Futures are contracts obligating the exchange of an asset at a predetermined date and price, notable for their high leverage (e.g., 5-fold) and liquidity (e.g., trillions of dollars) and, therefore, thrive in the Crypto market. Reinforcement learning (RL) has been widely applied in various quantitative tasks. However, most methods focus on the spot (e.g., stock) and could not be directly applied to the futures market with high leverage because of 2 key challenges. First, high leverage amplifies reward fluctuations, making RL training highly stochastic and difficult to converge. Second, prior works lacked self-awareness of capability boundaries, exposing them to the risk of significant capital loss when encountering previously unseen market state representations (e.g., during a black swan event like COVID-19). To tackle these challenges, we propose the eFficient and rIsk-aware eNsemble rEinforcement learning for Futures Trading (FineFT), a novel three-stage ensemble RL framework with stable training and proper risk management. In stage I, ensemble Q learners are selectively updated by ensemble temporal difference (TD) errors, i.e., TD errors across different learners, to improve convergence and performance. In stage II, we filter the Q-learners based on their profitabilities under different market dynamics and train variational autoencoders (VAEs) on market representations of each dynamic to identify the capability boundaries of the filtered learners. In stage III, we dynamically choose from the filtered ensemble and a conservative policy, guided by trained VAEs, to maintain profitability and mitigate risk with new market states. Through extensive experiments on crypto futures in a high-frequency trading environment with high fidelity and 5x leverage, we demonstrate that FineFT significantly outperforms 12 state-of-the-art baselines in 6 widely-used financial metrics, reducing risk by more than 40% while achieving superior profitability compared to the runner-up. Visualization of the selective update mechanism shows that different agents specialize in distinct market dynamics, and ablation studies certify routing with VAEs reduces maximum drawdown effectively, and selective update improves convergence and performance.

Visual-textual cross-modal hashing is an efficient method for large-scale multi-source heterogeneous data retrieval. However, existing unsupervised methods predominantly learn coarse-grained representations and establish global matching relationships, neglecting fine-grained alignment. Furthermore, image-text pairs exhibit partial semantic gaps and noise, leading to inaccurate similarity measurements. To tackle these issues, this paper proposes a Global-Regional Dual Hashing (GRDH) for unsupervised visual-textual retrieval, aiming to bridge the semantic ambiguity between image-text pairs and align multi-granularity representations within a unified transformer-based framework through concept similarity guidance. Specifically, we design the visual-textual transformers to extract global-region tokens from multimodal data. The region token refinement module is designed to eliminate semantically ambiguous region tokens, effectively reducing computational overhead for global-region interactions while promoting attention-based region information coordination. Secondly, under the guidance of concept-enhanced similarity, we constructed the global-regional semantic alignment loss that enables token-level and hash-level alignment in both continuous Euclidean space and discrete Hamming space. Finally, the global-regional dual hashing module generates hash codes of global-regional branches. Through cross-modal consistent hashing learning, it maintains semantic distribution consistency of cross-modal hash codes. Extensive experiments conducted on two baseline datasets (i.e., MIRFLICKR-25K and NUS-WIDE) demonstrate that our GRDH model outperforms recent state-of-the-art unsupervised methods, validating the effectiveness of our approach.

This paper proposes FastAlign, a faster, cheaper, and practical end-to-end solution for sequence alignment using commercial CPUs. It introduces two key innovations: a multi-stage seeding algorithm that improves search performance while maintaining low memory consumption, and an intra-query parallel seed-extension algorithm that eliminates redundancy and increases SIMD utilization. Evaluation results show that FastAlign achieves 2.27× ∼ 3.28× throughput speedup and 2.54× ∼ 5.65× cost reduction compared to state-of-the-art CPU and GPU baselines while guaranteeing 100% identical output to the de facto software BWA-MEM. FastAlign is open-sourced at https://github.com/zzhofict/BWA-FastAlign.git.

