Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Generative Imputation Model for Missing Not At Random Data

Data analysis usually suffers from the Missing Not At Random (MNAR) problem, where the cause of the value missing is not fully observed. Compared to the naive Missing Completely At Random (MCAR) problem, it is more in line with the realistic scenario whereas more complex and challenging. Existing statistical methods model the MNAR mechanism by different decomposition of the joint distribution of the complete data and the missing mask. But we empirically find that directly incorporating these statistical methods into deep generative models is sub-optimal. Specifically, it would neglect the confidence of the reconstructed mask during the MNAR imputation process, which leads to insufficient information extraction and less-guaranteed imputation quality. In this paper, we revisit the MNAR problem from a novel perspective that the complete data and missing mask are two modalities of incomplete data on an equal footing. Along with this line, we put forward a generative-model-specific joint probability decomposition method, conjunction model, to represent the distributions of two modalities in parallel and extract sufficient information from both complete data and missing mask. Taking a step further, we exploit a deep generative imputation model, namely GNR, to process the real-world missing mechanism in the latent space and concurrently impute the incomplete data and reconstruct the missing mask. The experimental results show that our GNR surpasses state-of-the-art MNAR baselines with significant margins (averagely improved from 9.9% to 18.8% in RMSE) and always gives a better mask reconstruction accuracy which makes the imputation more principle.
Emergent Software Service Platform and its Application in a Smart Mobility Setting

The development dynamics of digital innovations for industry, business, and society are producing complex system conglomerates that can no longer be designed centrally and hierarchically in classic development processes. Instead, systems are evolving in DevOps processes in which heterogeneous actors act together on an open platform. Influencing and controlling such dynamically and autonomously changing system landscapes is currently a major challenge and a fundamental interest of service users and providers, as well as operators of the platform infrastructures. In this paper, we propose an architecture for such an emergent software service platform. A software platform that implements this architecture with the underlying engineering methodology is demonstrated by a smart parking lot scenario.
AIGC In China: Current Developments And Future Outlook

The increasing attention given to AI Generated Content (AIGC) has brought a profound impact on various aspects of daily life, industrial manufacturing, and the academic sector. Recognizing the global trends and competitiveness in AIGC development, this study aims to analyze China's current status in the field. The investigation begins with an overview of the foundational technologies and current applications of AIGC. Subsequently, the study delves into the market status, policy landscape, and development trajectory of AIGC in China, utilizing keyword searches to identify relevant scholarly papers. Furthermore, the paper provides a comprehensive examination of AIGC products and their corresponding ecosystem, emphasizing the ecological construction of AIGC. Finally, this paper discusses the challenges and risks faced by the AIGC industry while presenting a forward-looking perspective on the industry's future based on competitive insights in AIGC.
Transforming Sentiment Analysis in the Financial Domain with ChatGPT

Financial sentiment analysis plays a crucial role in decoding market trends and guiding strategic trading decisions. Despite the deployment of advanced deep learning techniques and language models to refine sentiment analysis in finance, this study breaks new ground by investigating the potential of large language models, particularly ChatGPT 3.5, in financial sentiment analysis, with a strong emphasis on the foreign exchange market (forex). Employing a zero-shot prompting approach, we examine multiple ChatGPT prompts on a meticulously curated dataset of forex-related news headlines, measuring performance using metrics such as precision, recall, f1-score, and Mean Absolute Error (MAE) of the sentiment class. Additionally, we probe the correlation between predicted sentiment and market returns as an additional evaluation approach. ChatGPT, compared to FinBERT, a well-established sentiment analysis model for financial texts, exhibited approximately 35\% enhanced performance in sentiment classification and a 36\% higher correlation with market returns. By underlining the significance of prompt engineering, particularly in zero-shot contexts, this study spotlights ChatGPT's potential to substantially boost sentiment analysis in financial applications. By sharing the utilized dataset, our intention is to stimulate further research and advancements in the field of financial services.
False Discovery Rate Control for Lesion-Symptom Mapping with Heterogeneous data via Weighted P-values

Lesion-symptom mapping studies provide insight into what areas of the brain are involved in different aspects of cognition. This is commonly done via behavioral testing in patients with a naturally occurring brain injury or lesions (e.g., strokes or brain tumors). This results in high-dimensional observational data where lesion status (present/absent) is non-uniformly distributed with some voxels having lesions in very few (or no) subjects. In this situation, mass univariate hypothesis tests have severe power heterogeneity where many tests are known a priori to have little to no power. Recent advancements in multiple testing methodologies allow researchers to weigh hypotheses according to side-information (e.g., information on power heterogeneity). In this paper, we propose the use of p-value weighting for voxel-based lesion-symptom mapping (VLSM) studies. The weights are created using the distribution of lesion status and spatial information to estimate different non-null prior probabilities for each hypothesis test through some common approaches. We provide a monotone minimum weight criterion which requires minimum a priori power information. Our methods are demonstrated on dependent simulated data and an aphasia study investigating which regions of the brain are associated with the severity of language impairment among stroke survivors. The results demonstrate that the proposed methods have robust error control and can increase power. Further, we showcase how weights can be used to identify regions that are inconclusive due to lack of power.
Ranking-aware Uncertainty for Text-guided Image Retrieval

