Beyond Probability Partitions: Calibrating Neural Networks with Semantic Aware Grouping

Research has shown that deep networks tend to be overly optimistic about their predictions, leading to an underestimation of prediction errors. Due to the limited nature of data, existing studies have proposed various methods based on model prediction probabilities to bin the data and evaluate calibration error. We propose a more generalized definition of calibration error called Partitioned Calibration Error (PCE), revealing that the key difference among these calibration error metrics lies in how the data space is partitioned. We put forth an intuitive proposition that an accurate model should be calibrated across any partition, suggesting that the input space partitioning can extend beyond just the partitioning of prediction probabilities, and include partitions directly related to the input. Through semantic-related partitioning functions, we demonstrate that the relationship between model accuracy and calibration lies in the granularity of the partitioning function. This highlights the importance of partitioning criteria for training a calibrated and accurate model. To validate the aforementioned analysis, we propose a method that involves jointly learning a semantic aware grouping function based on deep model features and logits to partition the data space into subsets. Subsequently, a separate calibration function is learned for each subset. Experimental results demonstrate that our approach achieves significant performance improvements across multiple datasets and network architectures, thus highlighting the importance of the partitioning function for calibration

Deadline Constrained Cloud Computing Resources Scheduling through an Ant Colony System Approach

Cloud computing resources scheduling is essential for executing workflows in the cloud platform because it relates to both execution time and execution cost. In this paper, we adopt a model that optimizes the execution cost while meeting deadline constraints. In solving this problem, we propose an Improved Ant Colony System (IACS) approach featuring two novel strategies. Firstly, a dynamic heuristic strategy is used to calculate a heuristic value during an evolutionary process by taking the workflow topological structure into consideration. Secondly, a double search strategy is used to initialize the pheromone and calculate the heuristic value according to the execution time at the beginning and to initialize the pheromone and calculate heuristic value according to the execution cost after a feasible solution is found. Therefore, the proposed IACS is adaptive to the search environment and to different objectives. We have conducted extensive experiments based on workflows with different scales and different cloud resources. We compare the result with a particle swarm optimization (PSO) approach and a dynamic objective genetic algorithm (DOGA) approach. Experimental results show that IACS is able to find better solutions with a lower cost than both PSO and DOGA do on various scheduling scales and deadline conditions

Reparameterized Policy Learning for Multimodal Trajectory Optimization

We investigate the challenge of parametrizing policies for reinforcement learning (RL) in high-dimensional continuous action spaces. Our objective is to develop a multimodal policy that overcomes limitations inherent in the commonly-used Gaussian parameterization. To achieve this, we propose a principled framework that models the continuous RL policy as a generative model of optimal trajectories. By conditioning the policy on a latent variable, we derive a novel variational bound as the optimization objective, which promotes exploration of the environment. We then present a practical model-based RL method, called Reparameterized Policy Gradient (RPG), which leverages the multimodal policy parameterization and learned world model to achieve strong exploration capabilities and high data efficiency. Empirical results demonstrate that our method can help agents evade local optima in tasks with dense rewards and solve challenging sparse-reward environments by incorporating an object-centric intrinsic reward. Our method consistently outperforms previous approaches across a range of tasks. Code and supplementary materials are available on the project page https://haosulab.github.io/RPG

An Efficient General-Purpose Modular Vision Model via Multi-Task Heterogeneous Training

We present a model that can perform multiple vision tasks and can be adapted to other downstream tasks efficiently. Despite considerable progress in multi-task learning, most efforts focus on learning from multi-label data: a single image set with multiple task labels. Such multi-label data sets are rare, small, and expensive. We say heterogeneous to refer to image sets with different task labels, or to combinations of single-task datasets. Few have explored training on such heterogeneous datasets. General-purpose vision models are still dominated by single-task pretraining, and it remains unclear how to scale up multi-task models by leveraging mainstream vision datasets designed for different purposes. The challenges lie in managing large intrinsic differences among vision tasks, including data distribution, architectures, task-specific modules, dataset scales, and sampling strategies. To address these challenges, we propose to modify and scale up mixture-of-experts (MoE) vision transformers, so that they can simultaneously learn classification, detection, and segmentation on diverse mainstream vision datasets including ImageNet, COCO, and ADE20K. Our approach achieves comparable results to single-task state-of-the-art models and demonstrates strong generalization on downstream tasks. Due to its emergent modularity, this general-purpose model decomposes into high-performing components, efficiently adapting to downstream tasks. We can fine-tune it with fewer training parameters, fewer model parameters, and less computation. Additionally, its modularity allows for easy expansion in continual-learning-without-forgetting scenarios. Finally, these functions can be controlled and combined to meet various demands of downstream tasks

