Miracle-dz/UniFAR-FTUs huggingface dataset:
ON THE GENERALIZED BESSEL HEAT EQUATION RELATED TO THE GENERALIZED BESSEL DIAMOND OPERATOR

In this article, we study the equation @ @t u(x,t) = c 2 m,k B u(x,t) with the initial condition u(x,0) = f(x) for x 2 R +. Here the operator m,k B is called the Generalized Bessel Diamond Operator, iterated k times, and is defined by m,k B = � Bx a constant. We obtain the solution of this equation, which is related to the spectrum and the kernel, the so called generalized Bessel diamond heat kernel. Moreover, the generalized Bessel diamond heat kernel is shown to have interesting properties and to be related to the kernel of an extension of the heat equation.
A landing pad system for multicopy gene integration in Issatchenkia orientalis

The robust nature of the non-conventional yeast Issatchenkia orientalis allows it to grow under highly acidic conditions and therefore, has gained increasing interest in producing organic acids using a variety of carbon sources. Recently, the development of a genetic toolbox for I. orientalis, including an episomal plasmid, characterization of multiple promoters and terminators, and CRISPR-Cas9 tools, has eased the metabolic engineering efforts in I. orientalis. However, multiplex engineering is still hampered by the lack of efficient multicopy integration tools. To facilitate the construction of large, complex metabolic pathways by multiplex CRISPR-Cas9-mediated genome editing, we developed a bioinformatics pipeline to identify and prioritize genome-wide intergenic loci and characterized 47 sites. These loci are screened for guide RNA cutting efficiency, integration efficiency of a gene cassette, the resulting cellular fitness, and GFP expression level. We further developed a landing pad system using components from these well-characterized loci, which can aid in the integration of multiple genes using single guide RNA and multiple repair templates of the user’s choice. We have demonstrated the use of the landing pad for simultaneous integrations of 2, 3, 4, or 5 genes to the target loci with efficiencies greater than 80%. As a proof of concept, we showed how the production of 5-aminolevulinic acid can be improved by integrating five copies of genes at multiple sites in one step. We have further demonstrated the efficiency of this tool by constructing a metabolic pathway for succinic acid production by integrating five gene expression cassettes using a single guide RNA along with five different repair templates, leading to the production of 9 g/L of succinic acid in batch fermentations. This study demonstrates the effectiveness of a single gRNA-mediated CRISPR platform to build complex metabolic pathways in non-conventional yeast. This landing pad system will be a valuable tool for the metabolic engineering of I. orientalis. HIGHLIGHTS In silico screening was performed to identify 204 unique guide RNAs in the intergenic regions of the genome. 27 loci demonstrated high integration efficiency (>80%) and can be used for efficient gene or long pathway (∼18 kb) integration. An array of landing pad systems was installed at four loci for multiplex engineering. Multicopy integration of the gene cassettes (GFP, ALAS) resulted in a proportional increase in GFP fluorescence and 5-ALA production. A five-gene biosynthetic pathway was integrated into the chromosome in one step. This is the first study reporting the development of the landing pad system in Issatchenkia orientalis.
Unsupervised single-cell clustering with Asymmetric Within-Sample Transformation and per cluster supervised features selection

This chapter shows applying the Asymmetric Within-Sample Transformation [14] to single-cell RNA-Seq data matched with a previous dropout imputation. The asymmetric transformation is a special winsorization that flattens low-expressed intensities and preserves highly expressed gene levels. Before a standard hierarchical clustering algorithm, an intermediate step removes non-informative genes according to a threshold applied to a per-gene entropy estimate. Following the clustering, a time-intensive algorithm is shown to uncover the molecular features associated with each cluster. This step implements a resampling algorithm to generate a random baseline to measure up/down-regulated significant genes. To this aim, we adopt a GLM model [10] as implemented in DESeq2 [9] package. We render the results in graphical mode. While the tools are standard heat maps, we introduce some data scaling so that the results’ reliability is crystal clear.
Double Encryption Technique Using Polynomial Interpolation Method for Elliptic Curve Cryptosystem

