Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge Generation for Zero-shot Knowledge-based VQA

Previous solutions to knowledge-based visual question answering (K-VQA) retrieve knowledge from external knowledge bases and use supervised learning to train the K-VQA model.Recently pre-trained LLMs have been used as both a knowledge source and a zero-shot QA model for K-VQA and demonstrated promising results.However, these recent methods do not explicitly show the knowledge needed to answer the questions and thus lack interpretability.Inspired by recent work on knowledge generation from LLMs for text-based QA, in this work we propose and test a similar knowledge-generation-based K-VQA method, which first generates knowledge from an LLM and then incorporates the generated knowledge for K-VQA in a zero-shot manner. We evaluate our method on two K-VQA benchmarks and found that our method performs better than previous zero-shot K-VQA methods and our generated knowledge is generally relevant and helpful.
Geochronology and Genesis of the Tiegelongnan Porphyry Cu(Au) Deposit in Tibet: Evidence from U–Pb, Re–Os Dating and Hf, S, and H–O Isotopes

The Tiegelongnan Cu (Au) deposit is the largest copper deposit newly discovered in the Bangong–Nujiang metallogenic belt. The deposit has a clear alteration zoning consisting of, from core to margin, potassic to propylitic, superimposed by phyllic and advanced argillic alteration. The shallow part of the deposit consists of a high sulphidation‐state overprint, mainly comprising disseminated pyrite and Cu–S minerals such as bornite, covellite, digenite, and enargite. At depth porphyry‐type mineralization mainly comprises disseminated chalcopyrite, bornite, pyrite, and a minor vein molybdenite. Mineralization is disseminated and associated with veins contained within the porphyry intrusions and their surrounding rocks. The zircon U–Pb ages of the mineralized diorite porphyry and granodiorite porphyry are 123.1 ± 1.7 Ma (2σ) and 121.5 ± 1.5 Ma (2σ), respectively. The molybdenite Re–Os age is 121.2 ± 1.2 Ma, suggesting that mineralization was closely associated with magmatism. Andesite lava (zircon U–Pb age of 111.7 ± 1.6 Ma, 2σ) overlies the ore‐bodies and is the product of post‐mineralization volcanic activity that played a critical role in preserving the ore‐bodies. Values of −4.6 ‰ to + 0.8 ‰ δ34S for the metal sulfides (mean − 1.55 ‰) suggest that S mainly has a deep magmatic source. The H and O isotopic composition is (δD = −87 ‰ to −64 ‰; δ18OH2O = 5.5 ‰ to 9.0 ‰), indicating that the ore‐forming fluids are mostly magmatic‐hydrothermal, possibly mixed with a small amount of meteoric water. The zircon εHf(t) of the diorite porphyry is 3.7 to 8.3, and the granodiorite porphyry is 1.8 to 7.5. Molybdenite has a high Re from 382.2 × 10−6 to 1600 × 10−6. Re and Hf isotope composition show that Tiegelongnan has some mantle source, maybe the juvenile lower crust from crust–mantle mixed source. Metallogenesis of the Tiegelongnan giant porphyry system was associated with intermediate to acidic magma in the Early Cretaceous (~120 Ma). The magma provenance of the Tiegelongnan deposit has some mantle‐derived composition, possibly mixed with the crust‐derived materials.
Estimation of conditional average treatment effects on distributed data: A privacy-preserving approach

Estimation of conditional average treatment effects (CATEs) is an important topic in various fields such as medical and social sciences. CATEs can be estimated with high accuracy if distributed data across multiple parties can be centralized. However, it is difficult to aggregate such data if they contain privacy information. To address this issue, we proposed data collaboration double machine learning (DC-DML), a method that can estimate CATE models with privacy preservation of distributed data, and evaluated the method through numerical experiments. Our contributions are summarized in the following three points. First, our method enables estimation and testing of semi-parametric CATE models without iterative communication on distributed data. Semi-parametric or non-parametric CATE models enable estimation and testing that is more robust to model mis-specification than parametric models. However, to our knowledge, no communication-efficient method has been proposed for estimating and testing semi-parametric or non-parametric CATE models on distributed data. Second, our method enables collaborative estimation between different parties as well as multiple time points because the dimensionality-reduced intermediate representations can be accumulated. Third, our method performed as well or better than other methods in evaluation experiments using synthetic, semi-synthetic and real-world datasets.
Modified Cramer’s Rule and its Application to Solve Linear Systems in WZ Factorization

