Miracle-dz/UniFAR-FTUs huggingface dataset:
Selective Domain-Invariant Feature for Generalizable Deepfake Detection

With diverse presentation forgery methods emerging continually, detecting the authenticity of images has drawn growing attention. Although existing methods have achieved impressive accuracy in training dataset detection, they still perform poorly in the unseen domain and suffer from forgery of irrelevant information such as background and identity, affecting generalizability. To solve this problem, we proposed a novel framework Selective Domain-Invariant Feature (SDIF), which reduces the sensitivity to face forgery by fusing content features and styles. Specifically, we first use a Farthest-Point Sampling (FPS) training strategy to construct a task-relevant style sample representation space for fusing with content features. Then, we propose a dynamic feature extraction module to generate features with diverse styles to improve the performance and effectiveness of the feature extractor. Finally, a domain separation strategy is used to retain domain-related features to help distinguish between real and fake faces. Both qualitative and quantitative results in existing benchmarks and proposals demonstrate the effectiveness of our approach.
RouterBench: A Benchmark for Multi-LLM Routing System

As the range of applications for Large Language Models (LLMs) continues to grow, the demand for effective serving solutions becomes increasingly critical. Despite the versatility of LLMs, no single model can optimally address all tasks and applications, particularly when balancing performance with cost. This limitation has led to the development of LLM routing systems, which combine the strengths of various models to overcome the constraints of individual LLMs. Yet, the absence of a standardized benchmark for evaluating the performance of LLM routers hinders progress in this area. To bridge this gap, we present RouterBench, a novel evaluation framework designed to systematically assess the efficacy of LLM routing systems, along with a comprehensive dataset comprising over 405k inference outcomes from representative LLMs to support the development of routing strategies. We further propose a theoretical framework for LLM routing, and deliver a comparative analysis of various routing approaches through RouterBench, highlighting their potentials and limitations within our evaluation framework. This work not only formalizes and advances the development of LLM routing systems but also sets a standard for their assessment, paving the way for more accessible and economically viable LLM deployments. The code and data are available at https://github.com/withmartian/routerbench.
Risk premium and rough volatility

One the one hand, rough volatility has been shown to provide a consistent framework to capture the properties of stock price dynamics both under the historical measure and for pricing purposes. On the other hand, market price of volatility risk is a well-studied object in Financial Economics, and empirical estimates show it to be stochastic rather than deterministic. Starting from a rough volatility model under the historical measure, we take up this challenge and provide an analysis of the impact of such a non-deterministic risk for pricing purposes.
Meta-Prompting for Automating Zero-shot Visual Recognition with LLMs

Prompt ensembling of Large Language Model (LLM) generated category-specific prompts has emerged as an effective method to enhance zero-shot recognition ability of Vision-Language Models (VLMs). To obtain these category-specific prompts, the present methods rely on hand-crafting the prompts to the LLMs for generating VLM prompts for the downstream tasks. However, this requires manually composing these task-specific prompts and still, they might not cover the diverse set of visual concepts and task-specific styles associated with the categories of interest. To effectively take humans out of the loop and completely automate the prompt generation process for zero-shot recognition, we propose Meta-Prompting for Visual Recognition (MPVR). Taking as input only minimal information about the target task, in the form of its short natural language description, and a list of associated class labels, MPVR automatically produces a diverse set of category-specific prompts resulting in a strong zero-shot classifier. MPVR generalizes effectively across various popular zero-shot image recognition benchmarks belonging to widely different domains when tested with multiple LLMs and VLMs. For example, MPVR obtains a zero-shot recognition improvement over CLIP by up to 19.8% and 18.2% (5.0% and 4.5% on average over 20 datasets) leveraging GPT and Mixtral LLMs, respectively
Benchmarking Analytical Query Processing in Intel SGXv2

The recently introduced second generation of Intel SGX (SGXv2) lifts memory size limitations of the first generation. Theoretically, this promises to enable secure and highly efficient analytical DBMSs in the cloud. To validate this promise, in this paper, we conduct the first in-depth evaluation study of running analytical query processing algorithms inside SGXv2. Our study reveals that state-of-the-art query operators like radix joins and SIMD-based scans can indeed achieve high performance inside SGXv2 enclaves. These operations are orders of magnitude faster than joins optimized for the discontinued SGXv1 hardware. However, substantial performance overheads are still caused by subtle hardware and software differences influencing code execution inside an SGX enclave. We investigate these differences and propose new optimizations to bring the performance inside the enclave on par with native code execution outside an enclave.
Prevalence of and Factors Associated with Fecal Incontinence in a Large Community Study of Older Individuals

