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Kinematics restrict the ability of rare charm decays to explore the charged Lepton Flavor Violation processes mediated by the quark-level $c\to u \ell \tau$ transition. To fill the gap, we propose exploring new physics (NP) through the quasielastic scattering process $e^-p\to \tau^-\Lambda_c$ and the polarization of the $\tau$ lepton. As analyzing modes for the $\tau$ polarization, we consider the decays $\tau^-\to \pi^-\nu_{\tau}$, $\tau^-\to \rho^-\nu_{\tau}$, and $\tau^- \to \ell^-\bar{\nu}_{\ell}\nu_{\tau}$, and show that the $\tau$ polarization components can be extracted from analyzing the kinematics of the $\tau$ visible decay products. In the framework of a general low-energy effective Lagrangian, we then perform a detailed analysis of the polarization components in various aspects and scrutinize possible NP signals. With one upcoming experimental setup, we finally demonstrate promising event rate can be expected for the cascade process and, even in the worst-case scenario -- no signals is observed at all -- it can still provide a competitive potential for constraining the NP, compared with those from the high-$p_T$ dilepton invariant mass tails at high-energy colliders., Comment: 20 pages, 4 figures, 5 tables

Deep learning techniques have led to remarkable breakthroughs in the field of object detection and have spawned a lot of scene-understanding tasks in recent years. Scene graph has been the focus of research because of its powerful semantic representation and applications to scene understanding. Scene Graph Generation (SGG) refers to the task of automatically mapping an image or a video into a semantic structural scene graph, which requires the correct labeling of detected objects and their relationships. In this paper, a comprehensive survey of recent achievements is provided. This survey attempts to connect and systematize the existing visual relationship detection methods, to summarize, and interpret the mechanisms and the strategies of SGG in a comprehensive way. Deep discussions about current existing problems and future research directions are given at last. This survey will help readers to develop a better understanding of the current researches.

Deguelin exhibits antiproliferative activity against various cancer cell types. Previous studies have reported that deguelin exhibits pro-apoptotic activity against human cancer cells. The current study aimed at further elaborating the anticancer effects of deguelin against multiple myeloma cells. Cell growth estimations were made through MTT assay. Phase contrast microscopy was used for the analysis of the viability of multiple myeloma cells. Colony formation from multiple myeloma cells was studied using a clonogenic assay. Antioxidative assays for determining levels of glutathione (GSH) and superoxide dismutase (SOD) were carried out after treating multiple myeloma cells with deguelin. The apoptosis of multiple myeloma cells was studied using AO/EB and Annexin V-FITC/PI staining methods. Multiple myeloma cell cycle analysis was performed through flow cytometry. mRNA expression levels were depicted using qRT-PCR. Migration and invasion of multiple myeloma cells were determined with the wound-healing and transwell assays, respectively. Deguelin specifically inhibited the multiple myeloma cell growth while the normal plasma cells were minimally affected. Multiple myeloma cells when treated with deguelin exhibited remarkably lower viability and colony-forming ability. Multiple myeloma cells treated with deguelin produced more SOD and had higher GSH levels. The multiple myeloma cell growth, migration, and invasion were significantly declined by in vitro administration of deguelin. In conclusion, deguelin treatment, when applied in vitro, induced apoptotic cell death and resulted in mitotic cessation at the G2/M phase through modulation of cell cycle regulatory mRNAs in multiple myeloma cells.

We propose a method based on the Hilbert-Huang transform (HHT) to recover the high-energy waveform of low-frequency quasi-periodic oscillations (LFQPOs). Based on the method, we successfully obtain the modulation of the phase-folded light curve above 170 keV using the QPO phase reconstructed at lower energies in MAXI J1535-571 with Insight-HXMT observations. A comprehensive simulation study is conducted to demonstrate that such modulation indeed originates from the QPO. Thus the highest energies turn out to significantly exceed the upper limit of ~100 keV for QPOs reported previously using the Fourier method, marking the first opportunity to study QPO properties above 100 keV in this source. Detailed analyses of these high-energy QPO profiles reveal different QPO properties between the 30-100 keV and 100-200 keV energy ranges: the phase lag remains relatively stable, and the amplitude slightly increases below ~100 keV, whereas above this threshold, soft phase lags and a decrease in amplitude are observed. Given the reports of a hard tail detection in broad spectroscopy, we propose that the newly discovered QPO properties above 100 keV are dominated by the hard tail component, possibly stemming from a relativistic jet. Our findings also indicate a strong correlation between the QPOs originating from the jet and corona, supporting the scenario of jet-corona coupling precssion. We emphasize that our proposed HHT-based method can serve as an efficient manner in expanding the high energy band for studying QPOs, thereby enhancing our understanding of their origin., Comment: 21 pages, 15 figures, accepted for publication in ApJL

We present the results obtained from an X-ray timing study of the new black hole candidate (BHC) Swift J1727.8-1613. The work is based on Hard X-ray Modulation Telescope (Insight-HXMT) observations carried out during the 2023 outburst. Prominent type-C low-frequency Quasi-periodic Oscillations (LFQPOs) are detected throughout the observations. With the substantial effective area of the Insight-HXMT at high energies, we examine the energy dependence of various parameters, including the centroid frequency, fractional rms, and phase lags of the type-C QPOs. Our findings align closely with those observed in high-inclination systems. During the initial stage of the outburst, a peaked noise component is also detected, the frequency of which is highly correlated with the LFQPO frequency, aligning with the Psaltis-Belloni-van der Klis (PBK) relation. By assuming that the peaked noise originates from the precession of the accretion disc, the spin of this source can be constrained. Our results suggest that this source may possess a high spin.