The growing scale of ad auctions on online advertising platforms has intensified competition, making manual bidding impractical and necessitating auto-bidding to help advertisers achieve their economic goals. Current auto-bidding methods have evolved to use offline reinforcement learning or generative methods to optimize bidding strategies, but they can sometimes behave counterintuitively due to the black-box training manner and limited mode coverage of datasets, leading to challenges in understanding task status and generalization in dynamic ad environments. Large language models (LLMs) offer a promising solution by leveraging prior human knowledge and reasoning abilities to improve auto-bidding performance. However, directly applying LLMs to auto-bidding faces difficulties due to the need for precise actions in competitive auctions and the lack of specialized auto-bidding knowledge, which can lead to hallucinations and suboptimal decisions. To address these challenges, we propose a hierarchical Large auto-Bidding Model (LBM) to leverage the reasoning capabilities of LLMs for developing a superior auto-bidding strategy. This includes a high-level LBM-Think model for reasoning and a low-level LBM-Act model for action generation. Specifically, we propose a dual embedding mechanism to efficiently fuse two modalities, including language and numerical inputs, for language-guided training of the LBM-Act; then, we propose an offline reinforcement fine-tuning technique termed GQPO for mitigating the LLM-Think's hallucinations and enhancing decision-making performance without simulation or real-world rollout like previous multi-turn LLM-based methods. Experiments demonstrate the superiority of a generative backbone based on our LBM, especially in an efficient training manner and generalization ability.

In real-world decision-making tasks, it is critical for reinforcement learning (RL) methods to be both stable and robust. Maximum entropy RL methods typically generate a robust policy with entropy augmented reward. While incorporating entropy into the reward offers the benefit of exploration, it presents limited universal applicability and persistent convergence difficulties, such as suboptimal policy stabilization and unstable $Q$ value update. From optimization, we define these two issues as tremulous policy and spiky Q-function, investigating their underlying causes and relationships. Analysis with this, the maximum entropy principle leads to a spiky $Q$ -function update, which ultimately results in a tremulous policy. We thus introduce a beta-symmetric Kullback-Leibler (KL) divergence objective to mitigate such issues under the maximum entropy framework. With this objective function, the tremulous nature of the policy could be controlled with a large beta value. The spiky $Q$ -function could be avoided by annealing the entropy in the target $Q$ value, as the beta-symmetric KL divergence is an upper bound of the original reverse KL divergence. Theoretically, we prove that minimizing our new objective function results in a new policy that presents an improvement in the $Q$ value. Guaranteed by these results, we ultimately derive the optimal policy by iteratively updating the $Q$ value and policy, and we call this method max-entropy stable optimization (MeSO). Experimental results on the Mujoco and Roboschool platforms demonstrate that our algorithm maintains stability while offering better flexibility and overall performance.

Optical imaging capable of resolving nanoscale features would revolutionize scientific research and engineering applications across biomedicine, smart manufacturing, and semiconductor quality control. However, due to the physical phenomenon of diffraction, the optical resolution is limited to approximately half the wavelength of light, which impedes the observation of subwavelength objects such as the native state coronavirus, typically smaller than 200 nm. Fortunately, deep learning methods have shown remarkable potential in uncovering underlying patterns within data, promising to overcome the diffraction limit by revealing the mapping pattern between diffraction images and their corresponding ground truth object images. However, the absence of suitable datasets has hindered progress in this field–collecting high-quality optical data of subwavelength objects is highly difficult as these objects are inherently invisible under conventional microscopy, making it impossible to perform standard visual calibration and drift correction. Therefore, we provide the first general optical imaging dataset based on the "building block" concept for challenging the diffraction limit. Drawing an analogy to modular construction principles, we construct a comprehensive optical imaging dataset comprising subwavelength fundamental elements, i.e., small square units that can be assembled into larger and more complex objects. We then frame the task as an image-to-image translation task and evaluate various vision methods. Experimental results validate our "building block" concept, demonstrating that models trained on basic square units can effectively generalize to realistic, more complex unseen objects. Most importantly, by highlighting this underexplored AI-for-science area and its potential, we aspire to advance optical science by fostering collaboration with the vision and machine learning communities.