The proposed modified methods of Cramer's rule consider the column vector as well as the coefficient matrix concurrently in the linear system. The modified methods can be applied since Cramer's rule is typically known for solving the linear systems in $WZ$ factorization to yield Z-matrix. Then, we presented our results to show that there is no tangible difference in performance time between Cramer's rule and the modified methods in the factorization from improved versions of MATLAB. Additionally, the Frobenius norm of the modified methods in the factorization is better than using Cramer's rule irrespective of the version of MATLAB used.
Secure Wireless Communication in Active RIS-Assisted DFRC System

This work considers a dual-functional radar and communication (DFRC) system with an active reconfigurable intelligent surface (RIS) and a potential eavesdropper. Our purpose is to maximize the secrecy rate (SR) of the system by jointly designing the beamforming matrix at the DFRC base station (BS) and the reflecting coefficients at the active RIS, subject to the signal-to-interference-plus-noise-ratio (SINR) constraint of the radar echo and the power consumption constraints at the DFRC-BS and active RIS. An alternating optimization (AO) algorithm based on semi-definite relaxation (SDR) and majorizationminimization (MM) is applied to solve the SR-maximization problem by alternately optimizing the beamforming matrix and the reflecting coefficients. Specifically, we first apply the SDR and successive convex approximation (SCA) methods to transform the two subproblems into more tractable forms, then the MM method is applied to derive a concave surrogate function and iteratively solve the subproblems. Finally, simulation results indicate that the active RIS can better confront the impact of"multiplicative fading"and outperforms traditional passive RIS in terms of both secure data rate and radar sensing performance.
Multi-scale fMRI time series analysis for understanding neurodegeneration in MCI

In this study, we present a technique that spans multi-scale views (global scale -- meaning brain network-level and local scale -- examining each individual ROI that constitutes the network) applied to resting-state fMRI volumes. Deep learning based classification is utilized in understanding neurodegeneration. The novelty of the proposed approach lies in utilizing two extreme scales of analysis. One branch considers the entire network within graph-analysis framework. Concurrently, the second branch scrutinizes each ROI within a network independently, focusing on evolution of dynamics. For each subject, graph-based approach employs partial correlation to profile the subject in a single graph where each ROI is a node, providing insights into differences in levels of participation. In contrast, non-linear analysis employs recurrence plots to profile a subject as a multichannel 2D image, revealing distinctions in underlying dynamics. The proposed approach is employed for classification of a cohort of 50 healthy control (HC) and 50 Mild Cognitive Impairment (MCI), sourced from ADNI dataset. Results point to: (1) reduced activity in ROIs such as PCC in MCI (2) greater activity in occipital in MCI, which is not seen in HC (3) when analysed for dynamics, all ROIs in MCI show greater predictability in time-series.
Traveling Ionospheric Disturbances Detected During the Solar Eclipse of 24 October 1995

During the total solar eclipse period on 24 October 1995, the ionospheric data derived from Faraday rotation, differential Doppler frequency shift, and HF Doppler sounding at Lunping Observatory were used to detect the traveling ionospheric disturbances associated with the solar eclipse of that day. These observed ionospheric disturbances were characterized by quasi­ periodic wave-like trains lasting for several cycles with wavelengths of around 193 km, periods of around 12-14 min and propagated with a hori­ zontal velocity of around 296 mis from the direction of the eclipse path to the observation site. By use of a simplified bow wave front model, these traveling ionospheric disturbances are interpreted as the freely propagated atmospheric gravity waves generated by the supersonic movement of the Moon's shadow through the Earth's atmosphere along the eclipse path. (
Global bank network connectedness revisited: What is common, idiosyncratic and when?

We revisit the problem of estimating high-dimensional global bank network connectedness. Instead of directly regularizing the high-dimensional vector of realized volatilities as in Demirer et al. (2018), we estimate a dynamic factor model with sparse VAR idiosyncratic components. This allows to disentangle: (I) the part of system-wide connectedness (SWC) due to the common component shocks (what we call the"banking market"), and (II) the part due to the idiosyncratic shocks (the single banks). We employ both the original dataset as in Demirer et al. (2018) (daily data, 2003-2013), as well as a more recent vintage (2014-2023). For both, we compute SWC due to (I), (II), (I+II) and provide bootstrap confidence bands. In accordance with the literature, we find SWC to spike during global crises. However, our method minimizes the risk of SWC underestimation in high-dimensional datasets where episodes of systemic risk can be both pervasive and idiosyncratic. In fact, we are able to disentangle how in normal times $\approx$60-80% of SWC is due to idiosyncratic variation and only $\approx$20-40% to market variation. However, in crises periods such as the 2008 financial crisis and the Covid19 outbreak in 2019, the situation is completely reversed: SWC is comparatively more driven by a market dynamic and less by an idiosyncratic one.
SOAP: A Social Authentication Protocol

