Miracle-dz/UniFAR-FTUs huggingface dataset:
Baclofen during alcohol detoxification reduces the need for additional diazepam: a randomized placebo-controlled trial.

Baclofen may reduce the symptoms of alcohol withdrawal, as an alternative or as an adjuvant for benzodiazepines, but the available data are insufficient to support baclofen-assisted alcohol withdrawal. This study investigated the need for diazepam during acute alcohol withdrawal in patients receiving baclofen. In a single-blind, dose-dependent randomized controlled trial with three study arms, 63 patients with alcohol use disorder, starting in-patient benzodiazepine-assisted alcohol detoxification, were randomly assigned to receive placebo (n = 18), baclofen 30 mg/day (N = 20), or baclofen 60 mg/day (N = 25) for 7 days. Diazepam was provided as needed based on the withdrawal symptoms stated by Clinical Institute Withdrawal Assessment for Alcohol-revised. The primary outcome measure was the number of patients in need of diazepam during alcohol detoxification. Secondary outcome measure included the between-group difference in the amount of diazepam needed during alcohol detoxification. Using baclofen 60 mg/day, 32% of patients needed additional diazepam compared to 35% on baclofen 30 mg/day and compared to 72% on placebo (P = .013). The median total amount of diazepam needed was significantly lower in patients receiving baclofen 60 mg/day (0 ± 10 mg diazepam) and baclofen 30 mg/day (0 ± 10 mg diazepam) compared to placebo (10 ± 43 mg diazepam; P = .017). Adverse events were comparable between patients on baclofen and placebo. Baclofen can reduce the withdrawal symptoms during alcohol detoxification. Baclofen was well tolerated and may be considered for the management of alcohol withdrawal syndrome, especially useful in situations where benzodiazepines should be withheld, such as patients with liver impairment.
Functional partnerships between GPI‐anchored proteins and adhesion GPCRs

Specific extracellular interaction between glycophosphatidylinositol (GPI)‐anchored proteins and adhesion G protein‐coupled receptors (aGPCRs) plays an important role in unique biological functions. GPI‐anchored proteins are derived from a novel post‐translational modification of single‐span membrane molecules, while aGPCRs are bona fide seven‐span transmembrane proteins with a long extracellular domain. Although various members of the two structurally‐distinct protein families are known to be involved in a wide range of biological processes, many remain as orphans. Interestingly, accumulating evidence has pointed to a complex interaction and functional synergy between these two protein families. I discuss herein current understanding of specific functional partnerships between GPI‐anchored proteins and aGPCRs.
Energetic Constraints on the ITCZ Position in Idealized Simulations With a Seasonal Cycle

The atmospheric energy budget has recently been shown to provide powerful constraints on the position and shifts of the zonal and annual mean intertropical convergence zone (ITCZ), which lies close to the latitude of zero vertically integrated energy transport (energy flux equator, EFE). Relatively little work has, however, explored the applicability of the energetic framework to ITCZ shifts on shorter time scales. This study investigates to what extent the EFE tracks the ITCZ on subseasonal time scales in idealized aquaplanet simulations with different mixed layer depths. It is shown that the ITCZ always lags the EFE, even in the simulation with the shallowest mixed layer depth, making it possible for the EFE and the ITCZ to reside on opposite sides of the equator. At these times, which occur as the winter cross‐equatorial Hadley circulation retreats from the summer hemisphere, the required energy balance is achieved not through shifts of the Hadley cell's ascending branch and ITCZ to track the EFE but through changes in the cell's vertical structure into one of negative gross moist stability. For any given position of the ascending branch, the winter cell is much weaker as it retreats from than as it expands into the summer hemisphere and develops a shallow return flow at middle‐to‐lower tropospheric levels where the moist static energy reaches its minimum, hence favoring a negative gross moist stability. It is argued that the asymmetry between the expanding and retreating phases of the winter Hadley cell is linked to the nonlinear seasonal evolution of near‐surface temperatures.
Immunovirology of cytomegalovirus infection in allogeneic stem cell transplant recipients undergoing prophylaxis with letermovir: A narrative review