OBJECTIVES:In this study, we describe the prevalence of fecal incontinence by race, age, sex, the presence of major chronic conditions of stroke and diabetes, and the use of certain psychoactive medications.METHODS:Study subjects are participants in the Chicago Health and Aging Project, a study of older Chicago residents of a geographically defined area. In the period 1993–1996, interviewers conducted a door-to-door census that identified 6,099 individuals who participated in in-home interviews. The interviews included a wide range of questions regarding demographics, medical history, and medication use. The question used to determine the presence of fecal incontinence was: “In the past few months have you ever lost control of your bowels when you didn't want to? ”RESULTS:Fecal incontinence was seen in 585 of 6,099 survey responders yielding an overall prevalence of 9.6%. The prevalence of fecal incontinence was strongly associated with age across all demographic groups. We did not observe significant differences in the prevalence for males and females once we adjusted for age. However, the increase in prevalence with age was significantly greater among Blacks than Whites. The use of psychoactive medications was found to be associated with significantly higher odds of fecal incontinence. Diabetes and stroke were associated with a higher prevalence of fecal incontinence.CONCLUSIONS:These cross-sectional analyses offer promising evidence that this common condition is correlated with the presence of certain conditions (e.g., stroke and diabetes) and use of certain psychoactive medications.
Comparison of different schedulings for the ADMM based LDPC decoding

This paper proposes a general framework aiming at describing and comparing LDPC decoders based on the ADMM approach. ADMM is a linear programming decoding method. When applied on LDPC codes, it acts as an iterative message passing technique and enhances the error correction performance compared with the usual belief propagation based decoders. In this paper, three ADMM decoding algorithms classified according to their scheduling type are described and compared. Simulation results show that the layered scheduling is efficient to enhance the error correction performance while decreasing the average number of decoding iterations for the considered LDPC codes.
Concomitants of paranoia in the general population

Background Paranoia is an unregarded but pervasive attribute of human populations. In this study we carried out the most comprehensive investigation so far of the demographic, economic, social and clinical correlates of self-reported paranoia in the general population. Method Data weighted to be nationally representative were analysed from the Adult Psychiatric Morbidity Survey in England (APMS 2007; n=7281). Results The prevalence of paranoid thinking in the previous year ranged from 18.6% reporting that people were against them, to 1.8% reporting potential plots to cause them serious harm. At all levels, paranoia was associated with youth, lower intellectual functioning, being single, poverty, poor physical health, poor social functioning, less perceived social support, stress at work, less social cohesion, less calmness, less happiness, suicidal ideation, a great range of other psychiatric symptoms (including anxiety, worry, phobias, post-traumatic stress and insomnia), cannabis use, problem drinking and increased use of treatment and services. Conclusions Overall, the results indicate that paranoia has the widest of implications for health, emotional well-being, social functioning and social inclusion. Some of these concomitants may contribute to the emergence of paranoid thinking, while others may result from it.
Becker Digital

Introduction: Delays in adequate antimicrobial treatment contribute to high cost and mortality in sepsis. Polymerase chain reaction (PCR) assays are used alongside conventional cultures to accelerate the identification of microorganisms. We analyze the impact on medical outcomes and healthcare costs if improved adequacy of antimicrobial therapy is achieved by providing immediate coverage after positive PCR reports. Methods: A mathematical prediction model describes the impact of PCR-based rapid adjustment of antimicrobial treatment. The model is applied to predict cost and medical outcomes for 221 sepsis episodes of 189 post-surgical and intensive care unit (ICU) sepsis patients with available PCR data from a prospective, observational trial of a multiplex PCR assay in five hospitals. While this trial demonstrated reduction of inadequate treatment days, data on outcomes associated with reduced inadequate initial antimicrobial treatment had to be obtained from two other, bigger, studies which involved 1,147 (thereof 316 inadequately treated) medical or surgical ICU patients. Our results are reported with the (5% to 95%) percentile ranges from Monte Carlo simulation in which the input parameters were randomly and independently varied according to their statistical characterization in the three underlying studies. The model allows predictions also for different patient groups or PCR assays. Results: A total of 13.1% of PCR tests enabled earlier adequate treatment. We predict that cost for PCR testing (300 € /test) can be fully recovered for patients above 717 € (605 € to 1,710 € ) daily treatment cost. A 2.6% (2.0 to 3.2%) absolute reduction of mortality is expected. Cost per incremental survivor calculates to 11,477 € (9,321 € to 14,977 € ) and incremental cost-effectiveness ratio to 3,107 € (2,523 € to 4,055 € ) per quality-adjusted life-year.
Beliefs about psychotropic medication and psychotherapy among primary care patients with anxiety disorders

