Miracle-dz/UniFAR-FTUs huggingface dataset:
Archangel: A Hybrid UAV-Based Human Detection Benchmark With Position and Pose Metadata

Learning to detect objects, such as humans, in imagery captured by an unmanned aerial vehicle (UAV) usually suffers from tremendous variations caused by the UAV’s position towards the objects. In addition, existing UAV-based benchmark datasets do not provide adequate dataset metadata, which is essential for precise model diagnosis and learning features invariant to those variations. In this paper, we introduce Archangel, the first UAV-based object detection dataset composed of real and synthetic subsets captured with similar imagining conditions and UAV position and object pose metadata. A series of experiments are carefully designed with a state-of-the-art object detector to demonstrate the benefits of leveraging the metadata during model evaluation. Moreover, several crucial insights involving both real and synthetic data during model optimization are presented. In the end, we discuss the advantages, limitations, and future directions regarding Archangel to highlight its distinct value for the broader machine learning community.
A general framework for the analysis of kernel-based tests

Kernel-based tests provide a simple yet effective framework that use the theory of reproducing kernel Hilbert spaces to design non-parametric testing procedures. In this paper we propose new theoretical tools that can be used to study the asymptotic behaviour of kernel-based tests in several data scenarios, and in many different testing problems. Unlike current approaches, our methods avoid using lengthy $U$ and $V$ statistics expansions and limit theorems, that commonly appear in the literature, and works directly with random functionals on Hilbert spaces. Therefore, our framework leads to a much simpler and clean analysis of kernel tests, only requiring mild regularity conditions. Furthermore, we show that, in general, our analysis cannot be improved by proving that the regularity conditions required by our methods are both sufficient and necessary. To illustrate the effectiveness of our approach we present a new kernel-test for the conditional independence testing problem, as well as new analyses for already known kernel-based tests.
Testing Causality in Scientific Modelling Software

From simulating galaxy formation to viral transmission in a pandemic, scientific models play a pivotal role in developing scientific theories and supporting government policy decisions that affect us all. Given these critical applications, a poor modelling assumption or bug could have far-reaching consequences. However, scientific models possess several properties that make them notoriously difficult to test, including a complex input space, long execution times, and non-determinism, rendering existing testing techniques impractical. In fields such as epidemiology, where researchers seek answers to challenging causal questions, a statistical methodology known as Causal Inference has addressed similar problems, enabling the inference of causal conclusions from noisy, biased, and sparse data instead of costly experiments. This paper introduces the Causal Testing Framework: a framework that uses Causal Inference techniques to establish causal effects from existing data, enabling users to conduct software testing activities concerning the effect of a change, such as Metamorphic Testing, a posteriori. We present three case studies covering real-world scientific models, demonstrating how the Causal Testing Framework can infer metamorphic test outcomes from reused, confounded test data to provide an efficient solution for testing scientific modelling software.
Fractional Brownian Motion and the Fractional Stochastic Calculus

This paper begins by giving an historical context to fractional Brownian Motion and its development. Section 2 then introduces the fractional calculus, from the Riemann-Liouville perspective. In Section 3, we introduce Brownian motion and its properties, which is the framework for deriving the It\^o integral. In Section 4 we finally introduce the It\^o calculus and discuss the derivation of the It\^o integral. Section 4.1 continues the discussion about the It\^o calculus by introducing the It\^o formula, which is the analogue to the chain rule in classical calculus. In Section 5 we present our formal definition of fBm and derive some of its properties that give motivation for the development of a stochastic calculus with respect to fBm. Finally, in Section 6 we define and characterize a stochastic integral with respect to fBm from a pathwise perspective.
On Biased Behavior of GANs for Face Verification

Deep Learning systems need large data for training. Datasets for training face verification systems are difficult to obtain and prone to privacy issues. Synthetic data generated by generative models such as GANs can be a good alternative. However, we show that data generated from GANs are prone to bias and fairness issues. Specifically, GANs trained on FFHQ dataset show biased behavior towards generating white faces in the age group of 20-29. We also demonstrate that synthetic faces cause disparate impact, specifically for race attribute, when used for fine tuning face verification systems.
Tesseract: Parallelize the Tensor Parallelism Efficiently

