Miracle-dz/UniFAR-FTUs huggingface dataset:
The synergy of Linear Regression, Fuzzy Functional Dependencies and Linguistic Summaries: A Case of Heat Islands in Bratislava

The last decade has witnessed a rapid development of computational intelligence models in many scientific and industry fields. One of challenges is the lack of interpretability and explainability of the mechanism underlying the models. In this paper, we propose a methodology for evaluating and explaining influences by linear regression and by fuzzy logic techniques, which have been shown as promising approaches for the explainable computational intelligence. Precisely, fuzzy functional dependencies are able to verify regression result and explain intensities linguistically, whereas linguistic summaries can reveal intensities of relations among the subdomains of considered features. The proposed synergy is experimentally evaluated on the problem of explaining the influence of geographical features on the urban heat islands in the Bratislava downtown. Urban planning and development should consider environmental aspects. Thus, a support which is validated by three diverse approaches is more reliable.
TraceArk: Towards Actionable Performance Anomaly Alerting for Online Service Systems

Performance anomaly alerting based on trace data plays an important role in assuring the quality of online service systems. However, engineers find that many anomalies reported by existing techniques are not of interest for them to take further actions. For a large scale online service with hundreds of different microservices, current methods either fire lots of false alarms by applying simple thresholds to temporal metrics (i.e., latency), or run complex end-to-end deep learning model with limited interpretability. Engineers often feel difficult to understand why anomalies are reported, which hinders the followup actions. In this paper, we propose an actionable anomaly alerting approach TraceArk. More specifically, we design an anomaly evaluation model by extracting service impact related anomalous features. A small amount of engineer experience (i.e., feedback) is also incorporated to learn the actionable anomaly alerting model. Comprehensive experiments on a real dataset of Microsoft Exchange service and an anomaly injection dataset collected from an open-source project demonstrate that TraceArk significantly outperforms the existing state-of-the-art approaches. The improvement in F1 is 50.47% and 20.34% on the two datasets, respectively. Furthermore, TraceArk has been running stably for four months in a real production environment and showing a 2.3x improvement in Precision over the previous approach. TraceArk also provides intrepretable alerting details for engineers to take further actions.
MACRO AND MICROSCOPICAL STUDIES ON THE PINEAL GLAND OF CAMEL WITHIMMUNOHISTOCHEMICALLOCALIZATION TO PINEALOCYTES AND GLIA CELLS MARKERS.

Rasha. R. Beheiry 1 and Attia. A. A. Moselhy 2 . 1. Department of Histology and Cytology Faculty of Veterinary Medicine, Zagazig University. Egypt. 2. Department of Anatomy and Embryology, Faculty of Veterinary Medicine, Zagazig University. Egypt. Manuscript Info Abstract . Manuscript History
Demonstrating the conservation of angular momentum using spherical magnets

10.1119/1.5002548. 1An experimental setup for demonstrating the conservation of angular momentum of rotating spherical magnets is described. Two spherical Nd-Fe-B magnets are placed on a double inclined plane and projected towards each other with pre-selected impact parameters ranging from zero to a few tens of millimeters. After impact, the two magnets either revolve vigorously around the common center of mass or stop immediately, depending on the value of the impact parameter. Using a pick-up coil connected to an oscilloscope, the angular frequency for the rotating magnets was measured, and an estimate for the angular momentum was obtained. A high-speed video camera captured the impact and was used for measuring linear and angular velocities of the magnets. A very good agreement between the initial angular momentum before the impact and the final angular momentum of the revolving dumbbell is observed. The two rotating magnets, and the rotating electromagnetic field emanating from them, can also be viewed.
The Tully-Fisher Relation and H0

The use of the Tully-Fisher (TF) relation for the determination of H0 relies on the availability of an adequate template TF relation and of reliable primary distances. Here we use a TF template relation with the best available kinematical zero point, obtained from a sample of 24 clusters of galaxies extending to cz ~ 9000 km s-1, and the most recent set of Cepheid distances for galaxies fit for TF use. The combination of these two ingredients yields H0 = 69 ± 5 km s-1 Mpc-1. The approach is significantly more accurate than the more common application with single cluster (e.g., Virgo, Coma) samples.
Ginga observations of isolated pulsars: PSR1509‐58 and the Crab pulsar

The energy spectra and the pulse light curve of PSR1509–58 in the X‐ray range 2–60 keV are presented. The X‐ray pulse spectrum can be fitted by a power‐law model with a photon index of ∼1.3. The non‐pulse component is softer, and has a photon index of 2.2 if modeled by a power‐law. The Crab pulsar was observed by Ginga in March 1987 and September 1991. The variation of single pulse intensity was evaluated with a best statistics in the 2–11 keV X‐ray, but no significant deviation from the Poisson fluctuation was found. The long‐term variation of the relative intensity of the two peaks in the pulse profile was studied using the data taken 4.5 years apart. Unlike in the gamma‐ray range, no significant variation of the pulse peaks were found. In this respect the soft X‐ray pulse in the Crab pulsar shares more characteristics in common with the optical pulserather than the gamma‐ray.
Global Existence for Quadratic Systems of Reaction-Diffusion

