Miracle-dz/UniFAR-FTUs huggingface dataset:
Higgs amplitudes from supersymmetric form factors Part II : N < 4 super

The study of form factors has many phenomenologically interesting applications, one of which is Higgs plus gluon amplitudes in QCD. Through effective field theory techniques these are related to form factors of various operators of increasing classical dimension. In this paper we extend our analysis of the first finite top-mass correction, arising from the operator Tr(F ), from N = 4 super Yang-Mills to theories with N <4, for the case of three gluons and up to two loops. We confirm our earlier result that the maximally transcendental part of the associated Catani remainder is universal and equal to that of the form factor of a protected trilinear operator in the maximally supersymmetric theory. The terms with lower transcendentality deviate from the N = 4 answer by a surprisingly small set of terms involving for example ζ2, ζ3 and simple powers of logarithms, for which we provide explicit expressions. §{a.brandhuber, m.m. kostacinska, g.travaglini}@qmul.ac. uk, b.penante@cern.ch ar X iv :1 80 4. 05 82 8v 2 [ he pth ] 2 4 A pr 2 01 8
Genome-wide identification of R-SNARE gene family in upland cotton and function analysis of GhVAMP72l response to drought stress

Soluble N-ethylmaleimide-sensitive factor attachment protein receptors (R-SNAREs) mainly promoted the assembly of the SNARE complex to drive the final membrane fusion step of membrane transport. Previous research on R-SNAREs has mainly focused on development and growth and has rarely been involved in abiotic stress, especially in cotton. Here, we performed a comprehensive analysis of R-SNARE genes in upland cotton. In total, 51 Gh-R-SNARE genes across six phylogenetic groups were unevenly distributed on 21 chromosomes. Cis elements related to plant growth and response to abiotic stress responses were found in the promoter region of Gh-R-SNAREs. Nine Gh-R-SNARE genes were obviously upregulated under drought stress conditions by RNA-seq and qRT–PCR analysis. Among them, GhVAMP72l might be the key candidate gene contributing to drought stress tolerance in cotton by virus-induced gene silencing (VIGS) assay. These results provide valuable insights for the functional analysis of cotton R-SNAREs in response to drought stress and highlight potential beneficial genes for genetic improvement and breeding in cotton.
The role of policy in supporting environmentally sustainable foodservice in healthcare: lessons from exemplar hospitals

Background Foodservice in hospitals contributes to the environmental footprint of healthcare delivery. There is little known about the role of policy in supporting environmentally sustainable foodservices. The aim of the study was to explore policy in exemplar environmentally sustainable hospital foodservices from the perspective of hospital staff, toward what makes a policy effective, the limitations of policy, and the influential levels and types of policy. Methods A generic qualitative inquiry approach was utilized. Staff involved in foodservices were interviewed about the role of policy during 2020–2021 from 14 hospitals across nine countries. Data were analyzed using framework and thematic analysis. Results Policies spanned across high level policies at the level of the healthcare organization, local hospital procedures and protocols, as well as public policy from local, state/provincial and national government. Internal organizational policy was used to embed practices within the organization in the long term and help to build a shared vision and goal where public policy had lacked guidance. The creation, content and methods of communication and creating accountability made internal organizational policy successful. Public policy was most effective when it was mandatory, had clearly defined targets and funding to assist implementation. These exemplar hospitals also demonstrated attributes of policy entrepreneurs by engaging with policy makers to share their stories and lobby government for policy change. Discussion Policy from within the healthcare organization is an important mechanism for enabling hospitals to deliver and maintain environmentally sustainable foodservice. Public policy must be designed considering the unique implementation challenges hospitals face to ensure they are successful.
Identification of a novel and ancestral machinery involved in 1 mitochondrial membrane branching in Trypanosoma brucei 2

