Institute of Computing Technology

Cloud‐Native and Distributed Systems for Efficient and Scalable Large Language Models—A Research Agenda

ABSTRACT Objective Large Language Models (LLMs) are rapidly transforming artificial intelligence applications, but their unprecedented computational, memory, communication, and data‐management demands pose significant challenges to existing computing infrastructures. Traditional...

RFEA: Reliable Forensic Evidence Aggregation for Deepfake Video Detection

Deepfake video detection can benefit from multiple forensic cues, but the resulting responses are not always equally reliable or directly comparable. Moreover, informative forgery evidence may be concentrated in only part of a video. Consequently, indiscriminate fusion can allow...

JustEvo: A Prior-Guided Adaptive Mixture-of-Experts Framework for Multifunctional Protein Directed Evolution

Abstract Machine-learning-guided directed evolution can reduce the experimental burden of protein engineering by prioritizing variants before they are tested. In practice, however, many campaigns begin with few or no target-specific measurements, face choices about how to use het...

EcoMLC: An Energy-Efficient Machine Learning Compilation Framework via Cache Reuse and Frequency Scaling

While modern machine learning compilers (MLCs) are pushing deep neural network (DNN) inference latency to its theoretical limits, the accompanying energy overhead has become a critical bottleneck for large-scale deployment. Excessive energy consumption not only increases electric...

Multi-Step Obstruction Reasoning for Target-Oriented Grasp Sequence Generation in Cluttered Scenes

Retrieving target objects in severe clutter requires multi-step reasoning to establish valid obstacle removal sequences. While recent Vision–Language Models (VLMs) have advanced instruction-driven clutter grasping, existing paradigms lack explicit construction of graph-constraine...

Towards Optimal Speculative Decoding: A Comprehensive Survey on Optimizations and Future Directions

Speculative decoding (SD) has emerged as a compelling approach to breaking the sequential bottleneck of autoregressive large language model (LLM) inference. By coupling lightweight drafting with parallel verification, SD reduces expensive target-model decoding while preserving ge...

Elevation of Prime Gaps to Vector Topological Distances on Calabi-Yau Three-Folds: Discrete-Continuous Geometric Embedding within the PDSM-NT Framework

Abstract As a core research object in analytic number theory, the prime gap $$g_n=p_{n+1}-p_n$$ has long been regarded as a one-dimensional non-negative integer scalar. Within the axiomatic framework of the PDSM-NT primitive vortex number theory and wormhole space theory, this pa...

Early-Enrichment Hit Discovery via Reversible-Work c(t) Estimation in Metadynamics (CTMD)

Abstract Virtual screening for small-molecule binders is often limited by false positives from approximate scoring functions and rigid-receptor assumptions. These can be addressed downstream through accurate but expensive free-energy calculations. At the same time, recent artific...

A System Architecture for Low Latency Multiprogramming Quantum Computing

As quantum systems scale, multiprogramming quantum computing (MPQC) provides a practical way to improve device utilization and throughput. However, because quantum executables are device-dependent, non-portable across qubit regions, and highly susceptible to noise and crosstalk,...

A Spectral Framework for Prime-Generating Operators and an Exploratory π-Polynomial Relation for the Fine Structure Constant

Euler’s formula ζ(2) = π2/6 reveals a deep connection between primes (via the Euler product)and the transcendental number π. This paper elevates the fact that “primes generate π”to a rigorous operator-spectral language: we define a prime-generating operator Agen,shuiwhose discret...

A Review of Neural Question Generation: Approaches, Challenges, and Future Directions

The goal of question generation is to automatically produce relevant and meaningful questions from diverse inputs such as knowledge bases, natural language texts, and images. With the rapid advancement of neural architectures, neural question generation (NQG) has attracted growin...

Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy

X‐ray absorption spectroscopy (XAS) is a critical technique for probing the local structural and electronic properties of materials. Advanced synchrotron radiation facilities generate complex, high‐dimensional spectra, which pose significant challenges for traditional analysis me...

Steady-State Characteristics of Normalized 11th-Order Prime Differences and Its Gradient Correlation with the Decimal Sequence of π

Abstract Classical number theory and transcendental number theory generally hold that the high-order differences of prime sequences are unbounded and disordered without finite steady-state periodic characteristics. As a typical transcendental number, the decimal expansion of π is...

Research on the Underlying Analytical Mechanism of 11th-Order Optimality and 64-bit Steady-State Scale and Prime Number Correlation

Abstract To reveal the deep coupling law between the high-order difference optimality criterion and the 2-adic scale steady-state system, this paper conducts interdisciplinary research integrating number theory difference operators, p-adic geometry, cyclotomic field theory, and C...

Steady-State Characteristics of Normalized 11th-Order Prime Differences and Its Gradient Correlation with the Decimal Sequence of π

Abstract Classical number theory and transcendental number theory generally hold that the high-order differences of prime sequences are unbounded and disordered without finite steady-state periodic characteristics. As a typical transcendental number, the decimal expansion of π is...

Research on the Underlying Analytical Mechanism of 11th-Order Optimality and 64-bit Steady-State Scale and Prime Number Correlation

Abstract To reveal the deep coupling law between the high-order difference optimality criterion and the 2-adic scale steady-state system, this paper conducts interdisciplinary research integrating number theory difference operators, p-adic geometry, cyclotomic field theory, and C...

Multicenter Privacy‐Preserving Federated Models for Predicting Postoperative Delirium and Acute Kidney Injury in Older Patients

ABSTRACT Postoperative delirium (POD) and acute kidney injury (AKI) are serious complications in older patients undergoing surgery, yet predictive model development is often constrained by single‐center data limitations and privacy concerns that preclude centralized data sharing....

A co-optimization qubit mapping algorithm via two-stage search and bidirectional look

Abstract Current quantum devices typically lack full qubit connectivity, making it difficult to directly execute logical circuits on quantum devices. This limitation necessitates quantum circuit mapping algorithms to insert SWAP gates, dynamically remapping logical qubits to phys...

Efficient Discovery of Motif Transition Process for Large-Scale Temporal Graphs

Understanding the dynamic transition of motifs in temporal graphs is essential for revealing how graph structures evolve over time, identifying critical patterns, and predicting future behaviors, yet existing methods often focus on predefined motifs, limiting their ability to com...

Complete Proof of the Riemann Hypothesis Based on PDSM-NT Primitive Vortex Number Theory and CY₃ Manifold Topology

Based on the independently constructed PDSM-NT (Primitive Discrete Symmetric Modular Number Theory) theoretical system, this paper innovatively reconstructs the Riemann zeta function on the basis of interdisciplinary integration of Calabi-Yau three-fold (CY₃) manifold topological...