Anomalous magnetohydrodynamics with temperature-dependent electric conductivity and application to the global polarization

We have derived the solutions of the relativistic anomalous magnetohydrodynamics with longitudinal Bjorken boost invariance and transverse electromagnetic fields in the presence of temperature or energy density dependent electric conductivity. We consider the equations of states in a high temperature limit or in a high chiral chemical potential limit. We obtain both perturbative analytic solutions up to the order of \hbar and numerical solutions in our configurations of initial electromagnetic fields and Bjorken flow velocity. Our results show that the temperature or energy density dependent electric conductivity plays an important role to the decaying of the energy density and electromagnetic fields. We also implement our results to the splitting of global polarization for \Lambda and \bar{\Lambda} hyperons induced by the magnetic fields. Our results for the splitting of global polarization disagree with the experimental data in low energy collisions, which implies that the contribution from gradient of chemical potential may dominate in the low energy collisions

Simulation Design of a Tomato Picking Manipulator

Simulation is an important way to verify the feasibility of design parameters and schemes for robots. Through simulation, this paper analyzes the effectiveness of the design parameters selected for a tomato picking manipulator, and verifies the rationality of the manipulator in motion planning for tomato picking. Firstly, the basic parameters and workspace of the manipulator were determined based on the environment of a tomato greenhouse; the workspace of the lightweight manipulator was proved as suitable for the picking operation through MATLAB simulation. Next, the maximum theoretical torque of each joint of the manipulator was solved through analysis, the joint motors were selected reasonably, and SolidWorks simulation was performed to demonstrate the rationality of the material selected for the manipulator and the strength design of the joint connectors. After that, the trajectory control requirements of the manipulator in picking operation were determined in view of the operation environment, and the feasibility of trajectory planning was confirmed with MATLAB. Finally, a motion control system was designed for the manipulator, according to the end trajectory control requirements, followed by the manufacturing of a prototype. The prototype experiment shows that the proposed lightweight tomato picking manipulator boasts good kinematics performance, and basically meets the requirements of tomato picking operation: the manipulator takes an average of 21 s to pick a tomato, and achieves a success rate of 78.67%

ReRoGCRL: Representation-based Robustness in Goal-Conditioned Reinforcement Learning

While Goal-Conditioned Reinforcement Learning (GCRL) has gained attention, its algorithmic robustness against adversarial perturbations remains unexplored. The attacks and robust representation training methods that are designed for traditional RL become less effective when applied to GCRL. To address this challenge, we first propose the Semi-Contrastive Representation attack, a novel approach inspired by the adversarial contrastive attack. Unlike existing attacks in RL, it only necessitates information from the policy function and can be seamlessly implemented during deployment. Then, to mitigate the vulnerability of existing GCRL algorithms, we introduce Adversarial Representation Tactics, which combines Semi-Contrastive Adversarial Augmentation with Sensitivity-Aware Regularizer to improve the adversarial robustness of the underlying RL agent against various types of perturbations. Extensive experiments validate the superior performance of our attack and defence methods across multiple state-of-the-art GCRL algorithms. Our tool ReRoGCRL is available at https://github.com/TrustAI/ReRoGCRL.Comment: This paper has been accepted in AAAI24 (https://aaai.org/aaai-conference/

Mitochondrial Genome of an 8,400-Year-Old Individual from Northern China Reveals a Novel Sub-Clade under C5d

Ancient DNA studies have always refreshed our understanding of the human past that can’t be tracked by modern DNA alone. Until recently, ancient mitochondrial genomic studies in East Asia are still very limited. Here, we retrieved the whole mitochondrial genome of an 8,400-year- old individual from Inner Mongolia, China. Phylogenetic analyses show that the individual belongs to a previously undescribed clade under haplogroup C5d that was most probably originated in northern Asia and may have a very low frequency in extant populations that is not yet sampled. We further characterized the demographic history of mitochondrial haplogroups C5 and C5d, and found that C5 experienced a sharp increase in population size starting from around 4,000 years before present (BP). The time when intensive millet farming was built by populations who are associated with the lower Xiajiadian culture and was widely adopted in northern China. We caution that people related to haplogroup C5 may added this farming technology to their original way of life and that the various subsistence may provide abundant food sources and may further contribute to the increase of the population size