Abstract We prove global existence in time of weak solutions to a class of quadratic reaction-diffusion systems for which a Lyapounov structure of L log L-entropy type holds. The approach relies on an a priori dimension-independent L2-estimate, valid for a wider class of systems including also some classical Lotka-Volterra systems, and which provides an L1-bound on the nonlinearities, at least for not too degenerate diffusions. In the more degenerate case, some global existence may be stated with the use of a weaker notion of renormalized solution with defect measure, arising in the theory of kinetic equations.
Development of Sustainable Hybrid Microbial Fuel Cell (MFC) System from Living Plants for Low-power Field Electronic Devices

Microbial Fuel Cells (MFCs) are a promising renewable and sustainable energy solution for low-power field electronic devices, but still remain in the research phase due to low power output efficiency. This research systematically studied three tubular MFC systems including standalone soil microbial fuel cells (S-MFCs), plant microbial fuel cells (P-MFCs), and a proposed hybrid MFC (H-MFC) system. All tubular MFC systems were carefully constructed using three types of soils (potting soil, yard soil, and river soil), four plants (Bonnie Curly Spider, Peace Lily, Lemongrass, and Tomato Plant), and two types of electrodes (zinc mesh as the cathode and copper spiral as the anode). Power density from each system was calculated as a function of the days during growth and was based on the experimental measurement of the open voltage of every setup. The results showed that a standalone S-MFC system had easy implementation and a higher power density initially than some P-MFCs but was not as sustainable as P-MFCs, whereas both P-MFC and hybrid systems could produce power densities for sustained periods of time. However, the difference in power density between P-MFC and hybrid systems was observed after 18 days for potting soil and 11 days for yard soil, and after 30 days, the power density of all hybrid systems was roughly 2 times that of all P-MFC setups. Furthermore, the hybrid systems displayed significant advantages over their S-MFC and P-MFC counterparts, especially in the sustained power output and ease of replaceability, meaning that future implementation of this technology in a scaled-up setting is more feasible. Therefore, it is suggested that hybrid MFC systems will be the future direction of S-MFC and P-MFC technology.
Micromechanical Modeling of Flexural Strength for Epoxy Polymer Concrete

Epoxy polymer concrete (EPC) has been widely used in civil engineering nowadays due to its excellent mechanical properties and advantages in processing. In this paper, a modeling study has been carried out on the flexural performance of EPC. Two classic micromechanics models, i.e. rule of mixture and Mori-Tanaka method, are introduced to predict the flexural strength of EPC with various epoxy resin contents. The comparison shows that the parallel model based on the rule of mixture attains a good agreement with the measured results when the epoxy resin content is sufficiently high to achieve strong adhesion between the aggregate and the epoxy resin. In contrast, the Mori-Tanaka method with the failure criterion dominated by the weakest phase fails to give acceptable prediction due to the unsuitability of its basic assumptions to EPC, particularly when the epoxy resin content is at relatively high levels.
Robust PCA and pairs of projections in a Hilbert space

Abstract: This is a study of principal component analysis performed on a statistical sample. We assume that this data sample is made of independent copies of some random variable ranging in a separable real Hilbert space. This covers data in function spaces as well as data represented in reproducing kernel Hilbert spaces. Based on some new inequalities about the perturbation of nonnegative self-adjoint operators, we provide new bounds for the statistical fluctuations of the principal component representation with the draw of the statistical sample. We suggest two kinds of improvements to decrease these fluctuations: the first is to use a robust estimate of the covariance operator, for which non-asymptotic bounds of the estimation error are available under weak polynomial moment assumptions. The second improvement is to use some modification of the projection on the principal components based on functional calculus applied to the covariance operator. Using this modified projection, we can obtain bounds that do not depend on the spectral gap but on some more favorable factor. In appendix, we provide a new approach to the analysis of the relative positions of two orthogonal projections that is useful for our proofs and that has an interest of its own.
HALAL TOTAL QUALITY MANAGEMENT TOWARDS QUALITY PERFORMANCE. A CONCEPTUAL FRAMEWORK

: Total Quality Management (TQM) has become a modern system of constant improvement of the quality of all company activities. This paper aimed conceptual compared the multidimensionality of total quality management and the impact on quality performance based on halal awareness. Based on existing studies, we consider eight scopes total quality management include leadership, decision making, employee management, system approach to management, supplier management, process management, customer focus and continual improvement. Basically, this study can be applied by halal policy and decision makers in halal industry and halal studies researcher.
Safe Assets: A Dynamic Retrading Perspective*

