Miracle-dz/UniFAR-FTUs huggingface dataset:
ALL OPTICAL REVERSIBLE DATA DISTRIBUTOR

Nowadays, It became the fashion among the researchers about creating the New Reversible Gates. In the Reversible Literature, already many gates are proposed but it is the first time to propose a Gate for a decoder(Data Distributor). The proposed GLG (Garbage Less Gate) has No Garbage output which denotes its power efficiency. In this paper 2:4 reversible decoder is constructed using GLG. The proposed gate is also extended to N:2N decoder using the proposed GLG Gate and the Fredkin Gate. The theoretical proposition is verified through Optisystem & Modelsim Software. A comparison with existing reversible decoders is also included.
NDT_A_244452 703..713

Guangyin Zhang 1 Shixin Xu Zhuo Yuan Li Shen 1Department of Psychosomatic Medicine, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China; 2Tianjin Key Laboratory of Traditional Research of TCM Prescription and Syndrome; Medical Experiment Center, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China Purpose: Despite advances in characterizing the neurobiology of emotional disorders, there is still a significant lack of scientific understanding of the pathophysiological mechanisms governing major depressive disorder (MDD). This study attempted to elucidate the molecular circuitry of MDD and to identify more potential genes associated with the pathogenesis of the disease. Patients and Methods: Microarray data from the GSE98793 dataset were downloaded from the NCBI Gene Expression Omnibus (GEO) database, including 128 patients with MDD and 64 healthy controls. Weighted gene coexpression network analysis (WGCNA) was performed to find modules of differentially expressed genes (DEGs) with high correlations followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to obtain further biological insight into the top three key modules. The protein-protein interaction (PPI) network, the modules from the PPI network, and the gene annotation enrichment of modules were analyzed, as well. Results: We filtered 3276 genes that were considered significant DEGs for further WGCNA analysis. By performing WGCNA, we found that the turquoise, blue and brown functional modules were all strongly correlated with MDD development, including immune response, neutrophil degranulation, ribosome biogenesis, T cell activation, glycosaminoglycan biosynthetic process, and protein serine/threonine kinase activator activity. Hub genes were identified in the key functional modules that might have a role in the progression of MDD. Functional annotation showed that these modules primarily enriched such KEGG pathways as the TNF signaling pathway, T cell receptor signaling pathway, primary immunodeficiency, Th1, Th2 and Th17 cell differentiation, autophagy and RNA degradation and oxidative phosphorylation. These results suggest that these genes are closely related to autophagy and cellular immune function. Conclusion: The results of this study may help to elucidate the pathophysiology of MDD development at the molecular level and explore the potential molecular mechanisms for new interventional strategies.
Threats and Protection on E-Sim

Threats involve various risks and threats are associated with the embedded SIM technology, for instance, the Internet of things (IoT) identity. IoT refers to the working capabilities enabling the allocation of unique identifiers (UID) to effectively connect with the related devices thus enhancing communication. An e-SIM application cannot produce reliable and actual data used to obtain the subscriber’s anticipated outcome. The SIM technology does not provide some reliable data that can be employed by the user to formulate some serious productive outcomes. Failure by the technology to process and automatically provide the user with the notification suppose of any infringement or hacking. SIM-jacking is the other notable threats facing the embedded universal integrated connectivity card (e-UICC). Incompetent Log Rhythm Al Engine influences the fraudster hacking experience due to failure protections within the operational surrounding. The e-SIM technology system lacks timely threat, risk, and other various vital operations predictability to react to the experienced unbearable operations challenges induced by the fraudsters. Similarly, the embedded SIM incurs the insider threats whereby the service providers fail to secure the much-needed privacy concerning an individual’s vital information. The situations of personal data leakage are witnessed within the system operations.The e-SIM hijacking enables the fraudsters to secretly obtain the victim’s vital data of the subscriber, hijack, and receive the information intended to the individual to his/her personal phone. The process results to complete mobile account operations by the hacker resulting to further access to the victim’s bank information and transfer of cash. The other threat experienced by e-SIM users is the provision of false information. The SIM subscribers normally fall into traps of the fraudsters by receiving short messages (SMS) citing assistance kind of news from the service providers, thus drawing the victim’s bank amount. Identity fraud and device poisoning are other additional threats encountered in the application of e-SIM. Generally, the entire process of fraud invasion and victimization influence the victim’s business decisions of the affected individuals. Protections focuses on the embedded SIM provides greater security in addition to a re-programmable technological system, unlike the physical SIM card. The subscriber's personal information is not contained within the e-SIM but with the service providers, thus enhancing its effectiveness. An e-SIM enables the consumers to effectively shift carriers between the T-Mobile and Sprint without physical movement, thus supportive of security systems. Despite the security measures put into place, e-SIM like any other SIM card experiences information theft. Therefore, the service providers should encounter the emerging fraudster effects by proper monitoring of the network system to enable security restrictions. The system should induce strict conditions that enable the evaluation and differentiation between the IoT and the non-IoT devices during their operation.
Detection of protein-protein interactions

