Miracle-dz/UniFAR-FTUs huggingface dataset:
Backdoor Federated Learning by Poisoning Backdoor-Critical Layers

Federated learning (FL) has been widely deployed to enable machine learning training on sensitive data across distributed devices. However, the decentralized learning paradigm and heterogeneity of FL further extend the attack surface for backdoor attacks. Existing FL attack and defense methodologies typically focus on the whole model. None of them recognizes the existence of backdoor-critical (BC) layers-a small subset of layers that dominate the model vulnerabilities. Attacking the BC layers achieves equivalent effects as attacking the whole model but at a far smaller chance of being detected by state-of-the-art (SOTA) defenses. This paper proposes a general in-situ approach that identifies and verifies BC layers from the perspective of attackers. Based on the identified BC layers, we carefully craft a new backdoor attack methodology that adaptively seeks a fundamental balance between attacking effects and stealthiness under various defense strategies. Extensive experiments show that our BC layer-aware backdoor attacks can successfully backdoor FL under seven SOTA defenses with only 10% malicious clients and outperform the latest backdoor attack methods.
Do Perceptually Aligned Gradients Imply Robustness?

Adversarially robust classifiers possess a trait that non-robust models do not -- Perceptually Aligned Gradients (PAG). Their gradients with respect to the input align well with human perception. Several works have identified PAG as a byproduct of robust training, but none have considered it as a standalone phenomenon nor studied its own implications. In this work, we focus on this trait and test whether \emph{Perceptually Aligned Gradients imply Robustness}. To this end, we develop a novel objective to directly promote PAG in training classifiers and examine whether models with such gradients are more robust to adversarial attacks. Extensive experiments on multiple datasets and architectures validate that models with aligned gradients exhibit significant robustness, exposing the surprising bidirectional connection between PAG and robustness. Lastly, we show that better gradient alignment leads to increased robustness and harness this observation to boost the robustness of existing adversarial training techniques.
Some Students are Bigger than Others, Some Students’ Peers are Bigger than Other Students’ Peers

This paper analyses the extent to which peer influence on adolescent weight differs in a typical southern European country and in the United States, two geographical areas characterised by different economic, socio-cultural and environmental patterns. Our study is based on a survey of secondary school students containing a rich set of personal data and a wide range of school characteristics and parental backgrounds. After accounting for a large set of control factors and controlling for a combination of school- and neighbourhood-specific fixed effects, instrumental variable estimation and alternative definitions of peers, our results support a more powerful positive and significant effect of friends’ mean BMI on adolescent weight than that reported in previous US-based research.
Shepherd: A Critic for Language Model Generation

As large language models improve, there is increasing interest in techniques that leverage these models' capabilities to refine their own outputs. In this work, we introduce Shepherd, a language model specifically tuned to critique responses and suggest refinements, extending beyond the capabilities of an untuned model to identify diverse errors and provide suggestions to remedy them. At the core of our approach is a high quality feedback dataset, which we curate from community feedback and human annotations. Even though Shepherd is small (7B parameters), its critiques are either equivalent or preferred to those from established models including ChatGPT. Using GPT-4 for evaluation, Shepherd reaches an average win-rate of 53-87% compared to competitive alternatives. In human evaluation, Shepherd strictly outperforms other models and on average closely ties with ChatGPT.
TextPainter: Multimodal Text Image Generation with Visual-harmony and Text-comprehension for Poster Design

Text design is one of the most critical procedures in poster design, as it relies heavily on the creativity and expertise of humans to design text images considering the visual harmony and text-semantic. This study introduces TextPainter, a novel multimodal approach that leverages contextual visual information and corresponding text semantics to generate text images. Specifically, TextPainter takes the global-local background image as a hint of style and guides the text image generation with visual harmony. Furthermore, we leverage the language model and introduce a text comprehension module to achieve both sentence-level and word-level style variations. Besides, we construct the PosterT80K dataset, consisting of about 80K posters annotated with sentence-level bounding boxes and text contents. We hope this dataset will pave the way for further research on multimodal text image generation. Extensive quantitative and qualitative experiments demonstrate that TextPainter can generate visually-and-semantically-harmonious text images for posters.
Constraining the global mean surface temperature during 1850-1880 with new statistical physical model

