Extrudate Swell Phenomenon

The extrudate swell, i.e., the geometrical modifications that take place when the flowing material leaves the confined flow inside a channel and moves freely without the restrictions that are promoted by the walls, is a relevant phenomenon in several polymer processing techniques. For instance, in profile extrusion, the extrudate cross-section is subjected to a number of distortions that are motivated by the swell, which are very difficult to anticipate, especially for complex geometries. As happens in many industrial processes, numerical modelling might provide useful information to support design tasks, i.e., to allow for identifying the best strategy to compensate the changes promoted by the extrudate swell. This study reports the development of an improved interface tracking algorithm that employs the least-squares volume-to-point interpolation method for the grid movement. The formulation is enriched further with the consistent second-order time-accurate non-iterative Pressure-Implicit with Splitting of Operators (PISO) algorithm, which allows for efficiently simulating free-surface flows. The accuracy and robustness of the proposed solver is illustrated through the simulation of the steady planar and asymmetric extrudate swell flows of Newtonian fluids. The role of inertia on the extrudate swell is studied, and the results that are obtained with the newly improved solver show good agreement with reference data that are found in the scientific literature.
Harnessing Natural Killer Cells in Cancer Immunotherapy: A Review of Mechanisms and Novel Therapies

Simple Summary Cancer immunotherapy has mostly focused on harnessing adaptive immunity to promote the destruction of neoplastic cells by B and T lymphocytes. We are now increasingly recognizing the important role of the innate immune system in the body’s response to neoplastic cells, providing an opportunity for the development of innovative cancer treatments. Specifically, emerging therapies targeting natural killer (NK) cells, which are integral to the innate immune response, are being investigated for the treatment of various tumor types. Successful efforts would pave the way for entirely new immunotherapeutic strategies in cancer treatment, with numerous possible targets. Abstract Natural killer (NK) cells are lymphocytes that are integral to the body’s innate immunity, resulting in a rapid immune response to stressed or infected cells in an antigen-independent manner. The innate immune system plays an important role in the recognition of tumor-derived stress-related factors and is critical to subsequent adaptive immune responses against tumor antigens. The aim of this review is to discuss mechanisms by which tumor cells evade NK cells and to outline strategies that harness NK cells for cancer immunotherapy. We discuss strategies to relieve the exhausted state of NK cells, recent therapies focused on targeting NK-cell-specific activating and inhibitory receptors, the use of cytokines IL-2 and IL-15 to stimulate autologous or allogeneic NK cells, and ongoing trials exploring the use of genetically modified NK cells and chimeric antigen-receptor-modified NK (CAR-NK) cells.
Mobile Network Performance and Technical Feasibility of LTE-Powered Unmanned Aerial Vehicle

Conventional and license-free radio-controlled drone activities are limited to a line-of-sight (LoS) operational range. One of the alternatives to operate the drones beyond the visual line-of-sight (BVLoS) range is replacing the drone wireless communications system from the conventional industrial, scientific, and medical (ISM) radio band to a licensed cellular-connected system. The Long Term Evolution (LTE) technology that has been established for the terrestrial area allows command-and-control and payload communications between drone and ground station in real-time. However, with increasing height above the ground, the radio environment changes, and utilizing terrestrial cellular networks for drone communications may face new challenges. In this regard, this paper aims to develop an LTE-based control system prototype for low altitude small drones and investigate the feasibility and performance of drone cellular connectivity at different altitudes with measuring parameters such as latency, handover, and signal strength. The measurement results have shown that by increasing flight height from ground to 170 m the received signal power and the signal quality levels were reduced by 20 dBm and 10 dB respectively, the downlink data rate decreased to 70%, and latency increased up to 94 ms. It is concluded that although the existing LTE network can provide a minimum requirement for drone cellular connectivity, further improvements are still needed to enhance aerial coverage, eliminate interference, and reduce network latency.
Short- and Long-Term Effects of a Scapular-Focused Exercise Protocol for Patients with Shoulder Dysfunctions—A Prospective Cohort

Current clinical practice lacks consistent evidence in the management of scapular dyskinesis. This study aims to determine the short- and long-term effects of a scapular-focused exercise protocol facilitated by real-time electromyographic biofeedback (EMGBF) on pain and function, in individuals with rotator cuff related pain syndrome (RCS) and anterior shoulder instability (ASI). One-hundred and eighty-three patients were divided into two groups (n = 117 RCS and n = 66 ASI) and guided through a structured exercise protocol, focusing on scapular dynamic control. Values of pain and function (shoulder pain and disability index (SPADI) questionnaire, complemented by the numeric pain rating scale (NPRS) and disabilities of the arm, shoulder, and hand (DASH) questionnaire) were assessed at the initial, 4-week, and 2-year follow-up and compared within and between. There were significant differences in pain and function improvement between the initial and 4-week assessments. There were no differences in the values of DASH 1st part and SPADI between the 4-week and 2-year follow-up. There were no differences between groups at the baseline and long-term, except for DASH 1st part and SPADI (p < 0.05). Only 29 patients (15.8%) had a recurrence episode at follow-up. These results provide valuable information on the positive results of the protocol in the short- and long-term.
Transmission of Major and Minor Serum Proteins during Microfiltration of Skim Milk: Effects of Pore Diameters, Concentration Factors and Processing Stages

