Systematic investigations of transient response of nuclear spins in the presence of polarized electrons

We electrically probed the transient response of nuclear spins in an n-GaAs channel by performing Hanle signal and spin-valve signal measurements on an all-electrical spin-injection device having a half-metallic spin source of Co2MnSi. Furthermore, we simulated the Hanle and spin-valve signals by using the time evolution of nuclear-spin polarization under the presence of polarized electron spins by taking both T-1e and T-1 into consideration, where T-1e(-1) is the polarization rate of nuclear spins through the transfer of angular momentum from polarized electron spins and T-1(-1) is the depolarization rate of nuclear spins through the interaction with the lattice. The simulation results reproduced our experimental results on all the nuclear-spin-related phenomena appearing in the Hanle and spin-valve signals at different measurement conditions, providing quantitative explanation for the transient response of nuclear spins in GaAs to a change in magnetic fields and an estimate of the time scales of T-1e and T-1. These experimental and simulated results will deepen the understanding of nuclear-spin dynamics in semiconductors

Predator-Prey Coevolution Drives Productivity-Richness Relationships in Planktonic Systems

The relationship between environmental productivity and species richness often varies among empirical studies, and despite much research, simple explanations for this phenomenon remain elusive. We investigated how phytoplankton and zooplankton coevolution shapes productivity-richness relationships in both phytoplankton and zooplankton, using a simple nutrient-phytoplankton-zooplankton model that incorporates size-dependent metabolic rates summarized from empirical studies. The model allowed comparisons of evolved species richness across productivity levels and at different evolutionary times. Our results show that disruptive selection leads to evolutionary branching of phytoplankton and zooplankton. Both the time required for evolutionary branching and the number of evolved species in phytoplankton and zooplankton tend to increase with productivity, producing a transient unimodal or positive productivity-richness relationship but followed by a positive productivity-richness relationship for both groups over long enough evolutionary time. Our findings suggest that coevolution between phytoplankton and zooplankton can drive the two common forms (unimodal and positive) of productivity-richness relationships in nature

FedRec+: Enhancing Privacy and Addressing Heterogeneity in Federated Recommendation Systems

Preserving privacy and reducing communication costs for edge users pose significant challenges in recommendation systems. Although federated learning has proven effective in protecting privacy by avoiding data exchange between clients and servers, it has been shown that the server can infer user ratings based on updated non-zero gradients obtained from two consecutive rounds of user-uploaded gradients. Moreover, federated recommendation systems (FRS) face the challenge of heterogeneity, leading to decreased recommendation performance. In this paper, we propose FedRec+, an ensemble framework for FRS that enhances privacy while addressing the heterogeneity challenge. FedRec+ employs optimal subset selection based on feature similarity to generate near-optimal virtual ratings for pseudo items, utilizing only the user's local information. This approach reduces noise without incurring additional communication costs. Furthermore, we utilize the Wasserstein distance to estimate the heterogeneity and contribution of each client, and derive optimal aggregation weights by solving a defined optimization problem. Experimental results demonstrate the state-of-the-art performance of FedRec+ across various reference datasets.Comment: Accepted by 59th Annual Allerton Conference on Communication, Control, and Computin

Selective gas detection using Mn3O4/WO3 composites as a sensing layer

Pure WO3 sensors and Mn3O4/WO3 composite sensors with different Mn concentrations (1 atom %, 3 atom % and 5 atom %) were successfully prepared through a facile hydrothermal method. As gas sensing materials, their sensing performance at different temperatures was systematically investigated for gas detection. The devices displayed different sensing responses toward different gases at specific temperatures. The gas sensing performance of Mn3O4/WO3 composites (especially at 3 atom % Mn) were far improved compared to sensors based on pure WO3, where the improvement is related to the heterojunction formed between the two metal oxides. The sensor based on the Mn3O4/WO3 composite with 3 atom % Mn showed a high selective response to hydrogen sulfide (H2S), ammonia (NH3) and carbon monoxide (CO) at working temperatures of 90 degrees C, 150 degrees C and 210 degrees C, respectively. The demonstrated superior selectivity opens the door for potential applications in gas recognition and detection

Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM

The analysis and mining of user heterogeneous behavior are of paramount importance in recommendation systems. However, the conventional approach of incorporating various types of heterogeneous behavior into recommendation models leads to feature sparsity and knowledge fragmentation issues. To address this challenge, we propose a novel approach for personalized recommendation via Large Language Model (LLM), by extracting and fusing heterogeneous knowledge from user heterogeneous behavior information. In addition, by combining heterogeneous knowledge and recommendation tasks, instruction tuning is performed on LLM for personalized recommendations. The experimental results demonstrate that our method can effectively integrate user heterogeneous behavior and significantly improve recommendation performance.Comment: Accepted at RecSys 202

Collaborative Planning for Catching and Transporting Objects in Unstructured Environments

Multi-robot teams have attracted attention from industry and academia for their ability to perform collaborative tasks in unstructured environments, such as wilderness rescue and collaborative transportation.In this paper, we propose a trajectory planning method for a non-holonomic robotic team with collaboration in unstructured environments.For the adaptive state collaboration of a robot team to catch and transport targets to be rescued using a net, we model the process of catching the falling target with a net in a continuous and differentiable form.This enables the robot team to fully exploit the kinematic potential, thereby adaptively catching the target in an appropriate state.Furthermore, the size safety and topological safety of the net, resulting from the collaborative support of the robots, are guaranteed through geometric constraints.We integrate our algorithm on a car-like robot team and test it in simulations and real-world experiments to validate our performance.Our method is compared to state-of-the-art multi-vehicle trajectory planning methods, demonstrating significant performance in efficiency and trajectory quality