Text-guided image retrieval is to incorporate conditional text to better capture users' intent. Traditionally, the existing methods focus on minimizing the embedding distances between the source inputs and the targeted image, using the provided triplets $\langle$source image, source text, target image$\rangle$. However, such triplet optimization may limit the learned retrieval model to capture more detailed ranking information, e.g., the triplets are one-to-one correspondences and they fail to account for many-to-many correspondences arising from semantic diversity in feedback languages and images. To capture more ranking information, we propose a novel ranking-aware uncertainty approach to model many-to-many correspondences by only using the provided triplets. We introduce uncertainty learning to learn the stochastic ranking list of features. Specifically, our approach mainly comprises three components: (1) In-sample uncertainty, which aims to capture semantic diversity using a Gaussian distribution derived from both combined and target features; (2) Cross-sample uncertainty, which further mines the ranking information from other samples' distributions; and (3) Distribution regularization, which aligns the distributional representations of source inputs and targeted image. Compared to the existing state-of-the-art methods, our proposed method achieves significant results on two public datasets for composed image retrieval.
Error estimates of a bi-fidelity method for a multi-phase Navier-Stokes-Vlasov-Fokker-Planck system with random inputs

Uniform error estimates of a bi-fidelity method for a kinetic-fluid coupled model with random initial inputs in the fine particle regime are proved in this paper. Such a model is a system coupling the incompressible Navier-Stokes equations to the Vlasov-Fokker-Planck equations for a mixture of the flows with distinct particle sizes. The main analytic tool is the hypocoercivity analysis for the multi-phase Navier-Stokes-Vlasov-Fokker-Planck system with uncertainties, considering solutions in a perturbative setting near the global equilibrium. This allows us to obtain the error estimates in both kinetic and hydrodynamic regimes.
Benchmarking Adversarial Robustness of Compressed Deep Learning Models

The increasing size of Deep Neural Networks (DNNs) poses a pressing need for model compression, particularly when employed on resource constrained devices. Concurrently, the susceptibility of DNNs to adversarial attacks presents another significant hurdle. Despite substantial research on both model compression and adversarial robustness, their joint examination remains underexplored. Our study bridges this gap, seeking to understand the effect of adversarial inputs crafted for base models on their pruned versions. To examine this relationship, we have developed a comprehensive benchmark across diverse adversarial attacks and popular DNN models. We uniquely focus on models not previously exposed to adversarial training and apply pruning schemes optimized for accuracy and performance. Our findings reveal that while the benefits of pruning enhanced generalizability, compression, and faster inference times are preserved, adversarial robustness remains comparable to the base model. This suggests that model compression while offering its unique advantages, does not undermine adversarial robustness.
Constrained Global Optimization by Smoothing

This paper proposes a novel technique called"successive stochastic smoothing"that optimizes nonsmooth and discontinuous functions while considering various constraints. Our methodology enables local and global optimization, making it a powerful tool for many applications. First, a constrained problem is reduced to an unconstrained one by the exact nonsmooth penalty function method, which does not assume the existence of the objective function outside the feasible area and does not require the selection of the penalty coefficient. This reduction is exact in the case of minimization of a lower semicontinuous function under convex constraints. Then the resulting objective function is sequentially smoothed by the kernel method starting from relatively strong smoothing and with a gradually vanishing degree of smoothing. The finite difference stochastic gradient descent with trajectory averaging minimizes each smoothed function locally. Finite differences over stochastic directions sampled from the kernel estimate the stochastic gradients of the smoothed functions. We investigate the convergence rate of such stochastic finite-difference method on convex optimization problems. The"successive smoothing"algorithm uses the results of previous optimization runs to select the starting point for optimizing a consecutive, less smoothed function. Smoothing provides the"successive smoothing"method with some global properties. We illustrate the performance of the"successive stochastic smoothing"method on test-constrained optimization problems from the literature.
Estimation of the future rates of oil and gas discoveries in the western Gulf of Mexico. Professional paper

