Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Evidential Deep Learning via a Mixture of Dirichlet Distributions

This paper explores a modern predictive uncertainty estimation approach, called evidential deep learning (EDL), in which a single neural network model is trained to learn a meta distribution over the predictive distribution by minimizing a specific objective function. Despite their strong empirical performance, recent studies by Bengs et al. identify a fundamental pitfall of the existing methods: the learned epistemic uncertainty may not vanish even in the infinite-sample limit. We corroborate the observation by providing a unifying view of a class of widely used objectives from the literature. Our analysis reveals that the EDL methods essentially train a meta distribution by minimizing a certain divergence measure between the distribution and a sample-size-independent target distribution, resulting in spurious epistemic uncertainty. Grounded in theoretical principles, we propose learning a consistent target distribution by modeling it with a mixture of Dirichlet distributions and learning via variational inference. Afterward, a final meta distribution model distills the learned uncertainty from the target model. Experimental results across various uncertainty-based downstream tasks demonstrate the superiority of our proposed method, and illustrate the practical implications arising from the consistency and inconsistency of learned epistemic uncertainty.
RQP-SGD: Differential Private Machine Learning through Noisy SGD and Randomized Quantization

The rise of IoT devices has prompted the demand for deploying machine learning at-the-edge with real-time, efficient, and secure data processing. In this context, implementing machine learning (ML) models with real-valued weight parameters can prove to be impractical particularly for large models, and there is a need to train models with quantized discrete weights. At the same time, these low-dimensional models also need to preserve privacy of the underlying dataset. In this work, we present RQP-SGD, a new approach for privacy-preserving quantization to train machine learning models for low-memory ML-at-the-edge. This approach combines differentially private stochastic gradient descent (DP-SGD) with randomized quantization, providing a measurable privacy guarantee in machine learning. In particular, we study the utility convergence of implementing RQP-SGD on ML tasks with convex objectives and quantization constraints and demonstrate its efficacy over deterministic quantization. Through experiments conducted on two datasets, we show the practical effectiveness of RQP-SGD.
Eliminating Information Leakage in Hard Concept Bottleneck Models with Supervised, Hierarchical Concept Learning

Concept Bottleneck Models (CBMs) aim to deliver interpretable and interventionable predictions by bridging features and labels with human-understandable concepts. While recent CBMs show promising potential, they suffer from information leakage, where unintended information beyond the concepts (either when concepts are represented with probabilities or binary states) are leaked to the subsequent label prediction. Consequently, distinct classes are falsely classified via indistinguishable concepts, undermining the interpretation and intervention of CBMs. This paper alleviates the information leakage issue by introducing label supervision in concept predication and constructing a hierarchical concept set. Accordingly, we propose a new paradigm of CBMs, namely SupCBM, which achieves label predication via predicted concepts and a deliberately-designed intervention matrix. SupCBM focuses on concepts that are mostly relevant to the predicted label and only distinguishes classes when different concepts are presented. Our evaluations show that SupCBM outperforms SOTA CBMs over diverse datasets. It also manifests better generality across different backbone models. With proper quantification of information leakage in different CBMs, we demonstrate that SupCBM significantly reduces the information leakage.
Taking Class Imbalance Into Account in Open Set Recognition Evaluation

In recent years Deep Neural Network-based systems are not only increasing in popularity but also receive growing user trust. However, due to the closed-world assumption of such systems, they cannot recognize samples from unknown classes and often induce an incorrect label with high confidence. Presented work looks at the evaluation of methods for Open Set Recognition, focusing on the impact of class imbalance, especially in the dichotomy between known and unknown samples. As an outcome of problem analysis, we present a set of guidelines for evaluation of methods in this field.
Environmental Study on the Social and Economic Impact of Beijing’s Haze Problem: Based on DPSIR Model

As people pay more and more attention to environmental pollution, haze has become a hot word in China. Recent years have witnessed the worsening of air pollution in Beijing. The impact of haze on Beijing’s economic and social development has reached an alarming level. Based on the related statistics data, using the DPSIR framework to describe the interactions between haze and social economy in Beijing, this paper analyzes the effects of the haze, to the social economy in Beijing by mean-squared deviation weight method. The results show that the sources of pollution are decreasing and related treatment scheme is feasible up to now. Yet the scheme doesn’t apparently temper the loss in social economy. To solve the problem, Beijing should establish the cross-regional mechanism, develop green technology as well as advocate green consumption and green lifestyle.
Hierarchical Transformers are Efficient Meta-Reinforcement Learners