By accurately measuring composition and energy spectrum of cosmic ray, the origin problem of so called "keen" region (energy > 1 PeV) can be solved. However, up to the present, the results of the spectrum in the knee region obtained by several previous experiments have shown obvious differences, so they cannot give effective evidence for judging the theoretical models on the origin of the knee. Recently, the Large High Altitude Air Shower Observatory (LHAASO) has reported several major breakthroughs and important results in astro-particle physics field. Relying on its advantages of wide-sky survey, high altitude location and large area detector arrays, the research content of LHAASO experiment mainly includes ultra high-energy gamma-ray astronomy, measurement of cosmic ray spectra in the knee region, searching for dark matter and new phenomena of particle physics at higher energy. The electron and Thermal Neutron detector (EN-Detector) is a new scintillator detector which applies thermal neutron detection technology to measure cosmic ray extensive air shower (EAS). This technology is an extension of LHAASO. The EN-Detector Array (ENDA) can highly efficiently measure thermal neutrons generated by secondary hadrons so called "skeleton" of EAS. In this paper, we perform the optimization of ENDA configuration, and obtain expectations on the ENDA results, including thermal neutron distribution, trigger efficiency and capability of cosmic ray composition separation. The obtained real data results are consistent with those by the Monte Carlo simulation.

The timing properties of the Z-type low-mass X-ray binaries provide insights into the emission components involved in producing the unique Z-shaped track in the hardness-intensity diagrams of these sources. In this work, we investigate the AstroSat and NICER observations of the GX 340+0 covering the complete 'Z'-track from the horizontal branch (HB) to the extended flaring branch (EFB). For the first time, we present the Z-track as seen in soft X-rays using the AstroSat/SXT and NICER (the soft colour is defined as a ratio of 3-6 keV to 0.5-3 keV). The shape of the track is distinctly different in soft X-rays, strongly suggesting the presence of additional components active in soft X-rays. The detailed timing analysis revealed significant quasi-periodic oscillation throughout the HB and the normal branch (NB) using LAXPC and the first NICER detection of 33.1 +/- 1.1 Hz horizontal branch oscillation (HBO) in 3-6 keV. The oscillations at the HB/NB vertex are observed to have higher frequencies (41-52 Hz) than the HB oscillations (16-31 Hz) and NB oscillations (6.2-8 Hz) but significantly lower rms (~1.6%). The HB oscillation is also limited to the energy range of 3-20 keV, indicating an association of HBO origin with the non-thermal component. It is also supported by earlier studies that found the strongest X-ray polarisation during HB., Comment: 15 pages, 12 figures, 4 tables, accepted for publication in the MNRAS

We analyzed an observation with the Nuclear Spectroscopic Telescope Array of the black-hole X-ray binary MAXI J1535-571 in the soft intermediate state, in which we detected a 2.5-ks long flare. Our spectral fitting results suggest that MAXI J1535-571 possesses a high spin of 0.97 (-0.10/+0.02) and a low inclination of approximately 24 deg. We observed a gradual increase in the inner disc radius, as determined from fits to the continuum spectrum. This trend is inconsistent with an increased flux ratio of the thermal component, as well as the source evolving towards the soft state. This inconsistency may be attributed to a gradual decrease of the color correction factor. Additionally, with a flare velocity of approximately 0.5 c and a higher hardness ratio during the flare period, the quasi-simultaneous detection of a type-B QPO in the Neutron Star Interior Composition Explorer data, and quasi-simultaneous ejecta launch through radio observations collectively provide strong evidence supporting the possibility that the flare originated from a discrete jet ejection., Comment: 11 pages, 8 figures, 3 tables; accepted to be published in MNRAS

Blazars are radio-loud Active Galactic Nuclei (AGN) whose jets have a very small angle to our line of sight. Observationally, the radio emission are mostly compact or a compact-core with a 1-sided jet. With 2.5$^{\prime\prime}$ resolution at 3 GHz, the Very Large Array Sky Survey (VLASS) enables us to resolve the structure of some blazar candidates in the sky north of Decl. $-40$ deg. We introduce an algorithm to classify radio sources as either blazar-like or non-blazar-like based on their morphology in the VLASS images. We apply our algorithm to three existing catalogs, including one of known blazars (Roma-BzCAT) and two of blazar candidates identified by WISE colors and radio emission (WIBRaLS, KDEBLLACS). We show that in all three catalogs, there are objects with morphology inconsistent with being blazars. Considering all the catalogs, more than 12% of the candidates are unlikely to be blazars, based on this analysis. Notably, we show that 3% of the Roma-BzCAT "confirmed" blazars could be a misclassification based on their VLASS morphology. The resulting table with all sources and their radio morphological classification is available online., Comment: 21 pages, 11 figures, accepted for publication in The Astrophysical Journal

We present the first polarimetric analysis of Quasi-Periodic Oscillations (QPO) in a black hole binary utilizing \textit{IXPE} data. Our study focuses on Swift J1727.8--1613, which experienced a massive outburst that was observed by various telescopes across different wavelengths. The \textit{IXPE} observation we studied was conducted during the Hard-Intermediate state. The polarization degree (PD) and polarization angle (PA) were measured at 4.28$\pm$0.20\% and $1.9^{\circ}\pm1.4^{\circ}$, respectively. Remarkably, significant QPO signals were detected during this observation, with a QPO frequency of approximately 1.34 Hz and a fractional root-mean-square (RMS) amplitude of about 12.3\%. Furthermore, we conducted a phase-resolved analysis of the QPO using the Hilbert-Huang transform technique. The photon index showed a strong modulation with respect to the QPO phase. In contrast, the PD and PA exhibit no modulations in relation to the QPO phase, which is inconsistent with the expectation of the Lense-Thirring precession of the inner flow. Further theoretical studies are needed to conform with the observational results., Comment: Accepted for publication in APJL