A discovery-process model was used to predict, as of January 1, 1977, the expected number and size distribution of oil and gas fields remaining to be discovered in the study area in the Gulf of Mexico and also to forecast the rate at which these fields will be discovered in the future. These predictions and forecasts were done separately for the combined Miocene-Pliocene trend area, which occupies 123,027 km/sup 2/ of the study area, and the Pleistocene trend area, which occupies 33,612 km/sup 2/. In both these areas, water depths ranged from 0 to 200 m. As of January 1, 1977, more than 2700 oil and gas fields were still expected to be discovered in both trends. These fields range in size from 0.76 million to 194.3 million barrels of producible oil equivalent. Most of these fields (94.5 percent) are predicted to contain less than 12.14 million BOE (barrels of oil equivalent) each. The rate at which these oil and gas fields will be discovered is forecasted to start at approximately 1.25 million BOE per wildcat well. This rate is forecasted to decline to about half this yield per wildcat well after the drilling of 2600 wildcat wells in the more » combined Miocene-Pliocene trend area and 1200 wildcat wells in the Pleistocene trend area. Analysis of data from areas where exploration has been relatively unrestricted indicates that the size distribution of oil and gas fields has a log-geometric form. As a result of this analysis, a new procedure was devised to estimate the form of the underlying size distribution of oil and gas fields in areas, such as the study area, where the discovery-data series has been truncated by economic factors. 13 references, 18 figures, 6 tables. « less
Stochastic Controlled Averaging for Federated Learning with Communication Compression

Communication compression, a technique aiming to reduce the information volume to be transmitted over the air, has gained great interests in Federated Learning (FL) for the potential of alleviating its communication overhead. However, communication compression brings forth new challenges in FL due to the interplay of compression-incurred information distortion and inherent characteristics of FL such as partial participation and data heterogeneity. Despite the recent development, the performance of compressed FL approaches has not been fully exploited. The existing approaches either cannot accommodate arbitrary data heterogeneity or partial participation, or require stringent conditions on compression. In this paper, we revisit the seminal stochastic controlled averaging method by proposing an equivalent but more efficient/simplified formulation with halved uplink communication costs. Building upon this implementation, we propose two compressed FL algorithms, SCALLION and SCAFCOM, to support unbiased and biased compression, respectively. Both the proposed methods outperform the existing compressed FL methods in terms of communication and computation complexities. Moreover, SCALLION and SCAFCOM accommodates arbitrary data heterogeneity and do not make any additional assumptions on compression errors. Experiments show that SCALLION and SCAFCOM can match the performance of corresponding full-precision FL approaches with substantially reduced uplink communication, and outperform recent compressed FL methods under the same communication budget.
A Kitaev-type spin liquid on a quasicrystal

We develop an exactly solvable model with Kitaev-type interactions and study its phase diagram on the dual lattice of the quasicrystalline Ammann-Beenker lattice. Our construction is based on the $\Gamma$-matrix generalization of the Kitaev model and utilizes the cut-and-project correspondence between the four-dimensional simple cubic lattice and the Ammann-Beenker lattice to designate four types of bonds. We obtain a rich phase diagram with gapped (chiral and abelian) and gapless spin liquid phases via Monte Carlo simulations and variational analysis. We show that the ground state can be further tuned by the inclusion of an onsite term that selects 21 different vison configurations while maintaining the integrability of the model. Our results highlight the rich physics at the intersection of quasicrystals and quantum magnetism.
Chiral kinetic theory with self-energy corrections and neutrino spin Hall effect

We systematically derive the chiral kinetic theory for chiral fermions with collisions, including the self-energy corrections, from quantum field theories. We find that the Wigner functions and chiral kinetic equations receive both the classical and quantum corrections from the self-energies and their spacetime gradients. We also apply this formalism to study nonequilibrium neutrino transport due to the interaction with thermalized electrons and nucleons, as realized in core-collapse supernovae. We derive neutrino currents along magnetic fields and neutrino spin Hall effect induced by the density gradient at first order in the Fermi constant $G_{\rm F}$ for anisotropic neutrino distributions.
Learning to Generate Semantic Layouts for Higher Text-Image Correspondence in Text-to-Image Synthesis

Existing text-to-image generation approaches have set high standards for photorealism and text-image correspondence, largely benefiting from web-scale text-image datasets, which can include up to 5 billion pairs. However, text-to-image generation models trained on domain-specific datasets, such as urban scenes, medical images, and faces, still suffer from low text-image correspondence due to the lack of text-image pairs. Additionally, collecting billions of text-image pairs for a specific domain can be time-consuming and costly. Thus, ensuring high text-image correspondence without relying on web-scale text-image datasets remains a challenging task. In this paper, we present a novel approach for enhancing text-image correspondence by leveraging available semantic layouts. Specifically, we propose a Gaussian-categorical diffusion process that simultaneously generates both images and corresponding layout pairs. Our experiments reveal that we can guide text-to-image generation models to be aware of the semantics of different image regions, by training the model to generate semantic labels for each pixel. We demonstrate that our approach achieves higher text-image correspondence compared to existing text-to-image generation approaches in the Multi-Modal CelebA-HQ and the Cityscapes dataset, where text-image pairs are scarce. Codes are available at https://pmh9960.github. io/research/GCDP.
Is Self-Supervised Pretraining Good for Extrapolation in Molecular Property Prediction?