In this research, we incorporate the polynomial interpolation method in the elliptic curve discrete logarithm problem based cryptosystem. In this study, the polynomial interpolation method to be focused is the Lagrange polynomial interpolation which is the simplest polynomial interpolation method. This method will be incorporated in the encryption algorithm of the elliptic curve ElGamal cryptosystem. The scheme is a modification of the elliptic curve ElGamal cryptosystem. Two polynomials are constructed based on the encrypted points using Lagrange polynomial interpolation. Some examples on the original and the modified algorithms are shown. KeywordsLagrange polynomial interpolation, Discrete logarithm problem, Elliptic curve, Cryptosystem
Senior-subordinated structure: buffer or signal in securitisation?

By exploiting a unique and proprietary dataset comprising granular deal- and tranche-level data on a global portfolio of securitisation deals, we empirically test the buffer vs the signalling hypothesis of credit enhancements in securitisation. We do so by focusing on one internal credit enhancement associated with the design of financial securities in securitisation, i.e. subordination. This study provides novel evidence on the role played by subordination in securitisation, suggesting that a real dichotomy between the buffer and signalling effects does not hold. Our findings indeed highlight that subordination serves both as a buffer against observable risk and as a signal of unobservable credit quality to third-party investors in the market; moreover, our results are robust to a wide battery of robustness tests. Our findings, of international relevance, contribute to the literature on information asymmetries between originators and investors and offer new policy insights in light of the recent agreement reached by European lawmakers with national governments to revive the European securitisation market.
Joint CFO and channel estimation using pilot aided interpolation for high performance MIMO-OFDM

ABSTRACT Multiple Input Multiple Output (MIMO) system with orthogonal frequency division multiplexing (OFDM) is a promising physical layer candidate for the current and future wireless standards. MIMO increases system throughput while OFDM maximizes spectrum efficiency. To reduce the impacts of frequency selective fading and increase the capacity, MIMO requires precise channel state information (CSI). The presence of carrier frequency offset (CFO) introduces intercarrier interference (ICI) among the subcarriers which degrades the system performance. MIMO channel estimation is done under the assumption of perfect frequency synchronisation between the transmitter and receiver, or zero CFO. However, such an assumption is incorrect for time-varying and frequency-selective wireless channels, and the presence of CFO has an impact on channel estimation accuracy. In the existing methods, CFO and channel estimations are carried out independently using separate training sequences which reduces the spectral efficiency. As a result, the pilot aided interpolation approach is proposed in this paper for the joint estimate and compensation of CFO and channel effects using the same set of pilot tones. Theresults show that the proposed method offers precise CFO and channel estimation which improves the bit error rate performance and carrier to interference power ratio (CIR) of MIMO-OFDM system.
Assessment of implementation barriers of blockchain technology in public healthcare: evidences from developing countries

ABSTRACT The widespread use of Blockchain technology (BT) in nations that are developing remains in its early stages, necessitating a more comprehensive evaluation using efficient and adaptable approaches. The need for digitalization to boost operational effectiveness is growing in the healthcare sector. Despite BT's potential as a competitive option for the healthcare sector, insufficient research has prevented it being fully utilised. This study intends to identify the main sociological, economical, and infrastructure obstacles to BT adoption in developing nations' public health systems. To accomplish this goal, the study employs a multi-level analysis of blockchain hurdles using hybrid approach. The study's findings provide decision- makers with guidance on how to proceed, as well as insight into implementation challenges.
Readiness for Enterprise Social Media: A Multi-Country Investigation of User Cultural Values and Intention to Use

