Miracle-dz/UniFAR-FTUs huggingface dataset:
Sleep deprivation exacerbates microglial reactivity and Aβ deposition in a TREM2-dependent manner in mice

Sleep loss is associated with cognitive decline in the aging population and is a risk factor for Alzheimer’s disease (AD). Considering the crucial role of immunomodulating genes such as that encoding the triggering receptor expressed on myeloid cells type 2 (TREM2) in removing pathogenic amyloid-β (Aβ) plaques and regulating neurodegeneration in the brain, our aim was to investigate whether and how sleep loss influences microglial function in mice. We chronically sleep-deprived wild-type mice and the 5xFAD mouse model of cerebral amyloidosis, expressing either the humanized TREM2 common variant, the loss-of-function R47H AD-associated risk variant, or without TREM2 expression. Sleep deprivation not only enhanced TREM2-dependent Aβ plaque deposition compared with 5xFAD mice with normal sleeping patterns but also induced microglial reactivity that was independent of the presence of parenchymal Aβ plaques. We investigated lysosomal morphology using transmission electron microscopy and found abnormalities particularly in mice without Aβ plaques and also observed lysosomal maturation impairments in a TREM2-dependent manner in both microglia and neurons, suggesting that changes in sleep modified neuro-immune cross-talk. Unbiased transcriptome and proteome profiling provided mechanistic insights into functional pathways triggered by sleep deprivation that were unique to TREM2 and Aβ pathology and that converged on metabolic dyshomeostasis. Our findings highlight that sleep deprivation directly affects microglial reactivity, for which TREM2 is required, by altering the metabolic ability to cope with the energy demands of prolonged wakefulness, leading to further Aβ deposition, and underlines the importance of sleep modulation as a promising future therapeutic approach. Description Sleep deprivation exacerbates microglial reactivity and amyloid-β deposition in mice in a TREM2-dependent fashion. The benefits of sleep Sleep loss is associated with cognitive decline in older adults and is thought to be a risk factor for Alzheimer’s disease (AD), but the underlying mechanisms remain unclear. In a new study, Parhizkar et al. used different genetic mouse models that overproduce amyloid deposits in the brain to investigate the effects of sleep loss. The authors report that sleep loss reduces the ability of immune cells called microglia to remove brain amyloid deposits (a hallmark of AD). These findings suggest that sleep loss could contribute to cognitive decline by reducing immune cell function in the brain. —OMS
Patient-Reported Outcome–Based Prediction for Postdischarge Complications after Lung Surgery

Abstract Background Patients undergoing lung tumor surgery may experience various complications after discharge from the hospital. Using patient-reported outcomes (PROs), this study attempted to identify relevant indicators of postdischarge complications after lung tumor surgery and develop a predictive nomogram model to evaluate the risk for individual patients. Methods Patients who underwent lung tumor surgery between December 2021 and June 2022 were included in this study. PROs were assessed using the Perioperative Symptom Assessment for Lung Surgery scale and were assessed preoperatively at baseline, on postoperative day 1 (POD1) 1 to POD4, and then weekly until the fourth week. A random forest machine learning prediction model was built to rank the importance of each PRO score of patients on POD1 to POD4. We then selected the top 10 variables in terms of importance for the multivariable logistic regression analysis. Finally, a nomogram was developed. Results PROs, including coughing (POD3 and POD4), daily activity (POD1), and pain (POD1 and POD2), were associated with postdischarge complications in patients undergoing lung tumor surgery. The predictive model showed good performance in estimating the risk of postdischarge complications, with an area under the curve of 0.833 (95% confidence interval: 0.753–0.912), while maintaining good calibration and clinical value. Conclusion We found that PRO scores on POD1 to POD4 were associated with postdischarge complications after lung tumor surgery, and we developed a helpful nomogram model to predict the risk of postdischarge complications.
A New Species and a New Record of the Genus Phaeophyscia Moberg (Lecanorales, Physciaceae) from Pakistan Supported by Phenotypic and Molecular Phylogenetic Analyses

