Miracle-dz/UniFAR-FTUs huggingface dataset:
A Feasible IP Traceback Framework throughDynamic Deterministic Packet Marking

—It is long known attackers might utilize designed supply science location to hide their real areas. DDOS attack source traceback is an open and difficult drawback. Deterministic Packet Marking (DPM) is an easy and effective traceback mechanism, but the current DPM based mostly traceback schemes aren't sensible because of their measurability constraint [1]. However, due to the challenges of deployment, there has been not a widely adopted science traceback resolution, at least at the net level. As a result, the mist on the locations of spoofers has never been dissipated until currently. This paper proposes feasible science (FIT) traceback that bypasses the preparation difficulties of science traceback techniques. FIT investigates web management Message Protocol (ICMP) error messages (named path backscatter) triggered by spoofing traffic, and tracks the spoofers based on public obtainable info (e.g., topology). In order to tracebackto involved attack supply, what we would like to try and do is to mark these concerned ingress routers mistreatment the standard DPM strategy[2]. Similar toexisting schemes, we need participated routers to install a traffic monitor on these lines, FIT will discover the spoofers with no arrangement necessity. This paper represents the reasons, accumulation, and the factual results on way break up, exhibits the procedures and adequacy of FIT, and demonstrates the caught areas of spoofers through applying FIT on the method break up info set. These results can facilitate additional reveal science spoofing, which has been studied for long however never well understood [3]. Though work cannot work in all the spoofing attacks, it may be the foremost helpful mechanism to trace spoofers before an Internet-level traceback system has been deployed in real.
Electroencephalogram-Driven Machine-Learning Scenario for Assessing Impulse Control Disorder Comorbidity in Parkinson’s Disease Using a Low-Cost, Custom LEGO-Like Headset

Patients with Parkinson’s disease (PD) may develop cognitive symptoms of impulse control disorders (ICDs) when chronically treated with dopamine agonist (DA) therapy for motor deficits. Motor and cognitive comorbidities critically increase the disability and mortality of the affected patients. This study proposes an electroencephalogram (EEG)-driven machine-learning scenario to automatically assess ICD comorbidity in PD. We employed a classic Go/NoGo task to appraise the capacity of cognitive and motoric inhibition with a low-cost, custom LEGO-like headset to record task-relevant EEG activity. Further, we optimized a support vector machine (SVM) and support vector regression (SVR) pipeline to learn discriminative EEG spectral signatures for the detection of ICD comorbidity and the estimation of ICD severity, respectively. With a dataset of 21 subjects with typical PD, 9 subjects with PD and ICD comorbidity (ICD), and 25 healthy controls (HC), the study results showed that the SVM pipeline differentiated subjects with ICD from subjects with PD with an accuracy of 66.3% and returned an around-chance accuracy of 53.3% for the classification of PD versus HC subjects without the comorbidity concern. Furthermore, the SVR pipeline yielded significantly higher severity scores for the ICD group than for the PD group and resembled the ICD vs. PD distinction according to the clinical questionnaire scores, which was barely replicated by random guessing. Without a commercial, high-precision EEG product, our demonstration may facilitate deploying a wearable computer-aided diagnosis system to assess the risk of DA-triggered cognitive comorbidity in patients with PD in their daily environment.
Nonlinear strong ground motion in the ML 5.4 Chittenden earthquake: Evidence that preexisting damage increases susceptibility to further damage

We use 55 repeating earthquake sequences located near the 1989 Mw 6.9 Loma Prieta earthquake to identify time dependent velocity changes in the shallow crust. In addition to large delays caused by the Loma Prieta mainshock, the ML 5.4 Chittenden earthquake, an aftershock of Loma Prieta, caused direct S wave delays of up to 6ms and S coda delays exceeding 15ms. We attribute the delays to cracks formed or opened during the strong shaking of the Chittenden earthquake, the same mechanism believed responsible for the delays observed following Loma Prieta. The magnitude of the delays caused by Chittenden strongly correlate with those caused by Loma Prieta. This suggests that rocks recently damaged by nonlinear strong ground motion are particularly susceptible to further damage until they are completely healed. Therefore, we expect that the onset of nonlinearity will occur at substantially lower ground motion for large aftershocks than would otherwise be anticipated.
Correlation Between Coronavirus Disease 2019 Severity and Noninvasive Assessment of Liver Fibrosis in Patients with Metabolic Dysfunction-Associated Fatty Liver Disease