ABSTRACT Enterprise Social Media (ESM) empowers connections throughout an organization in real-time. However, research reveals that enterprise-wide adoption is a challenge for businesses. Research indicates that culture is one of the impediments to enterprise-wide adoption. Thus, a deeper understanding of the cultural influence on organizational readiness will help organizations plan more effectively. Schwartz’s cultural framework was used in a quantitative study to investigate the intention to use ESM at various levels in multiple countries. The participants’ responses were analyzed using structural equation modeling, including multiple group analysis. The test results suggest significant effects of autonomy and egalitarianism on individuals’ intention to adopt ESM. However, contrary to previous research, the effects of egalitarianism were found to be negative. This study emphasizes the importance of cultural values in shaping individuals’ intentions to use ESM, which contributes to organizational readiness for ESM adoption. The findings of this study provide organizations with awareness of cultural influences on intention to use ESM and can help organizations identify potential barriers to ESM adoption readiness.
C5 inhibitors are not C5a inhibitors

With great interest, we read the systematic review and metaanalysis by Tsai and colleagues describing the efficacy and safety of complement C5a inhibitors for patients with severe COVID-19 [1]. Their analysis show an important role for C5a inhibitors in reducing mortality in patients with severe COVID19. However, in their study, the authors erroneously classified C5 inhibitors ravulizumab, eculizumab, and zilucoplan as C5a inhibitors. Upon activation of the final common pathway of the complement cascade, C5 is cleaved into C5a and C5b. C5a is an anaphylatoxin causing a strong pro-inflammatory response, whereas C5b couples with other complement factors to form the C5b-9 membrane attack complex (MAC) [2]. The latter is important for bacterial killing, and inhibition of C5b increases the risk of bacterial infections [2–5]. C5 inhibitors, therefore, exert different effects by blocking both C5a and C5b when compared to specific C5a inhibitors, such as the monoclonal antibody vilobelimab [5–7]. In the non-controlled study by Annane and colleagues [8], the C5 inhibitor eculizumab did not reduce C5a levels in severe COVID-19 patients [9]. Previous research show that substantial inhibition of C5a requires a specifically targeted inhibition [10]. Eculizumab blocks C5 activation only if mediated by C5convertase formation, but does not block C5a generation due to direct cleavage by trypsin and thrombin. We previously showed that vilobelimab efficiently inhibits C5a in patients with severe COVID-19 [11]. C5a concentrations were significantly suppressed after the first infusion in the treated COVID-19 patients compared to the control group, which was maintained until at least day 8. Thereby, C5a seems to be key in severely ill COVID-19 patients rather than C5 [2,5,6,12]. Furthermore, the authors state that the use of C5 inhibitors was associated with a similar risk of serious adverse events (SAEs) and did not increase the risk of infection. However, C5 inhibition in COVID-19 has been associated with an increased risk of infection [2]. In the study by Annane [8], eculizumab treatment in COVID-19 patients led to significantly higher rate of infectious complications including ventilator-associated pneumonia, despite prophylactic antibiotics against meningococcal infection. Recent results of a phase 3 randomized controlled trial (NCT04369469) investigating the effects of ravulizumab in severe COVID-19 patients showed a higher incidence of infections and infestations in the treated group, compared to the control group. Another advantage of specifically inhibiting C5a over C5 and upstream inhibitors is that C5a inhibition does not affect MAC formation and is not associated with increased risk of infection [6,7]. In conclusion, C5 and C5a inhibitors should not be considered to have a similar mechanism of action and effectivity.
What does the common look like?

Urban commoning projects, seen as part of those practices referred to as ‘spatial agency’, deal with the production of space focusing on the construction of relations and processes rather than on the production of objects, buildings, or images. The refusal of architectural formalism is seen as a way to bypass commercialism and the superficiality of traditional architectural practices. However, architect-led urban commoning activities still cannot avoid the production of very recognisable images, reinforcing a more or less involuntary aesthetics of the commons. This article posits that the production of images is too important to be left to commercial architectural practice, and that it should be considered as an integral part of spatial agency. Rather than privileging a return to a depoliticised and formalist architectural practice, this article argues that commoning practices, if seen from the point of view of the theory of the common in the singular, have an intrinsic capacity to produce a new political and aesthetic strategy. Some projects from DOGMA and Aristide Antonas are presented as practices that take the production of the common as a theme while at the same time problematising the role of image- and form-making, providing new formulations of the role of the project of architecture in contemporary production.
Strategies of discrediting: attacks on feminist activists in Turkey