ABSTRACT Phaeophyscia kaghanensis Niazi, Nadeem, Afshan & Khalid, sp. nov. is described from Himalayan moist temperate forest in Pakistan. It is characterized by a greyish white to grey thallus, absence of asexual propagules, flat to slightly concave lobes, white medulla, black lower surface, large Physciatype ascospores (24-30×12-17 µm) and absence of secondary substances. In addition, P. microspora Aptroot & Schumm is reported as new to Pakistan, and ITS sequences were generated from this species for the first time. RÉSUMÉ Espèce nouvelle et signalement nouveau pour le genre Phaeophyscia Moberg (Lecanorales, Physciaceae) au Pakistan, confirmés par analyses phénotypique et phylogénétique. Phaeophyscia kaghanensis Niazi, Nadeem, Afshan & Khalid, sp. nov. est nouvellement décrite de la forêt humide himalayenne au Pakistan. Elle se caractérise par un thalle gris-blanc à gris, l'absence de propagules asexuées, des lobes plats à légèrement concaves, une médulle blanche, une face inférieure noire, de grandes ascospores de type Physcia (24-30 × 12-17 µm) et l'absence de substances secondaires. En outre, P. microspora Aptroot & Schumm est nouvellement signalée au Pakistan, et des séquences ITS ont été générées à partir de cette espèce pour la première fois.
Fast ion generation by combined RF-NBI heating in W7-X

Good fast ion confinement in quasi-isodynamic stellarators still has to be demonstrated experimentally. In the absence of fusion alphas, auxiliary heating systems can be used to generate fast ions. For this purpose, W7-X has neutral beam injection and its newly installed ion cyclotron heating system. In practice, both systems can run simultaneously, the synergetic running of both is the focus of the current modelling development. With the upgrades to the SCENIC code described here, we find that, compared with pure neutral beam injection (NBI), the fast ion tail is significantly enhanced when radio frequency (RF) heating is enabled. But the effectiveness of heating and fast ion tail generation strongly depends on the plasma parameters and antenna frequency, which calls for further study, both numerical and experimental.
HoloTile: a new high-speed and speckle-suppressed digital holography modality

We report on HoloTile, a new approach to speckle-reduced and rapidly generated holograms. By way of an analytical approach to Gaussian beam shaping, we modify the Point Spread Function (PSF) of a holographic system into flat-top squares. The width of each flat-top output “pixel” is determined from the inter-spectral distance arising from designed tiling on a Spatial Light Modulator (SLM). The resultant hologram consists of the product of a once-calculated PSF shaping hologram and a tiled object hologram. With HoloTile, we achieve speckle-reduced reconstructions and extremely fast hologram generation, due to the reduced resolution of the object hologram.
Some Homotopy Properties of Topological Groups and Homogeneous Spaces

Because of the continuous group operations, a topological group has some peculiar homotopy properties. It is well-known that the fundamental group of a topological group is commutative, [4, p. 54]. 2 The higher homotopy groups of a topological group have been investigated by W. Ifurewicz [11], B. Eckmann [5], and others. According to J. H. C. Whitehead [13], an arcwise connected topological group is n-simple for each n > 1 in the sense of S. Eilenberg [6]. According to R. H. Fox [81], the torus homotopy groups of a topological group G are all commutative; then it follows that the Whitehead products, [14], between the elements of the homotopy groups of G are always zero. The object of the present paper is to generalize these results to the class of special homogeneous spaces, defined in ? 3, including the spheres Sn as particular cases. With an aim to do this, we shall give in ? 2 some additional homotopy properties of a topological group. Our main results3 are embodied in the theorems of ? 3. Throughout this paper, let G be a locally connected topological group. Since the components of G are homeomorphic to each other, we assume G to be arcwise connected. Let II be an arewise connected closed subgroup of G. A mapping is a continuous transformation, and a homotopy is a continuous family of mappings ft, (0 < t ? 1).
Lightweight source localization for large-scale social networks