As IPCC ARs stated, global warming is estimated based on the average from 1850 to 1900 (global average temperature of preindustrialization estimated from relatively sparse observations). Given the impossibility of massive increasing observation data in the early stages, accurately constraining this baseline has become an unresolved issue. Here we developed a new statistical physical model to quantify the contribution of external forcings to global warming as a"deterministic trend"of the surface temperature series (instead of as non-stationary processes that yield a stochastic trend) and constrained the reconstruction of the early time series (1850-1880). We find that the existing datasets slightly overestimated the temperature anomalies in this period, thus the speed of global warming since pre-industrialization is still underestimated.
Merging in a Coupled Driving Simulator: How do drivers resolve conflicts?

Traffic interactions between merging and highway vehicles are a major topic of research, yielding many empirical studies and models of driver behaviour. Most of these studies on merging use naturalistic data. Although this provides insight into human gap acceptance and traffic flow effects, it obscures the operational inputs of interacting drivers. Besides that, researchers have no control over the vehicle kinematics (i.e., positions and velocities) at the start of the interactions. Therefore the relationship between initial kinematics and the outcome of the interaction is difficult to investigate. To address these gaps, we conducted an experiment in a coupled driving simulator with a simplified, top-down view, merging scenario with two vehicles. We found that kinematics can explain the outcome (i.e., which driver merges first) and the duration of the merging conflict. Furthermore, our results show that drivers use key decision moments combined with constant acceleration inputs (intermittent piecewise-constant control) during merging. This indicates that they do not continuously optimize their expected utility. Therefore, these results advocate the development of interaction models based on intermittent piecewise-constant control. We hope our work can contribute to this development and to the fundamental knowledge of interactive driver behaviour.
Resource Constrained Model Compression via Minimax Optimization for Spiking Neural Networks

Brain-inspired Spiking Neural Networks (SNNs) have the characteristics of event-driven and high energy-efficient, which are different from traditional Artificial Neural Networks (ANNs) when deployed on edge devices such as neuromorphic chips. Most previous work focuses on SNNs training strategies to improve model performance and brings larger and deeper network architectures. It's difficult to deploy these complex networks on resource-limited edge devices directly. To meet such demand, people compress SNNs very cautiously to balance the performance and the computation efficiency. Existing compression methods either iteratively pruned SNNs using weights norm magnitude or formulated the problem as a sparse learning optimization. We propose an improved end-to-end Minimax optimization method for this sparse learning problem to better balance the model performance and the computation efficiency. We also demonstrate that jointly applying compression and finetuning on SNNs is better than sequentially, especially for extreme compression ratios. The compressed SNN models achieved state-of-the-art (SOTA) performance on various benchmark datasets and architectures. Our code is available athttps://github.com/chenjallen/Resource-Constrained-Compression-on-SNN .
Exponential mixing for the white-forced complex Ginzburg--Landau equation in the whole space

In the last two decades, there has been a significant progress in the understanding of ergodic properties of white-forced dissipative PDEs. The previous studies mostly focus on equations posed on bounded domains since they rely on different compactness properties and the discreteness of the spectrum of the Laplacian. In the present paper, we consider the damped complex Ginzburg--Landau equation on the real line driven by a white-in-time noise. Under the assumption that the noise is sufficiently non-degenerate, we establish the uniqueness of stationary measure and exponential mixing in the dual-Lipschitz metric. The proof is based on coupling techniques combined with a generalization of Foia\c{s}--Prodi estimate to the case of the real line and special space-time weighted estimates which help to handle the behavior of solutions at infinity.
Coherence measurements of polaritons in thermal equilibrium reveal a new power law for two-dimensional condensates