On November 7, 2017, the US Food and Drug Administration approved the use of letermovir (LMV) for prophylaxis of cytomegalovirus (CMV) infection in adult CMV‐seropositive allogeneic stem cell transplant recipients. After 6 years of use, a large body of real‐world experience has been accumulated that validates the Phase III clinical trial results, in which LMV was shown to significantly reduce the risk of clinically significant CMV infection—defined as CMV end‐organ disease or CMV DNAemia requiring pre‐emptive antiviral therapy (PET)—and increase survival up to Week 24 after treatment inception. Notwithstanding, several issues still need to be settled, thus further investigation is required. First, since viral DNA may accumulate as a result of LMV‐driven abortive CMV infection, what is the optimal viral load threshold in the blood that would prompt LMV prophylaxis interruption and PET inception? Should this be adapted to the patient's risk? Second, what is the impact of LMV prophylaxis on the reconstitution of functional CMV‐specific T‐cell responses? Would it be a wise approach to individually tailor the duration of LMV treatment according to the number of peripheral blood CMV‐specific T cells at the end of regular prophylaxis? Third, how frequently do LMV‐resistant strains arise while patients are on LMV prophylaxis and how could this be minimized? Here, we discuss the literature addressing these topics.
Proximal humerus fractures: deltopectoral open reduction and internal fixation vs deltosplit minimally invasive plate osteosynthesis: which surgical approach provides superior results?

Purpose To compare the two main surgical approaches to address proximal humerus fractures (PHFs) stratified for Neer fracture types, to demonstrate which approach gives the best result for each fracture type. Methods A literature search was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines in PubMed, Web of Science, and Cochrane databases up to 4 January 2022. Inclusion criteria were studies comparing open reduction and internal fixation (ORIF) with deltopectoral (DP) approach and minimally invasive plate osteosynthesis (MIPO) with deltosplit (DS) approach of PHFs. Patient’s demographic data, fracture type, Constant–Murley Score (CMS), operation time, blood loss, length of hospital stay, complications, fluoroscopy time, and radiological outcomes were extracted. Results were stratified for each type of Neer fracture. Results Eleven studies (798 patients) were included in the meta-analysis. No functional difference was found in the CMS between the two groups for each type of Neer (P = n.s.): for PHFs Neer II, the mean CMS was 72.5 (s.e. 5.9) points in the ORIF group and 79.6 (s.e. 2.5) points in the MIPO group; for Neer III, 77.8 (s.e. 2.0) in the ORIF and 76.4 (se 3.0) in the MIPO; and for Neer IV, 70.6 (s.e. 2.7) in the ORIF and 60.9 (s.e. 6.3) in the MIPO. The operation time in the MIPO group was significantly lower than in the ORIF group for both Neer II (P = 0.0461) and Neer III (P = 0.0037) fractures. Conclusion The MIPO with DS approach demonstrated no significant differences in the results to the ORIF with DP approach for the different Neer fractures in terms of functional results, with a similar outcome, especially for the Neer II and III fracture types. The MIPO technique proved to be as safe and effective as the ORIF approach.
Heavy element nucleosynthesis associated with a gamma-ray burst

Kilonovae are a novel class of astrophysical transients, and the only observationally-confirmed site of rapid neutron capture nucleosynthesis (the r-process) in the Universe. To date, only a handful of kilonovae have been detected, with just a single spectroscopically-observed event (AT 2017gfo). Spectra of AT 2017gfo provided evidence for the formation of elements heavier than iron; however, these spectra were collected during the first ~ 10 days, when emission from light r-process elements dominates the observations. Heavier elements, if synthesised, are expected to shape the late-time evolution of the kilonova, beyond the phases for which we have spectral observations. Here we present spectroscopic observations of a rapidly-reddening thermal transient, following the gamma-ray burst, GRB 230307A. Early (2.4 day) optical spectroscopy identifies the presence of a hot (T ~ 6700 K) thermal continuum. By 29 days, this component has expanded and cooled significantly (T ~ 640 K), yet it remains optically thick, indicating the presence of high-opacity ejecta. We show that these properties can only be explained by the merger of compact objects, and further, leads us to infer the production of the heavy lanthanide elements. We identify several spectral features (in both absorption and emission), whose cause can be explained by newly-synthesised heavy elements. This event marks only the second recorded spectroscopic evidence for the synthesis of r-process elements, and the first to be observed at such late times.
Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis

The success of graph neural network-based models (GNNs) has significantly advanced recommender systems by effectively modeling users and items as a bipartite, undirected graph. However, many original graph-based works often adopt results from baseline papers without verifying their validity for the specific configuration under analysis. Our work addresses this issue by focusing on the replicability of results. We present a code that successfully replicates results from six popular and recent graph recommendation models (NGCF, DGCF, LightGCN, SGL, UltraGCN, and GFCF) on three common benchmark datasets (Gowalla, Yelp 2018, and Amazon Book). Additionally, we compare these graph models with traditional collaborative filtering models that historically performed well in offline evaluations. Furthermore, we extend our study to two new datasets (Allrecipes and BookCrossing) that lack established setups in existing literature. As the performance on these datasets differs from the previous benchmarks, we analyze the impact of specific dataset characteristics on recommendation accuracy. By investigating the information flow from users’ neighborhoods, we aim to identify which models are influenced by intrinsic features in the dataset structure. The code to reproduce our experiments is available at: https://github.com/sisinflab/Graph-RSs-Reproducibility.
Caching-at-STARS: the Next Generation Edge Caching