The rapid diffusion of hazardous information in large-flow-based social media causes great economic losses and potential threats to society. It is crucial to infer the inner information source as early as possible to prevent further losses. However, existing localization methods wait until all deployed sensors obtain propagation information before starting source inference within a network, and hence the best opportunity to control propagation is missed. In this paper, we propose a new localization strategy based on finite deployed sensors, named Greedy-coverage-based Rapid Source Localization (GRSL), to rapidly, flexibly and accurately infer the source in the early propagation stage of large-scale networks. There are two phases in GRSL. In the first phase, the Greedy-based Strategy (GS) greedily deploys sensors to rapidly achieve wide area coverage at a low cost. In the second phase, when a propagation event within a network is observed by a part of the sensors, the Inference Strategy (IS) with an earlier response mechanism begins executing the source inference task in an earlier small infected area. Comprehensive experiments with the SOTA methods demonstrate the superior performance and robustness of GRSL in various application scenarios.
Online resource allocation in Markov Chains

A large body of work in Computer Science and Operations Research study online algorithms for stochastic resource allocation problems. The most common assumption is that the online requests have randomly generated i.i. d. types. This assumption is well justified for static markets and/or relatively short time periods. We consider dynamic markets, whose states evolve as a random walk in a market-specific Markov Chain. This is a new model that generalizes previous i.i. d. settings. We identify important parameters of the Markov chain that is crucial for obtaining good approximation guarantees to the expected value of the optimal offline algorithm which knows realizations of all requests in advance. We focus on a stylized single-resource setting and: (i) generalize the well-known Prophet Inequality from the optimal stopping theory (single-unit setting) to Markov Chain setting; (ii) in multi-unit setting, design a simple algorithm that is asymptotically optimal under mild assumptions on the underlying Markov chain.
Poster Abstract: Implementing Dynamic User Equilibrium in a Scaled City Environment with Duckietown and SUMO

Effective traffic routing is an important part of a city’s architecture, with numerous simulation based representations being developed throughout the years. An important part of simulation based solutions is the practicality of these solutions in a real environment. To this end, we propose a scheme to bring a theoretical approach known as Dynamic User Equilibrium (DUE) from the simulator to a scaled city with wirelessly connected miniature autonomous vehicles. We describe how we translate a DUE solution from traffic simulator Simulation of Urban Mobility (SUMO) to physical Duckietown Robots. Our goal is to develop a predictable DUE solution for Duckietown Robots using SUMO as not only a development environment but guiding tool.
Mining quantitative association rules

By introducing the problem of mining association rules in relational tables containing both quantitative and categorical attributes,some definition and put forward some method are given.The measures of partial completeness which quantify the information lost due to partitioning are indroduced, this measure can be used to decide whether or not to partition a quantitative attribute, and the number of partitions.
Poster Abstract: SmartAppZoo: a Repository of SmartThings Apps for IoT Benchmarking

A well-organized SmartApps dataset provides a valuable resource for researchers to evaluate their work on smart home automation systems. The IoTBench dataset created by Celik et al. 1 is a significant contribution to the IoT research community [1]. However, due to the fast growth of SmartApps and the retirement of some old apps, the IoTBench dataset becomes outdated. The research community is in need of a new large-scale and carefully cleaned benchmarking dataset. In this poster, we present a new repository, namely, SmartAppZoo, which contains 3,526 SmartApps collected from GitHub repositories, including 184 SmartThings official apps, 468 third-party apps from IoTBench, and 2,874 new third-party apps. SmartAppZoo is a manually-verified, comprehensive, clean, and diverse IoT benchmarking dataset. SmartAppZoo is available at: https://github.com/SmartAppZoo/SmartAppZoo.
A Multi-task Model for Emotion and Offensive Aided Stance Detection of Climate Change Tweets