We have created a spatially homogeneous polariton condensate in thermal equilibrium, up to very high condensate fraction. Under these conditions, we have measured the coherence as a function of momentum, and determined the total coherent fraction of this boson system from very low density up to density well above the condensation transition. These measurements reveal a consistent power law for the coherent fraction as a function total density over nearly three orders of its magnitude. The same power law is seen in numerical simulations solving the two-dimensional Gross-Pitaevskii equation for the equilibrium coherence. This power law has not been predicted by prior analytical theories.
RadGraph2: Modeling Disease Progression in Radiology Reports via Hierarchical Information Extraction

We present RadGraph2, a novel dataset for extracting information from radiology reports that focuses on capturing changes in disease state and device placement over time. We introduce a hierarchical schema that organizes entities based on their relationships and show that using this hierarchy during training improves the performance of an information extraction model. Specifically, we propose a modification to the DyGIE++ framework, resulting in our model HGIE, which outperforms previous models in entity and relation extraction tasks. We demonstrate that RadGraph2 enables models to capture a wider variety of findings and perform better at relation extraction compared to those trained on the original RadGraph dataset. Our work provides the foundation for developing automated systems that can track disease progression over time and develop information extraction models that leverage the natural hierarchy of labels in the medical domain.
Evaluating and Optimizing the Effectiveness of Neural Machine Translation in Supporting Code Retrieval Models: A Study on the CAT Benchmark

Neural Machine Translation (NMT) is widely applied in software engineering tasks. The effectiveness of NMT for code retrieval relies on the ability to learn from the sequence of tokens in the source language to the sequence of tokens in the target language. While NMT performs well in pseudocode-to-code translation[17], it might have challenges in learning to translate from natural language query to source code in newly curated real-world code documentation/ implementation datasets. In this work, we analyze the performance of NMT in natural language-to-code translation in the newly curated CAT benchmark[31] that includes the optimized versions of three Java datasets TLCodeSum, CodeSearchNet, Funcom, and a Python dataset PCSD. Our evaluation shows that NMT has low accuracy, measured by CrystalBLEU[10] and Meteor[9] metrics in this task. To alleviate the duty of NMT in learning complex representation of source code, we propose ASTTrans Representation, a tailored representation of an Abstract Syntax Tree (AST) using a subset of non-terminal nodes. We show that the classical approach NMT performs significantly better in learning ASTTrans Representation over code tokens with up to 36% improvement on Meteor score. Moreover, we leverage ASTTrans Representation to conduct combined code search processes from the state-of-the-art code search processes using GraphCodeBERT[13], and UniXcoder[12]. Our NMT models of learning ASTTrans Representation can boost the Mean Reciprocal Rank of these state-of-the-art code search processes by up to 3.08% and improve 23.08% of queries' results over the CAT benchmark.
Extrapolating Large Language Models to Non-English by Aligning Languages

Existing large language models show disparate capability across different languages, due to the imbalance in the training data. Their performances on English tasks are often stronger than on tasks of other languages. In this paper, we empower pre-trained LLMs on non-English languages by building semantic alignment across languages. We start from targeting individual languages by performing cross-lingual instruction-tuning (CoIT) on LLaMA, i.e. tuning it with translation task data and cross-lingual general task data to obtain cross-lingual models (x-LLaMAs), and formulate underlying scaling laws to investigate the advantages of using scalable translation data. Then we perform multilingual instruction-tuning (MuIT) with mixed resources to build multilingual m-LLaMA. We also illustrate how we leverage the scaling laws to optimize data allocation in a resource-constrained setting. Experiment results on cross-lingual benchmarks XQUAD and MLQA show that x-LLaMAs surpass the English instruction-tuned counterpart (Alpaca) by an average of 27.83% across six non-English languages. Evaluation results on translation dataset Flores-101 show that x-LLaMAs outperform previous LLaMA-based models by an average of 18.89%. Encouragingly, m-LLaMA achieves comparable performance to x-LLaMAs on individual languages and demonstrates the ability to follow multilingual instructions. Further analysis on response content and representation space reveals the alignment of the multilingual semantic space within the middle layers of m-LLaMA.
Generalized Lagrangian heterogeneous multiscale modelling of complex fluids