A simultaneously transmitting and reflecting surface (STARS) enabled edge caching system is proposed for reducing backhaul traffic and ensuring the quality of service. A novel Caching-at-STARS structure, where a dedicated smart controller and cache memory are installed at the STARS, is proposed to satisfy user demands with fewer hops and desired channel conditions. Then, a joint caching replacement and information-centric hybrid beamforming optimization problem is formulated for minimizing the network power consumption. As long-term decision processes, the optimization problems based on independent and coupled phase-shift models of Caching-at-STARS contain both continuous and discrete decision variables, and are suitable for solving with deep reinforcement learning (DRL) algorithm. For the independent phase-shift Caching-at-STARS model, we develop a frequency-aware based twin delayed deep deterministic policy gradient (FA-TD3) algorithm that leverages user historical request information to serialize high-dimensional caching replacement decision variables. For the coupled phase-shift Caching-at-STARS model, we conceive a cooperative TD3 \&deep-Q network (TD3-DQN) algorithm comprised of FA-TD3 and DQN agents to decide on continuous and discrete variables respectively by observing the network external and internal environment. The numerical results demonstrate that: 1) The Caching-at-STARS-enabled edge caching system has advantages over traditional edge caching, especially in scenarios where Zipf skewness factors or cache capacity is large; 2) Caching-at-STARS outperforms the RIS-assisted edge caching systems; 3) The proposed FA-TD3 and cooperative TD3-DQN algorithms are superior in reducing network power consumption than conventional TD3.
Task-Oriented Channel Attention for Fine-Grained Few-Shot Classification

The difficulty of the fine-grained image classification mainly comes from a shared overall appearance across classes. Thus, recognizing discriminative details, such as eyes and beaks for birds, is a key in the task. However, this is particularly challenging when training data is limited. To address this, we propose Task Discrepancy Maximization (TDM), a task-oriented channel attention method tailored for fine-grained few-shot classification with two novel modules Support Attention Module (SAM) and Query Attention Module (QAM). SAM highlights channels encoding class-wise discriminative features, while QAM assigns higher weights to object-relevant channels of the query. Based on these submodules, TDM produces task-adaptive features by focusing on channels encoding class-discriminative details and possessed by the query at the same time, for accurate class-sensitive similarity measure between support and query instances. While TDM influences high-level feature maps by task-adaptive calibration of channel-wise importance, we further introduce Instance Attention Module (IAM) operating in intermediate layers of feature extractors to instance-wisely highlight object-relevant channels, by extending QAM. The merits of TDM and IAM and their complementary benefits are experimentally validated in fine-grained few-shot classification tasks. Moreover, IAM is also shown to be effective in coarse-grained and cross-domain few-shot classifications.
Crowd Safety Manager: Towards Data-Driven Active Decision Support for Planning and Control of Crowd Events

This paper presents novel technology and methodology aimed at enhancing crowd management in both the planning and operational phases. The approach encompasses innovative data collection techniques, data integration, and visualization using a 3D Digital Twin, along with the incorporation of artificial intelligence (AI) tools for risk identification. The paper introduces the Bowtie model, a comprehensive framework designed to assess and predict risk levels. The model combines objective estimations and predictions, such as traffic flow operations and crowdedness levels, with various aggravating factors like weather conditions, sentiments, and the purpose of visitors, to evaluate the expected risk of incidents. The proposed framework is applied to the Crowd Safety Manager project in Scheveningen, where the DigiTwin is developed based on a wealth of real-time data sources. One noteworthy data source is Resono, offering insights into the number of visitors and their movements, leveraging a mobile phone panel of over 2 million users in the Netherlands. Particular attention is given to the left-hand side of the Bowtie, which includes state estimation, prediction, and forecasting. Notably, the focus is on generating multi-day ahead forecasts for event-planning purposes using Resono data. Advanced machine learning techniques, including the XGBoost framework, are compared, with XGBoost demonstrating the most accurate forecasts. The results indicate that the predictions are adequately accurate. However, certain locations may benefit from additional input data to further enhance prediction quality. Despite these limitations, this work contributes to a more effective crowd management system and opens avenues for further advancements in this critical field.
Graph Structure from Point Clouds: Geometric Attention is All You Need

The use of graph neural networks has produced significant advances in point cloud problems, such as those found in high energy physics. The question of how to produce a graph structure in these problems is usually treated as a matter of heuristics, employing fully connected graphs or K-nearest neighbors. In this work, we elevate this question to utmost importance as the Topology Problem. We propose an attention mechanism that allows a graph to be constructed in a learned space that handles geometrically the flow of relevance, providing one solution to the Topology Problem. We test this architecture, called GravNetNorm, on the task of top jet tagging, and show that it is competitive in tagging accuracy, and uses far fewer computational resources than all other comparable models.
Large-Scale Gravity Current over the Middle Hills of the Nepal Himalaya: Implications for Aircraft Accidents

