Miracle-dz/UniFAR-FTUs huggingface dataset:
MASTER: Market-Guided Stock Transformer for Stock Price Forecasting

Stock price forecasting has remained an extremely challenging problem for many decades due to the high volatility of the stock market. Recent efforts have been devoted to modeling complex stock correlations toward joint stock price forecasting. Existing works share a common neural architecture that learns temporal patterns from individual stock series and then mixes up temporal representations to establish stock correlations. However, they only consider time-aligned stock correlations stemming from all the input stock features, which suffer from two limitations. First, stock correlations often occur momentarily and in a cross-time manner. Second, the feature effectiveness is dynamic with market variation, which affects both the stock sequential patterns and their correlations. To address the limitations, this paper introduces MASTER, a MArkert-guided Stock TransformER, which models the momentary and cross-time stock correlation and leverages market information for automatic feature selection. MASTER elegantly tackles the complex stock correlation by alternatively engaging in intra-stock and inter-stock information aggregation. Experiments show the superiority of MASTER compared with previous works and visualize the captured realistic stock correlation to provide valuable insights.
Think and Retrieval: A Hypothesis Knowledge Graph Enhanced Medical Large Language Models

We explore how the rise of Large Language Models (LLMs) significantly impacts task performance in the field of Natural Language Processing. We focus on two strategies, Retrieval-Augmented Generation (RAG) and Fine-Tuning (FT), and propose the Hypothesis Knowledge Graph Enhanced (HyKGE) framework, leveraging a knowledge graph to enhance medical LLMs. By integrating LLMs and knowledge graphs, HyKGE demonstrates superior performance in addressing accuracy and interpretability challenges, presenting potential applications in the medical domain. Our evaluations using real-world datasets highlight HyKGE's superiority in providing accurate knowledge with precise confidence, particularly in complex and difficult scenarios. The code will be available until published.
Olfactory Label Prediction on aroma-chemical Pairs

The application of deep learning techniques on aroma-chemicals has resulted in models more accurate than human experts at predicting olfactory qualities. However, public research in this domain has been limited to predicting the qualities of single molecules, whereas in industry applications, perfumers and food scientists are often concerned with blends of many odorants. In this paper, we apply both existing and novel approaches to a dataset we gathered consisting of labeled pairs of molecules. We present a publicly available model capable of generating accurate predictions for the non-linear qualities arising from blends of aroma-chemicals.
A theory of volumetric representations for opaque solids

We develop a theory for the representation of opaque solids as volumetric models. Starting from a stochastic representation of opaque solids as random indicator functions, we prove the conditions under which such solids can be modeled using exponential volumetric transport. We also derive expressions for the volumetric attenuation coefficient as a functional of the probability distributions of the underlying indicator functions. We generalize our theory to account for isotropic and anisotropic scattering at different parts of the solid, and for representations of opaque solids as implicit surfaces. We derive our volumetric representation from first principles, which ensures that it satisfies physical constraints such as reciprocity and reversibility. We use our theory to explain, compare, and correct previous volumetric representations, as well as propose meaningful extensions that lead to improved performance in 3D reconstruction tasks.
A Comprehensive Evaluation of Parameter-Efficient Fine-Tuning on Software Engineering Tasks

Pre-trained models (PTMs) have achieved great success in various Software Engineering (SE) downstream tasks following the ``pre-train then fine-tune'' paradigm. As fully fine-tuning all parameters of PTMs can be computationally expensive, a widely used solution is parameter-efficient fine-tuning (PEFT), which freezes PTMs while introducing extra parameters. Though work has been done to test PEFT methods in the SE field, a comprehensive evaluation is still lacking. This paper aims to fill in this gap by evaluating the effectiveness of five PEFT methods on eight PTMs and four SE downstream tasks. For different tasks and PEFT methods, we seek answers to the following research questions: 1) Is it more effective to use PTMs trained specifically on source code, or is it sufficient to use PTMs trained on natural language text? 2) What is the impact of varying model sizes? 3) How does the model architecture affect the performance? Besides effectiveness, we also discuss the efficiency of PEFT methods, concerning the costs of required training time and GPU resource consumption. We hope that our findings can provide a deeper understanding of PEFT methods on various PTMs and SE downstream tasks. All the codes and data are available at \url{https://github.com/zwtnju/PEFT.git}.
Factorial Moments of the Geometric Distribution of Order $k$