Social authentication has been suggested as a usable authentication ceremony to replace manual key authentication in messaging applications. Using social authentication, chat partners authenticate their peers using digital identities managed by identity providers. In this paper, we formally define social authentication, present a protocol called SOAP that largely automates social authentication, formally prove SOAP's security, and demonstrate SOAP's practicality in two prototypes. One prototype is web-based, and the other is implemented in the open-source Signal messaging application. Using SOAP, users can significantly raise the bar for compromising their messaging accounts. In contrast to the default security provided by messaging applications such as Signal and WhatsApp, attackers must compromise both the messaging account and all identity provider-managed identities to attack a victim. In addition to its security and automation, SOAP is straightforward to adopt as it is built on top of the well-established OpenID Connect protocol.
$C^*$-Algebraic Machine Learning: Moving in a New Direction

Machine learning has a long collaborative tradition with several fields of mathematics, such as statistics, probability and linear algebra. We propose a new direction for machine learning research: $C^*$-algebraic ML $-$ a cross-fertilization between $C^*$-algebra and machine learning. The mathematical concept of $C^*$-algebra is a natural generalization of the space of complex numbers. It enables us to unify existing learning strategies, and construct a new framework for more diverse and information-rich data models. We explain why and how to use $C^*$-algebras in machine learning, and provide technical considerations that go into the design of $C^*$-algebraic learning models in the contexts of kernel methods and neural networks. Furthermore, we discuss open questions and challenges in $C^*$-algebraic ML and give our thoughts for future development and applications.
Dynamic Test Case Prioritization in Industrial Test Result Datasets

Regression testing in software development checks if new software features affect existing ones. Regression testing is a key task in continuous development and integration, where software is built in small increments and new features are integrated as soon as possible. It is therefore important that developers are notified about possible faults quickly. In this article, we propose a test case prioritization schema that combines the use of a static and a dynamic prioritization algorithm. The dynamic prioritization algorithm rearranges the order of execution of tests on the fly, while the tests are being executed. We propose to use a conditional probability dynamic algorithm for this. We evaluate our solution on three industrial datasets and utilize Average Percentage of Fault Detection for that. The main findings are that our dynamic prioritization algorithm can: a) be applied with any static algorithm that assigns a priority score to each test case b) can improve the performance of the static algorithm if there are failure correlations between test cases c) can also reduce the performance of the static algorithm, but only when the static scheduling is performed at a near optimal level.
High-dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategy

Bayesian Optimization (BO) is an effective method for finding the global optimum of expensive black-box functions. However, it is well known that applying BO to high-dimensional optimization problems is challenging. To address this issue, a promising solution is to use a local search strategy that partitions the search domain into local regions with high likelihood of containing the global optimum, and then use BO to optimize the objective function within these regions. In this paper, we propose a novel technique for defining the local regions using the Covariance Matrix Adaptation (CMA) strategy. Specifically, we use CMA to learn a search distribution that can estimate the probabilities of data points being the global optimum of the objective function. Based on this search distribution, we then define the local regions consisting of data points with high probabilities of being the global optimum. Our approach serves as a meta-algorithm as it can incorporate existing black-box BO optimizers, such as BO, TuRBO, and BAxUS, to find the global optimum of the objective function within our derived local regions. We evaluate our proposed method on various benchmark synthetic and real-world problems. The results demonstrate that our method outperforms existing state-of-the-art techniques.
The Role of the TLR4-MyD88 Signaling Pathway in the Immune Response of the Selected Scallop Strain “Hongmo No. 1” to Heat Stress

Simple Summary The innate immunity of marine bivalves is challenged upon exposure to heat stress, but there is limited knowledge of its molecular mechanism. In this study, genes in the TLR4-MyD88 signaling pathway were investigated in a new scallop strain: “Hongmo No. 1” (Bohai Red ♂ × A. irradians concentricus ♀). This is the first study reporting that acute heat stress obviously inhibited the TLR4-MyD88 signaling pathway of bivalves, even with LPS stimulation. The subsequent RNAi targeting TLR4 gene in the new scallop strain “Hongmo No. 1” indicated the key role of TLR4-MyD88 signal transduction in the immune response. Acute heat stress seemed to affect the response of downstream mediator molecules to the inhibition of HMTLR4. These results provide more information about bivalve immunity under heat stress, which is expected to improve heat tolerance to cope with more frequent and extreme heat wave events. Abstract The innate immunity of marine bivalves is challenged upon exposure to heat stress, especially with increases in the frequency and intensity of heat waves. TLR4 serves a classical pattern recognition receptor in recognizing pathogenic microorganisms and activating immune responses. In this study, three genes, HMTLR4, HMMyD88 and HMTRAF6, were characterized as homologs of genes in the TLR4-MyD88 signaling pathway in the selected scallop strain “Hongmo No. 1”. According to RT-PCR, acute heat stress (32 °C) inhibited genes in the TLR4-MyD88 signaling pathway, and LPS stimulation-induced activation of TLR4-MyD88 signal transduction was also negatively affected at 32 °C. ELISA showed LPS-induced tumor necrosis factor alpha (TNF-α) or lysozyme (LZM) activity, but this was independent of temperature. RNA interference (RNAi) confirmed that HMTLR4 silencing suppressed the expression of its downstream gene, whether at 24 °C or at 32 °C. The level of TNF-α and the activity of LZM also decreased after injection with dsRNA, indicating a negative effect on the innate immunity of scallops. Additionally, acute heat stress affected the suppression of downstream gene expression when compared with that at 24 °C, which led us to the hypothesis that heat stress directly influences the downstream targets of HMTLR4. These results enrich the knowledge of scallop immunity under heat stress and can be beneficial for the genetic improvement of new scallop strains with higher thermotolerance.
Genesis of westerly wind bursts over the equatorial western Pacific during the onset of the strong 2015–2016 El Niño