Context. In September 2022, the transient neutron star low-mass X-ray binary XTE J1701-462 went into a new outburst. Aims. The objective of this work is to examine the evolution of the accretion geometry of XTE J1701-462 by studying the spectro-polarimetric properties along the Z track of this source. The simultaneous observations archived by the Insight-Hard X-ray Modulation Telescope (HXMT) and the Imaging X-ray Polarimetry Explorer (IXPE) give us the opportunity. Methods. We present a comprehensive X-ray spectro-polarimetric analysis of XTE J1701-462, using simultaneous observations from IXPE, Insight-HXMT and NuSTAR. For IXPE observations, two methods are employed to measure the polarization: a model-independent measurement with PCUBE and a model-dependent polarization-spectral analysis with XSPEC. The corresponding spectra from Insight-HXMT and NuSTAR are studied with two configurations that correspond to a slab-like corona and a spherical shell-like corona, respectively. Results. Significant polarization characteristics are detected in XTE J1701-462. The polarization degree shows a decreasing trend along the Z track, reducing from (4.84 $\pm$ 0.37)% to (3.76 $\pm$ 0.43)% on the horizontal branch and jumping to less than 1% on the normal branch. The simultaneous spectral analysis from Insight-HXMT and NuSTAR suggests that the redistribution between the thermal and Comptonized emission could be the reason for the PD evolution along the Z track. Based on the correlated spectro-polarimetric properties, we propose that this source likely has a slab coronal geometry and the size/thickness of the corona decreases along the Z track.

Kang Liu, Xiaochun Liu, Tao Cao, Xianmei Cui, Pengyu Sun, Liang Zhang, Xiaoqin Wu Department of Obstetrics and Gynecology, Shanxi Bethune Hospital & Shanxi Academy of Medical Sciences, Shanxi Bethune Hospital Affiliated to Shanxi Medical University, Taiyuan, 030032, People’s Republic of ChinaCorrespondence: Xiaoqin Wu, Department of Obstetrics and Gynecology, Shanxi Bethune Hospital & Shanxi Academy of Medical Sciences, Shanxi Bethune Hospital affiliated to Shanxi Medical University, Taiyuan, 030032, People’s Republic of China, Email liukang1212@sxmu.edu.cnObjective: This study explores the causal relationship between endometriosis and pelvic inflammatory diseases (PID).Methods: The study utilized genome-wide association study (GWAS) datasets for endometriosis (“finn-b-N14_ENDOMETRIOSIS“) and PID (”finn-b-N14_OTHFEMPELINF”). Subsequently, two-sample Mendelian randomization (MR) analyses were conducted using inverse variance weighting (IVW), Egger regression (MR-Egger), and weighted median (WM) methods. Heterogeneity was evaluated using Cochran’s Q test, and in case of detected outliers, they were removed for re-evaluation of MR causality.Results: From the endometriosis GWAS dataset, 33 single nucleotide polymorphisms (SNPs) were selected as instrumental variables. All three methods, IVW (OR = 1.39, P < 1× 10− 8), MR-Egger (OR = 1.41, P = 0.003), and WM (OR = 1.37, P = 1.16× 10− 5) confirmed a causal relationship between endometriosis and PID. The association between endometriosis and pelvic inflammation remained unaffected by the exclusion of individual SNPs. Lastly, Cochran’s Q test and funnel plots showed no evidence of SNP asymmetry.Conclusion: The results of the MR analysis support a potential causal relationship between endometriosis and an increased risk of PID.Keywords: endometriosis, pelvic inflammatory diseases, genome-wide association study, Mendelian randomization

The Circular Electron Positron Collider (CEPC) has been proposed to enable more thorough and precise measurements of the properties of Higgs, W, and Z bosons, as well as to search for new physics. In response to the stringent performance requirements of the vertex detector for the CEPC, a baseline vertex detector prototype was tested and characterized for the first time using a 6 GeV electron beam at DESY II Test Beam Line 21. The baseline vertex detector prototype is designed with a cylindrical barrel structure that contains six double-sided detector modules (ladders). Each side of the ladder includes TaichuPix-3 sensors based on Monolithic Active Pixel Sensor (MAPS) technology, a flexible printed circuit, and a carbon fiber support structure. Additionally, the readout electronics and the Data Acquisition system were also examined during this beam test. The performance of the prototype was evaluated using an electron beam that passed through six ladders in a perpendicular direction. The offline data analysis indicates a spatial resolution of about 5 um, with detection efficiency exceeding 99 % and an impact parameter resolution of about 5.1 um. These promising results from this baseline vertex detector prototype mark a significant step toward realizing the optimal vertex detector for the CEPC.

With the rise of large language models (LLMs), recent works have leveraged LLMs to improve the performance of click-through rate (CTR) prediction. However, we argue that a critical obstacle remains in deploying LLMs for practical use: the efficiency of LLMs when processing long textual user behaviors. As user sequences grow longer, the current efficiency of LLMs is inadequate for training on billions of users and items. To break through the efficiency barrier of LLMs, we propose Behavior Aggregated Hierarchical Encoding (BAHE) to enhance the efficiency of LLM-based CTR modeling. Specifically, BAHE proposes a novel hierarchical architecture that decouples the encoding of user behaviors from inter-behavior interactions. Firstly, to prevent computational redundancy from repeated encoding of identical user behaviors, BAHE employs the LLM's pre-trained shallow layers to extract embeddings of the most granular, atomic user behaviors from extensive user sequences and stores them in the offline database. Subsequently, the deeper, trainable layers of the LLM facilitate intricate inter-behavior interactions, thereby generating comprehensive user embeddings. This separation allows the learning of high-level user representations to be independent of low-level behavior encoding, significantly reducing computational complexity. Finally, these refined user embeddings, in conjunction with correspondingly processed item embeddings, are incorporated into the CTR model to compute the CTR scores. Extensive experimental results show that BAHE reduces training time and memory by five times for CTR models using LLMs, especially with longer user sequences. BAHE has been deployed in a real-world system, allowing for daily updates of 50 million CTR data on 8 A100 GPUs, making LLMs practical for industrial CTR prediction., Comment: Accepted by the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2024