We derive a simple expression for the $r^{th}$ factorial moment $\mu_{(r)}$ of the geometric distribution of order $k$ with success parameter $p\in(0,1)$ (and $q=1-p$) in terms of its probability mass function $f_k(n)$. Specifically, $\mu_{(r)} = r! f_k((r+1)k+r)/((qp^k)^{r+1})$.
AutoTask: Executing Arbitrary Voice Commands by Exploring and Learning from Mobile GUI

Voice command interfaces (VCIs) have gained increasing importance, enabling hands-free and eyes-free interaction with digital devices. However, the inherent complexity in constructing effective voice interfaces has limited the VCIs' functionalities to only a small fraction of GUI applications and tasks. This paper presents AutoTask, a VCI capable of automating any task in any mobile application without configuration or modification from developers or end users. The primary challenge for AutoTask is the lack of knowledge, as it needs to accomplish unknown tasks (e.g., user commands) within an unknown environment (e.g., GUI). To address this challenge, AutoTask employs two strategies: (1) trial and error: AutoTask explores the GUI, attempts potential operation sequences, and recovers from errors through backtracking; (2) learning from the environment: AutoTask accumulates experiences during exploration and summarizes correct knowledge from these experiences. We implemented AutoTask on Android devices and conducted an evaluation study, which proved the feasibility of AutoTask.
PKBOIN-12: A Bayesian optimal interval Phase I/II design incorporating pharmacokinetics outcomes to find the optimal biological dose

Immunotherapies and targeted therapies have gained popularity due to their promising therapeutic effects across multiple treatment areas. The focus of early phase dose-finding clinical trials has shifted from finding the maximum tolerated dose (MTD) to identifying the optimal biological dose (OBD), which aims to balance the toxicity and efficacy outcomes, thereby optimizing the risk-benefit trade-off. These trials often collect multiple pharmacokinetics (PK) outcomes to assess drug exposure, which has shown correlations with toxicity and efficacy outcomes but has not been utilized in the current dose-finding designs for OBD selection. Moreover, PK outcomes are usually available within days after initial treatment, much faster than toxicity and efficacy outcomes. To bridge this gap, we introduce the innovative model-assisted PKBOIN-12 design, which enhances the BOIN12 design by integrating PK information into both the dose-finding algorithm and the final OBD determination process. We further extend PKBOIN-12 to the TITE-PKBOIN-12 design to address the challenges of late-onset toxicity and efficacy outcomes. Simulation results demonstrate that the PKBOIN-12 design more effectively identifies the OBD and allocates a greater number of patients to it than BOIN12. Additionally, PKBOIN-12 decreases the probability of selecting inefficacious doses as the OBD by excluding those with low drug exposure. Comprehensive simulation studies and sensitivity analysis confirm the robustness of both PKBOIN-12 and TITE-PKBOIN-12 designs in various scenarios.
Surface termination effect of SrTiO3 substrate on ultrathin SrRuO3