The strong 2015–2016 El Niño was initiated by several strong westerly wind bursts over the equatorial western Pacific in March and May 2015. These westerly wind bursts trigger eastward propagating warm Kelvin waves and lead to large sea surface temperature (SST) warming in the equatorial eastern Pacific. The first burst of westerly winds in early March was mainly induced by the Arctic Oscillation (AO) event. These westerly wind anomalies were enhanced subsequently due to the Madden‐Julian Oscillation activity and northerly cold surges from East Asia‐western Pacific in mid‐March. Another westerly wind burst in May, induced by anomalous southerly winds from the Australian continent, further increased the SST anomaly in the equatorial eastern Pacific. This study provides an evidence of the AO influence on this strong El Niño‐Southern Oscillation (ENSO) event and demonstrates the complexity in the genesis of westerly wind bursts during the El Niño outbreak, which may help improve the prediction of ENSO.
On the Performance of Image Quality Measures with Application to Multifocused Image Fusion

Due to limited depth of field of machine vision cameras, multifocused image fusion is finding importance to produce a single image called fused image from various images of the same scene being imaged. To have focused images of all the objects in the scene, the fused image is formed by combining important features of various images. This in turn increases the importance of ability to assess the quality of the fused image more accurately. To be accurate, a typical image quality measure should be independent of image content, robust to noise, monotonic with respect to image blur and calculated with minimal computation complexity. In this paper, the performance of nine image quality measures were assessed through various experiments by applying image blur, adding image noise, changing image contrast and image saturation level. Experiments were also conducted on six sets of images to find the best image quality measure for multifocused image fusion.
Cholera Deaths During Outbreaks in Uvira, Eastern Democratic Republic of the Congo, 10–35 Months After Mass Vaccination

Abstract Our understanding of the burden and drivers of cholera mortality is hampered by limited surveillance and confirmation capacity. Leveraging enhanced clinical and laboratory surveillance in the cholera-endemic community of Uvira, eastern Democratic Republic of Congo, we describe cholera deaths across 3 epidemics between September 2021 and September 2023 following mass vaccination.
Estimation on the concentration of total suspended matter in Lombok Coastal using Landsat 8 OLI, Indonesia

Total Suspended Matter (TSM) are fine materials which suspended and floated in water column. Water column could be turbid due to TSM that reduces the depth of light penetration and causes low productivity in coastal waters. The objective of this study was to estimate TSM concentration using Landsat 8 OLI data in Lombok coastal waters Indonesia by using empirical and analytic approach between three visible bands of Landsat 8 OLI subsurface reflectance (OLI 2, OLI 3 and OLI 4) and field data. The accuracy of model was tested using error estimation and statistical analysis. Colour of waters, transparency and reflectance values showed, the clear water has high transparency and low reflectance while the turbid waters have low transparency and high reflectance. The estimation of TSM concentrations in Lombok coastal waters are 0.39 to 20.7 mg/l. TSM concentrations becoming high when it is on coast and low when it is far from the coast. The statistical analysis showed that TSM model from Landsat 8 OLI data could describe TSM from field measurement with correlation 91.8% and RMSE value 0.52. The t-test and f-test showed that the TSM derived from Landsat 8 OLI and TSM measured in field were not significantly different.
Distribution Locational Marginal Price: Approximations, Solution Algorithm and Organization

In this thesis, a proposal is made to formulate, solve and organize distribution locational marginal price (DLMP) in a manner which is similar to the already existing transmission grid level locational marginal price (LMP). In this way, the proposed DLMP of this thesis has a higher practical realization, due to i) the readily transferable organizational lessons learned from implementing the LMP and ii) understanding/interpreting the price structure from the perspective of an electricity market.