Together with the improvements in state-of-the-art accuracies of various tasks, deep learning models are getting significantly larger. However, it is extremely difficult to implement these large models because limited GPU memory makes it impossible to fit large models into a single GPU or even a GPU server. Besides, it is highly necessary to reduce the training time for large models. Previous methods like Megatron-LM implemented a 1-Dimensional distributed method to use GPUs to speed up the training. However, these methods have a high communication overhead and a low scaling efficiency on large-scale clusters. To solve these problems, we propose Tesseract, highly scalable tensor parallelism with a novel design. It increases efficiency by reducing communication overhead and lowers the memory required for each GPU. By introducing the novel dimension into tensor parallelism, Tesseract greatly increases the memory capacity of tensor parallelism. Concretely, this new dimension furthermore increases the degree of tensor parallelism. Compared to previous 1-D and 2-D methods, Tesseract manages to reduce the communication cost on each layer, resulting in speedups of 1.38x and 1.53x respectively with strong scaling. In weak scaling experiments, Tesseract achieves a maximum of 4.0/1.7 times inference speedup and 3.4/1.7 times throughput improvement compared to 1-D/2-D methods, respectively. By introducing Tesseract, we offer a more efficient and scalable way to implement large deep learning models with limited GPU resources.
Biological logics are restricted

Networks of gene regulation govern morphogenesis, determine cell identity and regulate cell function. But we have little understanding, at the local level, of which logics are biologically preferred or even permitted. To solve this puzzle, we studied the consequences of a fundamental aspect of gene regulatory networks: genes and transcription factors talk to each other but not themselves. Remarkably, this bipartite structure severely restricts the number of logical dependencies that a gene can have on other genes. We developed a theory for the number of permitted logics for diﬀerent regulatory building blocks of genes and transcription factors. We tested our predictions against a simulation of the 19 simplest building blocks, and found com-plete agreement. The restricted range of biological logics is a key insight into how information is processed at the genetic level. It constraints global network function and makes it easier to reverse engineer regulatory networks from observed behavior.
Cube sum problem and an explicit Gross-Zagier formula

A nonzero rational number is called a {\it cube sum} if it is of the form $a^3+b^3$ with $a,b\in{\Bbb Q}^\times$. In this paper, we prove that for any odd integer $k\geq 1$, there exist infinitely many cube-free odd integers $n$ with exactly $k$ distinct prime factors such that $2n$ is a cube sum (resp. not a cube sum). We present also a general construction of Heegner points and obtain an explicit Gross-Zagier formula which is used to prove the Birch and Swinnerton-Dyer conjecture for certain elliptic curves related to the cube sum problem.
Equidecomposability and discrepancy; a solution of Tarski's circle-squaring problem

Tarski's circle-squaring problem asks whether a disc is equidecomposable to a square; that is, whether a disc can be decomposed into finitely many parts which can be rearranged to obtain a partition of a square [16]. The problem was motivated by the well-known Banach-Tarski theorem stating that in /S* any two sets are equidecomposable provided that they are bounded and have non-empty interior. In particular, any ball is equidecomposable to any cube. On the other band, the existence of a Banach measure on the plane shows that a disc and a square can be equidecomposable only if they have the same area. (As for the proofs of these theorems, the further development of the theory and also for the history of Tarski's problem we refer to S. Wagon's book [19]).
Comparing the Growth Performance and Fatty Acid Profile of Brown Trout (Salmo trutta macrostigma) and Rainbow Trout (Oncorhynchus mykiss) Reared in Concrete Ponds and Net Cages