Background/Aims: Metabolic dysfunction-associated fatty liver disease is a crucial global health concern. Studies have shown that metabolic dysfunction-associated fatty liver disease patients are at higher risk of severe coronavirus disease 2019. However, there are no precise measures of the correlation between the degree of metabolic dysfunction-associated fatty liver disease fibrosis and coronavirus disease 2019 severity. This study evaluated the association between metabolic dysfunction-associated fatty liver disease with varying degrees of fibrosis and coronavirus disease 2019 prognosis. Materials and Methods: All hospitalized coronavirus disease 2019 patients who had liver steatosis as determined by computed tomography scan were included. Metabolic dysfunction-associated fatty liver disease was diagnosed in accordance with international consensus criteria. Liver fibrosis was assessed using the nonalcoholic fatty liver disease fibrosis score, FIB-4 and FIB-8 indexes. Coronavirus disease 2019 severity was defined using World Health Organization criteria. Logistic regression was used to determine the associations between varying degrees of fibrosis and the severity of coronavirus disease 2019. Results: A total of 996 confirmed hospitalized coronavirus disease 2019 cases with complete data were reviewed; of these, 296 (29.7%) cases of metabolic dysfunction-associated fatty liver disease were diagnosed. Metabolic dysfunction-associated fatty liver disease patients with any fibrotic state had more severe coronavirus disease 2019 than nonmetabolic dysfunction-associated fatty liver disease patients (adjusted odds ratio 1.912, 95% CI 1.363-2.684; P < .05). Multiple logistic regression analysis showed that metabolic dysfunction-associated fatty liver disease patients with significant fibrosis according to the FIB-8 score were more likely to have severe coronavirus disease 2019 (adjusted odds ratio 5.458, 95% CI 1.481-20.110; P < .05). Conclusion: The presence of metabolic dysfunction-associated fatty liver disease in hospitalized coronavirus disease 2019 patients strongly correlated with the severity of coronavirus disease 2019. The hepatic FIB-8 index appears to provide the best prognostic value among the fibrosis scores in metabolic dysfunction-associated fatty liver disease patients with coronavirus disease 2019.
Seismic anisotropy in sedimentary rocks, part 2: Laboratory data

Part one of this paper presents a method for measuring seismic velocities and transverse isotropy in rocks using a single core plug. This method saves at least two‐thirds of the time for preparing core samples and measuring velocities in transversely isotropic (TI) rocks. Using this method, we have measured velocity and anisotropy of many shale and reservoir rocks from oil and gas fields around the world. We present some of the data in this paper, which include seismic velocity and anisotropy in 17 brine‐saturated shale samples, 1 gas‐ and brine‐saturated coal sample, 8 brine‐saturated sands, 12 gas‐saturated sands, 32 gas‐saturated carbonate samples, and 25 brine‐saturated carbonate samples. The results show that clays and fine layering in sedimentary rocks are the main causes of seismic anisotropy. Very little intrinsic anisotropy exists in unfractured reservoir rocks such as sands, sandstones, and carbonates under reservoir conditions. In contrast, all shales were found seismically anisotropic: anisotr.
Therapeutic considerations of bioactive compounds in Alzheimer's disease and Parkinson's disease: Dissecting the molecular pathways

Leading neurodegenerative diseases Alzheimer's disease (AD) and Parkinson's disease (PD) are characterized by the impairment of memory and motor functions, respectively. Despite several breakthroughs, there exists a lack of disease‐modifying treatment strategies for these diseases, as the available drugs provide symptomatic relief and bring along side effects. Bioactive compounds are reported to bear neuroprotective properties with minimal toxicity, however, a detailed elucidation of their modes of neuroprotection is lacking. The review elucidates the neuroprotective mechanism(s) of some of the major phyto‐compounds in pre‐clinical and clinical studies of AD and PD to understand their potential in combating these diseases. Curcumin, eugenol, resveratrol, baicalein, sesamol and so on have proved efficient in countering the pathological hallmarks of AD and PD. Curcumin, resveratrol, caffeine and so on have reached the clinical phases of these diseases, while aromadendrin, delphinidin, cyanidin and xanthohumol are yet to be extensively explored in pre‐clinical phases. The review highlights the need for extensive investigation of these compounds in the clinical stages of these diseases so as to utilize their disease‐modifying abilities in the real field of treatment. Moreover, poor pharmacokinetic properties of natural compounds are constraints to their therapeutic yields and this review suggests a plausible contribution of nanotechnology in overcoming these limitations.
The detection of climate impacts: some methodological considerations

Recently, the search for evidence of climate‐change impacts has been intense. Long‐term and not so very long‐term data are being examined for evidence of climatic response. Three commonly used methods involve (i) correlation with an ever‐increasing number of climatic variables, (ii) examining trends through time and (iii) a comparison between two points in time. In this paper we provide examples designed to emphasize the need for impassionate and critical examination of findings, the so‐called ‘eclectic’ approach, involving a combination of empirical and conceptual evidence. Copyright © 2005 Royal Meteorological Society
Associations of the P300 Event-Related Potentials and Self-Reported Craving in Substance Use Disorders: A Systematic Review