The price of a safe asset reflects not only the expected discounted future cash flows but also future service flows, since retrading allows partial insurance of idiosyncratic risk in an incomplete markets setting. This lowers the issuers’ interest burden. As idiosyncratic risk rises during recessions, so does the value of the service flows bestowing the safe asset with a negative β. The resulting exorbitant privilege resolves government debt valuation puzzles and allows the government to run a permanent (primary) deficit without ever paying back its debt, but the government faces a “Debt Laffer Curve”.
Selenium deficiency-induced high concentration of reactive oxygen species restricts hypertrophic growth of skeletal muscle in juvenile zebrafish by suppressing TORC1-mediated protein synthesis

Abstract Se deficiency causes impaired growth of fish skeletal muscle due to the retarded hypertrophy of muscle fibres. However, the inner mechanisms remain unclear. According to our previous researches, we infer this phenomenon is associated with Se deficiency-induced high concentration of reactive oxygen species (ROS), which could suppress the target of rapamycin complex 1 (TORC1) pathway-mediated protein synthesis by inhibiting protein kinase B (Akt), an upstream protein of TORC1. To test this hypothesis, juvenile zebrafish (45 d post-fertilisation) were fed a basal Se-adequate diet or a basal Se-deficient diet or them supplemented with an antioxidant (DL-α-tocopherol acetate, designed as VE) or a TOR activator (MHY1485) for 30 d. Zebrafish fed Se-deficient diets exhibited a clear Se-deficient status in skeletal muscle, which was not influenced by dietary VE and MHY1485. Se deficiency significantly elevated ROS concentrations, inhibited Akt activity and TORC1 pathway, suppressed protein synthesis in skeletal muscle, and impaired hypertrophy of skeletal muscle fibres. However, these negative effects of Se deficiency were partly (except that on ROS concentration) alleviated by dietary MHY1485 and completely alleviated by dietary VE. These data strongly support our speculation that Se deficiency-induced high concentration of ROS exerts a clear inhibiting effect on TORC1 pathway-mediated protein synthesis by regulating Akt activity, thereby restricting the hypertrophy of skeletal muscle fibres in fish. Our findings provide a mechanistic explanation for Se deficiency-caused retardation of fish skeletal muscle growth, contributing to a better understanding of the nutritional necessity and regulatory mechanisms of Se in fish muscle physiology.
Unsupervised Domain Adaptation for 3D Keypoint Prediction from a Single Depth Scan

In this paper, we introduce a novel unsupervised domain adaptation technique for the task of 3D keypoint prediction from a single depth scan/image. Our key idea is to utilize the fact that predictions from different views of the same or similar objects should be consistent with each other. Such view consistency provides effective regularization for keypoint prediction on unlabeled instances. In addition, we introduce a geometric alignment term to regularize predictions in the target domain. The resulting loss function can be effectively optimized via alternating minimization. We demonstrate the effectiveness of our approach on real datasets and present experimental results showing that our approach is superior to state-of-the-art general-purpose domain adaptation techniques.
Navigating the Complexity of Generative AI Adoption in Software Engineering

This paper explores the adoption of Generative Artificial Intelligence (AI) tools within the domain of software engineering, focusing on the influencing factors at the individual, technological, and social levels. We applied a convergent mixed-methods approach to offer a comprehensive understanding of AI adoption dynamics. We initially conducted a questionnaire survey with 100 software engineers, drawing upon the Technology Acceptance Model (TAM), the Diffusion of Innovation Theory (DOI), and the Social Cognitive Theory (SCT) as guiding theoretical frameworks. Employing the Gioia Methodology, we derived a theoretical model of AI adoption in software engineering: the Human-AI Collaboration and Adaptation Framework (HACAF). This model was then validated using Partial Least Squares – Structural Equation Modeling (PLS-SEM) based on data from 183 software engineers. Findings indicate that at this early stage of AI integration, the compatibility of AI tools within existing development workflows predominantly drives their adoption, challenging conventional technology acceptance theories. The impact of perceived usefulness, social factors, and personal innovativeness seems less pronounced than expected. The study provides crucial insights for future AI tool design and offers a framework for developing effective organizational implementation strategies.
On Pseudolinear Codes for Correcting Adversarial Errors