The present invention uses fluorescence, interaction, a method for detecting the peptide to peptide interactions in detail. Fluorescent protein fragments, peptides of interest, as well as, inserted between the the peptide and the fluorescent protein fragments of interest, a nucleic acid encoding the linker portions of different lengths, provided with a method of using the system and it peptide interactions . The
The Gender Gap in Earnings Losses after Job Displacement

We compare men and women who are displaced from similar jobs by applying an event study design combined with propensity score matching and reweighting to administrative data from Germany. After a mass layoff, women’ s earnings losses are about 35% higher than men’ s, with the gap persisting five years after displacement. This is partly explained by women taking up more part-time employment, but even women’ s full-time wage losses are almost 50% higher than men’ s. Parenthood magnifies the gender gap sharply. Finally, displaced women spend less time on job search and apply for lower-paid jobs, highlighting the importance of labor supply decisions.
Design of Programmable Transmitarray Antenna with Independent Controls of Transmission Amplitude and Phase

A design method of programmable transmitarray antenna with independent controls of transmission amplitude and phase is proposed in C-band. The unit cell with cascaded structures mainly consists of four parts, including the receiving antenna, reconfigurable attenuator with PIN diodes, reconfigurable phase shifter with varactors and transmitting antenna. Correspondingly, various manipulations of spatial electromagnetic (EM) fields are achieved by varying the bias voltages of PIN diodes and varactors in the transmitarray antenna. The fabricated unit cell is measured in a standard waveguide, and the whole array with 8×8 unit cells is measured with two horns for calibration of the programmable EM features. The experimental results show that the transmission amplitude can range from -16 dB to -3.6 dB and the transmission phase achieves 270° coverage independently at the operating frequency of 5.67GHz under 16-bit programmable control. To further exhibit the capability and functionality of the proposed transmitarray antenna, the waveform engineering of adaptive beamforming and power-allocation beamforming are separately realized in the experiment. The measured results have good agreements with our theoretical calculations, verifying the validity of our design method.
On the size of disjunctive formulas in the μ-calculus

A key result in the theory of the modal μ-calculus is the disjunctive normal form theorem by Janin & Walukiewicz, stating that every μ-calculus formula is semantically equivalent to a so-called disjunctive formula. These disjunctive formulas have good computational properties and play a pivotal role in the theory of the modal μ-calculus. It is therefore an interesting question what the best normalisation procedure is for rewriting a formula into an equivalent disjunctive formula of minimal size. The best constructions that are known from the literature are automata-theoretic in nature and consist of a guarded transformation, i.e., the constructing of an equivalent guarded alternating automaton from a μ-calculus formula, followed by a Simulation Theorem stating that any such alternating automaton can be transformed into an equivalent non-deterministic one. Both of these transformations are exponential constructions, making the best normalisation procedure doubly exponential. Our key contribution presented here shows that the two parts of the normalisation procedure can be integrated, leading to a procedure that is single-exponential in the closure size of the formula.
Quantum Newton's method for solving system of nonlinear algebraic equations

While quantum computing provides an exponential advantage in solving system of linear equations, there is little work to solve system of nonlinear equations with quantum computing. We propose quantum Newton's method (QNM) for solving $N$-dimensional system of nonlinear equations based on Newton's method. In QNM, we solve the system of linear equations in each iteration of Newton's method with quantum linear system solver. We use a specific quantum data structure and $l_{\infty}$ tomography with sample error $\epsilon_s$ to implement the classical-quantum data conversion process between the two iterations of QNM, thereby constructing the whole process of QNM. The complexity of QNM in each iteration is $O(\log^4N/\epsilon_s^2)$. Through numerical simulation, we find that when $\epsilon_s>>1/\sqrt{N}$, QNM is still effective, so the complexity of QNM is sublinear with $N$, which provides quantum advantage compared with the optimal classical algorithm.
Beyond Average Performance - exploring regions of deviating performance for black box classification models

Machine learning models are becoming increasingly popular in different types of settings. This is mainly caused by their ability to achieve a level of predictive performance that is hard to match by human experts in this new era of big data. With this usage growth comes an increase of the requirements for accountability and understanding of the models' predictions. However, the degree of sophistication of the most successful models (e.g. ensembles, deep learning) is becoming a large obstacle to this endeavour as these models are essentially black boxes. In this paper we describe two general approaches that can be used to provide interpretable descriptions of the expected performance of any black box classification model. These approaches are of high practical relevance as they provide means to uncover and describe in an interpretable way situations where the models are expected to have a performance that deviates significantly from their average behaviour. This may be of critical relevance for applications where costly decisions are driven by the predictions of the models, as it can be used to warn end users against the usage of the models in some specific cases.
SLAW: Scaled Loss Approximate Weighting for Efficient Multi-Task Learning