Effects of pore diameters (100, 50, and 20 nm), concentration factors (1–8) and processing stages (1–5) on the transmission of major serum proteins (β-lactoglobulin and α-lactalbumin) and minor serum proteins (immunoglobulin (Ig) G, IgA, IgM, lactoferrin (LF), lactoperoxidase (LPO), xanthine oxidase (XO)) during ceramic microfiltration (MF) of skim milk were studied. Holstein skim milk was microfiltered at a temperature of 50 °C, a transmembrane pressure of 110 kPa and a crossflow velocity of 6.7 m/s, using a tubular single stainless steel module that consisted of three ceramic tubes, each with 19 channels (3.5 mm inner diameter) and a length of 0.5 m. For MF with 100 nm and 50 nm pore diameters, the recovery yield of major serum proteins in permeate was 44.3% and 44.1%, while the recovery yield of minor serum proteins was slightly less by 0%–8% than 50 nm MF. MF with 20 nm pore diameters showed a markedly lower (by 12%–45%) recovery yield for both major and minor serum proteins, corresponding with its lower membrane flux. Flux sharply decreased with an increasing concentration factor (CF) up to four, and thereafter remained almost unchanged. Compared to the decrease (88%) of flux, the transmission of major and minor serum proteins was decreased by 4%–15% from CF = one to CF = eight. With increasing processing stages, the flux gradually increased, and the recovery yield of both major and minor proteins in the permeate gradually decreased and reached a considerably low value at stage five. After four stages of MF with 100 nm pore diameter and a CF of four for each stage, the cumulative recovery yield of major serum proteins, IgG, IgA, IgM, LF, LPO, and XO reached 95.7%, 90.8%, 68.5%, 34.1%, 15.3%, 39.1% and 81.2% respectively.
Favorable Effects of Voriconazole Trough Concentrations Exceeding 1 μg/mL on Treatment Success and All-Cause Mortality: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis examined the optimal trough concentration of voriconazole for adult patients with invasive fungal infections. We used stepwise cutoffs of 0.5–2.0 μg/mL for efficacy and 3.0–6.0 μg/mL for safety. Studies were included if they reported the rates of all-cause mortality and/or treatment success, hepatotoxicity, and nephrotoxicity according to the trough concentration. Twenty-five studies involving 2554 patients were included. The probability of mortality was significantly decreased using a cutoff of ≥1.0 μg/mL (odds ratio (OR) = 0.34, 95% confidence interval (CI) = 0.15–0.80). Cutoffs of 0.5 (OR = 3.48, 95% CI = 1.45–8.34) and 1.0 μg/mL (OR = 3.35, 95% CI = 1.52–7.38) also increased the treatment success rate. Concerning safety, significantly higher risks of hepatotoxicity and neurotoxicity were demonstrated at higher concentrations for all cutoffs, and the highest ORs were recorded at 4.0 μg/mL (OR = 7.39, 95% CI = 3.81–14.36; OR = 5.76, 95% CI 3.14–10.57, respectively). Although further high-quality trials are needed, our findings suggest that the proper trough concentration for increasing clinical success while minimizing toxicity is 1.0–4.0 μg/mL for adult patients receiving voriconazole therapy.
Polymeric Nanoparticle Delivery of Combination Therapy with Synergistic Effects in Ovarian Cancer

Treatment of ovarian cancer is challenging due to late stage diagnosis, acquired drug resistance mechanisms, and systemic toxicity of chemotherapeutic agents. Combination chemotherapy has the potential to enhance treatment efficacy by activation of multiple downstream pathways to overcome drug resistance and reducing required dosages. Sequence of delivery and the dosing schedule can further enhance treatment efficacy. Formulation of drug combinations into nanoparticles can further enhance treatment efficacy. Due to their versatility, polymer-based nanoparticles are an especially promising tool for clinical translation of combination therapies with tunable dosing schedules. We review polymer nanoparticle (e.g., micelles, dendrimers, and lipid nanoparticles) carriers of drug combinations formulated to treat ovarian cancer. In particular, the focus on this review is combinations of platinum and taxane agents (commonly used first line treatments for ovarian cancer) combined with other small molecule therapeutic agents. In vitro and in vivo drug potency are discussed with a focus on quantifiable synergistic effects. The effect of drug sequence and dosing schedule is examined. Computational approaches as a tool to predict synergistic drug combinations and dosing schedules as a tool for future nanoparticle design are also briefly discussed.
Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge

Adopting effective techniques to automatically detect and identify small drones is a very compelling need for a number of different stakeholders in both the public and private sectors. This work presents three different original approaches that competed in a grand challenge on the “Drone vs. Bird” detection problem. The goal is to detect one or more drones appearing at some time point in video sequences where birds and other distractor objects may be also present, together with motion in background or foreground. Algorithms should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds, nor being confused by the rest of the scene. In particular, three original approaches based on different deep learning strategies are proposed and compared on a real-world dataset provided by a consortium of universities and research centers, under the 2020 edition of the Drone vs. Bird Detection Challenge. Results show that there is a range in difficulty among different test sequences, depending on the size and the shape visibility of the drone in the sequence, while sequences recorded by a moving camera and very distant drones are the most challenging ones. The performance comparison reveals that the different approaches perform somewhat complementary, in terms of correct detection rate, false alarm rate, and average precision.