OpenRaiser/Intern-Atlas huggingface dataset:
MTMD: Multi-Scale Temporal Memory Learning and Efficient Debiasing Framework for Stock Trend Forecasting
Venue: arXiv cs.CE 2025
Year: 2025

The endeavor of stock trend forecasting is principally focused on predicting the future trajectory of the stock market, utilizing either manual or technical methodologies to optimize profitability. Recent advancements in machine learning technologies have showcased their efficacy in discerning authentic profit signals within the realm of stock trend forecasting, predominantly employing temporal data derived from historical stock price patterns. Nevertheless, the inherently volatile and dynamic characteristics of the stock market render the learning and capture of multi-scale temporal dependencies and stable trading opportunities a formidable challenge. This predicament is primarily attributed to the difficulty in distinguishing real profit signal patterns amidst a plethora of mixed, noisy data. In response to these complexities, we propose a Multi-Scale Temporal Memory Learning and Efficient Debiasing (MTMD) model. This innovative approach encompasses the creation of a learnable embedding coupled with external attention, serving as a memory module through self-similarity. It aims to mitigate noise interference and bolster temporal consistency within the model. The MTMD model adeptly amalgamates comprehensive local data at each timestamp while concurrently focusing on salient historical patterns on a global scale. Furthermore, the incorporation of a graph network, tailored to assimilate global and local information, facilitates the adaptive fusion of heterogeneous multi-scale data. Rigorous ablation studies and experimental evaluations affirm that the MTMD model surpasses contemporary state-of-the-art methodologies by a substantial margin in benchmark datasets. The source code can be found at https://github.com/MingjieWang0606/MDMT-Public.
Aggregating Soft Labels from Crowd Annotations Improves Uncertainty Estimation Under Distribution Shift
Venue: arXiv cs.CL 2025
Year: 2025

Selecting an effective training signal for machine learning tasks is difficult: expert annotations are expensive, and crowd-sourced annotations may not be reliable. Recent work has demonstrated that learning from a distribution over labels acquired from crowd annotations can be effective both for performance and uncertainty estimation. However, this has mainly been studied using a limited set of soft-labeling methods in an in-domain setting. Additionally, no one method has been shown to consistently perform well across tasks, making it difficult to know a priori which to choose. To fill these gaps, this paper provides the first large-scale empirical study on learning from crowd labels in the out-of-domain setting, systematically analyzing 8 soft-labeling methods on 4 language and vision tasks. Additionally, we propose to aggregate soft-labels via a simple average in order to achieve consistent performance across tasks. We demonstrate that this yields classifiers with improved predictive uncertainty estimation in most settings while maintaining consistent raw performance compared to learning from individual soft-labeling methods or taking a majority vote of the annotations. We additionally highlight that in regimes with abundant or minimal training data, the selection of soft labeling method is less important, while for highly subjective labels and moderate amounts of training data, aggregation yields significant improvements in uncertainty estimation over individual methods. Code can be found at https://github.com/copenlu/aggregating-crowd-annotations-ood.
Fuzzing: On Benchmarking Outcome as a Function of Benchmark Properties
Venue: arXiv cs.SE 2025
Year: 2025

Characteristics of a benchmarking setup clearly can have some impact on the benchmark outcome. In this paper, we explore two methodologies to quantify the impact of the specific properties on the benchmarking outcome. Our first methodology is the controlled experiment to identify a causal relationship between a single property in isolation and the benchmarking outcome. However, manipulating one property exactly may not always be practical or possible. Hence, our second methodology is randomization and non-parametric regression to identify the strength of the relationship between arbitrary benchmark properties (i.e., covariates) and outcome. Together, these two fundamental aspects of experimental design, control and randomization, can provide a comprehensive picture of the impact of various properties of the current benchmark on the fuzzer ranking. These analyses can be used to guide fuzzer developers towards areas of improvement in their tools and allow researchers to make more nuanced claims about fuzzer effectiveness. We instantiate each approach on a subset of properties suspected of impacting the relative effectiveness of fuzzers and quantify the effects of these properties on the evaluation outcome. In doing so, we identify multiple novel properties which can have statistically significant effect on the relative effectiveness of fuzzers.
Policy learning "without" overlap: Pessimism and generalized empirical Bernstein's inequality
Venue: arXiv cs.LG 2025
Year: 2025