Primary health care clinics are increasingly providing psychiatric/psychological treatment of anxiety disorders, particularly for patients who do not have adequate access to specialty mental health services. Adequate treatment requires knowledge of and attention to patients' beliefs about available treatment options. The current investigation examined beliefs about psychotropic medications and psychotherapy among a sample of primary care patients with anxiety disorders. The influence of key demographic variables on strength of these beliefs was also explored. The presence of specific anxiety disorders was not found to impact strength of beliefs about either type of treatment. In contrast, there was a trend for the presence of depression to relate to more favorable attitudes toward psychotropic medication. Consistent with previous studies, ethnic minority patients reported less favorable attitudes toward both psychotropic medications and psychotherapy. These findings underscore the importance of assessing patient beliefs prior to the initiation of either psychotropic medications or psychotherapy across diagnostic and demographic groups. Practitioners should be particularly alert to the possibility that patients with anxiety disorders and members of ethnic minority groups may have less favorable attitudes toward treatment options. Treatment adherence may therefore be increased by addressing these beliefs directly. © 2005 Wiley‐Liss, Inc.
Knowledge, Attitude, and Practices regarding Pharmacovigilance and Adverse Drug Reaction reporting among Dental Students in a Teaching Hospital, Jodhpur, India: A Cross-sectional Study.

INTRODUCTION This is a cross-sectional knowledge, attitude, and practices (KAPs) study on pharmacovigilance (PV) and adverse drug reaction (ADR) reporting among dental students in a teaching hospital in India. AIM The aim of this study was to assess the KAP of dental students regarding PV, ADR reporting, and barriers toward the same. MATERIALS AND METHODS A cross-sectional survey using a self-administered, investigator-developed, close-ended questionnaire was conducted in an academic dental hospital in India. All prescribers including third year students, final year students, and house surgeons of the same institute were included for assessment of KAP regarding PV using 16, 8, and 8 items respectively. Data regarding barriers toward ADR reporting and demographics were also collected. Mann-Whitney U-test and Kruskal-Wallis test were applied followed by post hoc test. RESULTS A total of 241 of 275 respondents participated in the study with a response rate of 87.5%. Overall, 64% reported that they had no idea about the term PV. Age was significantly associated with knowledge (p = 0.045) and attitude (p = 0.016). Barriers contributing to underreporting were difficulty in deciding whether or not an ADR has occurred (52.0%), concerns that the report may be wrong (37%), lack of confidence to discuss ADR with colleagues (29%), and almost no financial benefits (24%). CONCLUSION Participants had a comparatively favorable attitude toward PV, but their knowledge and practice need considerable improvements. This study highlights the need for appropriate dental curriculum changes and further multicentric studies to shed more light on important issues of PV among dentists in India. CLINICAL SIGNIFICANCE This study explores dentists' knowledge, attitude, and behavior regarding PV, which could help to improve patient's safety and care. The favorable attitude of dentists is an indication that PV could be added in depth in the curriculum and in general practice. Information on barriers for reporting the ADRs could help to find possible solutions for removing the barriers precisely.
SETA: Semantic-Aware Token Augmentation for Domain Generalization

Domain generalization (DG) aims to enhance the model robustness against domain shifts without accessing target domains. A prevalent category of methods for DG is data augmentation, which focuses on generating virtual samples to simulate domain shifts. However, existing augmentation techniques in DG are mainly tailored for convolutional neural networks (CNNs), with limited exploration in token-based architectures, i.e., vision transformer (ViT) and multi-layer perceptrons (MLP) models. In this paper, we study the impact of prior CNN-based augmentation methods on token-based models, revealing their performance is suboptimal due to the lack of incentivizing the model to learn holistic shape information. To tackle the issue, we propose the SEmantic-aware Token Augmentation (SETA) method. SETA transforms token features by perturbing local edge cues while preserving global shape features, thereby enhancing the model learning of shape information. To further enhance the generalization ability of the model, we introduce two stylized variants of our method combined with two state-of-the-art style augmentation methods in DG. We provide a theoretical insight into our method, demonstrating its effectiveness in reducing the generalization risk bound. Comprehensive experiments on five benchmarks prove that our method achieves SOTA performances across various ViT and MLP architectures. Our code is available at https://github.com/lingeringlight/SETA.
Ultra-High-Resolution Image Synthesis with Pyramid Diffusion Model