We consider error-correction coding schemes for adversarial wiretap channels (AWTCs) in which the channel can a) read a fraction of the codeword bits up to a bound r and b) flip a fraction of the bits up to a bound p. The channel can freely choose the locations of the bit reads and bit flips via a process with unbounded computational power. Codes for the AWTC are of broad interest in the area of information security, as they can provide data resiliency in settings where an attacker has limited access to a storage or transmission medium. We investigate a family of non-linear codes known as pseudolinear codes, which were first proposed by Guruswami and Indyk (FOCS 2001) for constructing list-decodable codes independent of the AWTC setting. Unlike general non-linear codes, pseudolinear codes admit efficient encoders and have succinct representations. We focus on unique decoding and show that random pseudolinear codes can achieve rates up to the binary symmetric channel (BSC) capacity $1-H_{2}(p)$ for any $p, r$ in the less noisy region: $p\lt1/2$ and $r\lt1-H_{2}(p)$ where $H_{2}(\cdot)$ is the binary entropy function. Thus, pseudolinear codes are the first known optimal-rate binary code family for the less noisy AWTC that admit efficient encoders. The above result can be viewed as a derandomization result of random general codes in the AWTC setting, which in turn opens new avenues for applying derandomization techniques to randomized constructions of AWTC codes. Our proof applies a novel concentration inequality for sums of random variables with limited independence which may be of interest as an analysis tool more generally.
TransPose: A Transformer-based 6D Object Pose Estimation Network with Depth Refinement

As demand for robotics manipulation application increases, accurate vision-based 6D pose estimation becomes essential for autonomous operations. Convolutional Neural Networks (CNNs) based approaches for pose estimation have been previously introduced. However, the quest for better performance still persists especially for accurate robotics manipulation. This quest extends to the Agri-robotics domain. In this paper, we propose TransPose, an improved Transformer-based 6D pose estimation with a depth refinement module. The architecture takes in only an RGB image as input with no additional supplementing modalities such as depth or thermal images. The architecture encompasses an innovative lighter depth estimation network that estimates depth from an RGB image using feature pyramid with an up-sampling method. A transformer-based detection network with additional prediction heads is proposed to directly regress the object's centre and predict the 6D pose of the target. A novel depth refinement module is then used alongside the predicted centers, 6D poses and depth patches to refine the accuracy of the estimated 6D pose. We extensively compared our results with other state-of-the-art methods and analysed our results for fruit-picking applications. The results we achieved show that our proposed technique outperforms the other methods available in the literature.
Decalf: A Directed, Effectful Cost-Aware Logical Framework

We present decalf, a directed, effectful cost-aware logical framework for studying quantitative aspects of functional programs with effects. Like calf, the language is based on a formal phase distinction between the extension and the intension of a program, its pure behavior as distinct from its cost measured by an effectful step-counting primitive. The type theory ensures that the behavior is unaffected by the cost accounting. Unlike calf, the present language takes account of effects, such as probabilistic choice and mutable state. This extension requires a reformulation of calf’s approach to cost accounting: rather than rely on a ”separable” notion of cost, here a cost bound is simply another program. To make this formal, we equip every type with an intrinsic preorder, relaxing the precise cost accounting intrinsic to a program to a looser but nevertheless informative estimate. For example, the cost bound of a probabilistic program is itself a probabilistic program that specifies the distribution of costs. This approach serves as a streamlined alternative to the standard method of isolating a cost recurrence and readily extends to higher-order, effectful programs. The development proceeds by first introducing the decalf type system, which is based on an intrinsic ordering among terms that restricts in the extensional phase to extensional equality, but in the intensional phase reflects an approximation of the cost of a program of interest. This formulation is then applied to a number of illustrative examples, including pure and effectful sorting algorithms, simple probabilistic programs, and higher-order functions. Finally, we justify decalf via a model in the topos of augmented simplicial sets.
FAIRO: Fairness-aware Adaptation in Sequential-Decision Making for Human-in-the-Loop Systems

Achieving fairness in sequential-decision making systems within Human-in-the-Loop (HITL) environments is a critical concern, especially when multiple humans with different behavior and expectations are affected by the same adaptation decisions in the system. This human variability factor adds more complexity since policies deemed fair at one point in time may become discriminatory over time due to variations in human preferences resulting from inter- and intra-human variability. This paper addresses the fairness problem from an equity lens, considering human behavior variability, and the changes in human preferences over time. We propose FAIRO, a novel algorithm for fairness-aware sequential-decision making in HITL adaptation, which incorporates these notions into the decision-making process. In particular, FAIRO decomposes this complex fairness task into adaptive sub-tasks based on individual human preferences through leveraging the Options reinforcement learning framework. We design FAIRO to generalize to three types of HITL application setups that have the shared adaptation decision problem. Furthermore, we recognize that fairness-aware policies can sometimes conflict with the application's utility. To address this challenge, we provide a fairness-utility tradeoff in FAIRO, allowing system designers to balance the objectives of fairness and utility based on specific application requirements. Extensive evaluations of FAIRO on the three HITL applications demonstrate its generalizability and effectiveness in promoting fairness while accounting for human variability. On average, FAIRO can improve fairness compared with other methods across all three applications by 35.36%.