Adaptive Test-Time Personalization for Federated Learning

Personalized federated learning algorithms have shown promising results in adapting models to various distribution shifts. However, most of these methods require labeled data on testing clients for personalization, which is usually unavailable in real-world scenarios. In this paper, we introduce a novel setting called test-time personalized federated learning (TTPFL), where clients locally adapt a global model in an unsupervised way without relying on any labeled data during test-time. While traditional test-time adaptation (TTA) can be used in this scenario, most of them inherently assume training data come from a single domain, while they come from multiple clients (source domains) with different distributions. Overlooking these domain interrelationships can result in suboptimal generalization. Moreover, most TTA algorithms are designed for a specific kind of distribution shift and lack the flexibility to handle multiple kinds of distribution shifts in FL. In this paper, we find that this lack of flexibility partially results from their pre-defining which modules to adapt in the model. To tackle this challenge, we propose a novel algorithm called ATP to adaptively learns the adaptation rates for each module in the model from distribution shifts among source domains. Theoretical analysis proves the strong generalization of ATP. Extensive experiments demonstrate its superiority in handling various distribution shifts including label shift, image corruptions, and domain shift, outperforming existing TTA methods across multiple datasets and model architectures. Our code is available at https://github.com/baowenxuan/ATP .Comment: Accepted by NeurIPS 202

Boosting Adversarial Transferability by Block Shuffle and Rotation

Adversarial examples mislead deep neural networks with imperceptible perturbations and have brought significant threats to deep learning. An important aspect is their transferability, which refers to their ability to deceive other models, thus enabling attacks in the black-box setting. Though various methods have been proposed to boost transferability, the performance still falls short compared with white-box attacks. In this work, we observe that existing input transformation based attacks, one of the mainstream transfer-based attacks, result in different attention heatmaps on various models, which might limit the transferability. We also find that breaking the intrinsic relation of the image can disrupt the attention heatmap of the original image. Based on this finding, we propose a novel input transformation based attack called block shuffle and rotation (BSR). Specifically, BSR splits the input image into several blocks, then randomly shuffles and rotates these blocks to construct a set of new images for gradient calculation. Empirical evaluations on the ImageNet dataset demonstrate that BSR could achieve significantly better transferability than the existing input transformation based methods under single-model and ensemble-model settings. Combining BSR with the current input transformation method can further improve the transferability, which significantly outperforms the state-of-the-art methods

In Vitro and In Silico Characterization of the Aggregation of Thrombi on Ventricular Assist Device Cannula

The unacceptably high stroke rate of HeartMate III VAD without signs of adherent pump thrombosis is hypothesized to be the result of the thrombi originating on the inflow cannula, ingesting and ejecting emboli from the VAD. Therefore, inflow cannula thrombosis has been an emerging focus. The inflow cannula of contemporary VADs, which incorporate both polished and rough regions serve as useful benchmarks to study the effects of roughness and shear on thrombogenesis. An in vitro study was conducted to emulate the micro-hemodynamic condition on a sintered inflow cannula, and to observe the deposition and detachment patterns. Together with a computational fluid dynamic tool, this study aimed to provide insight into the optimization of inflow cannula and potentially reducing adverse neurological events due to upstream thrombus

Kick Bad Guys Out! Zero-Knowledge-Proof-Based Anomaly Detection in Federated Learning

Federated learning (FL) systems are vulnerable to malicious clients that submit poisoned local models to achieve their adversarial goals, such as preventing the convergence of the global model or inducing the global model to misclassify some data. Many existing defense mechanisms are impractical in real-world FL systems, as they require prior knowledge of the number of malicious clients or rely on re-weighting or modifying submissions. This is because adversaries typically do not announce their intentions before attacking, and re-weighting might change aggregation results even in the absence of attacks. To address these challenges in real FL systems, this paper introduces a cutting-edge anomaly detection approach with the following features: i) Detecting the occurrence of attacks and performing defense operations only when attacks happen; ii) Upon the occurrence of an attack, further detecting the malicious client models and eliminating them without harming the benign ones; iii) Ensuring honest execution of defense mechanisms at the server by leveraging a zero-knowledge proof mechanism. We validate the superior performance of the proposed approach with extensive experiments

Successor Liability in Bankruptcy: Some Unifying Themes of Intertemporal Creditor Priorities Created by Running Covenants, Products Liability, and Toxic-Waste Cleanup