A uniform one-unit-cell-high step on the SrTiO3 substrate is a prerequisite for growing high-quality epitaxial oxide heterostructures. However, it is inevitable that defects induced by mixed substrate surface termination exist at the interface, significantly impacting the properties of ultrathin films. In this study, we microscopically identify the origin for the lateral inhomogeneity in the growth of ultrathin SrRuO3 films due to the step effects of SrTiO3(001). By using atomic-resolved scanning transmission electron microscopy, we observe two distinct types of step propagation along the [011] and [0-11]crystallographic direction in SrTiO3-SrRuO3 heterostructures, respectively. In particular, the type-II [0-11] step results in lateral discontinuity of monolayer SrRuO3 and originates from the SrO-terminated regions along the TiO2-terminated step edge. Such an induced lateral discontinuity should be responsible for the distinct electronic and magnetic properties of monolayer SrRuO3. Our findings underscore the critical importance of using single termination STO substrate to achieve high-quality termination selective films and to unveil the intrinsic properties of epitaxial films in the atomic limit.
Overcoming the Coherence Time Barrier in Quantum Machine Learning on Temporal Data

The practical implementation of many quantum algorithms known today is believed to be limited by the coherence time of the executing quantum hardware and quantum sampling noise. Here we present a machine learning algorithm, NISQRC, for qubit-based quantum systems that enables processing of temporal data over durations unconstrained by the finite coherence times of constituent qubits. NISQRC strikes a balance between input encoding steps and mid-circuit measurements with reset to endow the quantum system with an appropriate-length persistent temporal memory to capture the time-domain correlations in the streaming data. This enables NISQRC to overcome not only limitations imposed by finite coherence, but also information scrambling or thermalization in monitored circuits. The latter is believed to prevent known parametric circuit learning algorithms even in systems with perfect coherence from operating beyond a finite time period on streaming data. By extending the Volterra Series analysis of dynamical systems theory to quantum systems, we identify measurement and reset conditions necessary to endow a monitored quantum circuit with a finite memory time. To validate our approach, we consider the well-known channel equalization task to recover a test signal of $N_{ts}$ symbols that is subject to a noisy and distorting channel. Through experiments on a 7-qubit quantum processor and numerical simulations we demonstrate that $N_{ts}$ can be arbitrarily long not limited by the coherence time.
Get a Grip: Reconstructing Hand-Object Stable Grasps in Egocentric Videos

We address in-the-wild hand-object reconstruction for a known object category in egocentric videos, focusing on temporal periods of stable grasps. We propose the task of Hand-Object Stable Grasp Reconstruction (HO-SGR), the joint reconstruction of frames during which the hand is stably holding the object. We thus can constrain the object motion relative to the hand, effectively regularising the reconstruction and improving performance. By analysing the 3D ARCTIC dataset, we identify temporal periods where the contact area between the hand and object vertices remain stable. We showcase that objects within stable grasps move within a single degree of freedom (1~DoF). We thus propose a method for jointly optimising all frames within a stable grasp by minimising the object's rotation to that within a latent 1 DoF. We then extend this knowledge to in-the-wild egocentric videos by labelling 2.4K clips of stable grasps from the EPIC-KITCHENS dataset. Our proposed EPIC-Grasps dataset includes 390 object instances of 9 categories, featuring stable grasps from videos of daily interactions in 141 environments. Our method achieves significantly better HO-SGR, both qualitatively and by computing the stable grasp area and 2D projection labels of mask overlaps.
Battery-Care Resource Allocation and Task Offloading in Multi-Agent Post-Disaster MEC Environment

Being an up-and-coming application scenario of mobile edge computing (MEC), the post-disaster rescue suffers multitudinous computing-intensive tasks but unstably guaranteed network connectivity. In rescue environments, quality of service (QoS), such as task execution delay, energy consumption and battery state of health (SoH), is of significant meaning. This paper studies a multi-user post-disaster MEC environment with unstable 5G communication, where device-to-device (D2D) link communication and dynamic voltage and frequency scaling (DVFS) are adopted to balance each user's requirement for task delay and energy consumption. A battery degradation evaluation approach to prolong battery lifetime is also presented. The distributed optimization problem is formulated into a mixed cooperative-competitive (MCC) multi-agent Markov decision process (MAMDP) and is tackled with recurrent multi-agent Proximal Policy Optimization (rMAPPO). Extensive simulations and comprehensive comparisons with other representative algorithms clearly demonstrate the effectiveness of the proposed rMAPPO-based offloading scheme.
Pseudorandom and Pseudoentangled States from Subset States