ABSTRACT The Istanbul Feminist Night March has been organised since 2003, and it has the distinction of being the most participatory feminist protest organised in Turkey. As the space for civil society in Turkey shrinks, feminists are the only dissident group that continue to take to the streets. However, the Feminist Night March has been banned by either the governor or the police under the pretext of ‘non-permitted places for protests’ in recent years. Despite the ban, hundreds of women gathered each year, but the intensity of the police violence escalated, and several cases have been taken to court against protestors since 2021. These attacks on feminist activists and the Feminist Night March take place against the backdrop of multiple attacks on women’s rights and gender equality in Turkey, which is symbolised in Turkey’s withdrawal from the Istanbul Convention. In this article, the attacks on the March and activists will be discussed within the framework of how the Turkish government attempts to discredit feminists and feminist protest to achieve their anti-gender and family-oriented agenda.
Study of Long-lived Radioactive Sources in the Galaxy with INTEGRAL/SPI

The large-scale spectral characteristics of both 26Al and 60Fe in the Galaxy are studied with INTEGRAL/SPI. Both two lines are broadened slightly. The flux ratio of 60Fe to 26Al is 0.15. 26Al line energy shifts along Galactic longitudes are detected, which is consistent with expectation from Galactic rotation. 26Al emission signal toward high latitudes is detected, which would originate from local star-formation regions in the Gould Belt. Finally, 26Al spectra for three nearby star-formation regions, Cygnus, Vela and Sco-Cen, are obtained with SPI.
Common or Not? Variations in E-Commerce Adoption Factors Between India and the USA

ABSTRACT The e-commerce adoption rate is not the same across countries, and there are significant variations. While most technology adoption research has been carried out in on ecommerce across countries, this study focuses on introducing novel constructs with a mix of cross-cultural study. The present study therefore uses a sample mix from a developed nation (the USA) and a developing nation (India) to explore the e-commerce adoption factors. In this study, the supportive factors of perceived trust are hypothesized as causal factors for e-commerce adoption and studied with USA and India results. The factors considered in the proposed model are: hedonic motivation; comparison shopping; and perceived trust (further classified as honesty, benevolence, and competence). The study used Amazon’s MTurk to collect data from Indian (n = 471) and US (n = 440) respondents. The results of the multi-group moderation technique reveal that honesty, comparison shopping, and hedonic motivation were stronger for the US sample, while benevolence and comfort were stronger for the Indian sample. Based on the results, theoretical contributions, managerial implications, and future research directions are discussed.
Value Inference in Sociotechnical Systems

As artificial agents become increasingly embedded in our society, we must ensure that their behavior aligns with human values. Value alignment entails value inference , the process of identifying values and reasoning about how humans prioritize values. We introduce a holistic framework that connects the technical (AI) components necessary for value inference. Subsequently, we discuss how hybrid intelligence—the synergy of human and artificial intelligence—is instrumental to the success of value inference. Finally, we illustrate how value inference both poses significant challenges and provides novel opportunities for multiagent systems research.
Model-Based Actor-Critic for Multi-Objective Reinforcement Learning with Dynamic Utility Functions

Many real-world problems require a trade-off between multiple conflicting objectives. Decision-makers’ preferences over solutions to such problems are determined by their utility functions, which convert multi-objective values to scalars. In some settings, utility functions change over time, and the goal is to find methods that can efficiently adapt an agent’s policy to changes in utility. Previous work on learning with dynamic utility functions has focused on model-free methods, which often suffer from poor sample efficiency. In this work, we instead propose a model-based actor-critic, which explores with diverse utility functions through imagined rollouts within a learned world model between interactions with the real environment. An experimental evaluation on Minecart, a well-known benchmark for multi-objective reinforcement learning, shows that by learning a model of the environment the quality of the agent’s policy is improved compared to model-free algorithms.
Towards Explaining Sequences of Actions in Multi-Agent Deep Reinforcement Learning Models