We introduce the Pyramid Diffusion Model (PDM), a novel architecture designed for ultra-high-resolution image synthesis. PDM utilizes a pyramid latent representation, providing a broader design space that enables more flexible, structured, and efficient perceptual compression which enable AutoEncoder and Network of Diffusion to equip branches and deeper layers. To enhance PDM's capabilities for generative tasks, we propose the integration of Spatial-Channel Attention and Res-Skip Connection, along with the utilization of Spectral Norm and Decreasing Dropout Strategy for the Diffusion Network and AutoEncoder. In summary, PDM achieves the synthesis of images with a 2K resolution for the first time, demonstrated on two new datasets comprising images of sizes 2048x2048 pixels and 2048x1024 pixels respectively. We believe that this work offers an alternative approach to designing scalable image generative models, while also providing incremental reinforcement for existing frameworks.
Spanning Euler Tours in Hypergraphs

Motivated by generalizations of de Bruijn cycles to various combinatorial structures (Chung, Diaconis, and Graham), we study various Euler tours in set systems. Let $\mathcal{G}$ be a hypergraph whose corank and rank are $c\geq 3$ and $k$, respetively. The minimum $t$-degree of $\mathcal{G}$ is the fewest number of edges containing every $t$-subset of vertices. An Euler tour (family, respectively) in $\mathcal{G}$ is a (family of, respectively) closed walk(s) that (jointly, respectively) traverses each edge of $\mathcal{G}$ exactly once. An Euler tour is spanning if it traverses all the vertices of $\mathcal{G}$. We show that $\mathcal{G}$ has an Euler family if its incidence graph is $(1+\lceil k/c \rceil)$-edge-connected. Provided that the number of vertices of $\mathcal{G}$ meets a reasonable lower bound, and either $2$-degree is at least $k$ or $t$-degree is at least one for $t\geq 3$, we show that $\mathcal{G}$ has a spanning Euler tour. To exhibit the usefulness of our results, we solve a number of open problems concerning ordering blocks of a design (these have applications in other fields such as erasure-correcting codes). Answering a question of Horan and Hurlbert, we show that a Steiner quadruple system of order $n$ has a (spanning) Euler tour if and only if $n\geq 8$ and $n\equiv 2,4 \pmod 6$, and we prove a similar result for all Steiner systems, as well as all designs except for 2-designs whose index $\lambda$ is less than the largest block size. We nearly solve a conjecture of Dewar and Stevens on the existence of universal cycles in pairwise balanced designs. Motivated by R.L. Graham's question on the existence of Hamiltonian cycles in block-intersection graphs of Steiner triple systems, we establish the Hamiltonicity of the block-intersection graph of a large family of (not necessarily uniform) designs. All our results are constructive and of polynomial time complexity.
Fully abstract translations between functional languages

We examine the problem of finding fully abstract translations between programming languages, i.e., translations that preserve code equivalence and nonequivalence. We present three examples of fully abstract translations: one from call-by-value to lazy PCF, one from call-by-name to call-by-value PCF, and one from lazy to call-by-value PCF. The translations yield lower bounds on decision procedures for proving equivalences of code. We define a notion of ‘functional translation’ that captures the essence of the proofs of full abstraction, and show that some languages cannot be translated into others.
Boundary Attention: Learning to Localize Boundaries under High Noise

We present a differentiable model that infers explicit boundaries, including curves, corners and junctions, using a mechanism that we call boundary attention. Boundary attention is a boundary-aware local attention operation that, when applied densely and repeatedly, progressively refines a field of variables that specify an unrasterized description of the local boundary structure in every overlapping patch within an image. It operates in a bottom-up fashion, similar to classical methods for sub-pixel edge localization and edge-linking, but with a higher-dimensional description of local boundary structure, a notion of spatial consistency that is learned instead of designed, and a sequence of operations that is end-to-end differentiable. We train our model using simple synthetic data and then evaluate it using photographs that were captured under low-light conditions with variable amounts of noise. We find that our method generalizes to natural images corrupted by real sensor noise, and predicts consistent boundaries under increasingly noisy conditions where other state-of-the-art methods fail.
Normalized Validity Scores for DNNs in Regression based Eye Feature Extraction

We propose an improvement to the landmark validity loss. Landmark detection is widely used in head pose estimation, eyelid shape extraction, as well as pupil and iris segmentation. There are numerous additional applications where landmark detection is used to estimate the shape of complex objects. One part of this process is the accurate and fine-grained detection of the shape. The other part is the validity or inaccuracy per landmark, which can be used to detect unreliable areas, where the shape possibly does not fit, and to improve the accuracy of the entire shape extraction by excluding inaccurate landmarks. We propose a normalization in the loss formulation, which improves the accuracy of the entire approach due to the numerical balance of the normalized inaccuracy. In addition, we propose a margin for the inaccuracy to reduce the impact of gradients, which are produced by negligible errors close to the ground truth.