ABSTRACT Engin, S. and Altan, O. , 2022. Comparing the growth performance and fatty acid profile of brown trout (Salmo trutta macrostigma) and rainbow trout (Oncorhynchus mykiss) reared in concrete ponds and net cages. Journal of Coastal Research, 38(5), 968–975. Coconut Creek (Florida), ISSN 0749-0208. This study aimed to compare the growth performance and fatty acid profiles of rainbow trout (Oncorhynchus mykiss) and brown trout (Salmo trutta macrostigma) fry reared in concrete ponds and net cages. In the study, which lasted for 270 days under commercial production conditions, an initial live weight of 5.03 g per fish was used and placed in concrete ponds and net cages in two replications and equal numbers. The specific growth rate ranged between 1.78 and 1.81%, but there was no significant difference among the experimental groups (p >0.05). In the study, although the feed conversion ratio varied between 1.04 and 1.06, no significant difference was determined among the experimental groups (p >0.05). On the contrary, daily average weight gain was higher for both species in net cages and was significantly different from that of fish in the pond (p <0.05). As a result of the fatty acid analysis of the study, it was determined that the saturated fatty acid, monounsaturated fatty acid, and polyunsaturated fatty acid values were different among the groups (p <0.05). The results show that brown trout in fish farm conditions succeeded as an efficient aquaculture business at least as much as rainbow trout.
An intersectional analysis of historical and contemporary structural racism on non-fatal shootings in Baltimore, Maryland

Introduction Non-fatal shooting rates vary tremendously within cities in the USA. Factors related to structural racism (both historical and contemporary) could help explain differences in non-fatal shooting rates at the neighbourhood level. Most research assessing the relationship between structural racism and firearm violence only includes one dimension of structural racism. Our study uses an intersectional approach to examine how the interaction of two forms of structural racism is associated with spatial non-fatal shooting disparities in Baltimore, Maryland. Methods We present three additive interaction measures to describe the relationship between historical redlining and contemporary racialized economic segregation on neighbourhood-level non-fatal shootings. Results Our findings revealed that sustained disadvantage census tracts (tracts that experience contemporary socioeconomic disadvantage and were historically redlined) have the highest burden of non-fatal shootings. Sustained disadvantage tracts had on average 24 more non-fatal shootings a year per 10 000 residents compared with similarly populated sustained advantage tracts (tracts that experience contemporary socioeconomic advantage and were not historically redlined). Moreover, we found that between 2015 and 2019, the interaction between redlining and racialized economic segregation explained over one-third of non-fatal shootings (approximately 650 shootings) in sustained disadvantage tracts. Conclusion These findings suggest that the intersection of historical and contemporary structural racism is a fundamental cause of firearm violence inequities in Baltimore. Intersectionality can advance injury prevention research and practice by (1) serving as an analytical tool to expose inequities in injury-related outcomes and (2) informing the development and implementation of injury prevention interventions and policies that prioritise health equity and racial justice.
Supersymmetry breaking in gauge theories and string theory

OF THE DISSERTATION Supersymmetry Breaking in Gauge Theories and String Theory by Kuver Sinha Dissertation Director: Professor Duiliu-Emanuel Diaconescu We study aspects of supersymmetry breaking in gauge theories and string theory. On the gauge theory side, we explore metastable vacua in a SQCD-like model with an extra sector connected by a singlet. The model combines dynamical supersymmetry breaking with an O’Raifeartaigh mechanism in terms of confined variables. On the string theory side, we study the dynamics of non-supersymmetric magnetized D-brane configurations on Calabi-Yau spaces. We also study the stabilization of the supersymmetry breaking runaway quiver.
Exploring and exploiting ant colony optimization algorithm for vertical highway alignment development