Multi-task learning (MTL) is a subfield of machine learning with important applications, but the multi-objective nature of optimization in MTL leads to difficulties in balancing training between tasks. The best MTL optimization methods require individually computing the gradient of each task's loss function, which impedes scalability to a large number of tasks. In this paper, we propose Scaled Loss Approximate Weighting (SLAW), a method for multi-task optimization that matches the performance of the best existing methods while being much more efficient. SLAW balances learning between tasks by estimating the magnitudes of each task's gradient without performing any extra backward passes. We provide theoretical and empirical justification for SLAW's estimation of gradient magnitudes. Experimental results on non-linear regression, multi-task computer vision, and virtual screening for drug discovery demonstrate that SLAW is significantly more efficient than strong baselines without sacrificing performance and applicable to a diverse range of domains.
Trust in Prediction Models: a Mixed-Methods Pilot Study on the Impact of Domain Expertise

People’s trust in prediction models can be affected by many factors, including domain expertise like knowledge about the application domain and experience with predictive modelling. However, to what extent and why domain expertise impacts people’s trust is not entirely clear. In addition, accurately measuring people’s trust remains challenging. We share our results and experiences of an exploratory pilot study in which four people experienced with predictive modelling systematically explore a visual analytics system with an unknown prediction model. Through a mixed-methods approach involving Likert-type questions and a semi-structured interview, we investigate how people’s trust evolves during their exploration, and we distil six themes that affect their trust in the prediction model. Our results underline the multi-faceted nature of trust, and suggest that domain expertise alone cannot fully predict people’s trust perceptions.
A Nivat Theorem for Weighted Alternating Automata over Commutative Semirings

This paper connects the classes of weighted alternating finite automata (WAFA), weighted finite tree automata (WFTA), and polynomial automata (PA). First, we investigate the use of trees in the run semantics for weighted alternating automata and prove that the behavior of a weighted alternating automaton can be characterized as the composition of the behavior of a weighted finite tree automaton and a specific tree homomorphism, if weights are taken from a commutative semiring. Based on this, we give a Nivat-like characterization for weighted alternating automata. Moreover, we show that the class of series recognized by weighted alternating automata is closed under inverses of homomorphisms, but not under homomorphisms. Additionally, we give a logical characterization of weighted alternating automata, which uses weighted MSO logic for trees. Finally, we investigate the strong connection between weighted alternating automata and polynomial automata. We prove: A weighted language is recognized by a weighted alternating automaton iff its reversal in recognized by a polynomial automaton. Using the corresponding result for polynomial automata, we are able to prove that the ZERONESS problem for weighted alternating automata with weights taken from the rational numbers decidable.
TANet: A new Paradigm for Global Face Super-resolution via Transformer-CNN Aggregation Network

Recently, face super-resolution (FSR) methods either feed whole face image into convolutional neural networks (CNNs) or utilize extra facial priors (e.g., facial parsing maps, facial landmarks) to focus on facial structure, thereby maintaining the consistency of the facial structure while restoring facial details. However, the limited receptive fields of CNNs and inaccurate facial priors will reduce the naturalness and fidelity of the reconstructed face. In this paper, we propose a novel paradigm based on the self-attention mechanism (i.e., the core of Transformer) to fully explore the representation capacity of the facial structure feature. Specifically, we design a Transformer-CNN aggregation network (TANet) consisting of two paths, in which one path uses CNNs responsible for restoring fine-grained facial details while the other utilizes a resource-friendly Transformer to capture global information by exploiting the long-distance visual relation modeling. By aggregating the features from the above two paths, the consistency of global facial structure and fidelity of local facial detail restoration are strengthened simultaneously. Experimental results of face reconstruction and recognition verify that the proposed method can significantly outperform the state-of-the-art methods.
Allocating Indivisible Goods to Strategic Agents: Pure Nash Equilibria and Fairness

