Miracle-dz/UniFAR-FTUs huggingface dataset:
The conformal group SO(4,2) and Robertson-Walker spacetimes

The Robertson-Walker spacetimes are conformally flat and so are conformally invariant under the action of the Lie group SO (4,2), the conformal group of Minkowski spacetime. We find a local coordinate transformation allowing the Robertson-Walker metric to be written in a manifestly conformally flat form for all values of the curvature parameter k continuously and use this to obtain the conformal Killing vectors of the Robertson-Walker spacetimes directly from those of the Minkowski spacetime. The map between the Minkowski and Robertson-Walker spacetimes preserves the structure of the Lie algebra so (4,2). Thus the conformal Killing vector basis obtained does not depend upon k , but has the disadvantage that it does not contain explicitly a basis for the Killing vector subalgebra. We present an alternative set of bases that depend (continuously) on k and contain the Killing vector basis as a sub-basis (these are compared with a previously published basis). In particular, bases are presented which include the Killing vectors for all Robertson-Walker spacetimes with additional symmetry, including the Einstein static spacetimes and the de Sitter family of spacetimes, where the basis depends on the Ricci scalar R
A Decentralized Blockchain-Enabled Federated Learning Approach for Vehicular Networks

Data from interconnected vehicles may contain sensitive information such as location, driving behavior, personal identifiers, etc. Without adequate safeguards, sharing this data jeopardizes data privacy and system security. The current cen-tralized data-sharing paradigm in these systems raises particular concerns about data privacy. Recognizing these challenges, the shift towards decentralized interactions in technology, as echoed by the principles of Industry 5.0, becomes paramount. This work is closely aligned with these principles, emphasizing decentralized, human-centric, and secure technological interactions in an interconnected vehicular ecosystem. To embody this, we propose a practical approach that merges two emerging technologies: Federated Learning (FL) and Blockchain. The integration of these technologies enables the creation of a decentralized vehicular network. In this setting, vehicles can learn from each other without compromising privacy while also ensuring data integrity and accountability. Initial experiments show that compared to conventional decentralized federated learning techniques, our proposed approach significantly enhances the performance and security of vehicular networks. The system's accuracy stands at 91.92%. While this may appear to be low in comparison to state-of-the-art federated learning models, our work is noteworthy because, unlike others, it was achieved in a malicious vehicle setting. Despite the challenging environment, our method maintains high accuracy, making it a competent solution for preserving data privacy in vehicular networks.
Scalable Schemes for Mobile Networks with Multiple Services

The introduction of new service categories with di erent bandwidth requirements, e.g., data and multimedia, to cellular mobile radio networks makes many of the traditional mechanisms unusable or less e cient. The call admission and the handover handling are of the most sensitive issues to this extension. The performance of all services including the traditional voice and the new services can be dramatically affected if no appropriate schemes are used. In this paper, we propose call admission and handover handling schemes for a cellular mobile network that o ers two service types: voice and data. The data connections are assumed to transmit at di erent transmission rates that are integer multiples to that of one radio channel. In the case of congestion, the base station asks the active data connections to reduce their transmission rate in order to provide free channels for the newly arrived request of both service types. This is basically intended for incoming handover requests. The request will be rejected if the transmission rate of the active connections reaches a given minimum rate. Similar mechanism can also be used for new call arrivals, but some priority can be given to handovers by setting a higher transmission rate threshold for the rejection. As an extension to the proposed scalability, a queuing of new calls is also proposed and analyzed. Analytical models were built for the two proposed schemes together with the traditional channel reservation scheme. The e ect of di erent tra c and con guration parameters on the performance measures like the grade of service, blocking probabilities, and utilization, are studied using the proposed technique. Results show that the proposed schemes provide very good performance and more fairness among the di erent service types.
Conditional Backdoor Attack via JPEG Compression