Intelligent Tutoring Systems (ITSs) have significantly enhanced adult literacy training, a key factor for societal participation, employment opportunities, and lifelong learning. Our study investigates the application of advanced AI models, including Large Language Models (LLMs) like GPT-4, for predicting learning performance in adult literacy programs in ITSs. This research is motivated by the potential of LLMs to predict learning performance based on its inherent reasoning and computational capabilities. By using reading comprehension datasets from the ITS, AutoTutor, we evaluate the predictive capabilities of GPT-4 versus traditional machine learning methods in predicting learning performance through five-fold cross-validation techniques. Our findings show that the GPT-4 presents the competitive predictive abilities with traditional machine learning methods such as Bayesian Knowledge Tracing, Performance Factor Analysis, Sparse Factor Analysis Lite (SPARFA-Lite), tensor factorization and eXtreme Gradient Boosting (XGBoost). While XGBoost (trained on local machine) outperforms GPT-4 in predictive accuracy, GPT-4-selected XGBoost and its subsequent tuning on the GPT-4 platform demonstrates superior performance compared to local machine execution. Moreover, our investigation into hyper-parameter tuning by GPT-4 versus grid-search suggests comparable performance, albeit with less stability in the automated approach, using XGBoost as the case study. Our study contributes to the field by highlighting the potential of integrating LLMs with traditional machine learning models to enhance predictive accuracy and personalize adult literacy education, setting a foundation for future research in applying LLMs within ITSs., Comment: 26TH International Conference on Human-Computer Interaction

Structure information is critical for understanding the semantics of text-rich images, such as documents, tables, and charts. Existing Multimodal Large Language Models (MLLMs) for Visual Document Understanding are equipped with text recognition ability but lack general structure understanding abilities for text-rich document images. In this work, we emphasize the importance of structure information in Visual Document Understanding and propose the Unified Structure Learning to boost the performance of MLLMs. Our Unified Structure Learning comprises structure-aware parsing tasks and multi-grained text localization tasks across 5 domains: document, webpage, table, chart, and natural image. To better encode structure information, we design a simple and effective vision-to-text module H-Reducer, which can not only maintain the layout information but also reduce the length of visual features by merging horizontal adjacent patches through convolution, enabling the LLM to understand high-resolution images more efficiently. Furthermore, by constructing structure-aware text sequences and multi-grained pairs of texts and bounding boxes for publicly available text-rich images, we build a comprehensive training set DocStruct4M to support structure learning. Finally, we construct a small but high-quality reasoning tuning dataset DocReason25K to trigger the detailed explanation ability in the document domain. Our model DocOwl 1.5 achieves state-of-the-art performance on 10 visual document understanding benchmarks, improving the SOTA performance of MLLMs with a 7B LLM by more than 10 points in 5/10 benchmarks. Our codes, models, and datasets are publicly available at https://github.com/X-PLUG/mPLUG-DocOwl/tree/main/DocOwl1.5., Comment: 21 pages, 15 figures

Constrained variational inequality problems are recognized for their broad applications across various fields including machine learning and operations research. First-order methods have emerged as the standard approach for solving these problems due to their simplicity and scalability. However, they typically rely on projection or linear minimization oracles to navigate the feasible set, which becomes computationally expensive in practical scenarios featuring multiple functional constraints. Existing efforts to tackle such functional constrained variational inequality problems have centered on primal-dual algorithms grounded in the Lagrangian function. These algorithms along with their theoretical analysis often require the existence and prior knowledge of the optimal Lagrange multipliers. In this work, we propose a simple primal method, termed Constrained Gradient Method (CGM), for addressing functional constrained variational inequality problems, without necessitating any information on the optimal Lagrange multipliers. We establish a non-asymptotic convergence analysis of the algorithm for variational inequality problems with monotone operators under smooth constraints. Remarkably, our algorithms match the complexity of projection-based methods in terms of operator queries for both monotone and strongly monotone settings, while utilizing significantly cheaper oracles based on quadratic programming. Furthermore, we provide several numerical examples to evaluate the efficacy of our algorithms.

Reinforcement Learning from AI Feedback (RLAIF) has the advantages of shorter annotation cycles and lower costs over Reinforcement Learning from Human Feedback (RLHF), making it highly efficient during the rapid strategy iteration periods of large language model (LLM) training. Using ChatGPT as a labeler to provide feedback on open-domain prompts in RLAIF training, we observe an increase in human evaluators' preference win ratio for model responses, but a decrease in evaluators' satisfaction rate. Analysis suggests that the decrease in satisfaction rate is mainly due to some responses becoming less helpful, particularly in terms of correctness and truthfulness, highlighting practical limitations of basic RLAIF. In this paper, we propose Hybrid Reinforcement Learning from AI Feedback (HRLAIF). This method enhances the accuracy of AI annotations for responses, making the model's helpfulness more robust in training process. Additionally, it employs AI for Red Teaming, further improving the model's harmlessness. Human evaluation results show that HRLAIF inherits the ability of RLAIF to enhance human preference for outcomes at a low cost while also improving the satisfaction rate of responses. Compared to the policy model before Reinforcement Learning (RL), it achieves an increase of 2.08\% in satisfaction rate, effectively addressing the issue of a decrease of 4.58\% in satisfaction rate after basic RLAIF., Comment: 18 pages, 7 figures

Large Multimodal Models (LMMs) often suffer from multimodal hallucinations, wherein they may create content that is not present in the visual inputs. In this paper, we explore a new angle of this issue: overly detailed training data hinders the model's ability to timely terminate generation, leading to continued outputs beyond visual perception limits. By investigating how the model decides to terminate generation with EOS, the special end-of-sentence token, we find that the model assesses the completeness of the entire sequence by comparing the generated text with the image. This observation suggests that the model possesses an inherent potential of making proper EOS decisions based on its visual perception to avoid overly lengthy outputs. To take advantage of such potential, we explore two methods to mitigate multimodal hallucinations: a training objective that enables the model to reduce hallucinations by learning from regular instruction data, and a data filtering strategy to prevent harmful training data from exacerbating model hallucinations. Both methods significantly improve the hallucination performance of LMMs, without requiring any additional data or knowledge., Comment: Accepted to ACL 2024