The exceptional sensitivity of mammalian hearing organs is attributed to an active process, where force produced by sensory cells boost sound-induced vibrations, making soft sounds audible. This process is thought to be local, with each section of the hearing organ capable of amplifying sound-evoked movement, and nearly instantaneous, since amplification can work for sounds at frequencies up to 100 kHz in some species. To test these fundamental precepts, we developed a method for focally stimulating the living hearing organ with light. Light pulses caused intense and highly damped mechanical responses followed by traveling waves that developed with considerable delay. The delayed response was identical to movements evoked by click-like sounds. This shows that the active process is neither local nor instantaneous, but requires mechanical waves traveling from the cochlear base toward its apex. A physiologically-based mathematical model shows that such waves engage the active process, enhancing hearing sensitivity.Funding Agencies|NIH [DC-004554, DC-004084]; Swedish Research Council [K2011-63X-14061-11-39]; Research Council for Health, Working Life and Welfare [2006-1526]; Horselskadades Riksforbund; Tysta skolan foundation</p

Time-Dependent Pricing for High-Speed Railway in China Based on Revenue Management

High-speed railway (HSR) is recognized as a green transportation mode with lower energy consumption and less pollution emission than other transportation. At present, China has the largest HSR network globally, but the maximum revenue of railway transportation corporations has not been realized. In order to make HSR achieve a favorable position within the fierce competition in the market, increase corporate revenue, and achieve the sustainable development of HSR and railway corporations, we introduce the concept of revenue management in HSR operations and propose an innovative model to optimize the price and seat allocation for HSR simultaneously. In the study, we formulate the optimization problem as a mixed-integer nonlinear programming (MINLP) model, which appropriately captures passengers&rsquo choice behavior. To reduce the computational complexity, we further transform the proposed MINLP model into an equivalent model. Finally, the effectiveness of both the proposed model and solution algorithm are tested and validated by numerical experiments. The research results show that the model can flexibly adjust the price and seat allocation of the corresponding ticketing period according to the passenger demand, and increase the total expected revenue by 5.92% without increasing the capacity. Document type: Articl

Templateâ based protein structure prediction in CASP11 and retrospect of Iâ TASSER in the last decade

We report the structure prediction results of a new composite pipeline for templateâ based modeling (TBM) in the 11th CASP experiment. Starting from multiple structure templates identified by LOMETS based metaâ threading programs, the QUARK ab initio folding program is extended to generate initial fullâ length models under strong constraints from template alignments. The final atomic models are then constructed by Iâ TASSER based fragment reassembly simulations, followed by the fragmentâ guided molecular dynamic simulation and the MQAPâ based model selection. It was found that the inclusion of QUARKâ TBM simulations as an intermediate modeling step could help improve the quality of the Iâ TASSER models for both Easy and Hard TBM targets. Overall, the average TMâ score of the first Iâ TASSER model is 12% higher than that of the best LOMETS templates, with the RMSD in the same threadingâ aligned regions reduced from 5.8 to 4.7 à . Nevertheless, there are nearly 18% of TBM domains with the templates deteriorated by the structure assembly pipeline, which may be attributed to the errors of secondary structure and domain orientation predictions that propagate through and degrade the procedures of template identification and final model selections. To examine the record of progress, we made a retrospective report of the Iâ TASSER pipeline in the last five CASP experiments (CASP7â 11). The data show no clear progress of the LOMETS threading programs over PSIâ BLAST; but obvious progress on structural improvement relative to threading templates was witnessed in recent CASP experiments, which is probably attributed to the integration of the extended ab initio folding simulation with the threading assembly pipeline and the introduction of atomicâ level structure refinements following the reduced modeling simulations. Proteins 2016; 84(Suppl 1):233â 246. © 2015 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134137/1/prot24918.pd

Observation of an anisotropic ultrafast spin relaxation process in large-area WTe2films

Weyl semimetal Td-WTe2 hosts the natural broken inversion symmetry and strong spin-orbit coupling, which contains profound spin-related physics within a picosecond timescale. However, the comprehensive understanding of ultrafast spin behaviors in WTe2 is lacking due to its limited quality of large-scale films. Here, we report on an anisotropic ultrafast spin dynamics in highly oriented Td-WTe2 films using a femtosecond pump-probe technique at room temperature. A transient spin polarization-flip transition as fast as 0.8 ps is observed upon photoexcitation. The inversed spin is subsequently scattered by defects with a duration of about 5.9 ps. The whole relaxation process exhibits an intriguing dual anisotropy of sixfold and twofold symmetries, which stems from the energy band anisotropy of the WTe2 crystalline structure and the matrix element effect, respectively. Our work enriches the insights into the ultrafast opto-spintronics in topological Weyl semimetals