Although Multi-agent Deep Reinforcement Learning (MADRL) has shown promising results in solving complex real-world problems, the applicability and reliability of MADRL models are often limited by a lack of understanding of their inner workings for explaining the decisions made. To address this issue, this paper proposes a novel method for explaining MADRL by generalizing the sequences of action events performed by agents into high-level abstract strategies using a spatio-temporal neural network model. Specifically, an interval-based memory retrieval procedure is developed to generalize the encoded sequences of action events over time into short sequential patterns. In addition, two abstraction algorithms are introduced, one for abstracting action events across multiple agents and the other for further abstracting the episodes over time into short sequential patterns, which can then be translated into symbolic form for interpretation. We evaluate the proposed method using the StarCraft Multi Agent Challenge (SMAC) benchmark task, which shows that the method is able to derive high-level explanations of MADRL models at various levels of granularity.
Revenue Maximization Mechanisms for an Uninformed Mediator with Communication Abilities

Consider a market where a seller owns an item for sale and a buyer wants to purchase it. Each player has private information, known as their type. It can be costly and difficult for the players to reach an agreement through direct communication. However, with a mediator as a trusted third party, both players can communicate privately with the mediator without worrying about leaking too much or too little information. The mediator can design and commit to a multi-round communication protocol for both players, in which they update their beliefs about the other player's type. The mediator cannot force the players to trade but can influence their behaviors by sending messages to them. We study the problem of designing revenue-maximizing mechanisms for the mediator. We show that the mediator can, without loss of generality, focus on a set of direct and incentive-compatible mechanisms. We then formulate this problem as a mathematical program and provide an optimal solution in closed form under a regularity condition. Our mechanism is simple and has a threshold structure. We also discuss some interesting properties of the optimal mechanism, such as situations where the mediator may lose money.
Strategy Extraction for Transfer in AI Agents

We propose an approach to knowledge transfer for improved life-long learning in AI agents, using behavioural strategies as a form of transferable knowledge, influenced by the human cognitive ability to develop strategies. A strategy is defined as a partial sequence of actions an agent can take to reach some predefined event of interest. This information acts as guidance or a partial solution that an agent can generalise and use to predict how to handle unknown observed phenomena. As a first step toward this goal, we present an approach for extracting strategies from an agent’s existing knowledge that can be applied in multiple contexts. Our approach uses a combination of observed action frequency information with local sequence alignment techniques to find patterns of significance that form a strategy. We demonstrate our approach in two environments: Pac-man; and a dungeon-crawling video game. Our evaluation serves as a promising first step towards efficient and robust generalisation to support lifelong learning across a wider class of tasks.
Bilevel Entropy based Mechanism Design for Balancing Meta in Video Games

We address a mechanism design problem where the goal of the designer is to maximize the entropy of a player’s mixed strategy at a Nash equilibrium. This objective is of special relevance to video games where game designers wish to diversify the players’ inter-action with the game. To solve this design problem, we propose a bi-level alternating optimization technique that (1) approximates the mixed strategy Nash equilibrium using a Nash Monte-Carlo reinforcement learning approach and (2) applies a gradient-free optimization technique (Covariance-Matrix Adaptation Evolutionary Strategy) to maximize the entropy of the mixed strategy obtained in level (1). The experimental results show that our approach achieves comparable results to the state-of-the-art approach on three benchmark domains “Rock-Paper-Scissors-Fire-Water”, “Workshop War-fare” and “Pokemon Video Game Championship”. Next, we show that, unlike previous state-of-the-art approaches, the computational complexity of our proposed approach scales significantly better in larger combinatorial strategy spaces.