Toward Understanding the Influence of Individual Clients in Federated Learning

Federated learning allows mobile clients to jointly train a global model without sending their private data to a central server. Extensive works have studied the performance guarantee of the global model, however, it is still unclear how each individual client influences the collaborative training process. In this work, we defined a new notion, called {\em Fed-Influence}, to quantify this influence over the model parameters, and proposed an effective and efficient algorithm to estimate this metric. In particular, our design satisfies several desirable properties: (1) it requires neither retraining nor retracing, adding only linear computational overhead to clients and the server; (2) it strictly maintains the tenets of federated learning, without revealing any client's local private data; and (3) it works well on both convex and non-convex loss functions, and does not require the final model to be optimal. Empirical results on a synthetic dataset and the FEMNIST dataset demonstrate that our estimation method can approximate Fed-Influence with small bias. Further, we show an application of Fed-Influence in model debugging.Comment: Accepted at AAAI 202

Open-TransMind: A New Baseline and Benchmark for 1st Foundation Model Challenge of Intelligent Transportation

With the continuous improvement of computing power and deep learning algorithms in recent years, the foundation model has grown in popularity. Because of its powerful capabilities and excellent performance, this technology is being adopted and applied by an increasing number of industries. In the intelligent transportation industry, artificial intelligence faces the following typical challenges: few shots, poor generalization, and a lack of multi-modal techniques. Foundation model technology can significantly alleviate the aforementioned issues. To address these, we designed the 1st Foundation Model Challenge, with the goal of increasing the popularity of foundation model technology in traffic scenarios and promoting the rapid development of the intelligent transportation industry. The challenge is divided into two tracks: all-in-one and cross-modal image retrieval. Furthermore, we provide a new baseline and benchmark for the two tracks, called Open-TransMind. According to our knowledge, Open-TransMind is the first open-source transportation foundation model with multi-task and multi-modal capabilities. Simultaneously, Open-TransMind can achieve state-of-the-art performance on detection, classification, and segmentation datasets of traffic scenarios. Our source code is available at https://github.com/Traffic-X/Open-TransMind

Electric Field Switching of Magnon Spin Current in a Compensated Ferrimagnet

Manipulation of directional magnon propagation, known as magnon spin current, is essential for developing magnonic memory and logic devices featuring nonvolatile functionalities and ultralow power consumption. Magnon spin current can usually be modulated by magnetic field or current-induced spin torques. However, these approaches may lead to energy dissipation caused by Joule heating. Electric-field switching of magnon spin current without charge current is highly desired but very challenging to realize. By integrating magnonic and piezoelectric materials, we demonstrate manipulation of the magnon spin current generated by the spin Seebeck effect in the ferrimagnetic insulator Gd3Fe5O12 (GdIG) film on a piezoelectric substrate. We observe reversible electric-field switching of magnon polarization without applied charge current. Through strain-mediated magnetoelectric coupling, the electric field induces the magnetic compensation transition between two magnetic states of the GdIG, resulting in its magnetization reversal and the simultaneous switching of magnon spin current. Our work establishes a prototype material platform that pave the way for developing magnon logic devices characterized by all electric field reading and writing and reveals the underlying physics principles of their functions

Alcohol consumption and risks of more than 200 diseases in Chinese men

Alcohol consumption accounts for ~3 million annual deaths worldwide, but uncertainty persists about its relationships with many diseases. We investigated the associations of alcohol consumption with 207 diseases in the 12-year China Kadoorie Biobank of >512,000 adults (41% men), including 168,050 genotyped for ALDH2-rs671 and ADH1B-rs1229984, with >1.1 million ICD-10 coded hospitalized events. At baseline, 33% of men drank alcohol regularly. Among men, alcohol intake was positively associated with 61 diseases, including 33 not defined by the World Health Organization as alcohol-related, such as cataract (n = 2,028; hazard ratio 1.21; 95% confidence interval 1.09–1.33, per 280 g per week) and gout (n = 402; 1.57, 1.33–1.86). Genotype-predicted mean alcohol intake was positively associated with established (n = 28,564; 1.14, 1.09–1.20) and new alcohol-associated (n = 16,138; 1.06, 1.01–1.12) diseases, and with specific diseases such as liver cirrhosis (n = 499; 2.30, 1.58–3.35), stroke (n = 12,176; 1.38, 1.27–1.49) and gout (n = 338; 2.33, 1.49–3.62), but not ischemic heart disease (n = 8,408; 1.04, 0.94–1.14). Among women, 2% drank alcohol resulting in low power to assess associations of self-reported alcohol intake with disease risks, but genetic findings in women suggested the excess male risks were not due to pleiotropic genotypic effects. Among Chinese men, alcohol consumption increased multiple disease risks, highlighting the need to strengthen preventive measures to reduce alcohol intake

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Impact of Element Pattern on the Performance of GNSS Power-Inversion Adaptive Arrays

Power-inversion (PI) adaptive arrays are widely used in Global Navigation Satellite System (GNSS) receivers for interference mitigation. The effects of element patterns on the performance of PI adaptive arrays are investigated in this paper. To this end, the performance of adaptive arrays is investigated by Monte Carlo simulations, using CST Microwave Studio (Dassault Systems, Vélizy-Villacoublay, France) to calculate the radiation patterns of circular microstrip elements which are used to compute the adaptive weight and the adaptive array gain. It is shown that the performance of PI adaptive arrays is mainly dependent on the gain pattern of the reference antenna element rather than the non-reference elements because the algorithm essentially pushes the elements into an unequal position. Furthermore, the results show that the impact of mutual coupling on the performance of the antenna array can be associated with the radiation patterns of the reference element, which is helpful in selecting the optimum reference element without increasing computational complexity, especially for small GNSS arrays

Sunlight Induced Chlorophyll fluorescence in the Near-Infrared Spectral Region in Natural Waters: Interpretations of the Narrow Reflectance Peak around 761nm

Sunlight induced chlorophyll a fluorescence (SICF) can be used as a probe to estimate chlorophyll a concentrations (Chl) and infer phytoplankton physiology. SICF at ∼685 nm has been widely applied to studies of natural waters. SICF around 740 nm has been demonstrated to cause a narrow reflectance peak at ∼761 nm in the reflectance spectra of terrestrial vegetation. This narrow peak has also been observed in the reflectance spectra of natural waters, but its mechanism and applications have not yet been investigated and it has often been treated as measurement artifacts. In this study, we aimed to interpret this reflectance peak at ∼761 nm and discuss its potential applications for remote monitoring of natural waters. A derivative analysis of the spectral reflectance suggests that the 761 nm peak is due to SICF. It was also found that the fluorescence line height (FLH) at 761 nm significantly and linearly correlates with Chl. FLH(761 nm) showed a tighter relationship with Chl than the relationship between FLH(∼685 nm) and Chl mainly due to weaker perturbations by nonalgal materials around 761 nm. While it is not conclusive, a combination of FLH(761 nm) and FLH(∼685 nm) might have some potentials to discriminate cyanobacteria from other phytoplankton due to their different fluorescence responses at the two wavelengths. It was further found that reflectance spectra with a 5 nm spectral resolution are adequate to capture the spectral SICF feature at ∼761 nm. These preliminary results suggest that FLH(761 nm) need to be explored more for future applications in optically complex coastal and inland waters