Despite their success in specific scenarios, existing foundation agents still struggle to generalize across various virtual scenarios, mainly due to the dramatically different encapsulations of environments with manually designed observation and action spaces. To handle this issue, we propose the General Computer Control (GCC) setting to restrict foundation agents to interact with software through the most unified and standardized interface, i.e., using screenshots as input and keyboard and mouse actions as output. We introduce Cradle, a modular and flexible LMM-powered framework, as a preliminary attempt towards GCC. Enhanced by six key modules, Information Gathering, Self-Reflection, Task Inference, Skill Curation, Action Planning, and Memory, Cradle is able to understand input screenshots and output executable code for low-level keyboard and mouse control after high-level planning and information retrieval, so that Cradle can interact with any software and complete long-horizon complex tasks without relying on any built-in APIs. Experimental results show that Cradle exhibits remarkable generalizability and impressive performance across four previously unexplored commercial video games (Red Dead Redemption 2, Cities:Skylines, Stardew Valley and Dealer’s Life 2), five software applications (Chrome, Outlook, Feishu, Meitu and CapCut), and a comprehensive benchmark, OSWorld. With a unified interface to interact with any software, Cradle greatly extends the reach of foundation agents thus paving the way for generalist agents.

Sequence alignment is a fundamental and often time-consuming step in genomic data analysis. Typically, it adheres to the seed-and-extension paradigm and numerous accelerator-based approaches have been proposed to optimize either of the kernels. However, these approaches often increase costs and contribute minimally to the overall alignment process. To address this, we have designed an optimized full pipeline, FastBWA, which seeks to enhance performance while keeping costs low and explores the potential of CPU computing resources. Our implementation demonstrates that FastBWA achieves up to 2.5× and 1.8× in end-to-end alignment throughput compared to BWA-MEM and its newer version, BWA-MEM2.

Auto-bidding, with its strong capability to optimize bidding decisions within dynamic and competitive online environments, has become a pivotal strategy for advertising platforms. Existing approaches typically employ rule-based strategies or Reinforcement Learning (RL) techniques. However, rule-based strategies lack the flexibility to adapt to time-varying market conditions, and RL-based methods struggle to capture essential historical dependencies and observations within Markov Decision Process (MDP) frameworks. Furthermore, these approaches often face challenges in ensuring strategy adaptability across diverse advertising objectives. Additionally, as offline training methods are increasingly adopted to facilitate the deployment and maintenance of stable online strategies, the issues of documented behavioral patterns and behavioral collapse resulting from training on fixed offline datasets become increasingly significant. To address these limitations, this paper introduces a novel offline Generative Auto-bidding framework with Value-Guided Explorations (GAVE). GAVE accommodates various advertising objectives through a score-based Return-To-Go (RTG) module. Moreover, GAVE integrates an action exploration mechanism with an RTG-based evaluation method to explore novel actions while ensuring stability-preserving updates. A learnable value function is also designed to guide the direction of action exploration and mitigate Out-of-Distribution (OOD) problems. Experimental results on two offline datasets and real-world deployments demonstrate that GAVE outperforms state-of-the-art baselines in both offline evaluations and online A/B tests. By applying the core methods of this framework, we proudly secured first place in the NeurIPS 2024 competition, 'AIGB Track: Learning Auto-Bidding Agents with Generative Models'.

SUMMARY: Duplicate marking is a critical preprocessing step in gene sequence analysis to flag redundant reads arising from polymerase chain reaction amplification and sequencing artifacts. Although Picard MarkDuplicates is widely recognized as the gold-standard tool, its single-threaded implementation and reliance on global sorting result in significant computational and resource overhead, limiting its efficiency on large-scale datasets. Here, we introduce FastDup: a high-performance, scalable solution that follows the speculation-and-test mechanism. FastDup achieves up to 20× throughput speedup with 32 threads and guarantees 100% identical output compared to Picard MarkDuplicates. AVAILABILITY AND IMPLEMENTATION: FastDup is a C++ program available from Zenodo https://zenodo.org/records/15727829, Bioconda https://anaconda.org/bioconda/fastdup and GitHub https://github.com/zzhofict/FastDup.git under the MIT license.