The vertical alignment optimization is about developing a minimum cost curvilinear vertical profile of constrained grade sections and appropriate non‐overlapping vertical curves passing through fixed control points with elevation constraints. Variations in ground profile and discreteness in unit cutting and filling costs make it a non‐convex, noisy, constrained optimization problem with many local minima. Further, the gradient related constraints and vertical curvature are non‐linear. This paper presents an innovative exploring and exploiting ant colony optimization (E&E‐ACO) algorithm with an appropriate point sampling, vertical curve fitting strategies, and an intuitive feasible region identification approach for solving the vertical alignment optimization problem. The E&E‐ACO algorithm extensively explores the feasible search space to generate a set of potential solutions and effectively exploit the space around the potential solutions for developing the optimized vertical alignment. The efficacy of the proposed method is demonstrated using two case studies. In one case study, the optimized solution by the proposed method had a marginally better objective function value and about three times lesser computational time than the solution by the mesh adaptive direct search method. The optimized alignment satisfied the elevation constraints of fixed control points and imitated the manually designed real‐world vertical alignment. The linearly varying exploration and exploitation parameters had better convergence rate than the other tested variations. Further, the proposed method at the end of 1000 iterations yielded about six times better result than the traditional ACO algorithm.
SELENBP1 inhibits progression of colorectal cancer by suppressing epithelial–mesenchymal transition

Abstract Selenium-binding protein 1 (SELENBP1) is frequently dysregulated in various malignancies including colorectal cancer (CRC); however, its roles in progression of CRCs and the underlying mechanism remain to be elucidated. In this study, we compared the expression of SELENBP1 between CRCs and colorectal normal tissues (NTs), as well as between primary and metastatic CRCs; we determined the association between SELENBP1 expression and CRC patient prognoses; we conducted both in vitro and in vivo experiments to explore the functional roles of SELENBP1 in CRC progression; and we characterized the potential underlying mechanisms associated with SELENBP1 activities. We found that the expression of SELENBP1 was significantly and consistently decreased in CRCs than that in adjacent NTs, while significantly and frequently decreased in metastatic than primary CRCs. High expression of SELENBP1 was an independent predictor of favorable prognoses in CRC patients. Overexpression of SELENBP1 suppressed, while silencing of SELENBP1 promoted cell proliferation, migration and invasion, and in vivo tumorigenesis of CRC. Mechanically, SELENBP1 may suppress CRC progression by inhibiting the epithelial–mesenchymal transition.
Segmentation of Living and ablated Tumor parts in CT images Using ResLU-Net

Abstract Computed tomography (CT) is widely used as the imaging modality for the treatment of tumors in Microwave Ablation (MWA) therapy. In order to accurately perform ablation of liver tumors and prevent tumor recurrence it is necessary to segment both the living tumor and the ablated tissue on the CT images. The U-Net model has outperformed other methods in biomedical image segmentation. However, because of the low contrast between tumor and liver tissue texture, the traditional U-net network cannot perform an accurate segmentation of the CT images of liver during MWA therapy. The aim of this study is to improve the U-net model network to achieve a higher segmentation performance on the CT images of liver tumor inMWA therapy. To achieve this, residual block is added in the first steps of up-sampling to deepen the network depth and enhance the segmentation result. We compare the proposed method named as ‘ResLU-Net’ with a conventional U-Net model. The results show that the ResLU-Net method has a good performance in tumor segmentation with a structure similarity index (SSIM) value of 0.97. This new method can help physicians in the MWA therapy process.
Worst-case asymptotic properties of /spl Hscr//sub /spl infin// identification

This paper studies asymptotic properties of /spl Hscr//sub /spl infin// identification problems and algorithms. The sample complexity of time- and frequency-domain /spl Hscr//sub /spl infin// identification problems is estimated, which exhibits a polynomial growth requirement on the input observation duration for the time-domain /spl Hscr//sub /spl infin// identification problem, and a linear growth rate of frequency response samples required for the frequency-domain /spl Hscr//sub /spl infin// identification problem. The divergence behavior is also established for linear algorithms for the time- and frequency-domain problems. The results extend previous work to more restricted sets of linear time-invariant systems with more refined a priori information, specifically imposed on the stability degree and the steady-state gain of the systems, thus demonstrating that no robustly convergent linear algorithms can exist even for a small set of exponentially stable systems.
Mathematics of hysteretic phenomena : the T(x) model for the description of hysteresis