Abstract We introduce a fully Lagrangian heterogeneous multiscale method (LHMM) to model complex fluids with microscopic features that can extend over large spatio/temporal scales, such as polymeric solutions and multiphasic systems. The proposed approach discretizes the fluctuating Navier–Stokes equations in a particle-based setting using smoothed dissipative particle dynamics (SDPD). This multiscale method uses microscopic information derived on-the-fly to provide the stress tensor of the momentum balance in a macroscale problem, therefore bypassing the need for approximate constitutive relations for the stress. We exploit the intrinsic multiscale features of SDPD to account for thermal fluctuations as the characteristic size of the discretizing particles decreases. We validate the LHMM using different flow configurations (reverse Poiseuille flow, flow passing a cylinder array and flow around a square cavity) and fluid (Newtonian and non-Newtonian). We show the framework's flexibility to model complex fluids at the microscale using multiphase and polymeric systems. We also show that stresses are adequately captured and passed from micro to macro scales, leading to richer fluid response at the continuum. In general, the proposed methodology provides a natural link between variations at a macroscale, whereas accounting for memory effects of microscales.
The effect of marital status on life expectancy: Is cohabitation as protective as marriage?

Abstract It is well-known that marital status is an important predictor for life expectancy. However, non-married individuals are often misclassified as singles which ignores the heterogeneity within the group. This paper shows the importance of distinguishing between types of singles, and in particular whether they are cohabiting, when predicting life expectancies. We use unique and detailed longitudinal register data to track marital status throughout the individual's lifetime. We find that all types of singles consistently benefit from living with a spouse, i.e., after divorce, becoming widower or being never married. This result holds for both men and women. For certain types of cohabiting singles we reject significant differences in life expectancy compared to married individuals. Finally, we use a case study to show that, like married individuals, all types of singles that cohabit also serve as informal caregivers and have the potential to limit the end-of-life long-term care expenditure levels.
Trajectories of cognitive processing speed and physical disability over 11 years following initiation of a first multiple sclerosis disease-modulating therapy

Background We analysed the COMparison Between All immunoTherapies for Multiple Sclerosis (NCT03193866), a Swedish nationwide observational study in relapsing-remitting multiple sclerosis (RRMS), to identify trajectories of processing speed and physical disability after disease-modulating therapy (DMT) start. Methods Using a group-modelling approach, we assessed trajectories of processing speed with oral Symbol Digit Modalities Test (SDMT) and physical disability with Expanded Disability Status Scale, from first DMT start among 1645 patients with RRMS followed during 2011–2022. We investigated predictors of trajectories using group membership as a multinomial outcome and calculated conditional probabilities linking membership across the trajectories. Results We identified 5 stable trajectories of processing speed: low SDMT scores (mean starting values=29.9; 5.4% of population), low/medium (44.3; 25.3%), medium (52.6; 37.9%), medium/high (63.1; 25.8%) and high (72.4; 5.6%). We identified 3 physical disability trajectories: no disability/stable (0.8; 26.8%), minimal disability/stable (1.6; 58.1%) and moderate disability (3.2; 15.1%), which increased to severe disability. Older patients starting interferons were more likely than younger patients starting rituximab to be on low processing speed trajectories. Older patients starting teriflunomide, with more than one comorbidity, and a history of pain treatment were more likely to belong to the moderate/severe physical disability trajectory, relative to the no disability one. There was a strong association between processing speed and physical disability trajectories. Conclusions In this cohort of actively treated RRMS, patients’ processing speed remained stable over the years following DMT start, whereas patients with moderate physical disability deteriorated in physical function. Nevertheless, there was a strong link between processing speed and disability after DMT start.
Identification des forts séismes passés sur les failles normales actives de la région Lazio-Abruzzo (Italie Centrale) par ‘datations cosmogéniques' (36Cl) de leurs escarpements