Learning representations to capture the very fundamental understanding of the world is a key challenge in machine learning. The hierarchical structure of explanatory factors hidden in data is such a general representation and could be potentially achieved with a hierarchical VAE. However, training a hierarchical VAE always suffers from the “posterior collapse”, where the data information is hard to propagate to the higher-level latent variables, hence resulting in a bad hierarchical representation. To address this issue, we first analyze the shortcomings of existing methods for mitigating the posterior collapse from an information theory perspective, then highlight the necessity of regularization for explicitly propagating data information to higher-level latent variables while maintaining the dependency between different levels. This naturally leads to formulating the inference of the hierarchical latent representation as a sequential decision process, which could benefit from applying reinforcement learning (RL). Aligning RL's objective with the regularization, we first introduce a skip-generative path to acquire a reward for evaluating the information content of an inferred latent representation, and then the developed Q-value function based on it could have a consistent optimization direction of the regularization. Finally, policy gradient, one of the typical RL methods, is employed to train a hierarchical VAE without introducing a gradient estimator. Experimental results firmly support our analysis and demonstrate that our proposed method effectively mitigates the posterior collapse issue, learns an informative hierarchy, acquires explainable latent representations, and significantly outperforms other hierarchical VAE-based methods in downstream tasks.

In recent years, the work on video-text retrieval has been well-developed due to the emergence of large-scale pre-training methods. However, these works focus solely on inter-modal interactions and contrasts, neglecting the contrasts of multigrained features within modalities, which makes the similarity measurement less accurate. Worse still, many of these works only contrast features of the same grain size, ignoring the important information implied by features of different grain sizes. For these reasons, we propose a joint modal similarity contrastive learning model, named JM-CLIP. Firstly, the method employs a multidimensional contrastive strategy of two modal features, including inter-modal and intra-modal multi-grained feature contrasts. Secondly, to fuse the various contrastive similarities well, we also design a joint modal attention module to fuse the various similarities into a final joint multigranularity similarity score. The significant performance improvement achieved on three popular video-text retrieval datasets demonstrates the effectiveness and superiority of our proposed method. The code is available at https://github.com/DannielGe/JM-CLIP.

Sequence alignment is the most time-consuming step in the genome analysis pipeline. Since sequence alignment generally follows the seed-and-extension paradigm, prior proposed hardware accelerators either opt to accelerate the seeding phase or the seed-extension phase. However, the diversity of each sequence in the alignment workflow leads to the pipeline stall or bubbles, which finally results in a decreased throughput for the end-to-end sequence alignment.In this paper, we propose NvWa, which is a hardware scheduling accelerator for sequence alignment. To solve the diversity problem, we propose three novel scheduling mechanisms and corresponding architecture, which target the seeding phase, the seed-extension phase, and the interaction between the two phases, respectively. For the seeding phase, we propose a Seeding Scheduler to schedule all idle seeding units in only one cycle. For the seed-extension phase, the Extension Scheduler can achieve both lower latency and higher parallelism when facing seed-extension tasks with different scales. Between the two phases, an efficient Coordinator caches and dispatches seeding hits to optimal and sub-optimal seed-extension units. Furthermore, to avoid algorithmic obsolescence for the new sequence technologies, we propose a loosely coupled design, which decouples the data path and the control scheduling path. Experimental results show that NvWa can achieve 493×, 200×, 12.11×, 2.30× speedup and 14.21×, 5.60×, 4.34×, 5.85× energy reduction when compared with a 16-thread CPU baseline, an NVIDIA A100 GPU, and two state-of-the-art accelerators, respectively.

Human motion prediction is a classical problem in computer vision and computer graphics, which has a wide range of practical applications. Previous effects achieve great empirical performance based on an encoding-decoding style. The methods of this style work by first encoding previous motions to latent representations and then decoding the latent representations into predicted motions. However, in practice, they are still unsatisfactory due to several issues, including complicated loss constraints, cumbersome training processes, and scarce switch of different categories of motions in prediction. In this paper, to address the above issues, we jump out of the foregoing style and propose a novel framework from a new perspective. Specifically, our framework works in a masked completion fashion. In the training stage, we learn a motion diffusion model that generates motions from random noise. In the inference stage, with a denoising procedure, we make motion prediction conditioning on observed motions to output more continuous and controllable predictions. The proposed framework enjoys promising algorithmic properties, which only needs one loss in optimization and is trained in an end-to-end manner. Additionally, it accomplishes the switch of different categories of motions effectively, which is significant in realistic tasks, e.g., the animation task. Comprehensive experiments on benchmarks confirm the superiority of the proposed framework. The project page is available at https://lhchen.top/Human-MAC.