Starting with the mathematical modeling of classical problems of hysteresis phenomena, this book provides an insight into the physics of the inverse hysteresis, higher order reversal loops, accommodation, viscosity, creep, wasp-waisted loops or hysteresic coupled systems. The author presents the T(x) model and applies it successfully to describe most, if not all, important phenomena in magnetism.
The why and how of trustworthy AI

Abstract Artificial intelligence is increasingly penetrating industrial applications as well as areas that affect our daily lives. As a consequence, there is a need for criteria to validate whether the quality of AI applications is sufficient for their intended use. Both in the academic community and societal debate, an agreement has emerged under the term “trustworthiness” as the set of essential quality requirements that should be placed on an AI application. At the same time, the question of how these quality requirements can be operationalized is to a large extent still open. In this paper, we consider trustworthy AI from two perspectives: the product and organizational perspective. For the former, we present an AI-specific risk analysis and outline how verifiable arguments for the trustworthiness of an AI application can be developed. For the second perspective, we explore how an AI management system can be employed to assure the trustworthiness of an organization with respect to its handling of AI. Finally, we argue that in order to achieve AI trustworthiness, coordinated measures from both product and organizational perspectives are required.
Beyong the Higgs

A Higgs-compatible boson has been observed at the LHC at CERN. We briefly review the role of the Higgs in particle physics and describe some of the current challenges in understanding the fundamental structure of the universe. Is there supersymmetry and is it instrumental in uniting gravity with the other three fundamental forces? What makes up dark matter and dark energy? We also report on the efforts in experimental particle physics by Malaysian collaborators to answer some of these questions.
An External Inverse Compton Emission Model of Gamma-Ray Burst High-Energy Lags

esz´ The Fermi satellite has been reporting the detailed temporal properties of gamma-ray bursts (GRBs) in an extremely broad spectral range, 8 keV - 300 GeV, in particular, the unexpected delays of the GeV emission onsets behind the MeV emission of some GRBs. We focus on GRB 080916C, one of the Fermi-LAT GRBs for which the data of the delayed high-energy emission are quite extensive, and we show that the behavior of the high-energy emission of this burst can be explained by a model in which the prompt emission consists of two components: one is the MeV component due to the synchrotron-self-Compton radiation of electrons accelerated in the internal shock of the jet and the other is the high-energy component due to inverse Compton scattering of the photospheric X-ray emission of the expanding cocoon off the same electrons in the jet. Such an external inverse Compton effect could be important for other Fermi-LAT GRBs, including short GRBs as well. In this model, the delay timescale is directly linked to the physical properties of GRB progenitor.
Quantitative Verification of a Force-based Model for Pedestrian Dynamics

This paper introduces a spatially continuous force-based model for simulating pedestrian dynamics. The main intention of this work is the quantitative description of pedestrian movement through bottlenecks and in corridors. Measurements of flow and density at bottlenecks will be presented and compared with empirical data. Furthermore the fundamental diagram for the movement in a corridor is reproduced. The results of the proposed model show a good agreement with empirical data.
Basis- and tripartition identification for quadratic programming and lineair complementary problems; From an interior solution to an optimal basis and viceversa

Optimal solutions of interior point algorithms for linear and quadratic programming and linear complementarity problems provide maximal complementary solutions. Maximal complementary solutions can be characterized by optimal (tri)partitions. On the other hand, the solutions provided by simplex--based pivot algorithms are given in terms of complementary bases. A basis identification algorithm is an algorithm which generates a complementary basis, starting from any complementary solution. A tripartition identification algorithm is an algorithm which generates a maximal complementary solution (and its corresponding tripartition), starting from any complementary solution. In linear programming such algorithms were respectively proposed by Megiddo in 1991 and Balinski and Tucker in 1969. In this paper we will present identification algorithms for quadratic programming and linear complementarity problems with sufficient matrices. The presented algorithms are based on the principal pivot transform and the orthogonality property of basis tableaus.