16 African trypanosomes are eukaryotic parasites that exist in two main replicative 17 forms; the procyclic form in the midgut of the insect vector, the tsetse fly Glossina spp. 18 and the bloodstream form responsible for diseases in humans and cattle. Unlike most 19 other eukaryotes, where mitochondria continuously fuse and divide, trypanosome 20 mitochondria form a single and continuously interconnected network that only divides 21 during cytokinesis. The machineries governing mitochondrial remodeling and 22 interconnection, however, remain largely unknown. We characterize a dynamin-related 23 protein (DRP) from T. brucei ( Tb DBF, previously called Tb MfnL) that depicts sequence 24 similarities with Opa1 and Mfn, mammalian DRPs involved mitochondrial fusion. We 25 showed that Tb DBF has closely related homologues in several organisms that are devoid 26 of Mfn and Opa1, such as eukaryotes from different phyla, prokaryotes and archaea. 27 Tb DBF is the first member of this new protein family to be functionally characterized. It 28 localizes to the mitochondrial periphery and, upon overexpression, induces a strong 29 increase in the interconnection and branching of mitochondrial filaments in a GTPase 30 dependent manner. Its overexpression also promotes a major increase in cellular and 31 mitochondrial volume and an increased consumption of the two major carbon sources 32 used by the parasite (glucose and proline), as well as ethanolamine, a precursor of 33 phosphatidyl-ethanolamine involved in membrane biogenesis and shaping. We propose 34 that mitochondrial Tb DBF is a component of an ancestral membrane remodeling 35 machinery that contributes to the formation of intermitochondrial connections. 36
Near-Term Suitability Assessment of Deploying DAC System at Airport: A Case Study of 52 Large Airports in China

This study is the first to propose the deployment of direct air capture (DAC) systems at large airports to provide solutions for achieving carbon neutrality in aviation transportation. Here, an estimating model for carbon dioxide (CO2) emissions in the landing and take-off (LTO) phase of large airports was developed, and the suitability of deploying DAC systems at airports was evaluated by the analytic hierarchy process (AHP). This study found that the annual CO2 emissions of 52 large airports in the LTO phase are about 23 Mt, accounting for about 23% of the total CO2 emissions of civil aviation in China. The four dimensions of airport transportation conditions, meteorological conditions, space resources, and security levels had a decreasing impact on the deployment of DAC systems in that order. The airports with suitable DAC systems are mainly located in the Yangtze River Delta, the Pearl River Delta, and the Chengdu-Chongqing Airport Cluster. This study provides a theoretical basis for the deployment of DAC systems at airports, which provides new CO2 emission reduction solutions for the aviation transportation industry.
Stuck in the MOS pit: A critical analysis of MOS test methodology in TTS evaluation

The Mean Opinion Score (MOS) is a prevalent metric in TTS evaluation. Although standards for collecting and reporting MOS exist, researchers seem to use the term inconsistently, and underreport the details of their testing methodologies. A survey of Interspeech and SSW papers from 2021-2022 shows that most authors do not report scale labels, increments, or instructions to participants, and those who do diverge in terms of their implementation. It is also unclear in many cases whether listeners were asked to rate naturalness, or overall quality. MOS obtained for natural speech using different testing methodologies vary in the surveyed papers: specifically, quality MOS is on average higher than naturalness MOS. We carried out several listening tests using the same stimuli but with differences in the scale increment and instructions about what participants should rate, and found that both of these variables affected MOS for some systems.
The Lack of Specialized Symbology and Visual Interaction Design Guidance for Sub-Sea Military Operations