Thermalization Effect in semiconductor Si, and metallic silicide NiSi2, CoSi2 by using Non-Adiabatic Molecular Dynamics Approach

Recently, cold source transistor (CSFET) with steep-slope subthreshold swing (SS) < 60 mV/decade has been proposed to overcome Boltzmann tyranny in its ballistic regime. However the scattering, especially by inelastic scattering may lead serious SS degradation through cold carrier thermalization. In this study, the electronic excitation/relaxation dynamic process is investigated theoretically by virtue of the state-of-the-art nonadiabatic molecular dynamics (NAMD) method, i.e., the mixed quantum-classical NAMD. The mixed quantum-classical NAMD considers both carrier decoherence and detailed balance to calculate the cold carrier thermalization and transfer processes in semiconductor Si, and metallic silicide (NiSi2 and CoSi2). The dependence of the thermalization factor, relaxation time, scattering time and scattering rate on energy level are obtained. The thermalization of carrier gradually increases from low energy to high energy. Partially thermalization from the ground state to reach the thermionic current window is realized with sub-100 $fs$ time scale. Fully thermalization to entail energy region depends on the barrier height sensitively, i.e., the scattering rate decreases exponentially as the energy of the out-scattering state increase. The scattering rate of NiSi2 and CoSi2 is 2 orders of magnitude higher than that of Si, arising from their higher density of states than that in Silicon This study can shed light on the material design for low power tunneling FET as well as the emerging CSFET.Comment: 14 pages, 17 figre

Characterization of myeloperoxidase and its contribution to antimicrobial effect on extracellular traps in flounder (Paralichthys olivaceus)

Myeloperoxidase (MPO) is a cationic leukocyte haloperoxidase and together with other proteins, they possess activities against various microorganisms and are involved in extracellular trap (ET) formation. The present work describes the gene and deduced protein sequences, and functions of MPO in flounder (PoMPO). The PoMPO possesses a 2313 bp open reading frame (ORF) that encodes a protein of 770 amino acids. The highest PoMPO mRNA expression levels were found in the head kidney, followed by peritoneal cells, gill, spleen, skin, muscle, and liver. PoMPO was expressed in MHCII+ and GCSFR+ cells which indicated that PoMPO mainly is expressed in flounder macrophages and granulocytes. Bacterial lipopolysaccharide-stimulated peritoneal leukocytes showed an increased protein level of PoMPO while it seemed that LPS also promoted the migration of MPO+ cells from the head kidney into the peripheral blood and peritoneal cavity. After phorbol 12-myristate 13-acetate (PMA) or bacterial stimulation, flounder leukocytes produced typical ET structures containing DNA with decoration by MPO. The ETs containing DNA and PoMPO effectively inhibited the proliferation of ET-trapped bacteria. Blocking PoMPO with antibodies decreased the enzymatic activity, which attenuated the antibacterial activity of ETs. This study pinpoints the involvement of ETs in flounder innate responses to pathogens

Differential effects of partial and complete loss of TREM2 on microglial injury response and tauopathy.

Alzheimer's disease (AD), the most common form of dementia, is characterized by the abnormal accumulation of amyloid plaques and hyperphosphorylated tau aggregates, as well as microgliosis. Hemizygous missense variants in Triggering Receptor Expressed on Myeloid Cells 2 (TREM2) are associated with elevated risk for developing late-onset AD. These variants are hypothesized to result in loss of function, mimicking TREM2 haploinsufficiency. However, the consequences of TREM2 haploinsufficiency on tau pathology and microglial function remain unknown. We report the effects of partial and complete loss of TREM2 on microglial function and tau-associated deficits. In vivo imaging revealed that microglia from aged TREM2-haploinsufficient mice show a greater impairment in their injury response compared with microglia from aged TREM2-KO mice. In transgenic mice expressing mutant human tau, TREM2 haploinsufficiency, but not complete loss of TREM2, increased tau pathology. In addition, whereas complete TREM2 deficiency protected against tau-mediated microglial activation and atrophy, TREM2 haploinsufficiency elevated expression of proinflammatory markers and exacerbated atrophy at a late stage of disease. The differential effects of partial and complete loss of TREM2 on microglial function and tau pathology provide important insights into the critical role of TREM2 in AD pathogenesis