Pseudorandom states (PRS) are an important primitive in quantum cryptography. In this paper, we show that subset states can be used to construct PRSs. A subset state with respect to $S$, a subset of the computational basis, is \[ \frac{1}{\sqrt{|S|}}\sum_{i\in S} |i\rangle. \] As a technical centerpiece, we show that for any fixed subset size $|S|=s$ such that $s = 2^n/\omega(\mathrm{poly}(n))$ and $s=\omega(\mathrm{poly}(n))$, where $n$ is the number of qubits, a random subset state is information-theoretically indistinguishable from a Haar random state even provided with polynomially many copies. This range of parameter is tight. Our work resolves a conjecture by Ji, Liu and Song. Since subset states of small size have small entanglement across all cuts, this construction also illustrates a pseudoentanglement phenomenon.
Semi-Bandit Learning for Monotone Stochastic Optimization

Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables. Exact or approximation algorithms have been obtained for several fundamental problems in this area. However, a significant limitation of this approach is that it requires full knowledge of the underlying probability distributions. Can we still get good (approximation) algorithms if these distributions are unknown, and the algorithm needs to learn them through repeated interactions? In this paper, we resolve this question for a large class of"monotone"stochastic problems, by providing a generic online learning algorithm with $\sqrt{T \log T}$ regret relative to the best approximation algorithm (under known distributions). Importantly, our online algorithm works in a semi-bandit setting, where in each period, the algorithm only observes samples from the r.v. s that were actually probed. Our framework applies to several fundamental problems in stochastic optimization such as prophet inequality, Pandora's box, stochastic knapsack, stochastic matchings and stochastic submodular optimization.
2D-Guided 3D Gaussian Segmentation

Recently, 3D Gaussian, as an explicit 3D representation method, has demonstrated strong competitiveness over NeRF (Neural Radiance Fields) in terms of expressing complex scenes and training duration. These advantages signal a wide range of applications for 3D Gaussians in 3D understanding and editing. Meanwhile, the segmentation of 3D Gaussians is still in its infancy. The existing segmentation methods are not only cumbersome but also incapable of segmenting multiple objects simultaneously in a short amount of time. In response, this paper introduces a 3D Gaussian segmentation method implemented with 2D segmentation as supervision. This approach uses input 2D segmentation maps to guide the learning of the added 3D Gaussian semantic information, while nearest neighbor clustering and statistical filtering refine the segmentation results. Experiments show that our concise method can achieve comparable performances on mIOU and mAcc for multi-object segmentation as previous single-object segmentation methods.
Isospectral CR manifolds with respect to the Kohn Laplacian

We prove that the spectrum of the Kohn Laplacian does not determine the equivalence classes of CR manifolds. We construct pairs of odd-dimensional elliptic manifolds that are not equivalent as CR manifolds but whose Kohn Laplacians have the same spectrum. These manifolds are endowed with the CR structures inherited from the canonical CR structure on the sphere of the same dimension. We provide three different constructions among lens spaces and an additional one among elliptic manifolds with non-cyclic fundamental groups.
Understanding driver-pedestrian interactions to predict driver yielding: naturalistic open-source dataset collected in Minnesota