Despite the great success of existing cross-modal retrieval methods, existing unsupervised cross-modal hashing methods still suffer from common problems. First, the features extracted from the text are too sparse. Second, the similarity matrices of each different modality cannot be fused adaptively. In this paper, we propose Deep Enhanced-Similarity Attention Hashing (DESAH) to alleviate the above problems. Firstly, we construct a text encoder expanding graph convolutional neural network to simultaneously extract features of samples and their semantic neighbors to enrich text features. Secondly, we propose an enhanced attention fusion mechanism. The mechanism is used to adaptively fuse the similarity matrices within different modalities to form a unified inter-modal similarity matrix to guide the learning of hash functions. Extensive experiments have demonstrated that DESAH provides significant improvements in cross-modal retrieval tasks compared to baseline methods.

Unsupervised cross-modal hashing (UCMH) has attracted increasing research due to its efficient retrieval performance and label irrelevance. However, existing methods have some bottlenecks: Firstly, the existing unsupervised methods suffer from inaccurate similarity measures due to the lack of correlation between features of different modalities and simple features cannot fully describe the fine-grained relationships of multi-modal data. Secondly, existing methods have rarely explored vision-language knowledge distillation schemes to distil multi-modal knowledge of these vision-language models to guide the learning of student networks. To address these bottlenecks, this paper proposes an effective unsupervised cross-modal hashing retrieval method, called Vision-Language Knowledge Distillation for Unsupervised Cross-Modal Hashing Retrieval (VLKD). VLKD uses the vision-language pre-training (VLP) model to encode features on multi-modal data, and then constructs a similarity matrix to provide soft similarity supervision for the student model. It distils the knowledge of the VLP model to the student model to gain an understanding of multi-modal knowledge. In addition, we designed an end-to-end unsupervised hashing learning model that incorporates a graph convolutional auxiliary network. The auxiliary network aggregates information from similar data nodes based on the similarity matrix distilled by the teacher model to generate more consistent hash codes. Finally, the teacher network does not require additional training, it only needs to guide the student network to learn high-quality hash representation, and VLKD is quite efficient in training and retrieval. Sufficient experiments on three multimedia retrieval benchmark datasets show that the proposed method achieves better retrieval performance compared to existing unsupervised cross-modal hashing methods, demonstrating the effectiveness of the proposed method.

Due to the advantages of low storage cost and fast retrieval efficiency, deep hashing methods are widely used in cross‐modal retrieval. Images are usually accompanied by corresponding text descriptions rather than labels. Therefore, unsupervised methods have been widely concerned. However, due to the modal divide and semantic differences, existing unsupervised methods cannot adequately bridge the modal differences, leading to suboptimal retrieval results. In this paper, we propose CLIP‐based cycle alignment hashing for unsupervised vision‐text retrieval (CCAH), which aims to exploit the semantic link between the original features of modalities and the reconstructed features. Firstly, we design a modal cyclic interaction method that aligns semantically within intramodality, where one modal feature reconstructs another modal feature, thus taking full account of the semantic similarity between intramodal and intermodal relationships. Secondly, introducing GAT into cross‐modal retrieval tasks. We consider the influence of text neighbour nodes and add attention mechanisms to capture the global features of text modalities. Thirdly, Fine‐grained extraction of image features using the CLIP visual coder. Finally, hash encoding is learned through hash functions. The experiments demonstrate on three widely used datasets that our proposed CCAH achieves satisfactory results in total retrieval accuracy. Our code can be found at: https://github.com/CQYIO/CCAH.git .

With the development of three-generation genome sequencing technology, targeted sequencing has become a fundamental need in virus detection, metagenomics expansion, and human polymorphisms detection. However, previous studies on CPU/GPU platforms have much lower throughput than Nanopore sequencers, and hardware designs have high design cost and flexibility issues. In this paper, we propose Crescent, a GPU-based targeted sequence selector. Specifically, on the algorithm level, we modify the popular-used algorithm without significantly decreasing the accuracy and then propose four key observations. On the architecture level, based on four key observations, we propose two-stage parallelism mechanisms (i.e., inter-task and intra-task) and other optimization strategies (i.e., data reuse and redundant computation elimination) to accelerate our modified algorithm. Our experimental results show that the throughput of Crescent on NVIDIA A100 is $10.84 \times$ and $2.30 \times$ higher than a state-of-the-art 16-thread and 72-thread CPU baseline, respectively.