This paper studies offline policy learning, which aims at utilizing observations collected a priori (from either fixed or adaptively evolving behavior policies) to learn an optimal individualized decision rule that achieves the best overall outcomes for a given population. Existing policy learning methods rely on a uniform overlap assumption, i.e., the propensities of exploring all actions for all individual characteristics must be lower bounded. As one has no control over the data collection process, this assumption can be unrealistic in many situations, especially when the behavior policies are allowed to evolve over time with diminishing propensities for certain actions. In this paper, we propose Pessimistic Policy Learning (PPL), a new algorithm that optimizes lower confidence bounds (LCBs) -- instead of point estimates -- of the policy values. The LCBs are constructed using knowledge of the behavior policies for collecting the offline data. Without assuming any uniform overlap condition, we establish a data-dependent upper bound for the suboptimality of our algorithm, which only depends on (i) the overlap for the optimal policy, and (ii) the complexity of the policy class we optimize over. As an implication, for adaptively collected data, we ensure efficient policy learning as long as the propensities for optimal actions are lower bounded over time, while those for suboptimal ones are allowed to diminish arbitrarily fast. In our theoretical analysis, we develop a new self-normalized type concentration inequality for inverse-propensity-weighting estimators, generalizing the well-known empirical Bernstein's inequality to unbounded and non-i.i.d. data. We complement our theory with an efficient optimization algorithm via Majorization-Minimization and policy tree search, as well as extensive simulation studies and real-world applications that demonstrate the efficacy of PPL.
Stream: Design Space Exploration of Layer-Fused DNNs on Heterogeneous Dataflow Accelerators
Venue: arXiv cs.AR 2025
Year: 2025

As the landscape of deep neural networks evolves, heterogeneous dataflow accelerators, in the form of multi-core architectures or chiplet-based designs, promise more flexibility and higher inference performance through scalability. So far, these systems exploit the increased parallelism by coarsely mapping a single layer at a time across cores, which incurs frequent costly off-chip memory accesses, or by pipelining batches of inputs, which falls short in meeting the demands of latency-critical applications. To alleviate these bottlenecks, this work explores a new fine-grain mapping paradigm, referred to as layer fusion, on heterogeneous dataflow accelerators through a novel design space exploration framework called Stream. Stream captures a wide variety of heterogeneous dataflow architectures and mapping granularities, and implements a memory and communication-aware latency and energy analysis validated with three distinct state-of-the-art hardware implementations. As such, it facilitates a holistic exploration of architecture and mapping, by strategically allocating the workload through constraint optimization. The findings demonstrate that the integration of layer fusion with heterogeneous dataflow accelerators yields up to 2.2x lower energy-delay product in inference efficiency, addressing both energy consumption and latency concerns. The framework is available open-source at: https://github.com/kuleuven-micas/stream.
Annotated History of Modern AI and Deep Learning
Venue: arXiv cs.NE 2025
Year: 2025

Machine learning (ML) is the science of credit assignment. It seeks to find patterns in observations that explain and predict the consequences of events and actions. This then helps to improve future performance. Minsky's so-called "fundamental credit assignment problem" (1963) surfaces in all sciences including physics (why is the world the way it is?) and history (which persons/ideas/actions have shaped society and civilisation?). Here I focus on the history of ML itself. Modern artificial intelligence (AI) is dominated by artificial neural networks (NNs) and deep learning, both of which are conceptually closer to the old field of cybernetics than what was traditionally called AI (e.g., expert systems and logic programming). A modern history of AI & ML must emphasize breakthroughs outside the scope of shallow AI text books. In particular, it must cover the mathematical foundations of today's NNs such as the chain rule (1676), the first NNs (circa 1800), the first practical AI (1914), the theory of AI and its limitations (1931-34), and the first working deep learning algorithms (1965-). From the perspective of 2025, I provide a timeline of the most significant events in the history of NNs, ML, deep learning, AI, computer science, and mathematics in general, crediting the individuals who laid the field's foundations. The text contains numerous hyperlinks to relevant overview sites. With a ten-year delay, it supplements my 2015 award-winning deep learning survey which provides hundreds of additional references. Finally, I will put things in a broader historical context, spanning from the Big Bang to when the universe will be many times older than it is now.
Tighter Bounds for Query Answering with Guarded TGDs
Venue: arXiv cs.LO 2025
Year: 2025

We consider the complexity of the open-world query answering problem, where we wish to determine certain answers to conjunctive queries over incomplete datasets specified by an initial set of facts and a set of guarded TGDs. This problem has been well-studied in the literature and is decidable but with a high complexity, namely, it is 2EXPTIME complete. Further, the complexity shrinks by one exponential when the arity is fixed. We show in this paper how we can obtain better complexity bounds when considering separately the arity of the guard atom and that of the additional atoms, called the side signature. Our results make use of the technique of linearizing guarded TGDs, introduced in Gottlob, Manna, and Pieris. Specifically, we present a variant of the linearization process, making use of a restricted version of the chase that we recently introduced. Our results imply that open-world query answering with guarded TGDs can be solved in EXPTIME with arbitrary-arity guard relations if we simply bound the arity of the side signature; and that the complexity drops to NP if we fix the side signature and bound the width of the dependencies.
A Theory of Conversion Relations for Prefixed Units of Measure
Venue: arXiv cs.PL 2025
Year: 2025