Deep neural network (DNN) models have been proven vulnerable to backdoor attacks. One trend of backdoor attacks is developing more invisible and dynamic triggers to make attacks stealthier. However, these invisible and dynamic triggers can be inadvertently mitigated by some widely used passive denoising operations, such as image compression, making the efforts under this trend questionable. Another trend is to exploit the full potential of backdoor attacks by proposing new triggering paradigms, such as hibernated or opportunistic backdoors. In line with these trends, our work investigates the first conditional backdoor attack, where the backdoor is activated by a specific condition rather than pre-defined triggers. Specifically, we take the JPEG compression as our condition and jointly optimize the compression operator and the target model's loss function, which can force the target model to accurately learn the JPEG compression behavior as the triggering condition. In this case, besides the conditional triggering feature, our attack is also stealthy and robust to denoising operations. Extensive experiments on the MNIST, GTSRB and CelebA verify our attack's effectiveness, stealthiness and resistance to existing backdoor defenses and denoising operations. As a new triggering paradigm, the conditional backdoor attack brings a new angle for assessing the vulnerability of DNN models, and conditioned over JPEG compression magnifies its threat due to the universal usage of JPEG.
SSR and SRAP marker-based linkage map of Vitis vinifera L.

An F1 population was created by the cross ‘87-1’ × ‘9-22’. The female parent ‘87-1’ was an extremely early maturing cultivar with strong flavour. The male parent was an excellent breeding line producing large berries maturing late. The mapping population included 149 randomly chosen individuals. Molecular genetic map for each parent and the consensus map were constructed using simple sequence repeat and sequence-related amplified polymorphism markers by software JoinMap 3.0. The ‘87-1’ map covers a total length of 1272.9 cM distributed in 21 linkage groups and consists of 163 molecular markers with an average distance between adjacent markers of 8.9 cM. The ‘9-22’ map covers a total length of 1267.4 cM distributed in 20 linkage groups and consists of 158 molecular markers with an average distance between adjacent markers of 9.1 cM. The consensus map covers a total length of 1537.1 cM distributed in 21 linkage groups and one doublet and consists of 217 molecular markers with an average distance of 7.8 cM between adjacent markers. The length of the linkage groups is 69.8 cM on average. The map covers the 19 chromosomes of the Vitis genome and can lay a solid foundation for further studies such as quantative trait loci (QTL) mapping of correlated traits and marker-assisted selection.
AFPTAS Results for Common Variants of Bin Packing: A New Method for Handling the Small Items

We consider two well-known natural variants of bin packing and show that these packing problems admit asymptotic fully polynomial time approximation schemes (AFPTASs). In bin packing problems, a set of one-dimensional items of size at most 1 is to be assigned (packed) to subsets of sum at most 1 (bins). It has been known for a while that the most basic problem admits an AFPTAS. In this paper, we develop new methods that allow us to extend this result to other variants of bin packing consisting of a family of two-parameter bin packing problems. We demonstrate the use of our methods by designing AFPTASs for the following problems. The first problem is bin packing with cardinality constraints, where a parameter $k$ is given such that a bin may contain up to $k$ items. The goal is to minimize the number of bins used. The second problem is bin packing with rejection, where every item has a rejection penalty associated with it. An item needs to be either packed to a bin or rejected, and the goal is to minimize the number of bins used and the total rejection penalty of unpacked items. This resolves the complexity of two important variants of the bin packing problem. Our approximation schemes use a novel method for packing the small items. This new method is the core of the improved running times of our schemes over the running times of the previous results, which are only asymptotic polynomial time approximationschemes.
Bridging the Design Methodologies of Burst-Mode Specifications and Signal Transition Graphs