This paper addresses the lack and need for specialized and visually effective interaction design guidance for sub-sea military operations. We identify gaps in the implementation of best practice visualization techniques, building upon our recently published survey on visual interfaces used in military decision support systems. Our analysis focuses on the current NATO sym-bology standard and several sub-sea military frontend systems to identify deficiencies and their underlying causes. Such origins of deficiencies include inadequate design consideration of environmental conditions, as well as incomplete hardware and software requirements for sub-sea conditions. While many such gaps exist, for the purposes of this paper, we narrow our focus to exploring the potential for a new sub-sea symbology for the maritime domain, drawing from insights gained and developed through our participation in the EDIDP (European Defence Industrial Development Programme) project CUIIS (Comprehensive Underwater Intervention Information System). We propose extending existing NATO military standards by creating a comprehensive framework for a new sub-sea symbology and visual interaction design. This framework includes a set of semi-otic communication symbols for military divers, which can easily be combined based on the most common messages required for effective communication between command and military divers. This paper concludes by highlighting the opportunities for improvement in NATO Military Symbology for sub-sea military operations.
A Hybridization of Spatial Modeling and Deep Learning for People’s Visual Perception of Urban Landscapes

The visual qualities of the urban environment influence people’s perception and reaction to their surroundings; hence the visual quality of the urban environment affects people’s mental states and can have detrimental societal effects. Therefore, people’s perception and understanding of the urban environment are necessary. This study used a deep learning-based approach to address the relationship between effective spatial criteria and people’s visual perception, as well as spatial modeling and preparing a potential map of people’s visual perception in urban environments. Dependent data on people’s visual perception of Tehran, Iran, was gathered through a questionnaire that contained information about 663 people, 517 pleasant places, and 146 unpleasant places. The independent data consisted of distances to industrial areas, public transport stations, recreational attractions, primary streets, secondary streets, local passages, billboards, restaurants, shopping malls, dilapidated areas, cemeteries, religious places, traffic volume, population density, night light, air quality index (AQI), and normalized difference vegetation index (NDVI). The convolutional neural network (CNN) algorithm created the potential map. The potential visual perception map was evaluated using the receiver operating characteristic (ROC) curve and area under the curve (AUC), with the estimates of AUC of 0.877 and 0.823 for pleasant and unpleasant visuals, respectively. The maps obtained using the CNN algorithm showed that northern, northwest, central, eastern, and some southern areas of the city are potent in pleasant sight, and southeast, some central, and southern regions had unpleasant sight potential. The OneR method results demonstrated that distance to local passages, population density, and traffic volume is most important for pleasant and unpleasant sights.
SymNet 3.0: Exploiting Long-Range Influences in Learning Generalized Neural Policies for Relational MDPs

We focus on the learning of generalized neural policies for Relational Markov Decision Processes (RMDPs) expressed in RDDL. Recent work ﬁrst converts the instances of a relational domain into an instance graph , and then trains a Graph Attention Network (GAT) of ﬁxed depth with parameters shared across instances to learn a state representation, which can be decoded to get the policy [Sharma et al., 2022]. Unfortunately, this approach struggles to learn policies that exploit long-range dependencies – a fact we formally prove in this paper. As a remedy, we ﬁrst construct a novel inﬂuence graph characterized by edges capturing one-step inﬂuence (dependence) between nodes based on the transition model. We then de-ﬁne inﬂuence distance between two nodes as the shortest path between them in this graph – a feature we exploit to represent long-range dependencies. We show that our architecture, referred to as Symbolic Inﬂuence Network (S YM N ET 3.0), with its distance-based features, does not suffer from the representational issues faced by earlier approaches. Extensive experimentation demonstrates that we are competitive with existing baselines on 12 standard IPPC domains, and perform signiﬁcantly better on six additional domains (including IPPC variants), designed to test a model’s capability in capturing long-range dependencies. Further analysis shows that S YM N ET 3.0 automatically learns to focus on nodes that have key information for representing policies that capture long-range dependencies.
Have I got a job for you? Automation, Market Structure and Skills