Notre capacite a anticiper les futurs forts seismes depend de notre connaissance des evenements passes. Or cette connaissance est limitee, faute de methodes simples pour les identifier et de donnees pour les decrire. Notre objectif a ainsi ete double : 1) Ameliorer une methode d'identification des seismes passes, -celle basee sur la datation des phases d'exhumation sismique de plans calcaires de failles normales par la mesure de leur contenu en 36Cl in-situ cosmogenique ; 2) Utiliser cette methode pour acquerir de nombreuses donnees documentant les derniers grands seismes sur des failles cibles. Nous avons ainsi developpe un nouveau protocole de modelisation des concentrations en 36Cl. L'originalite du protocole est de prendre en compte tous les facteurs intervenant dans la production du 36Cl et d'integrer leurs incertitudes. Nous ameliorons ainsi significativement la methode 36Cl et quantifions les incertitudes qu'elle entraine sur les nombres, âges et deplacements des seismes identifies. Nous avons aussi collecte ~1000 echantillons a la surface de 11 plans de faille normale exhumes sismiquement (15 sites), dans la region Lazio-Abruzzo (Italie) site des seismes de l'Aquila (04-2009, Mw 6.3, ~300 victimes) et d'Avezzano (1915, Mw 7, ~30 000 victimes). La modelisation de 500 mesures 36Cl effectuees documente le fonctionnement sismique passe (14 ka) de 4 failles majeures, et suggere que celles-ci ont principalement rompu lors de phases paroxysmales (3-4 forts seismes en 2-4 ka), separees par des phases quiescentes. Les courbes d'occurrence des seismes suggerent que la region du Fucino presente un risque sismique eleve.
ReForker: Patching x86_64 Binaries with the Fork Server to Improve Hardware-Assisted Fuzzing through Trampoline-Based Binary Rewriting

Coverage-based Greybox Fuzzing (CGF) is an efficient technique for detecting software vulnerabilities. However, efficient compile-time instrumentation utilized by some fuzzers is not available in testing closed-source binary. Therefore, researchers have proposed hardware-assisted fuzzing to address this. Though enhancing the efficiency in rebuilding coverage, hardware-assisted fuzzing performs poorly on some binaries due to the absence of the fork server, which is an efficient mechanism in modern fuzzers. The program patched with the fork server goes through initialization stages only once during repeated testing, which can significantly improve fuzzing performance. However, existing hardware-assisted fuzzers cannot place the fork server in some (statically linked) binaries. In this paper, we propose ReForker, a trampoline x86_64 binary rewriter to instrument the binary with the fork server for improving the efficiency of hardware-assisted fuzzing. Different from some rewriters in CGF, ReForker focuses on additive instrumentation and is not limited by symbolic information or Position-Independent Executable (PIE) code. It searches the sections in ELF for an executable one as the trampoline section, to insert the fork server. Then ReForker rewrites the instruction storing the address of the main function to redirect the control flow to the fork server, avoiding disturbing the layout of the original code and data sections. The evaluation shows that ReForker effectively works on real-world programs, including statically linked, PIE, and stripped binaries, with only increasing the file size by 4KB and introducing negligible runtime overhead on SPEC INT2006.
RETRACT: Expressive Designated Verifier Anonymous Credentials

Anonymous credentials (ACs) are secure digital versions of credentials that allow selective proof of possession of encoded attributes without revealing additional information. Attributes can include basic personal details (e.g., passport, medical records) and also claims about existing attributes (e.g., age > 18), which can be revealed without disclosing any concrete information. However, embedding all possible claims in a credential is impractical. To address this, we propose verifiers defining policies as high-level programs executed by holders on their credentials. We also propose making the proofs designated verifier to prevent the misuse or leakage of sensitive information by dishonest verifiers to any unwanted third party.