Ancient mitochondrial genomes reveal extensive genetic influence of the steppe pastoralists in Western Xinjiang

The population prehistory of Xinjiang has been a hot topic among geneticists, linguists, and archaeologists. Current ancient DNA studies in Xinjiang exclusively suggest an admixture model for the populations in Xinjiang since the early Bronze Age. However, almost all of these studies focused on the northern and eastern parts of Xinjiang; the prehistoric demographic processes that occurred in western Xinjiang have been seldomly reported. By analyzing complete mitochondrial sequences from the Xiabandi (XBD) cemetery (3,500–3,300 BP), the up-to-date earliest cemetery excavated in western Xinjiang, we show that all the XBD mitochondrial sequences fall within two different West Eurasian mitochondrial DNA (mtDNA) pools, indicating that the migrants into western Xinjiang from west Eurasians were a consequence of the early expansion of the middle and late Bronze Age steppe pastoralists (Steppe_MLBA), admixed with the indigenous populations from Central Asia. Our study provides genetic links for an early existence of the Indo-Iranian language in southwestern Xinjiang and suggests that the existence of Andronovo culture in western Xinjiang involved not only the dispersal of ideas but also population movement.Introduction Materials and methods - Archaeological Background, Sampling, and Sequencing - Sequence Mapping and Mitochondrial DNA Haplogroup Determination - Analysis of Xiabandi Mitochondrial DNA Genomes Results - Mitochondrial DNA Authentication and Contamination Assessment - Major Bronze Age Steppe Pastoralist Origin of the Xiabandi Mitochondrial Haplogroups - Expansion of the Bronze Age Steppe Pastoralists as a Dynamic Process to Form the Genetic Landscape of Xiabandi Individuals Discussion Conclusion

Adaptive Deep Neural Network Inference Optimization with EENet

Well-trained deep neural networks (DNNs) treat all test samples equally during prediction. Adaptive DNN inference with early exiting leverages the observation that some test examples can be easier to predict than others. This paper presents EENet, a novel early-exiting scheduling framework for multi-exit DNN models. Instead of having every sample go through all DNN layers during prediction, EENet learns an early exit scheduler, which can intelligently terminate the inference earlier for certain predictions, which the model has high confidence of early exit. As opposed to previous early-exiting solutions with heuristics-based methods, our EENet framework optimizes an early-exiting policy to maximize model accuracy while satisfying the given per-sample average inference budget. Extensive experiments are conducted on four computer vision datasets (CIFAR-10, CIFAR-100, ImageNet, Cityscapes) and two NLP datasets (SST-2, AgNews). The results demonstrate that the adaptive inference by EENet can outperform the representative existing early exit techniques. We also perform a detailed visualization analysis of the comparison results to interpret the benefits of EENet

GeoGauss: Strongly Consistent and Light-Coordinated OLTP for Geo-Replicated SQL Database

Multinational enterprises conduct global business that has a demand for geo-distributed transactional databases. Existing state-of-the-art databases adopt a sharded master-follower replication architecture. However, the single-master serving mode incurs massive cross-region writes from clients, and the sharded architecture requires multiple round-trip acknowledgments (e.g., 2PC) to ensure atomicity for cross-shard transactions. These limitations drive us to seek yet another design choice. In this paper, we propose a strongly consistent OLTP database GeoGauss with full replica multi-master architecture. To efficiently merge the updates from different master nodes, we propose a multi-master OCC that unifies data replication and concurrent transaction processing. By leveraging an epoch-based delta state merge rule and the optimistic asynchronous execution, GeoGauss ensures strong consistency with light-coordinated protocol and allows more concurrency with weak isolation, which are sufficient to meet our needs. Our geo-distributed experimental results show that GeoGauss achieves 7.06X higher throughput and 17.41X lower latency than the state-of-the-art geo-distributed database CockroachDB on the TPC-C benchmark