A bstract . The paper asks a simple question. If individuals demand products from the ﬁrms in the economy and supply their skills concomitantly to the ﬁrms, then why should there be skill gap in the society, i.e., mismatch between skills that individuals possess and ﬁrm’s demand. After all, in a decentralised economy prices of various products and skill speciﬁc wages should adjust to ensure that there is match between demand and supply of skills. The paper suggests that there are three factors that keep skill gap alive. The ﬁrst two are the web of dynamic complementarities in the way human beings acquire skills and the way they invent new technologies. The third factor is the lack of competition in the product market that accompanies period of accelerated technical change. The papers looks at the impact automation and globalisation has had in opening up the skill gap and suggest policies that would help close the skill gap. With accelerated changes in technology and concomitant social changes, it is ever so more important to have a learning environment that allows workers and citizens in the society to learn and adapt ﬂexibly.
An Anonymous Authentication Scheme Based on Chinese Residue Theorem in Wireless Body Area Networks

Considering the resource limitations of low-cost wireless sensors, there is a growing inclination to utilize cryptographic primitives that are optimized for efficiency, such as symmetric key encryption/decryption and hash functions, when designing authentication schemes. However, designing a lightweight authentication scheme that can meet various security requirements poses a significant challenge. In recent years, numerous lightweight authentication schemes have been proposed in order to address these security needs. Nevertheless, recent research has revealed that many of these schemes exhibit security vulnerabilities and design deficiencies, including challenges related to asynchronization and impractical gateway-node search operations. Due to the inadequate security of existing schemes, this study introduces a novel privacy-preserving authentication scheme that aims to provide adaptive resilience against desynchronization attacks in wireless body area networks (WBANs). The proposed scheme utilizes lightweight cryptographic modules to optimize efficiency. To ensure user anonymity, the Chinese Remainder Theorem technique is employed, whereas forward secrecy and resistance to desynchronization attacks are achieved through the use of one-way hash chains and serial numbers, respectively. Through extensive analysis and comparisons, the proposed scheme is demonstrated to strike a fine balance between security and efficiency.
Bioinspired Dynamic Control of Amphibious Articulated Creatures with Spiking Neural Networks

We present a biologically plausible, compact spiking neural network for controlling the crawling and swimming behaviors of amphibious creatures with articulated skeletons. Prior methods for learning efficient control policies for such creatures are resource-greedy, both in terms of computational time and energy requirements due to the high number of degrees of freedom introduced by the many joints present in the creature skeleton. Our approach takes a radical departure from prior work and exploits the physiology of amphibious creatures. Specifically, we emulate experimentally identified biological controllers for amphibious creatures with a network of spiking neurons, which alleviates the need for training altogether and can potentially provide the additional benefit of utilizing minimal resources in terms of energy. Our approach is robust and allows the amphibious crea-ture to avoid both static and dynamic obstacles when exhibiting different movement patterns, and also adaptively control its swimming speed. Moreover, we show that the creature can seamlessly transition between crawling and swimming behaviors as it moves from land to water or vice-versa, similar to its real-world counter-part. Our approach presents an efficient and scalable alternative for producing vivid and lively motion, as we demonstrate through a complex scene where multiple amphibious creatures interact with each other, successfully avoiding collisions while moving across a pool of water. Our approach is generalizable to other creatures also, as we show through the design of a controller for a quadruped.
Query-Focused Multi-document Summarization Survey

—With the exponential growth of textual information on the web and in multimedia, query-focused multi-document summarization (QFMS) has emerged as a critical research area. QFMS aims to generate concise summaries that address user queries and satisfy their information needs. This paper provides a comprehensive survey of state-of-the-art approaches in QFMS, focusing specifically on graph-based and clustering-based methods. Each approach is examined in detail, highlighting its advantages and disadvantages. The survey covers ranking algorithms, sentence selection techniques, redundancy removal methods, evaluation metrics, and available datasets. The principal aim of this paper is to present a thorough analysis of QFMS approaches, providing researchers and practitioners with valuable insights into the field. By surveying existing techniques, the paper identifies the challenges and issues faced in QFMS and discusses potential future directions. Moreover, the paper emphasizes the importance of addressing coherency, ambiguity, vague references, evaluation methods, redundancy, and diversity in QFMS. Performance standards and competing approaches are also discussed, showcasing the advancements made in QFMS. The paper acknowledges the need for improving summarization coherence, readability, and semantic efficiency, while balancing compression ratios and summarizing quality. Additionally, it highlights the potential of hybrid methods and the integration of extractive and abstractive techniques to achieve more human-like summaries.
Unravelling Vaccine Scepticism in South Tyrol, Italy: A Qualitative Analysis of Personal, Relational, and Structural Factors Influencing Vaccination Decisions