Asynchronous circuits are a promising type of digital circuit that still see moderate usage in today’s commercial products, which has often been linked to the adaptation challenges that are posed within industry, e.g. time required to develop new tools and train designers versus using existing synchronous tools to quickly meet market demands. Several formal models were introduced to aid with the design of asynchronous circuits, including Burst-Mode (BM) Specifications and Signal Transition Graphs (STGs). BM specifications resemble synchronous Finite State Machines (FSMs) allowing circuit designers to easily adapt and use them, however their circuit implementations may be limited due to declining tool support. STGs have access to well-established tools that produce optimal hazard-free circuit implementations, but they are seen as too different by the industry. In this paper, we present a new ‘co-design’ methodology that bridges the gap between BM specifications and STGs by using a formal model called Burst Automaton (BA). BA is a generic FSM-like model that acts as a framework for enabling interoperability between many different formal models, and offers several benefits that BM specifications and STGs can leverage. Our ‘co-design’ methodology is implemented in Workcraft, and is evaluated on several benchmarks showing an improved synthesis flow.
Enhancing the Opto-Electronic Performance of Thin-Film Solar Cells Using Embedded Nanorods Comprised of Single Walled Carbon Nanotubes

This computational study uses the Finite-Difference Time Domain (FDTD) method to investigate the opto-electronic performance of amorphous silicon thin-film solar cells (Si TFSCs) that are embedded with arrays of periodic nanorods composed of randomly-oriented single-walled carbon nanotubes (SWCNTs). The diameter of the nanorods were varied between 20 nm-70 nm, and for each diameter, the side-to-side distances between neighboring nanorods (pitch) were varied from 5 nm-30 nm, keeping the nanorod height equal to the absorber layer thickness (550 mm), The optimal short circuit current density obtained was 32.13 mA/cm2 for a nanorod diameter of 30 nm and pitch of 5 nm, yielding a 351% increase compared to bare Si TFSCs 7.12 mA/cm2. Additionally, the efficiency increased from 1.62% for bare Si TFSCs to 8.50% for the embedded SWCNT nanorod structure. This study shows the immense potential of using novel carbon-based nanostructures to significantly enhance the opto-electronic performance of solar cells, as well as contributes towards sustainable technology substituting fossil fuels to minimize global warming.
Ontology-inferred phylogeny reconstruction for analyzing the evolutionary relationships between species: Ontological inference versus cladistics

We propose the use of formal ontological inferencing, rather than cladistics, to reconstruct phylogeny trees and to analyze the evolutionary relationships between species. For this experiment, we focused on the phylogeny of fungi. Lexical chaining technique has been used for incremental population of evolving ontological elements. Also category theory has been employed to provide an underlying formalism for capturing and analyzing the evolutionary behavior of the system.
Identification of ex-sand mining area using optical and SAR imagery

Open mining activities in Sumedang Regency has been operated since 1984 impacted to degradation of environment due to large area of ex-mining. Therefore, identification of ex-mining area which generally been used for sand mining is crucial and important to detect and monitor recent environmental degradation impacted from the ex-mining activities. In this research, identification ex-sand mining area using optical and SAR data in Sumedang Regency will be discussed. We use Landsat 5 TM acquisition date August 01, 2009 and Landsat 8 OLI acquired on June 24, 2016 to identify location of sand mining area, processed using Tasselled Cap Trasformation (TCT), while the landform deformation approached using ALOS PALSAR in 2009 and ALOS PALSAR 2 in 2016 processed using SAR interferometry (InSAR) method. The results show that TCT and InSAR method can can be used to identify the areas of ex-sand mining clearly. In 2016 the total area of ex-mining were 352.92 Ha. The land deformation show that during 7 years period since 2009 has impacted to the deformation at 7 meters.
Exploiting Semantic Features for Image Retrieval at CLEF 2005

This paper presents the MIRACLE’s team approach to text-based image retrieval at ImageCLEF 2005 adhoc task. The experiments defined this year try to use semantic information sources, like semantic dictionaries or text structure. For this purpose EuroWordnet has been considered and a new algorithm to extract synonyms from the semantic database has been developed. This new algorithm implementation is based on the proximity of words in the EuroWordnet tree and has been previously studied in [11]. On the other side, semantic information is implicitly included in the fields in which image descriptions are structured.
Accuracy Assessments for Headwater Stream Maps in Western North Carolina