We introduce Hierarchical Transformers for Meta-Reinforcement Learning (HTrMRL), a powerful online meta-reinforcement learning approach. HTrMRL aims to address the challenge of enabling reinforcement learning agents to perform effectively in previously unseen tasks. We demonstrate how past episodes serve as a rich source of information, which our model effectively distills and applies to new contexts. Our learned algorithm is capable of outperforming the previous state-of-the-art and provides more efficient meta-training while significantly improving generalization capabilities. Experimental results, obtained across various simulated tasks of the Meta-World Benchmark, indicate a significant improvement in learning efficiency and adaptability compared to the state-of-the-art on a variety of tasks. Our approach not only enhances the agent's ability to generalize from limited data but also paves the way for more robust and versatile AI systems.
A generalized formulation for gradient schemes in unstructured finite volume method

We present a generic framework for gradient reconstruction schemes on unstructured meshes using the notion of a dyadic sum-vector product. The proposed formulation reconstructs centroidal gradients of a scalar from its directional derivatives along specific directions in a suitably defined neighbourhood. We show that existing gradient reconstruction schemes can be encompassed within this framework by a suitable choice of the geometric vectors that define the dyadic sum tensor. The proposed framework also allows us to re-interpret certain hybrid schemes, which might not be derivable through traditional routes. Additionally, a generalization of flexible gradient schemes is proposed that can be employed to enhance the robustness of consistent gradient schemes without compromising on the accuracy of the computed gradients.
Scaling Artificial Intelligence for Digital Wargaming in Support of Decision-Making

In this unprecedented era of technology-driven transformation, it becomes more critical than ever that we aggressively invest in developing robust artificial intelligence (AI) for wargaming in support of decision-making. By advancing AI-enabled systems and pairing these with human judgment, we will be able to enhance all-domain awareness, improve the speed and quality of our decision cycles, offer recommendations for novel courses of action, and more rapidly counter our adversary's actions. It therefore becomes imperative that we accelerate the development of AI to help us better address the complexity of modern challenges and dilemmas that currently requires human intelligence and, if possible, attempt to surpass human intelligence--not to replace humans, but to augment and better inform human decision-making at machine speed. Although deep reinforcement learning continues to show promising results in intelligent agent behavior development for the long-horizon, complex tasks typically found in combat modeling and simulation, further research is needed to enable the scaling of AI to deal with these intricate and expansive state-spaces characteristic of wargaming for either concept development, education, or analysis. To help address this challenge, in our research, we are developing and implementing a hierarchical reinforcement learning framework that includes a multi-model approach and dimension-invariant observation abstractions.
Implementation of large momentum transfer without swapping the directions of the Raman beams

Large momentum transfer (LMT) is an important technique for magnifying the phase shift accumulated in an atom interferometer. Existing approaches to implement Raman-transition-based LMT all involve physically swapping the propagation directions of the two counterpropagating Raman beams repeatedly, which could significantly complicate the experimental system. Here, we demonstrate a simpler approach for Raman-transition-based LMT that does not involve a physical swap of the directions of the Raman beams. In this approach, both Raman beams are retroreflected, and a Doppler shift induced by a bias velocity of the atoms is used to separate the transition frequencies of the two pairs of counterpropagating Raman beams. Therefore, an effective swap of the directions of the Raman beams can be achieved by shifting the relative frequency between the two Raman beams from the resonant frequency of one pair of the Raman beams to that of the other pair. We demonstrate the use of this technique for LMT-augmented accelerometry using atoms released from a magneto-optic trap.
Quasi van der Waals Epitaxial Growth of GaAsSb Nanowires on Graphitic Substrate for Photonic Applications

III-V semiconductor nanowires are considered promising building blocks for advanced photonic devices. One of the key advantages is that the lattice mismatch can easily be accommodated in 1D structures, resulting in superior heteroepitaxial quality compared to thin films. However, few reports break the limitation of using bulk crystalline materials as substrates for epitaxial growth of high-quality photonic 1D components, making monolithic integration of III-V components on arbitrary substrates challenging. In this work, we show that the growth of self-catalyzed GaAsSb nanowires on graphitic substrates can be promoted by creating step edges of monolayer thickness on kish graphite before the growth. By further alternating the deposition sequence of the group-III element Al and the group-V elements As and Sb, it was found that triangular crystallites form when Al is deposited first. This indicates that the surface binding energy between the graphitic surface and the III-V nucleus profoundly influences the epitaxial growth of III-V materials on graphitic surfaces. Using the optimized growth recipe with an AlAsSb buffer nuclei, vertical [111]-oriented GaAsSb/GaAs nanowires with GaAsSb-based multiple axial superlattices were grown on exfoliated graphite, which was attached to a (001) AlAs/GaAs distributed Bragg reflector (DBR) using the simple Scotch tape method. Fabry-P\'{e}rot resonance modes were observed under optical excitation at room temperature, indicating a successful monolithic integration with optical feedback from the DBR system. These results demonstrate the great potential for flexible integration of high-efficiency III-V nanowire photonic devices on arbitrary photonic platforms using a 2D material buffer layer, e.g., graphene, without breaking the orientation registry.
Shuffle approach to wreath Pieri operators