XML configurations are integral to the Android development framework, particularly in the realm of UI display. However, these configurations can introduce compatibility issues (bugs), resulting in divergent visual outcomes and system crashes across various Android API versions (levels). In this study, we systematically investigate LLM-based approaches for detecting and repairing configuration compatibility bugs. Our findings highlight certain limitations of LLMs in effectively identifying and resolving these bugs, while also revealing their potential in addressing complex, hard-to-repair issues that traditional tools struggle with. Leveraging these insights, we introduce the LLM-CompDroid framework, which combines the strengths of LLMs and traditional tools for bug resolution. Our experimental results demonstrate a significant enhancement in bug resolution performance by LLM-CompDroid, with LLM-CompDroid-GPT-3.5 and LLM-CompDroid-GPT-4 surpassing the state-of-the-art tool, ConfFix, by at least 9.8% and 10.4% in both Correct and Correct@k metrics, respectively. This innovative approach holds promise for advancing the reliability and robustness of Android applications, making a valuable contribution to the field of software development.

Graph contrastive learning (GCL) has emerged as a pivotal technique in the domain of graph representation learning. A crucial aspect of effective GCL is the caliber of generated positive and negative samples, which is intrinsically dictated by their resemblance to the original data. Nevertheless, precise control over similarity during sample generation presents a formidable challenge, often impeding the effective discovery of representative graph patterns. To address this challenge, we propose an innovative framework: Adversarial Curriculum Graph Contrastive Learning (ACGCL), which capitalizes on the merits of pair-wise augmentation to engender graph-level positive and negative samples with controllable similarity, alongside subgraph contrastive learning to discern effective graph patterns therein. Within the ACGCL framework, we have devised a novel adversarial curriculum training methodology that facilitates progressive learning by sequentially increasing the difficulty of distinguishing the generated samples. Notably, this approach transcends the prevalent sparsity issue inherent in conventional curriculum learning strategies by adaptively concentrating on more challenging training data. Finally, a comprehensive assessment of ACGCL is conducted through extensive experiments on six well-known benchmark datasets, wherein ACGCL conspicuously surpasses a set of state-of-the-art baselines.

The potential of Machine Learning Control (MLC) in HVAC systems is hindered by its opaque nature and inference mechanisms, which is challenging for users and modelers to fully comprehend, ultimately leading to a lack of trust in MLC-based decision-making. To address this challenge, this paper investigates and explores Interpretable Machine Learning (IML), a branch of Machine Learning (ML) that enhances transparency and understanding of models and their inferences, to improve the credibility of MLC and its industrial application in HVAC systems. Specifically, we developed an innovative framework that combines the principles of Shapley values and the in-context learning feature of Large Language Models (LLMs). While the Shapley values are instrumental in dissecting the contributions of various features in ML models, LLM provides an in-depth understanding of rule-based parts in MLC; combining them, LLM further packages these insights into a coherent, human-understandable narrative. The paper presents a case study to demonstrate the feasibility of the developed IML framework for model predictive control-based precooling under demand response events in a virtual testbed. The results indicate that the developed framework generates and explains the control signals in accordance with the rule-based rationale.

The rapid progression in artificial intelligence has facilitated the emergence of large language models like ChatGPT, offering potential applications extending into specialized engineering modeling, especially physics-based building energy modeling. This paper investigates the innovative integration of large language models with building energy modeling software, focusing specifically on the fusion of ChatGPT with EnergyPlus. A literature review is first conducted to reveal a growing trend of incorporating of large language models in engineering modeling, albeit limited research on their application in building energy modeling. We underscore the potential of large language models in addressing building energy modeling challenges and outline potential applications including 1) simulation input generation, 2) simulation output analysis and visualization, 3) conducting error analysis, 4) co-simulation, 5) simulation knowledge extraction and training, and 6) simulation optimization. Three case studies reveal the transformative potential of large language models in automating and optimizing building energy modeling tasks, underscoring the pivotal role of artificial intelligence in advancing sustainable building practices and energy efficiency. The case studies demonstrate that selecting the right large language model techniques is essential to enhance performance and reduce engineering efforts. Besides direct use of large language models, three specific techniques were utilized: 1) prompt engineering, 2) retrieval-augmented generation, and 3) multi-agent large language models. The findings advocate a multidisciplinary approach in future artificial intelligence research, with implications extending beyond building energy modeling to other specialized engineering modeling.

Learning performance data (e.g., quiz scores and attempts) is significant for understanding learner engagement and knowledge mastery level. However, the learning performance data collected from Intelligent Tutoring Systems (ITSs) often suffers from sparsity, impacting the accuracy of learner modeling and knowledge assessments. To address this, we introduce the 3DG framework (3-Dimensional tensor for Densification and Generation), a novel approach combining tensor factorization with advanced generative models, including Generative Adversarial Network (GAN) and Generative Pre-trained Transformer (GPT), for enhanced data imputation and augmentation. The framework operates by first representing the data as a three-dimensional tensor, capturing dimensions of learners, questions, and attempts. It then densifies the data through tensor factorization and augments it using Generative AI models, tailored to individual learning patterns identified via clustering. Applied to data from an AutoTutor lesson by the Center for the Study of Adult Literacy (CSAL), the 3DG framework effectively generated scalable, personalized simulations of learning performance. Comparative analysis revealed GAN's superior reliability over GPT-4 in this context, underscoring its potential in addressing data sparsity challenges in ITSs and contributing to the advancement of personalized educational technology.

Large Language Models (LLMs) generate responses to questions; however, their effectiveness is often hindered by sub-optimal quality of answers and occasional failures to provide accurate responses to questions. To address these challenges, a fine-tuning process is employed, involving feedback and examples to refine models. The objective is to enhance AI models through continuous feedback loops, utilizing metrics such as cosine similarity, LLM evaluation and Rouge-L scores to evaluate the models. Leveraging LLMs like GPT-3.5, GPT4ALL, and LLaMA2, and Claude, this approach is benchmarked on financial datasets, including the FinanceBench and RAG Instruct Benchmark Tester Dataset, illustrating the necessity of fine-tuning. The results showcase the capability of fine-tuned models to surpass the accuracy of zero-shot LLMs, providing superior question and answering capabilities. Notably, the combination of fine-tuning the LLM with a process known as Retrieval Augmented Generation (RAG) proves to generate responses with improved accuracy.