AbstractNepal has been the location of a series of fatal aircraft accidents, raising serious concerns about civil aviation security and the safety of passengers. However, significant studies on weather patterns associated with the airports and air routes of the Himalayan complex terrain and their implications for aviation activities are yet to be carried out. The present study numerically reconstructs the prevailing weather conditions and puts forward some possible causes behind the most recent fatal aircraft accident in the foothills of the western Nepal Himalaya at 0730 UTC (1315 LST) 16 February 2014. The weather patterns have been numerically simulated at 1-km2 horizontal grid resolution using the Weather Research and Forecasting (WRF) modeling system. The reconstructed weather situation shows the existence of a low-level cloud ceiling, supercooled cloud water and hail, trapped mountain waves, supercritical descent of a strong tail wind, and the development of turbulence at the altitude of the flight .
Shape Completion with Prediction of Uncertain Regions

Shape completion, i.e., predicting the complete geometry of an object from a partial observation, is highly relevant for several downstream tasks, most notably robotic manipulation. When basing planning or prediction of real grasps on object shape reconstruction, an indication of severe geometric uncertainty is indispensable. In particular, there can be an irreducible uncertainty in extended regions about the presence of entire object parts when given ambiguous object views. To treat this important case, we propose two novel methods for predicting such uncertain regions as straightforward extensions of any method for predicting local spatial occupancy, one through postprocessing occupancy scores, the other through direct prediction of an uncertainty indicator. We compare these methods together with two known approaches to probabilistic shape completion. Moreover, we generate a dataset, derived from ShapeNet [1], of realistically rendered depth images of object views with ground-truth annotations for the uncertain regions. We train on this dataset and test each method in shape completion and prediction of uncertain regions for known and novel object instances and on synthetic and real data. While direct uncertainty prediction is by far the most accurate in the segmentation of uncertain regions, both novel methods outperform the two baselines in shape completion and uncertain region prediction, and avoiding the predicted uncertain regions increases the quality of grasps for all tested methods. Web: https://github.com/DLR-RM/shape-completion
Black-box Attacks Against Neural Binary Function Detection

Binary analyses based on deep neural networks (DNNs), or neural binary analyses (NBAs), have become a hotly researched topic in recent years. DNNs have been wildly successful at pushing the performance and accuracy envelopes in the natural language and image processing domains. Thus, DNNs are highly promising for solving binary analysis problems that are hard due to a lack of complete information resulting from the lossy compilation process. Despite this promise, it is unclear that the prevailing strategy of repurposing embeddings and model architectures originally developed for other problem domains is sound given the adversarial contexts under which binary analysis often operates. In this paper, we empirically demonstrate that the current state of the art in neural function boundary detection is vulnerable to both inadvertent and deliberate adversarial attacks. We proceed from the insight that current generation NBAs are built upon embeddings and model architectures intended to solve syntactic problems. We devise a simple, reproducible, and scalable black-box methodology for exploring the space of inadvertent attacks – instruction sequences that could be emitted by common compiler toolchains and configurations – that exploits this syntactic design focus. We then show that these inadvertent misclassifications can be exploited by an attacker, serving as the basis for a highly effective black-box adversarial example generation process. We evaluate this methodology against two state-of-the-art neural function boundary detectors: XDA and DeepDi. We conclude with an analysis of the evaluation data and recommendations for how future research might avoid succumbing to similar attacks.
Scaling symmetry, Smarr relation, and the extended first law in lower-dimensional Lovelock gravity

Recently, it was discovered that lower-dimensional versions of Lovelock gravity exist as scalar-tensor theories that are examples of Horndeski gravity. We study the thermodynamics of the static black hole solutions satisfying $g_{tt}g_{rr}=-1$ in these theories up to cubic order through Euclidean methods. Considering solutions with spherical, planar and hyperbolic event horizons ($k=+1, 0, -1$), we show that the universality of the thermodynamics for planar black holes ($k=0$) and the extended 1st law that include the variation of the couplings together with their associated potentials hold also in lower dimensions. We find that in $D=4, 6$ where the 2nd- and the 3rd-order Lovelock Lagrangians are boundary terms respectively, the Smarr relation is modified since the entropy is not a homogenous function in these dimensions. We also present a derivation of the Smarr relation and its modified version based on the global scaling properties of the reduced action that is used to obtain the solutions consistently. Unlike the other hairy black hole solutions that have been analyzed before, despite the terms in the reduced action that break the scaling symmetry, the derivation still follows from a conserved Noether charge.