The prediction of material properties plays a crucial role in the development and discovery of materials in diverse applications, such as batteries, semiconductors, catalysts, and pharmaceuticals. Recently, there has been a growing interest in employing data-driven approaches by using machine learning technologies, in combination with conventional theoretical calculations. In material science, the prediction of unobserved values, commonly referred to as extrapolation, is particularly critical for property prediction as it enables researchers to gain insight into materials beyond the limits of available data. However, even with the recent advancements in powerful machine learning models, accurate extrapolation is still widely recognized as a significantly challenging problem. On the other hand, self-supervised pretraining is a machine learning technique where a model is first trained on unlabeled data using relatively simple pretext tasks before being trained on labeled data for target tasks. As self-supervised pretraining can effectively utilize material data without observed property values, it has the potential to improve the model's extrapolation ability. In this paper, we clarify how such self-supervised pretraining can enhance extrapolation performance.We propose an experimental framework for the demonstration and empirically reveal that while models were unable to accurately extrapolate absolute property values, self-supervised pretraining enables them to learn relative tendencies of unobserved property values and improve extrapolation performance.
Evaluation of a Culturally Adapted Online Basic Psychosocial Skills Training Program for International Frontline Workers in Under-Resourced Contexts During the COVID-19 Pandemic

Abstract Objective: Frontline workers report negative mental health impacts of being exposed to the risk of COVID-19, and of supporting people struggling with the effects of the virus. Uptake of psychological first-aid resources is inconsistent, and they may not meet the needs of frontline workers in under-resourced contexts. This study evaluates a culturally adapted basic psychosocial skills (BPS) training program that aimed to meet the needs of frontline workers in under-resourced settings. Methods: A cross-sectional survey administered to frontline workers who completed the program between 2020 and 2022, investigated their perceived confidence, satisfaction, and skill development, as well as their views on relevance to context and accessibility of the program. Results: Out of the 1000 people who had undertaken the BPS program, 118 (11.8%) completed the survey. Participants reported high levels of satisfaction and improved confidence in, and knowledge of, psychosocial skills. Participants reported that the BPS program was culturally and contextually relevant, and some requested expansion of the program, including more interactivity, opportunities for anonymous participation, and adaption to other cultural contexts, including translation into languages other than English. Conclusion: Findings indicate a need for free, online, and culturally adapted psychosocial skills training program that is designed with key stakeholders to ensure relevance to social and cultural contexts.
Safety and Efficacy of Aspiration Catheter CAT6 and 5 Fr Navien in the Endovascular Treatment of Acute Ischemic Stroke

Background: Mechanical thrombectomy has become a key treatment option for acute ischemic stroke. This study compared the safety and efficacy of aspiration catheter CAT6 and 5 Fr Navien. Methods: Thrombectomy was performed in103 patients with the acute internal carotid artery, middle cerebral artery M1 or M2 occlusions, including the CAT6 group (n=53 with stent retriever and CAT6 aspiration) and the 5 Fr Navien group (n=50 with stent retriever and 5 Fr Navien aspiration) at the Advanced Stroke Center. Results: Overall, an aspiration catheter placement success rate was achieved in 93.2% of cases, 52 (98.11%) for CAT6, and 44 (88.00%) for 5 Fr Navien (P=0.042). Overall, 17 cases (16.51%) required additional guidewire rates, 5.66% for CAT6, and 13.592% for 5 Fr Navien (P=0.002). First-pass success rate (FPSR) was achieved in 38.84% of cases overall, a rate that did not differ significantly between catheters: 45.28% for CAT6; 32.00% for 5 Fr Navien (P=0.167). Final thrombolysis in cerebral infarction 2b or 3 reperfusion was achieved in 91.26% of cases overall, 51 (96.23%) for CAT6, and 43 (86%) for 5 Fr Navien (P=0.066). The participants had a mean number of passes for the index thrombus of 1.956 and a median procedure time of 65.82±21.8 minutes. There was no significant difference found in 90-day good outcome (mean 42.7%, modified Rankin Score 0 to 2) and 90-day mortality (17%) between CAT6 and 5 Fr Navien. Conclusion: Aspiration catheter placement success rate and first-pass success rate seemed to be higher for CAT6 and, moreover, the rate of additional guidewires was lower.