We describe a way to study and compute Pieri rules for wreath Macdonald polynomials using the quantum toroidal algebra. The Macdonald pairing can be naturally generalized to the wreath setting, but the wreath Macdonald polynomials are no longer collinear with their duals. We establish the relationship between these dual polynomials and the quantum toroidal algebra, and we outline a way to compute norm formulas. None of the aforementioned formulas are successfully computed in this paper.
TETRIS: Towards Exploring the Robustness of Interactive Segmentation

Interactive segmentation methods rely on user inputs to iteratively update the selection mask. A click specifying the object of interest is arguably the most simple and intuitive interaction type, and thereby the most common choice for interactive segmentation. However, user clicking patterns in the interactive segmentation context remain unexplored. Accordingly, interactive segmentation evaluation strategies rely more on intuition and common sense rather than empirical studies (e.g., assuming that users tend to click in the center of the area with the largest error). In this work, we conduct a real-user study to investigate real user clicking patterns. This study reveals that the intuitive assumption made in the common evaluation strategy may not hold. As a result, interactive segmentation models may show high scores in the standard benchmarks, but it does not imply that they would perform well in a real world scenario. To assess the applicability of interactive segmentation methods, we propose a novel evaluation strategy providing a more comprehensive analysis of a model's performance. To this end, we propose a methodology for finding extreme user inputs by a direct optimization in a white-box adversarial attack on the interactive segmentation model. Based on the performance with such adversarial user inputs, we assess the robustness of interactive segmentation models w.r. t click positions. Besides, we introduce a novel benchmark for measuring the robustness of interactive segmentation, and report the results of an extensive evaluation of dozens of models.
Aggregation of pairwise comparison matrices: A clustering approach

We consider clustering in group decision making where the opinions are given by pairwise comparison matrices. In particular, the k-medoids model is suggested to classify the matrices as it has a linear programming problem formulation. Its objective function depends on the measure of dissimilarity between the matrices but not on the weights derived from them. With one cluster, our methodology provides an alternative to the conventional aggregation procedures. It can also be used to quantify the reliability of the aggregation. The proposed theoretical framework is applied to a large-scale experimental dataset, on which it is able to automatically detect some mistakes made by the decision-makers.
Metabolic Reprogramming during Microglia Activation

Microglia, the specialized macrophages of the brain, can adopt different shapes and functions, some of which may be detrimental for nervous tissue. Similar to other immune cells, the metabolic program may determine the phenotypic features of microglia, and could constitute a therapeutic target in neurological diseases. Because the knowledge on microglial metabolism was sparse we here employed mouse primary microglia cells polarized into a pro- or anti-inflammatory state to define their metabolic features. After stimulation with either IL1β/IFNγ or IL4, the activity of glycolysis, glucose oxidation, glutamine oxidation, mitochondrial and peroxisomal fatty acid β-oxidation, and fatty acid synthesis, was assessed by using radiolabeled substrates. We complemented these data with transcriptome analysis of key enzymes orchestrating these metabolic pathways. Pro-inflammatory microglia exhibit increased glucose and glutamine metabolism and suppress both fatty acid oxidation and to a lesser extent fatty acid synthesis. On the other hand, anti-inflammatory microglia display changes only in fatty acid metabolism upregulating both fatty acid oxidation and fatty acid synthesis. Importantly, also human microglia-like cells differentiated from pluripotent stem cells upregulate glycolysis in pro-inflammatory conditions. Finally, we show that glycolytic enzymes are induced in a pro-inflammatory brain environment in vivo in mice. Taken together, the distinct metabolism in pro- and anti-inflammatory microglia can constitute a target to direct the microglial phenotype
On the Convergence Rate of the Stochastic Gradient Descent (SGD) and application to a modified policy gradient for the Multi Armed Bandit

We present a self-contained proof of the convergence rate of the Stochastic Gradient Descent (SGD) when the learning rate follows an inverse time decays schedule; we next apply the results to the convergence of a modified form of policy gradient Multi-Armed Bandit (MAB) with $L2$ regularization.
EasyFS: an Efficient Model-free Feature Selection Framework via Elastic Transformation of Features