In the realm of artificial intelligence, where a vast majority of data is unstructured, obtaining substantial amounts of labeled data to train supervised machine learning models poses a significant challenge. To address this, we delve into few-shot and active learning, where are goal is to improve AI models with human feedback on a few labeled examples. This paper focuses on understanding how a continuous feedback loop can refine models, thereby enhancing their accuracy, recall, and precision through incremental human input. By employing Large Language Models (LLMs) such as GPT-3.5, BERT, and SetFit, we aim to analyze the efficacy of using a limited number of labeled examples to substantially improve model accuracy. We benchmark this approach on the Financial Phrasebank, Banking, Craigslist, Trec, Amazon Reviews datasets to prove that with just a few labeled examples, we are able to surpass the accuracy of zero shot large language models to provide enhanced text classification performance. We demonstrate that rather than needing to manually label millions of rows of data, we just need to label a few and the model can effectively predict the rest.

Flowcharts and mind maps, collectively known as flowmind, are vital in daily activities, with hand-drawn versions facilitating real-time collaboration. However, there's a growing need to digitize them for efficient processing. Automated conversion methods are essential to overcome manual conversion challenges. Existing sketch recognition methods face limitations in practical situations, being field-specific and lacking digital conversion steps. Our paper introduces the Flowmind2digital method and hdFlowmind dataset to address these challenges. Flowmind2digital, utilizing neural networks and keypoint detection, achieves a record 87.3% accuracy on our dataset, surpassing previous methods by 11.9%. The hdFlowmind dataset, comprising 1,776 annotated flowminds across 22 scenarios, outperforms existing datasets. Additionally, our experiments emphasize the importance of simple graphics, enhancing accuracy by 9.3%.

In recent years, the recognition of free-hand sketches has remained a popular task. However, in some special fields such as the military field, free-hand sketches are difficult to sample on a large scale. Common data augmentation and image generation techniques are difficult to produce images with various free-hand sketching styles. Therefore, the recognition and segmentation tasks in related fields are limited. In this paper, we propose a novel adversarial generative network that can accurately generate realistic free-hand sketches with various styles. We explore the performance of the model, including using styles randomly sampled from a prior normal distribution to generate images with various free-hand sketching styles, disentangling the painters' styles from known free-hand sketches to generate images with specific styles, and generating images of unknown classes that are not in the training set. We further demonstrate with qualitative and quantitative evaluations our advantages in visual quality, content accuracy, and style imitation on SketchIME., Comment: 6 pages, 7 figures, ICSMD

Gesture recognition is an indispensable component of natural and efficient human-computer interaction technology, particularly in desktop-level applications, where it can significantly enhance people's productivity. However, the current gesture recognition community lacks a suitable desktop-level (top-view perspective) dataset for lightweight gesture capture devices. In this study, we have established a dataset named GR4DHCI. What distinguishes this dataset is its inherent naturalness, intuitive characteristics, and diversity. Its primary purpose is to serve as a valuable resource for the development of desktop-level portable applications. GR4DHCI comprises over 7,000 gesture samples and a total of 382,447 frames for both Stereo IR and skeletal modalities. We also address the variances in hand positioning during desktop interactions by incorporating 27 different hand positions into the dataset. Building upon the GR4DHCI dataset, we conducted a series of experimental studies, the results of which demonstrate that the fine-grained classification blocks proposed in this paper can enhance the model's recognition accuracy. Our dataset and experimental findings presented in this paper are anticipated to propel advancements in desktop-level gesture recognition research.

The current deep neural network algorithm still stays in the end-to-end training supervision method like Image-Label pairs, which makes traditional algorithm is difficult to explain the reason for the results, and the prediction logic is difficult to understand and analyze. The current algorithm does not use the existing human knowledge information, which makes the model not in line with the human cognition model and makes the model not suitable for human use. In order to solve the above problems, the present invention provides a deep neural network training method based on the human knowledge, which uses the human cognition model to construct the deep neural network training model, and uses the existing human knowledge information to construct the deep neural network training model. This paper proposes a multi-level hierarchical deep learning algorithm, which is composed of multi-level hierarchical deep neural network architecture and multi-level hierarchical deep learning framework. The experimental results show that the proposed algorithm can effectively explain the hidden information of the neural network. The goal of our study is to improve the interpretability of deep neural networks (DNNs) by providing an analysis of the impact of knowledge injection on the classification task. We constructed a knowledge injection dataset with matching knowledge data and image classification data. The knowledge injection dataset is the benchmark dataset for the experiments in the paper. Our model expresses the improvement in interpretability and classification task performance of hidden layers at different scales., Comment: 7 pages, 3 figures, 5 tables

Despite neural networks (NN) have been widely applied in various fields and generally outperforms humans, they still lack interpretability to a certain extent, and humans are unable to intuitively understand the decision logic of NN. This also hinders the knowledge interaction between humans and NN, preventing humans from getting involved to give direct guidance when NN's decisions go wrong. While recent research in explainable AI has achieved interpretability of NN from various perspectives, it has not yet provided effective methods for knowledge exchange between humans and NN. To address this problem, we constructed a two-way interaction interface that uses structured representations of visual concepts and their relationships as the "language" for knowledge exchange between humans and NN. Specifically, NN provide intuitive reasoning explanations to humans based on the class-specific structural concepts graph (C-SCG). On the other hand, humans can modify the biases present in the C-SCG through their prior knowledge and reasoning ability, and thus provide direct knowledge guidance to NN through this interface. Through experimental validation, based on this interaction interface, NN can provide humans with easily understandable explanations of the reasoning process. Furthermore, human involvement and prior knowledge can directly and effectively contribute to enhancing the performance of NN.