Low vaccine uptake in South Tyrol, particularly for non-coronavirus and SARS-CoV-2 vaccines, poses a significant public health challenge in the northernmost province of Italy. This qualitative study conducted in-depth interviews with a purposive sample of vaccine-sceptical parents to examine the factors that contribute to their vaccination decisions. The ten participants’ children had varied vaccination statuses, ranging from unvaccinated to partially vaccinated or vaccinated as late as possible. Only one adult participant received the SARS-CoV-2 vaccine. Using Grounded Theory analysis, the emergent meta-category of ‘self-relatedness’ was identified, highlighting the importance of individual experiences and the social context. The study found that participants’ social circles consisted of individuals with similar vaccination attitudes, often characterized by a shared affinity for nature. Although they accepted individuals with different views, they remained uninfluenced. Participants perceived healthcare professionals as one-sided and uncritical, expressing distrust toward state orders. They believed that parents should be responsible for their children’s well-being rather than the state. Distrust in the state and healthcare system, exacerbated by the SARS-CoV-2 pandemic, was rooted in negative experiences. In contrast, the participants had positive experiences with natural healing, homeopathy, and trusting the natural course of events. They perceived themselves as tolerant, non-radical, curious, health-conscious, yet critical and questioning. Participants resisted coercion, fear-mongering, and state sanctions and sought alternatives to mandatory vaccination. To address the complex social and behavioural factors underlying vaccination refusal, this study suggests that vaccination advocates, policymakers, and information providers should engage in appreciative, personal, and well-founded information exchanges with vaccine-hesitant individuals. Broad and comprehensible information dissemination, flexibility, and freedom of decision are essential for increasing informed decision making. Further research is required to better understand the epistemic basis of vaccine hesitancy.
Comparison of Next-Day Probabilistic Severe Weather Forecasts from Coarse- and Fine-Resolution CAMs and a Convection-Allowing Ensemble

AbstractGiven increasing computing power, an important question is whether additional computational resources would be better spent reducing the horizontal grid spacing of a convection-allowing model (CAM) or adding members to form CAM ensembles. The present study investigates this question as it applies to CAM-derived next-day probabilistic severe weather forecasts created by using forecast updraft helicity as a severe weather proxy for 63 days of the 2010 and 2011 NOAA Hazardous Weather Testbed Spring Forecasting Experiments. Forecasts derived from three sets of Weather Research and Forecasting Model configurations are tested: a 1-km deterministic model, a 4-km deterministic model, and an 11-member, 4-km ensemble. Forecast quality is evaluated using relative operating characteristic (ROC) curves, attributes diagrams, and performance diagrams, and forecasts from five representative cases are analyzed to investigate their relative quality and value in a variety of situations. While no statistically signif.
On The Diversity of ASR Hypotheses In Spoken Language Understanding

In Conversational AI, an Automatic Speech Recognition (ASR) system is used to transcribe the user’s speech, and the output of the ASR is passed as an input to a Spoken Language Understanding (SLU) system, which outputs semantic objects (such as intent, slot-act pairs, etc.). Recent work, including the state-of-the-art methods in SLU utilize either Word lattices or N-Best Hypotheses from the ASR. The intuition given for using N-Best instead of 1-Best is that the hypotheses provide extra information due to errors in the transcriptions of the ASR system, i.e., the performance gain is attributed to the word-error-rate (WER) of the ASR. We empirically show that the gain in using N-Best hypotheses is related to not WER but to the diversity of hypotheses. Code and datasets are available at this URL.