Headwater streams are essential to downstream water quality, therefore it is important they are properly represented on maps used for stream regulation. Current maps used for stream regulation, such as the United States Geological Survey (USGS) topographic maps and Natural Resources Conservation Service (NRCS) soil survey maps, are outdated and do not accurately nor consistently depict headwater streams. In order for new stream maps to be used for regulatory purposes, the accuracy must be known and the maps must show streams with a consistent level of accuracy. This study assessed the valley valley presence/absence and stream length accuracy of the new stream maps created by the North Carolina Center for Geographic Analysis (CGIA) for western North Carolina. The CGIA stream map does not depict headwater streams with a consistent level of accuracy. This study also compared the accuracy of stream networks modeled using the computer software program, Terrain Analysis using Digital Elevation Models (TauDEM), to the CGIA stream map. The stream networks modeled in TauDEM,also do not consistently predict the location of headwater streams across the mountain region of the state. The location of headwater streams could not be accurately nor consistently predicted by solely using aerial photography or elevation data. Other factors such as climate, soils, geology, land use, and vegetation cover should be considered to accurately and consistently model headwater stream networks.
Vision Transformer-based Model for Gastric Cancer Detection and Classification using Weakly Annotated Histopathological Images

Gastric Cancer (GC) is the fifth most diagnosed cancer worldwide. An early diagnosis is a hope for patients suffering from GC. A biopsy is a procedure that helps detect abnormal and suspicious areas to determine whether cancer cells are in the stomach. Tissue samples collected through biopsy are stained using Hematoxylin and Eosin (H&E) and digitalized through scanning to produce a whole slide image (WSI) needed for further analysis. Recently, most prognostics have proven effective using artificial intelligence techniques combined with related computer aid detection systems. This research used a vision transformer to detect and classify gastric cancer from weakly annotated tissue images. After acquiring normal and cancer histopathological samples, we applied the vision transformer (ViT) model for binary classification. We generalized our approach by performing region-based prediction on unannotated tissue samples. The proposed approach will ease diagnosis and support pathologists in decision-making.
Slot fed circularly polarized magneto-electric dipole antenna array fed by optimized printed microstrip gap waveguide network

A compact circularly polarized 8 × 8 antenna array is designed for the 60 GHz band. The array comprises circularly polarized magneto-electric dipoles (CP-ME-Dipole) excited by narrow slots. The slots are fed by a printed gap waveguide (PGWG) cooperative network optimized based on the termination of the effective impedance of the array elements. Thus, it accounts for the space mutual coupling of the antenna elements. A procedure based on the full-wave analysis of a 4 ´ 4 array is used to estimate each element’s 8 × 8 array effective port impedance. The cooperative feeding network is designed based on the known effective impedances. The array is divided into two half subarrays out of phase from each other, and a rectangular waveguide feeds both sides. The commonly measured bandwidth of 18.3% achieves return loss better than 10 dB and an axial ratio below 3 dB (AR) of less than 3 dB. A maximum gain of 26.2 dBic with a high radiation efficiency of 82% radiation efficiency.
The Optimum Feeding Frequency in Growing Korean Rockfish (Sebastes schlegeli) Rearing at the Temperature of 15°C and 19°C