We consider the problem of fairly allocating a set of indivisible goods to a set of strategic agents with additive valuation functions. We assume no monetary transfers, and therefore, a mechanism in our setting is an algorithm that takes as input the reported—rather than the true—values of the agents. Our main goal is to explore whether there exist mechanisms that have pure Nash equilibria for every instance and, at the same time, provide fairness guarantees for the allocations that correspond to these equilibria. We focus on two relaxations of envy-freeness, namely, envy-freeness up to one good (EF1) and envy-freeness up to any good (EFX), and we positively answer the preceding question. In particular, we study two algorithms that are known to produce such allocations in the nonstrategic setting: round-robin (EF1 allocations for any number of agents) and a cut-and-choose algorithm of Plaut and Roughgarden (EFX allocations for two agents). For round-robin, we show that all of its pure Nash equilibria induce allocations that are EF1 with respect to the underlying true values, whereas for the algorithm of Plaut and Roughgarden, we show that the corresponding allocations not only are EFX, but also satisfy maximin share fairness, something that is not true for this algorithm in the nonstrategic setting! Further, we show that a weaker version of the latter result holds for any mechanism for two agents that always has pure Nash equilibria, which all induce EFX allocations. Funding: This work was supported by the Horizon 2020 European Research Council Advanced “Algorithmic and Mechanism Design Research in Online Markets” [Grant 788893], the Ministero dell’Università e della Ricerca Research project of national interest (PRIN) “Algorithms, Games, and Digital Markets,” the Future Artificial Intelligence Research project funded by the NextGenerationEU program within the National Recovery and Resilience Plan (PNRR-PE-AI) scheme [M4C2, investment 1.3, line on Artificial Intelligence], the National Recovery and Resilience Plan-Ministero dell’Università e della Ricerca (PNRR-MUR) project IR0000013-SoBigData.it, the Nederlandse Organisatie voor Wetenschappelijk Onderzoek Veni Project [Grant VI.Veni. 192.153], and the National Recovery and Resilience Plan Greece 2.0 funded by the European Union under the NextGenerationEU Program [Grant MIS 5154714].
Structural Performance of Laterite soil Stabilised with Cement and Blue Gum (Eucalyptus Globulus) Wood Ash for Use as a Road base Material

This study examines the effect of partially replacing cement with blue gumwood ash (BGWA) in stabilizing laterite soil to be used as a potential road base material. Initially, Ordinary Portland Cement was introduced to the soil at varying contents from 0 to 12% in steps of 3% by weight of the soil sample. Each wet sample of the soil was subjected to the Californian bearing ratio (CBR) and unconfined compressive strength (UCS) tests in determining the optimal soil-cement mix. Results show that CBR and UCS values increased as cement content increased, and a 6% cement content corresponding to a UCS value of 2.88 MPa at 7 days of curing met the specifications of the Overseas Road Note 31 to be used in the construction of road bases. The second treatment involved partially replacing the 6% cement content with BGWA in decreasing steps of 1%. Peak CBR value of 348% at 2% BGWA content (>160% recommended by Kenya road design manual) and UCS value of 2.99 MPa at 7 days of curing were obtained. Thus, BGWA can partially replace cement in stabilizing laterite soil for use in the construction of road bases as per the Overseas Road Note 31 specifications.
Shan--Chen interacting vacuum cosmology

In this paper, we introduce a novel class of interacting vacuum models, based on recasting the equation of state originally developed in the context of lattice kinetic theory by Shan & Chen as the coupling between the vacuum and cold dark matter (CDM). This coupling allows the vacuum to evolve and is nonlinear around a characteristic energy scale ρ∗, changing into a linear coupling with a typical power law evolution at scales much lower and much higher than ρ∗. Focusing on the simplest sub-class of models where the interaction consists only of an energy exchange and the CDM remains geodesic, we first illustrate the various possible models that can arise from the Shan–Chen coupling, with several different behaviours at both early and late times depending on the values of the model parameters selected. We then place the first observational constraints on this Shan–Chen interacting vacuum scenario, performing an MCMC analysis to find those values of the model and cosmological parameters which are favoured by observational data. We focus on models where the nonlinearity of the coupling is relevant at late times, choosing for the reference energy scale ρ∗, the critical energy density in ΛCDM. We show that the observational data we use are compatible with a wide range of models which result in different cosmologies. However, we also show that ΛCDM is preferred over all of the Shan–Chen interacting vacuum models that we study, and comment on the inability of these models to relax the H0 and σ8 tensions.
Generating Explainable Rule Sets from Tree-Ensemble Learning Methods by Answer Set Programming

We propose a method for generating explainable rule sets from tree-ensemble learners using Answer Set Programming (ASP). To this end, we adopt a decompositional approach where the split structures of the base decision trees are exploited in the construction of rules, which in turn are assessed using pattern mining methods encoded in ASP to extract interesting rules. We show how user-defined constraints and preferences can be represented declaratively in ASP to allow for transparent and flexible rule set generation, and how rules can be used as explanations to help the user better understand the models. Experimental evaluation with real-world datasets and popular tree-ensemble algorithms demonstrates that our approach is applicable to a wide range of classification tasks.