Continuous-time approximation of Stochastic Gradient Descent (SGD) is a crucial tool to study its escaping behaviors from stationary points. However, existing stochastic differential equation (SDE) models fail to fully capture these behaviors, even for simple quadratic objectives. Built on a novel stochastic backward error analysis framework, we derive the Hessian-Aware Stochastic Modified Equation (HA-SME), an SDE that incorporates Hessian information of the objective function into both its drift and diffusion terms. Our analysis shows that HA-SME matches the order-best approximation error guarantee among existing SDE models in the literature, while achieving a significantly reduced dependence on the smoothness parameter of the objective. Further, for quadratic objectives, under mild conditions, HA-SME is proved to be the first SDE model that recovers exactly the SGD dynamics in the distributional sense. Consequently, when the local landscape near a stationary point can be approximated by quadratics, HA-SME is expected to accurately predict the local escaping behaviors of SGD.

Mathematical modeling of production systems is the foundation of all model-based approaches for production system analysis, design, improvement, and control. To construct such a model for the stochastic process of the production system more efficiently, a new modeling approach has been proposed that reversely identifies the model parameters using system performance metrics (e.g., production rate, work-in-process, etc.) derived from the parts flow data. This paper extends this performance metrics-based modeling approach to non-exponential serial production lines. Since no analytical expressions of performance metrics are available for non-exponential systems, we use neural network surrogate models to calculate those performance metrics as functions in terms of the system parameters. Then, based on the surrogate models and given performance metrics, the machine parameters are estimated by solving a constrained optimization problem that minimizes the mean square error of the performance metrics resulting from the estimated parameters compared to the true ones. With the designed multi-start particle swarm optimization algorithm, we find that multiple non-unique combinations of machine parameters can lead to practically the same system performance metrics and a linear relationship of the reliability parameters from these obtained estimations is observed. Besides, model sensitivity analysis is implemented to verify the robustness of the different combinations of machine parameters even under the potential improvement scenarios., Comment: 31 pages

The generation and collection of big data series are becoming an integral part of many emerging applications in sciences, IoT, finance, and web applications among several others. The terabyte-scale of data series has motivated recent efforts to design fully distributed techniques for supporting operations such as approximate kNN similarity search, which is a building block operation in most analytics services on data series. Unfortunately, these techniques are heavily geared towards achieving scalability at the cost of sacrificing the results' accuracy. State-of-the-art systems report accuracy below 10% and 40%, respectively, which is not practical for many real-world applications. In this paper, we investigate the root problems in these existing techniques that limit their ability to achieve better a trade-off between scalability and accuracy. Then, we propose a framework, called CLIMBER, that encompasses a novel feature extraction mechanism, indexing scheme, and query processing algorithms for supporting approximate similarity search in big data series. For CLIMBER, we propose a new loss-resistant dual representation composed of rank-sensitive and ranking-insensitive signatures capturing data series objects. Based on this representation, we devise a distributed two-level index structure supported by an efficient data partitioning scheme. Our similarity metrics tailored for this dual representation enables meaningful comparison and distance evaluation between the rank-sensitive and ranking-insensitive signatures. Finally, we propose two efficient query processing algorithms, CLIMBER-kNN and CLIMBER-kNN-Adaptive, for answering approximate kNN similarity queries. Our experimental study on real-world and benchmark datasets demonstrates that CLIMBER, unlike existing techniques, features results' accuracy above 80% while retaining the desired scalability to terabytes of data., Comment: 16 pages, 14 figures, 1 table

While neural networks have excelled in video action recognition tasks, their black-box nature often obscures the understanding of their decision-making processes. Recent approaches used inherently interpretable models to analyze video actions in a manner akin to human reasoning. These models, however, usually fall short in performance compared to their black-box counterparts. In this work, we present a new framework named Language-guided Interpretable Action Recognition framework (LaIAR). LaIAR leverages knowledge from language models to enhance both the recognition capabilities and the interpretability of video models. In essence, we redefine the problem of understanding video model decisions as a task of aligning video and language models. Using the logical reasoning captured by the language model, we steer the training of the video model. This integrated approach not only improves the video model's adaptability to different domains but also boosts its overall performance. Extensive experiments on two complex video action datasets, Charades & CAD-120, validates the improved performance and interpretability of our LaIAR framework. The code of LaIAR is available at https://github.com/NingWang2049/LaIAR., Comment: Accepted by CVPR 2024

Charts are important for presenting and explaining complex data relationships. Recently, multimodal large language models (MLLMs) have shown remarkable capabilities in various chart understanding tasks. However, the sheer size of these models in terms of parameters and computational requirements limits their use in resource-constrained environments. In this paper, we present TinyChart, an efficient MLLM for chart understanding with only 3B parameters. TinyChart overcomes two key challenges in efficient chart understanding: (1) reduce the burden of learning numerical computations through a Program-of-Thoughts (PoT) learning strategy, which trains the model to generate Python programs for numerical calculations, and (2) reduce lengthy vision feature sequences produced by the vision transformer for high-resolution images through a Vision Token Merging module, which gradually merges most similar vision tokens. Extensive experiments demonstrate that our 3B TinyChart achieves SOTA performance on a variety of chart understanding benchmarks including ChartQA, Chart-to-Text, Chart-to-Table, OpenCQA, and ChartX. It outperforms several chart understanding MLLM with up to 13B parameters such as ChartLlama and ChartAst, and close-sourced general-purpose MLLM GPT-4V on ChartQA. It also demonstrates its superior efficiency with higher throughput during inference due to a smaller model scale and more efficient vision encoding. Our code and model are available at https://github.com/X-PLUG/mPLUG-DocOwl/tree/main/TinyChart., Comment: 13 pages, 11 figures