Many factors influence the yielding result of a driver-pedestrian interaction, including traffic volume, vehicle speed, roadway characteristics, etc. While individual aspects of these interactions have been explored, comprehensive, naturalistic studies, particularly those considering the built environment's influence on driver-yielding behavior, are lacking. To address this gap, our study introduces an extensive open-source dataset, compiled from video data at 18 unsignalized intersections across Minnesota. Documenting more than 3000 interactions, this dataset provides a detailed view of driver-pedestrian interactions and over 50 distinct contextual variables. The data, which covers individual driver-pedestrian interactions and contextual factors, is made publicly available at https://github.com/tianyi17/pedestrian_yielding_data_MN. Using logistic regression, we developed a classification model that predicts driver yielding based on the identified variables. Our analysis indicates that vehicle speed, the presence of parking lots, proximity to parks or schools, and the width of major road crossings significantly influence driver yielding at unsignalized intersections. This study contributes to one of the most comprehensive driver-pedestrian datasets in the US, offering valuable insights for traffic safety improvements. By making this information available, our study will support communities across Minnesota and the United States in their ongoing efforts to improve road safety for pedestrians.
Improved Approximation Guarantees for Packing and Covering Integer Programs 1

Several important NP-hard combinatorial optimization problems can be posed as packing/covering integer programs; the randomized rounding technique of Raghavan & Thompson is a powerful tool to approximate them well. We present one elementary unifying property of all these integer programs (IPs), and use the FKG correlation inequality to derive an improved analysis of randomized rounding on them. This also yields a pessimistic estimator, thus presenting deterministic polynomial-time algorithms for them with approximation guarantees signi cantly better than those known.
Agent based modelling for continuously varying supply chains

Problem definition: Supply chains are constantly evolving networks. Reinforcement learning is increasingly proposed as a solution to provide optimal control of these networks. Academic/practical: However, learning in continuously varying environments remains a challenge in the reinforcement learning literature.Methodology: This paper therefore seeks to address whether agents can control varying supply chain problems, transferring learning between environments that require different strategies and avoiding catastrophic forgetting of tasks that have not been seen in a while. To evaluate this approach, two state-of-the-art Reinforcement Learning (RL) algorithms are compared: an actor-critic learner, Proximal Policy Optimisation(PPO), and a Recurrent Proximal Policy Optimisation (RPPO), PPO with a Long Short-Term Memory(LSTM) layer, which is showing popularity in online learning environments. Results: First these methods are compared on six sets of environments with varying degrees of stochasticity. The results show that more lean strategies adopted in Batch environments are different from those adopted in Stochastic environments with varying products. The methods are also compared on various continuous supply chain scenarios, where the PPO agents are shown to be able to adapt through continuous learning when the tasks are similar but show more volatile performance when changing between the extreme tasks. However, the RPPO, with an ability to remember histories, is able to overcome this to some extent and takes on a more realistic strategy. Managerial implications: Our results provide a new perspective on the continuously varying supply chain, the cooperation and coordination of agents are crucial for improving the overall performance in uncertain and semi-continuous non-stationary supply chain environments without the need to retrain the environment as the demand changes.
The hull variation problem for projective Reed-Muller codes and quantum error-correcting codes

Long quantum codes using projective Reed-Muller codes are constructed. Projective Reed-Muller are evaluation codes obtained by evaluating homogeneous polynomials at the projective space. We obtain asymmetric and symmetric quantum codes by using the CSS construction and the Hermitian construction, respectively. We provide entanglement-assisted quantum error-correcting codes from projective Reed-Muller codes with flexible amounts of entanglement by considering equivalent codes. Moreover, we also construct quantum codes from subfield subcodes of projective Reed-Muller codes.
Fluid Antenna Array Enhanced Over-the-Air Computation

Over-the-air computation (AirComp) has emerged as a promising technology for fast wireless data aggregation by harnessing the superposition property of wireless multiple-access channels. This paper investigates a fluid antenna (FA) array-enhanced AirComp system, employing the new degrees of freedom achieved by antenna movements. Specifically, we jointly optimize the transceiver design and antenna position vector (APV) to minimize the mean squared error (MSE) between target and estimated function values. To tackle the resulting highly non-convex problem, we adopt an alternating optimization technique to decompose it into three subproblems. These subproblems are then iteratively solved until convergence, leading to a locally optimal solution. Numerical results show that FA arrays with the proposed transceiver and APV design significantly outperform the traditional fixed-position antenna arrays in terms of MSE.