Traditional model-free feature selection methods treat each feature independently while disregarding the interrelationships among features, which leads to relatively poor performance compared with the model-aware methods. To address this challenge, we propose an efficient model-free feature selection framework via elastic expansion and compression of the features, namely EasyFS, to achieve better performance than state-of-the-art model-aware methods while sharing the characters of efficiency and flexibility with the existing model-free methods. In particular, EasyFS expands the feature space by using the random non-linear projection network to achieve the non-linear combinations of the original features, so as to model the interrelationships among the features and discover most correlated features. Meanwhile, a novel redundancy measurement based on the change of coding rate is proposed for efficient filtering of redundant features. Comprehensive experiments on 21 different datasets show that EasyFS outperforms state-of-the art methods up to 10.9\% in the regression tasks and 5.7\% in the classification tasks while saving more than 94\% of the time.
Algorithm-hardware co-design for Energy-Efficient A/D conversion in ReRAM-based accelerators

Deep neural networks are widely deployed in many fields. Due to the in-situ computation (known as processing in memory) capacity of the Resistive Random Access Memory (ReRAM) crossbar, ReRAM-based accelerator shows potential in accelerating DNN with low power and high performance. However, despite power advantage, such kind of accelerators suffer from the high power consumption of peripheral circuits, especially Analog-to-Digital Converter (ADC), which account for over 60 percent of total power consumption. This problem hinders the ReRAM-based accelerator to achieve higher efficiency. Some redundant Analog-to-Digital conversion operations have no contribution to maintaining inference accuracy, and such operations can be eliminated by modifying the ADC searching logic. Based on such observations, we propose an algorithm-hardware co-design method and explore the co-design approach in both hardware design and quantization algorithms. Firstly, we focus on the distribution output along the crossbar's bit-lines and identify the fine-grained redundant ADC sampling bits. % of weight and To further compress ADC bits, we propose a hardware-friendly quantization method and coding scheme, in which different quantization strategy was applied to the partial results in different intervals. To support the two features above, we propose a lightweight architectural design based on SAR-ADC\@. It's worth mentioning that our method is not only more energy efficient but also retains the flexibility of the algorithm. Experiments demonstrate that our method can reduce about $1.6 \sim 2.3 \times$ ADC power reduction.
Photon Number-Resolving Quantum Reservoir Computing

Neuromorphic processors improve the efficiency of machine learning algorithms through the implementation of physical artificial neurons to perform computations. However, whilst efficient classical neuromorphic processors have been demonstrated in various forms, practical quantum neuromorphic platforms are still in the early stages of development. Here we propose a fixed optical network for photonic quantum reservoir computing that is enabled by photon number-resolved detection of the output states. This significantly reduces the required complexity of the input quantum states while still accessing a high-dimensional Hilbert space. The approach is implementable with currently available technology and lowers the barrier to entry to quantum machine learning.
Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams

We propose a novel nonparametric sequential test for composite hypotheses for means of multiple data streams. Our proposed method, \emph{peeking with expectation-based averaged capital} (PEAK), builds upon the testing-as-betting framework and provides a non-asymptotic $\alpha$-level test across any stopping time. PEAK is computationally tractable and efficiently rejects hypotheses that are incorrect across all potential distributions that satisfy our nonparametric assumption, enabling joint composite hypothesis testing on multiple streams of data. We numerically validate our theoretical findings under the best arm identification and threshold identification in the bandit setting, illustrating both the competitive performance and the computational efficiency of our method against state-of-the-art testing methods.
Multisource Semisupervised Adversarial Domain Generalization Network for Cross-Scene Sea-Land Clutter Classification

Deep learning (DL)-based sea\textendash land clutter classification for sky-wave over-the-horizon-radar (OTHR) has become a novel research topic. In engineering applications, real-time predictions of sea\textendash land clutter with existing distribution discrepancies are crucial. To solve this problem, this article proposes a novel Multisource Semisupervised Adversarial Domain Generalization Network (MSADGN) for cross-scene sea\textendash land clutter classification. MSADGN can extract domain-invariant and domain-specific features from one labeled source domain and multiple unlabeled source domains, and then generalize these features to an arbitrary unseen target domain for real-time prediction of sea\textendash land clutter. Specifically, MSADGN consists of three modules: domain-related pseudolabeling module, domain-invariant module, and domain-specific module. The first module introduces an improved pseudolabel method called domain-related pseudolabel, which is designed to generate reliable pseudolabels to fully exploit unlabeled source domains. The second module utilizes a generative adversarial network (GAN) with a multidiscriminator to extract domain-invariant features, to enhance the model's transferability in the target domain. The third module employs a parallel multiclassifier branch to extract domain-specific features, to enhance the model's discriminability in the target domain. The effectiveness of our method is validated in twelve domain generalizations (DG) scenarios. Meanwhile, we selected 10 state-of-the-art DG methods for comparison. The experimental results demonstrate the superiority of our method.