Units of measure with prefixes and conversion rules are given a formal semantic model in terms of categorial group theory. Basic structures and both natural and contingent semantic operations are defined. Conversion rules are represented as a class of ternary relations with both group-like and category-like properties. A hierarchy of subclasses is explored, each satisfying stronger useful algebraic properties than the preceding, culminating in a direct efficient conversion-by-rewriting algorithm.
What-if Analysis for Business Professionals: Current Practices and Future Opportunities
Venue: arXiv cs.HC 2025
Year: 2025

What-if analysis (WIA) is essential for data-driven decision-making, allowing users to assess how changes in variables impact outcomes and explore alternative scenarios. Existing WIA research primarily supports the workflows of data scientists and analysts, and largely overlooks business professionals who engage in WIA through non-technical means. To bridge this gap, we conduct a two-part user study with 22 business professionals across marketing, sales, product, and operations roles. The first study examines their existing WIA practices, tools, and challenges. Findings reveal that business professionals perform many WIA techniques independently using rudimentary tools due to various constraints. We then implement representative WIA techniques in a visual analytics prototype and use it as a probe to conduct a follow-up study evaluating business professionals' practical use of the techniques. Results show that these techniques improve decision-making efficiency and confidence while underscoring the need for better data preparation, risk assessment, and domain knowledge integration support. Finally, we offer design recommendations to enhance future business analytics systems.
End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation
Venue: arXiv cs.LG 2025
Year: 2025

Multivariate time series forecasting with hierarchical structure is pervasive in real-world applications, demanding not only predicting each level of the hierarchy, but also reconciling all forecasts to ensure coherency, i.e., the forecasts should satisfy the hierarchical aggregation constraints. Moreover, the disparities of statistical characteristics between levels can be huge, worsened by non-Gaussian distributions and non-linear correlations. To this extent, we propose a novel end-to-end hierarchical time series forecasting model, based on conditioned normalizing flow-based autoregressive transformer reconciliation, to represent complex data distribution while simultaneously reconciling the forecasts to ensure coherency. Unlike other state-of-the-art methods, we achieve the forecasting and reconciliation simultaneously without requiring any explicit post-processing step. In addition, by harnessing the power of deep model, we do not rely on any assumption such as unbiased estimates or Gaussian distribution. Our evaluation experiments are conducted on four real-world hierarchical datasets from different industrial domains (three public ones and a dataset from the application servers of Alipay's data center) and the preliminary results demonstrate efficacy of our proposed method.
Scene Structure Guidance Network: Unfolding Graph Partitioning into Pixel-Wise Feature Learning
Venue: arXiv cs.CV 2025
Year: 2025

Understanding the informative structures of scenes is essential for low-level vision tasks. Unfortunately, it is difficult to obtain a concrete visual definition of the informative structures because influences of visual features are task-specific. In this paper, we propose a single general neural network architecture for extracting task-specific structure guidance for scenes. To do this, we first analyze traditional spectral clustering methods, which computes a set of eigenvectors to model a segmented graph forming small compact structures on image domains. We then unfold the traditional graph-partitioning problem into a learnable network, named \textit{Scene Structure Guidance Network (SSGNet)}, to represent the task-specific informative structures. The SSGNet yields a set of coefficients of eigenvectors that produces explicit feature representations of image structures. In addition, our SSGNet is light-weight ($\sim$ 56K parameters), and can be used as a plug-and-play module for off-the-shelf architectures. We optimize the SSGNet without any supervision by proposing two novel training losses that enforce task-specific scene structure generation during training. Our main contribution is to show that such a simple network can achieve state-of-the-art results for several low-level vision applications. We also demonstrate that our network generalizes well on unseen datasets, compared to existing methods which use structural embedding frameworks. We further propose a lighter version of SSGNet ($\sim$ 29K parameters) for depth computation, SSGNet-D, and successfully execute it on edge computing devices like Jetson AGX Orin, improving the performance of baseline network, even in the wild, with little computational delay.
Towards the Identifiability in Noisy Label Learning: A Multinomial Mixture Modelling Approach
Venue: arXiv cs.LG 2025
Year: 2025