Core needle biopsy (CNB) has been used with caution in pheochromocytoma and paraganglioma (PPGL) due to concerns about catecholamine-related complications. While it is unclear what scientific evidence supports this claim, it has limited the acquisition of biological samples for diagnostic purposes and research, especially in metastatic PPGL. We performed a systematic review and individual patient meta-analysis to evaluate the risk of complications after CNB in PPGL patients. The primary and secondary objectives were to investigate the risk of death and the occurrence of complications requiring intervention or hospitalization, respectively. Fifty-six articles describing 86 PPGL patients undergoing CNB were included. Of the patients (24/71), 34% had metastases and 53.4% (31/58) had catecholamine-related symptoms before CNB. Of the patients (14/41), 34.1% had catecholamine excess testing prior to the biopsy. No CNB-related deaths were reported. Four patients (14.8%, 4/27) experienced CNB-related complications requiring hospitalization or intervention. One case had a temporary duodenal obstruction caused by hematoma, two cases had myocardial infarction, and one case had Takotsubo cardiomyopathy. Eight patients (32%, 8/25) had CNB-related catecholamine symptoms, mainly transient hypertension, excessive diaphoresis, tachycardia, or hypertensive crisis. The scientific literature does not allow us to make any firm conclusion on the safety of CNB in PPGL. However, it is reasonable to argue that CNB could be conducted after thorough consideration, preparation, and with close follow-up for PPGL patients with a strong clinical indication for such investigation.

In this paper, we introduce a simple and efficient approach for the general reduction of one-loop integrals. Our method employs the introduction of an auxiliary vector and the identification of the tensor structure as an auxiliary propagator. This key insight allows us to express a wide range of one-loop integrals, encompassing both tensor structures and higher poles, in the Baikov representation. By establishing an integral-by-parts (IBP) relation, we derive a recursive formula that systematically solves the one-loop reduction problem, even in the presence of various degenerate cases. Our proposed strategy is characterized by its simplicity and effectiveness, offering a significant advancement in the field of one-loop calculations., Comment: 24 pages

This thesis describes a set of techniques and methods to create an autonomously interactive character as a virtual opponent in a Virtual Reality (VR) application for anticipation training in sports karate. Convincing karate movements are created by blending adequate motion capture data in real-time with motion blending. Attack techniques with a precise attacking position are achieved by using hybrid Inverse Kinematics. Stepping movements are created with a motion graph approach, enabling the virtual character to adapt the position relative to the user’s position. An offline preprocessing method is proposed to cope with a large amount of motion capture data. The realized character can autonomously and interactively respond to the trainee’s movement and perform a suitable hand attack. A user study among karate athletes shows a general acceptance of the implemented system as a useful tool for anticipation training.:List of Figures List of Tables 1 Introduction 2 Human Motion Synthesis and Animation 3 Combining Inverse Kinematics with Motion Blending 4 Automatic Motion Base Creation 5 Create an Autonomous Interactive Character 6 Conclusion and Discussion Appendices Bibliography

Neural Radiance Fields (NeRF) is a novel implicit 3D reconstruction method that shows immense potential and has been gaining increasing attention. It enables the reconstruction of 3D scenes solely from a set of photographs. However, its real-time rendering capability, especially for interactive real-time rendering of large-scale scenes, still has significant limitations. To address these challenges, in this paper, we propose a novel neural rendering system called UE4-NeRF, specifically designed for real-time rendering of large-scale scenes. We partitioned each large scene into different sub-NeRFs. In order to represent the partitioned independent scene, we initialize polygonal meshes by constructing multiple regular octahedra within the scene and the vertices of the polygonal faces are continuously optimized during the training process. Drawing inspiration from Level of Detail (LOD) techniques, we trained meshes of varying levels of detail for different observation levels. Our approach combines with the rasterization pipeline in Unreal Engine 4 (UE4), achieving real-time rendering of large-scale scenes at 4K resolution with a frame rate of up to 43 FPS. Rendering within UE4 also facilitates scene editing in subsequent stages. Furthermore, through experiments, we have demonstrated that our method achieves rendering quality comparable to state-of-the-art approaches. Project page: https://jamchaos.github.io/UE4-NeRF/.

Mingping He,1,&ast; Wenzheng Han,1,&ast; Chuan Shi,1 Ming Wang,1 Junheng Li,2 Wei He,2 Xinxin Xu,3 Qian Gan,1 Shaofeng Guan,1 Liang Zhang,1 Yang Chen,1 Xifeng Chang,1 Tianqi Li,1 Xinkai Qu1 1Department of Cardiology, Huadong Hospital Affiliated to Fudan University, Shanghai, People’s Republic of China; 2Department of Nuclear Medicine, Huadong Hospital Affiliated to Fudan University, Shanghai, People’s Republic of China; 3Clinical Research Center for Geriatric Medicine, Huadong Hospital Affiliated to Fudan University, Shanghai, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Wenzheng Han; Xinkai Qu, Department of Cardiology, Huadong Hospital Affiliated to Fudan University, No. 221, Yan’an Road (W), Shanghai, People’s Republic of China, Email wzhan_cardiology@foxmail.com; qxkchest@126.comBackground: Microvascular dysfunction in patients with non-obstructive epicardial coronary may aggravate patient’s symptoms or lead to various clinical events.Objective: To investigate the correlation between dynamic single photon emission computed tomography (D-SPECT) derived coronary flow reserve (CFR) and TIMI frame count (TFC) in patients with non-obstructive epicardial coronary patients.Methods: Patients with suspected or known stable CAD who were recommended to undergo invasive coronary angiography were prospectively enrolled in this study. Those who had non-obstructive coronary received TIMI frame count (TFC) and D-SPECT. A cut-off value of > 40 was defined as slow flow referred to TFC.Results: A total of 47 patients diagnosed with non-obstructive coronary were enrolled. The mean age of patients was 66.09 ± 8.36 years, and 46.8% were male. Dynamic SPECT derived coronary flow reserve (CFR) was significantly correlated with TIMI frame count in 3 epicardial coronary (LAD: r=− 0.506, P = 0.0003; LCX: r= − 0.532, P = 0.0001; RCA: r= − 0.657, P < 0.0001). The sensitivity and specificity of CFR in identi