Two feeding trials were conducted to determine the optimum feeding frequency in growing Korean rockfish, (Sebastes schlegeli) reared at the temperatures of 15°C and 19°C. Fish averaging 92.2±0.7 g (mean±standard deviation [SD]) at 15.0±0.5°C and 100.2±0.4 g (mean±SD) at 19.0±0.5°C water temperature were randomly distributed into each of 15 indoor tanks containing 250-L sea water from a semi-recirculation system. A total of five feeding frequency groups were set up in three replicates as follows: one meal in a day at 08:00 hour, two meals a day at 08:00 and 17:00 hours, three meals a day at 08:00, 14:00, and 20:00 hours, four meals a day at 08:00, 12:00, 16:00, and 20:00 hours, and one meal every 2 days at 08:00 hour. Fish were fed at the rate of 1.2% body weight (BW)/d at 15°C and 1.5% BW/d at 19°C. At the end of 8 wks of feeding trial weight gain and specific growth rate were significantly higher at the fish fed groups of one meal a day and two meals a day at 15°C and fish fed groups of 1 meal every 2 days at 19°C were significantly lower than those of all other fish fed groups. Glutamic oxaloacetic transaminase and glutamic pyruvic transaminase of fish fed group at 1 meal every 2 days was significantly higher than those of all other fish fed groups in both experiments. Weight gain, specific growth rate and condition factor were gradually decreased as the feeding frequency increased. The results indicate that growing Korean rockfish 92 and 100 g perform better at 15°C than 19°C water temperature. As we expected, current results have indicated that a feeding frequency of 1 meal a day is optimal for the improvement of weight gain in growing Korean rockfish grown from 92 g to 133 g at 15°C and 100 g to 132 g at 19°C water temperature.
A Flexible Robotic Framework for Autonomous Manufacturing Processes: Report from the European Robotics Challenge Stage 1

Common industrial automation approaches consist on heavy and fixed robotic manipulators working in separated and closed production lines. Recent advances in sensing and control yield flexible and versatile robotic manipulator platforms, which could work in barrier-free production areas and might be the next advance in industrial production. In our contribution, we present a sophisticated and completely autonomous software framework for handling complex pickand-place tasks using state-of-the-art tools and algorithms. It covers applicable solutions for 3D image processing, motion planning, grasping as well as error handling and task scheduling strategies. Its competitiveness in terms of robustness and performance has been proven by earning the first place among 39 teams from all over Europe in the simulation stage of the EuRoC. At the challenge 2 of this EU-funded project, a mobile robotic platform is used for intelligent, flexible and highly automated production systems. We discuss our results as well as the applicability of our framework to real industrial applications.
Polynomial homotopies on multicore workstations

Homotopy continuation methods to solve polynomial systems scale very well on parallel machines. We examine its parallel implementation on multiprocessor multicore workstations using threads. With more cores we speed up pleasingly parallel path tracking jobs. In addition, we compute solutions more accurately in about the same amount of time with threads, and thus achieve quality up. Focusing on polynomial evaluation and linear system solving (key ingredients of Newton's method) we can double the accuracy of the results with the quad doubles of QD-2.3. 9 in less than double the time, if all available eight cores are used.
Individual and synergistic antioxidative actions of melatonin: studies with vitamin E, vitamin C, glutathione and desferrrioxamine (desferoxamine) in rat liver homogenates

The pharmacological effects of melatonin, vitamin E, vitamin C, glutathione and desferrioxamine (desferoxamine) alone and in combination on iron‐induced membrane lipid damage in rat liver homogenates were examined by estimating levels of malondialdehyde and 4‐hydroxyalkenals (MDA + 4‐HDA). Individually, melatonin (2.5–1600 μM), vitamin E (0.5–50 μM), glutathione (100–7000 μM) and desferrioxamine (1–8 μM) inhibited lipid peroxidation in a concentration‐dependent manner. Vitamin C had both a pro‐oxidative (25–2000 μM) and an antioxidative (2600–5000 μM) effect. The IC50 (concentration that reduces damage by 50%) values were 4, 10, 426, 2290 and 4325 μM for vitamin E, desferrioxamine, melatonin, glutathione and vitamin C, respectively. The synergistic actions of melatonin with vitamin C, vitamin E, and glutathione were systematically investigated. When melatonin was combined with vitamin E, glutathione, or vitamin C, the protective effects against iron‐induced lipid peroxidation were dramatically enhanced. Even though melatonin was added at very low concentrations, it still showed synergistic effects with other antioxidants at certain concentrations. Furthermore, melatonin not only reversed the pro‐oxidative effects of vitamin C, but its efficacy in reducing lipid peroxidation was improved when it was combined with prooxidative concentrations of vitamin C. The results provide new information in terms of the possible pharmacological use of the combination of melatonin and classical antioxidants to treat free radical‐related conditions.