Learning from noisy labels (LNL) is crucial in deep learning, in which one of the approaches is to identify clean-label samples from poorly-annotated datasets. Such an identification is challenging because the conventional LNL problem, which assumes only one noisy label per instance, is non-identifiable, i.e., clean labels cannot be estimated theoretically without additional heuristics. This paper presents a novel data-driven approach that addresses this issue without requiring any heuristics about clean samples. We discover that the LNL problem becomes identifiable if there are at least $2C - 1$ i.i.d. noisy labels per instance, where $C$ is the number of classes. Our finding relies on the assumption of i.i.d. noisy labels and multinomial mixture modelling, making it easier to interpret than previous studies that require full-rank noisy-label transition matrices. To fulfil this condition without additional manual annotations, we propose a method that automatically generates additional i.i.d. noisy labels through nearest neighbours. These noisy labels are then used in the Expectation-Maximisation algorithm to infer clean labels. Our method demonstrably estimates clean labels accurately across various label noise benchmarks, including synthetic, web-controlled, and real-world datasets. Furthermore, the model trained with our method performs competitively with many state-of-the-art methods.
Plant species richness prediction from DESIS hyperspectral data: A comparison study on feature extraction procedures and regression models
Venue: arXiv cs.LG 2025
Year: 2025

The diversity of terrestrial vascular plants plays a key role in maintaining the stability and productivity of ecosystems. Airborne hyperspectral imaging has shown promise for measuring plant diversity remotely, but to operationalise these efforts over large regions we need to advance satellite-based alternatives. The advanced spectral and spatial specification of the recently launched DESIS (the DLR Earth Sensing Imaging Spectrometer) instrument provides a unique opportunity to test the potential for monitoring plant species diversity with spaceborne hyperspectral data. This study provides a quantitative assessment on the ability of DESIS hyperspectral data for predicting plant species richness in two different habitat types in southeast Australia. Spectral features were first extracted from the DESIS spectra, then regressed against on-ground estimates of plant species richness, with a two-fold cross validation scheme to assess the predictive performance. We tested and compared the effectiveness of Principal Component Analysis (PCA), Canonical Correlation Analysis (CCA), and Partial Least Squares analysis (PLS) for feature extraction, and Kernel Ridge Regression (KRR), Gaussian Process Regression (GPR), and Random Forest Regression (RFR) for species richness prediction. The best prediction results were $r=0.76$ and $\text{RMSE}=5.89$ for the Southern Tablelands region, and $r=0.68$ and $\text{RMSE}=5.95$ for the Snowy Mountains region. Relative importance analysis for the DESIS spectral bands showed that the red-edge, red, and blue spectral regions were more important for predicting plant species richness than the green bands and the near-infrared bands beyond red-edge. We also found that the DESIS hyperspectral data performed better than Sentinel-2 multispectral data in the prediction of plant species richness.
A Minimal Formulation of Session Types: The Sessions of Trios in Concert
Venue: arXiv cs.PL 2025
Year: 2025

Session types are a type-based approach to the verification of message-passing programs. They specify communication structures essential to enforcing program correctness; by relying on sequencing constructs, a session type can precisely describe the intended order of communication actions through a channel. In this paper we study a fragment of session types that makes a very limited use of sequencing; we call it minimal session types. In the context of a core process calculus with sessions and higher-order concurrency, we establish two technical results. First, we prove that every process P typable with standard session types can be compiled down into a process D(P) typable with minimal session types. Second, we prove that P and D(P) are behaviorally equivalent. These results show that having sequencing in both processes and session types is convenient, but that only sequencing in processes is truly indispensable, as it can correctly codify sequencing in types. Our developments draw inspiration from work by Parrow on behavior-preserving decompositions of untyped processes using trios, i.e., processes with exactly three nested prefixes. By casting Parrow's approach in the realm of typed processes, our developments reveal a conceptually simple formulation of session types, supported by static and dynamic correctness results.
Synthesizing Proxy Applications for MPI Programs
Venue: arXiv cs.DC 2025
Year: 2025

Proxy applications (proxy-apps) are basic tools for evaluating the performance of specific workloads on high-performance computing (HPC) systems. Since the development of high-fidelity proxy-apps, which exhibit similar performance characteristics as corresponding production applications, is labor-intensive, synthetic proxy-apps are created as a useful supplement to manually developed proxy-apps. To thoroughly resemble performance characteristics of HPC applications represented by Message Passing Interface (MPI) programs, we propose Siesta, a novel framework to automatically synthesize proxy-apps based on communication-computation traces. Given an MPI program, Siesta synthesizes parameterized code snippets to mimic computation behaviors in different execution periods, and combines the code snippets and MPI function records into an event trace. It then extracts program behavior patterns from the trace as grammars and finally transforms the grammars into a synthetic proxy-app. We evaluate the proposed methods on representative MPI programs with various environments. The results show that our synthetic proxy-apps can precisely approximate the performance characteristics of MPI programs.