Introduction: The phenomenon of craving and attention bias towards drug cues is theorized to operate cooperatively, owing to the principles of associative learning. In this context, the conditioned response to drug-related stimuli activates reward mechanisms within the brain, consequently inducing craving and fostering the underlying mechanisms that contribute to relapse in individuals with substance use disorders. Multiple studies have assessed the relationship between attention to substance-related cues and subjective craving through electroencephalography (EEG), but their findings have yet to be synthesized and examined. This review summarizes the association between the amplitude of the P300 event-related potential (ERP) and substance use craving, compares discrepancies in results by type of substance, and discusses gaps in the literature to inform future research. Methods: A systematic search was conducted on Embase, Web of Science, CINAHL, and PsychINFO databases. Studies were published in English and included peer-reviewed human research investigating the relationship between EEG P300 ERP and self-reported substance use craving. The included study samples comprised of in treatment or non-treatment-seeking participants who use substances. The primary outcomes of interest were those derived from inferential statistics assessing P300 amplitude and substance use craving. Results: Ten studies were included in the final search and were organized by substance type: three alcohol, three cocaine, two tobacco, one heroin, and one cannabis. Results were mixed for alcohol and cocaine. Studies on tobacco, heroin, and cannabis use were congruent for associations between the P300 amplitude and craving. Conclusions: Overall findings are mixed between studies addressing the association of the EEG P300 amplitude and craving. These results should be considered in the context of the limited sample size, underpowered analyses, and methodological differences that potentially contribute to discrepancies in outcomes. Further research is required to assess the role of craving assessment, EEG methodology, and substance-related factors on the association between P300 amplitude and self-reported craving.
Precision-Machine Learning for the Matrix Element Method

The matrix element method is the LHC inference method of choice for limited statistics. We present a dedicated machine learning framework, based on efficient phase-space integration, a learned acceptance and transfer function. It is based on a choice of INN and diffusion networks, and a transformer to solve jet combinatorics. We showcase this setup for the CP-phase of the top Yukawa coupling in associated Higgs and single-top production.
PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm

In contrast to numerous NLP and 2D vision foundational models, learning a 3D foundational model poses considerably greater challenges. This is primarily due to the inherent data variability and diversity of downstream tasks. In this paper, we introduce a novel universal 3D pre-training framework designed to facilitate the acquisition of efficient 3D representation, thereby establishing a pathway to 3D foundational models. Considering that informative 3D features should encode rich geometry and appearance cues that can be utilized to render realistic images, we propose to learn 3D representations by differentiable neural rendering. We train a 3D backbone with a devised volumetric neural renderer by comparing the rendered with the real images. Notably, our approach seamlessly integrates the learned 3D encoder into various downstream tasks. These tasks encompass not only high-level challenges such as 3D detection and segmentation but also low-level objectives like 3D reconstruction and image synthesis, spanning both indoor and outdoor scenarios. Besides, we also illustrate the capability of pre-training a 2D backbone using the proposed methodology, surpassing conventional pre-training methods by a large margin. For the first time, PonderV2 achieves state-of-the-art performance on 11 indoor and outdoor benchmarks, implying its effectiveness. Code and models are available at https://github.com/OpenGVLab/PonderV2.
Long-Tailed Classification Based on Coarse-Grained Leading Forest and Multi-Center Loss

Long-tailed (LT) classification is an unavoidable and challenging problem in the real world. Most existing long-tailed classification methods focus only on solving the class-wise imbalance while ignoring the attribute-wise imbalance. The deviation of a classification model is caused by both class-wise and attribute-wise imbalance. Due to the fact that attributes are implicit in most datasets and the combination of attributes is complex, attribute-wise imbalance is more difficult to handle. For this purpose, we proposed a novel long-tailed classification framework, aiming to build a multi-granularity classification model by means of invariant feature learning. This method first unsupervisedly constructs Coarse-Grained forest (CLF) to better characterize the distribution of attributes within a class. Depending on the distribution of attributes, one can customize suitable sampling strategies to construct different imbalanced datasets. We then introduce multi-center loss (MCL) that aims to gradually eliminate confusing attributes during feature learning process. The proposed framework does not necessarily couple to a specific LT classification model structure and can be integrated with any existing LT method as an independent component. Extensive experiments show that our approach achieves state-of-the-art performance on both existing benchmarks ImageNet-GLT and MSCOCO-GLT and can improve the performance of existing LT methods. Our codes are available on GitHub: \url{https://github.com/jinyery/cognisance}
Linear-in-temperature conductance in electron hydrodynamics