In this work, we address the United Nations Sustainable Development Goal 13: Climate Action by focusing on identifying public attitudes toward climate change on social media platforms such as Twitter. Climate change is threatening the health of the planet and humanity. Public engagement is critical to address climate change. However, climate change conversations on Twitter tend to polarize beliefs, leading to misinformation and fake news that influence public attitudes, often dividing them into climate change believers and deniers. Our paper proposes an approach to classify the attitude of climate change tweets (believe/deny/ambiguous) to identify denier statements on Twitter. Most existing approaches for detecting stances and classifying climate change tweets either overlook deniers’ tweets or do not have a suitable architecture. The relevant literature suggests that emotions and higher levels of toxicity are prevalent in climate change Twitter conversations, leading to a delay in appropriate climate action. Therefore, our work focuses on learning stance detection (main task) while exploiting the auxiliary tasks of recognizing emotions and offensive utterances. We propose a multimodal multitasking framework MEMOCLiC that captures the input data using different embedding techniques and attention frameworks, and then incorporates the learned emotional and offensive expressions to obtain an overall representation of the features relevant to the stance of the input tweet. Extensive experiments conducted on a novel curated climate change dataset and two benchmark stance detection datasets (SemEval-2016 and ClimateStance-2022) demonstrate the effectiveness of our approach.
Unsupervised Anomaly Detection on Microservice Traces through Graph VAE

The microservice architecture is widely employed in large Internet systems. For each user request, a few of the microservices are called, and a trace is formed to record the tree-like call dependencies among microservices and the time consumption at each call node. Traces are useful in diagnosing system failures, but their complex structures make it difficult to model their patterns and detect their anomalies. In this paper, we propose a novel dual-variable graph variational autoencoder (VAE) for unsupervised anomaly detection on microservice traces. To reconstruct the time consumption of nodes, we propose a novel dispatching layer. We find that the inversion of negative log-likelihood (NLL) appears for some anomalous samples, which makes the anomaly score infeasible for anomaly detection. To address this, we point out that the NLL can be decomposed into KL-divergence and data entropy, whereas lower-dimensional anomalies can introduce an entropy gap with normal inputs. We propose three techniques to mitigate this entropy gap for trace anomaly detection: Bernoulli & Categorical Scaling, Node Count Normalization, and Gaussian Std-Limit. On five trace datasets from a top Internet company, our proposed TraceVAE achieves excellent F-scores.
Deep Learning for Variable Renewable Energy: A Systematic Review

In recent years, both fields, AI and VRE, have received increasing attention in scientific research. Thus, this article’s purpose is to investigate the potential of DL-based applications on VRE and as such provide an introduction to and structured overview of the field. First, we conduct a systematic literature review of the application of Artificial Intelligence (AI), especially Deep Learning (DL), on the integration of Variable Renewable Energy (VRE). Subsequently, we provide a comprehensive overview of specific DL-based solution approaches and evaluate their applicability, including a survey of the most applied and best suited DL architectures. We identify ten DL-based approaches to support the integration of VRE in modern power systems. We find (I) solar PV and wind power generation forecasting, (II) system scheduling and grid management, and (III) intelligent condition monitoring as three high potential application areas.
Web Information Extraction for Social Good: Food Pantry Answering As an Example

Social determinants of health (SDH) are the conditions in which we are born, live, work, and age. Food insecurity (FI) is an important domain of SDH. FI is associated with poor health outcomes. Food bank/pantry (food pantry) directly addresses FI. Improving the availability and quality of food from food pantries could reduce FI, leading to improved health outcomes. However, it is difficult for a client to access food pantry information. In this study, we built a food pantry answering framework by combining location-aware information retrieval, web information extraction and domain-specific answering. Our proposed framework first retrieves pantry candidates based on geolocation of the client, and utilizes structural information from markup language to extract semantic chunks related to six common client requests. We use BERT and RoBERTa as information extraction models and compare three different web page segmentation methods in the experiments.
Heavy Traffic Queue Length Distribution without Resource Pooling in an Input-Queued Switch