Booming data volume has become an important challenge for data center storage and bandwidth resources. Consequently, fast and efficient compression architecture is becoming the most fundamental design in data centers. However, the compression ratio (CR) and compression throughput are often difficult to achieve at the same time on existing computing platforms. DEFLATE is a widely used compression format in data centers, which is an ideal case for hardware acceleration. Unfortunately, Deflate has an inherent connection among its special memory access pattern, which limits a higher throughput.

Deep topic models have been proven as a promising way to extract hierarchical latent representations from documents represented as high-dimensional bag-of-words vectors.However, the representation capability of existing deep topic models is still limited by the phenomenon of "posterior collapse", which has been widely criticized in deep generative models, resulting in the higher-level latent representations exhibiting similar or meaningless patterns.To this end, in this paper, we first develop a novel deep-coupling generative process for existing deep topic models, which incorporates skip connections into the generation of documents, enforcing strong links between the document and its multi-layer latent representations.After that, utilizing data augmentation techniques, we reformulate the deep-coupling generative process as a Markov decision process and develop a corresponding Policy Gradient (PG) based training algorithm, which can further alleviate the information reduction at higher layers.Extensive experiments demonstrate that our developed methods can effectively alleviate "posterior collapse" in deep topic models, contributing to providing higher-quality latent document representations.

Unsupervised out-of-distribution (OOD) detection is essential for the reliability of machine learning. In the literature, existing work has shown that higher-level semantics captured by hierarchical VAEs can be used to detect OOD instances.However, we empirically show that, the inherent issue of hierarchical VAEs, i.e., `posterior collapse'', would seriously limit their capacity for OOD detection.Based on a thorough analysis forposterior collapse'', we propose a novel informative hierarchical VAE to alleviate this issue through enhancing the connections between the data sample and its multi-layer stochastic latent representations during training.Furthermore, we propose a novel score function for unsupervised OOD detection, referred to as Adaptive Likelihood Ratio. With this score function, one can selectively aggregate the semantic information on multiple hidden layers of hierarchical VAEs, leading to a strong separability between in-distribution and OOD samples. Experimental results demonstrate that our method can significantly outperform existing state-of-the-art unsupervised OOD detection approaches.

Pluralistic image completion focuses on generating both visually realistic and diverse results for image completion. Prior methods enjoy the empirical successes of this task. However, their used constraints for pluralistic image completion are argued to be not well interpretable and unsatisfactory from two aspects. First, the constraints for visual reality can be weakly correlated to the objective of image completion or even redundant. Second, the constraints for diversity are designed to be task-agnostic, which causes the constraints to not work well. In this paper, to address the issues, we propose an end-to-end probabilistic method. Specifically, we introduce a unified probabilistic graph model that represents the complex interactions in image completion. The entire procedure of image completion is then mathematically divided into several sub-procedures, which helps efficient enforcement of constraints. The sub-procedure directly related to pluralistic results is identified, where the interaction is established by a Gaussian mixture model (GMM). The inherent parameters of GMM are task-related, which are optimized adaptively during training, while the number of its primitives can control the diversity of results conveniently. We formally establish the effectiveness of our method and demonstrate it with comprehensive experiments. The implementationis available at https://github.com/tmllab/PICMM.

Hierarchical topic models such as the gamma belief network (GBN) have delivered promising results in mining multi-layer document representations and discovering interpretable topic taxonomies. However, they often assume in the prior that the topics at each layer are independently drawn from the Dirichlet distribution, ignoring the dependencies between the topics both at the same layer and across different layers. To relax this assumption, we propose sawtooth factorial topic embedding guided GBN, a deep generative model of documents that captures the dependencies and semantic similarities between the topics in the embedding space. Specifically, both the words and topics are represented as embedding vectors of the same dimension. The topic matrix at a layer is factorized into the product of a factor loading matrix and a topic embedding matrix, the transpose of which is set as the factor loading matrix of the layer above. Repeating this particular type of factorization, which shares components between adjacent layers, leads to a structure referred to as sawtooth factorization. An auto-encoding variational inference network is constructed to optimize the model parameter via stochastic gradient descent. Experiments on big corpora show that our models outperform other neural topic models on extracting deeper interpretable topics and deriving better document representations.