Linear temperature dependence of transport coefficients in metals is habitually ascribed to non-Fermi-liquid physics. Here we establish the T-linear behavior for 2D electron fluids, systems in which carrier collisions assist conduction, leading to resistance decreasing with temperature. This behavior originates from exotic hydrodynamics described by Fermi surface modulations evolving in space and time in an amoeba-like loop manner that features a large family of long-lived excitations manifest as multiple viscous modes. A cascade of these modes results in a linear T dependence that extends down to lowest temperatures, as well as a Kolmogorov-like fractional power -5/3 scaling of conductivity vs. wavenumber. These dependences provide a smoking gun for nonclassical hydrodynamics and are expected to be generic for strongly-correlated 2D systems with simple near-circular Fermi surfaces.
Numerical study on the mechanism of drag modulation by dispersed drops in two-phase Taylor-Couette turbulence

The presence of a dispersed phase can significantly modulate the drag in turbulent systems. We derived a conserved quantity that characterizes the radial transport of azimuthal momentum in the fluid-fluid two-phase Taylor-Couette turbulence. This quantity consists of contributions from advection, diffusion, and two-phase interface, which are closely related to density, viscosity, and interfacial tension, respectively. We found that the presence of the two-phase interface consistently produces a positive contribution to the momentum transport and leads to drag enhancement, while decreasing the density and viscosity ratios of the dispersed phase to the continuous phase reduces the contribution of local advection and diffusion terms to the momentum transport, respectively, resulting in drag reduction. Therefore, we concluded that the decreased density ratio and the decreased viscosity ratio work together to compete with the presence of two-phase interface for achieving drag modulation in fluid-fluid two-phase turbulence.
Ontology Enrichment for Effective Fine-grained Entity Typing

Fine-grained entity typing (FET) is the task of identifying specific entity types at a fine-grained level for entity mentions based on their contextual information. Conventional methods for FET require extensive human annotation, which is time-consuming and costly. Recent studies have been developing weakly supervised or zero-shot approaches. We study the setting of zero-shot FET where only an ontology is provided. However, most existing ontology structures lack rich supporting information and even contain ambiguous relations, making them ineffective in guiding FET. Recently developed language models, though promising in various few-shot and zero-shot NLP tasks, may face challenges in zero-shot FET due to their lack of interaction with task-specific ontology. In this study, we propose OnEFET, where we (1) enrich each node in the ontology structure with two types of extra information: instance information for training sample augmentation and topic information to relate types to contexts, and (2) develop a coarse-to-fine typing algorithm that exploits the enriched information by training an entailment model with contrasting topics and instance-based augmented training samples. Our experiments show that OnEFET achieves high-quality fine-grained entity typing without human annotation, outperforming existing zero-shot methods by a large margin and rivaling supervised methods.
Tag Your Fish in the Broken Net: A Responsible Web Framework for Protecting Online Privacy and Copyright

The World Wide Web, a ubiquitous source of information, serves as a primary resource for countless individuals, amassing a vast amount of data from global internet users. However, this online data, when scraped, indexed, and utilized for activities like web crawling, search engine indexing, and, notably, AI model training, often diverges from the original intent of its contributors. The ascent of Generative AI has accentuated concerns surrounding data privacy and copyright infringement. Regrettably, the web's current framework falls short in facilitating pivotal actions like consent withdrawal or data copyright claims. While some companies offer voluntary measures, such as crawler access restrictions, these often remain inaccessible to individual users. To empower online users to exercise their rights and enable companies to adhere to regulations, this paper introduces a user-controlled consent tagging framework for online data. It leverages the extensibility of HTTP and HTML in conjunction with the decentralized nature of distributed ledger technology. With this framework, users have the ability to tag their online data at the time of transmission, and subsequently, they can track and request the withdrawal of consent for their data from the data holders. A proof-of-concept system is implemented, demonstrating the feasibility of the framework. This work holds significant potential for contributing to the reinforcement of user consent, privacy, and copyright on the modern internet and lays the groundwork for future insights into creating a more responsible and user-centric web ecosystem.
Do Not Marginalize Mechanisms, Rather Consolidate!

Structural causal models (SCMs) are a powerful tool for understanding the complex causal relationships that underlie many real-world systems. As these systems grow in size, the number of variables and complexity of interactions between them does, too. Thus, becoming convoluted and difficult to analyze. This is particularly true in the context of machine learning and artificial intelligence, where an ever increasing amount of data demands for new methods to simplify and compress large scale SCM. While methods for marginalizing and abstracting SCM already exist today, they may destroy the causality of the marginalized model. To alleviate this, we introduce the concept of consolidating causal mechanisms to transform large-scale SCM while preserving consistent interventional behaviour. We show consolidation is a powerful method for simplifying SCM, discuss reduction of computational complexity and give a perspective on generalizing abilities of consolidated SCM.