This paper studies the heavy-traffic joint distribution of queue lengths of an input-queued switch operating under the MaxWeight scheduling policy. Input-queued switch acts as a representative of SPNs that do not satisfy the so-called complete resource pooling (CRP) condition, and consequently exhibit a multidimensional state space collapse. Except in special cases, only mean queue lengths of such non-CRP systems have been obtained in the literature. In this paper, we develop the transform method to study the steady state distribution of non-CRP systems. The key challenge is in solving an implicit functional equation involving the Laplace transform of the heavy-traffic limiting distribution. We then consider the general n - n input-queued switch that has n2 queues. Under a conjecture on uniqueness of the solution of the functional equation, we obtain an exact joint distribution of the heavy-traffic limiting queue-lengths in terms of a nonlinear transformation of 2n iid exponentials.
Multi-aspect Diffusion Network Inference

To learn influence relationships between nodes in a diffusion network, most existing approaches resort to precise timestamps of historical node infections. The target network is customarily assumed as an one-aspect diffusion network, with homogeneous influence relationships. Nonetheless, tracing node infection timestamps is often infeasible due to high cost, and the type of influence relationships may be heterogeneous because of the diversity of propagation media. In this work, we study how to infer a multi-aspect diffusion network with heterogeneous influence relationships, using only node infection statuses that are more readily accessible in practice. Equipped with a probabilistic generative model, we iteratively conduct a posteriori, quantitative analysis on historical diffusion results of the network, and infer the structure and strengths of homogeneous influence relationships in each aspect. Extensive experiments on both synthetic and real-world networks are conducted, and the results verify the effectiveness and efficiency of our approach.
Transferring Audio Deepfake Detection Capability across Languages

The proliferation of deepfake content has motivated a surge of detection studies. However, existing detection methods in the audio area exclusively work in English, and there is a lack of data resources in other languages. Cross-lingual deepfake detection, a critical but rarely explored area, urges more study. This paper conducts the first comprehensive study on the cross-lingual perspective of deepfake detection. We observe that English data enriched in deepfake algorithms can teach a detector the knowledge of various spoofing artifacts, contributing to performing detection across language domains. Based on the observation, we first construct a first-of-its-kind cross-lingual evaluation dataset including heterogeneous spoofed speech uttered in the two most widely spoken languages, then explored domain adaptation (DA) techniques to transfer the artifacts detection capability and propose effective and practical DA strategies fitting the cross-lingual scenario. Our adversarial-based DA paradigm teaches the model to learn real/fake knowledge while losing language dependency. Extensive experiments over 137-hour audio clips validate the adapted models can detect fake audio generated by unseen algorithms in the new domain.
Towards Model Robustness: Generating Contextual Counterfactuals for Entities in Relation Extraction

The goal of relation extraction (RE) is to extract the semantic relations between/among entities in the text. As a fundamental task in information systems, it is crucial to ensure the robustness of RE models. Despite the high accuracy current deep neural models have achieved in RE tasks, they are easily affected by spurious correlations. One solution to this problem is to train the model with counterfactually augmented data (CAD) such that it can learn the causation rather than the confounding. However, no attempt has been made on generating counterfactuals for RE tasks. In this paper, we formulate the problem of automatically generating CAD for RE tasks from an entity-centric viewpoint, and develop a novel approach to derive contextual counterfactuals for entities. Specifically, we exploit two elementary topological properties, i.e., the centrality and the shortest path, in syntactic and semantic dependency graphs, to first identify and then intervene on the contextual causal features for entities. We conduct a comprehensive evaluation on four RE datasets by combining our proposed approach with a variety of RE backbones. Results prove that our approach not only improves the performance of the backbones but also makes them more robust in the out-of-domain test 1.
A product-form network for systems with job stealing policies

In this paper, we introduce a new product-form queueing network model where servers are always busy. This is obtained by defining a job movement policy that admits instantaneous migrations of jobs from non-empty waiting buffers to empty ones. Thus, the resulting routing scheme is statedependent. This class of networks tends to maximize the system throughput and can be used to model situations where successive refinements on jobs improve the processes quality but are not strictly required to obtain a result.