Miracle-dz/UniFAR-FTUs huggingface dataset:
Verifying the DaisyNFS concurrent and crash-safe file system with sequential reasoning

This paper develops a new approach to verifying a performant file system that isolates crash safety and concurrency reasoning to a transaction system that gives atomic access to the disk, so that the rest of the file system can be verified with sequential reasoning . We demonstrate this approach in DaisyNFS, a Network File System (NFS) server written in Go that runs on top of a disk. DaisyNFS uses GoTxn, a new verified, concurrent transaction system that extends GoJournal [9] with two-phase locking and an allocator. The transaction system’s specification formalizes under what conditions transactions can be verified with only sequential reasoning, and comes with a mechanized proof in Coq [37] that connects the specification to the implementation. As evidence that proofs enjoy sequential reasoning, DaisyNFS uses Dafny [26], a sequential verification language, to implement and verify all the NFS operations on top of GoTxn. The sequential proofs helped achieve a number of good properties in DaisyNFS: easy incremental development (for example, adding support for large files), a relatively short proof (only 2 × as many lines of proof as code), and a performant implementation (at least 60% the throughput of the Linux NFS server exporting ext4 across a variety of bench-marks).
Modeling Multicore Contention under Cyclic Executive Scheduling Master Thesis

Commercially available Off The Shelf (COTS) multicores have been assessed as the baseline computing platform even in the most conservative real-time domains. Multicore contention arising on shared hardware resources, with its circular dependence with scheduling, is among the most challenging issues that require urgent attention before multicores can be fully embraced for real-time computing. In the context of statically scheduled systems, still the most used scheduling approach in real-time industries, this Thesis proposes two different formulations for computing the worst-case contention delay potentially suffered by a task on account of the interference potentially incurred when sending requests to shared resources. These two models provide tight contention delay upper bounds under reasonable assumptions, by capturing the interdependence between timing interference of conflicting requests, issued in parallel by other cores, and the identification of the particular set of tasks co-running on those cores. This work shows, via extensive evaluation, that jointly accounting for worst-case task overlapping and request distribution scenarios provides tighter contention bounds when compared to state-of-the-art solutions. Furthermore, a fully automated framework for the implementation and execution of both methodologies has been developed. Modeling Multicore Contention under Cyclic Executive Scheduling v
O NTOLOGY - GUIDED AND T EXT - ENHANCED R EPRE - SENTATION FOR K NOWLEDGE G RAPH Z ERO - SHOT R E - LATIONAL L EARNING

Knowledge graph embedding (KGE) have been proposed and utilized to knowledge graph completion (KGC), but most KGE methods struggle in unseen relations. Previous studies focus on complete zero-shot relational learning by incorporating text-features and proximity relations, which are difficult to accurately represent the complete semantic of relations. To overcome the above-mentioned issues in zero-shot relation learning, we propose an ontology-guided and text-enhanced representation, which could improve the effect of current KGE for un-seen relations. In fact, each KG contain ontology and text descriptions that describe the meta-information of knowledge. To combine text-embedding space and graph-embedding space, we design TR-GCN to obtain the meta-representation of relations based on the ontology structure and their textual descriptions. It will be used directly to guide previous KGE methods such as TransE and RotatE on zero-shot relation learning. The experimental results on multiple public datasets demonstrate that the proposed ontology-guided and text-enhanced representation can enrich KGs embedding, and significantly improves the KGC performance on unseen relations.
Protection of Energy Network Infrastructures Applying a Dynamic Topology Virtualization

Rapid progress of computing and info-communication technologies (ICT) has changed the ecosystem of power production and delivery. Today, an energy network is a complex set of interrelated devices and information systems covering all areas of electric power operations and applying ICT based on open standards, such as IEC 60870, IEC 61850, and IEC 61970. According to IEC 62351, the energy networks are faced with high cybersecurity risks caused by open communications, security requirements rarely considered in the energy facilities, partial and difficult upgrades, and incompatibility of secure tools with industrial solutions. This situation results in new security challenges, e.g. denial of service attacks on the connected controllers, dispatching centers, process control systems, and terminals. IEC 62351 describes possible ways to comprehensive security in the energy networks. Most of them used in traditional networks (e.g., firewalls, intrusion detection systems) can be adapted to the energy networks. Honeypot systems as a protection measure help us to mitigate the attacks and maintain necessary security in the networks. Due to the large scale of an energy network and heterogeneity of its components, a new design, deployment, and management strategy for the honeypot systems are required. The paper suggests a new method for organizing a virtual network infrastructure of a hybrid honeypot system and a dynamic management method that adapts the network topology to the attacker’s actions according to the development graph of potential attacks. This technique allows us to dynamically build virtual networks of arbitrary scale. Because of the similarity of the virtual network to the virtualized origin and providing the level of interactivity of its nodes corresponding to real devices, this technique deploys an energy network indistinguishable from the real one for the attackers. A prototype of our honeypot system has been implemented, and experiments on it have demonstrated the more efficient use of the computing resources, the faster reaction to the attacker’s actions, and the deployment of different sizes of virtual networks for the given limits of the computing resources.
The Brand–Land Identity of Etna Volcano Valley Wines: A Policy Delphi Study

This is the first study on the brand–land link for quality wines with a strong identity produced in extreme territories using the Policy Delphi methodology. The objective of this study is to assess the existence of a relationship between the wine brand and the territory of origin for wines produced in the Etna valley in Sicily (southern Italy). Awareness among producers and stakeholders of the recognizability of local wines by the market was investigated. Moreover, the forecasts/reliability, issues/importance, options/feasibility, and goals/desirability of development factors for wines with a brand–land link in the Etna valley were analyzed. The results were used to design a model of the value chain for wines with a brand–land identity which is generalizable to other wine regions. In this study, the policy Delphi method was adapted, consolidated, and improved for marketing studies in the agri-food sector. This adapted method can be replicated in other studies focusing on similar contexts. The findings provide insights into the characteristics (type and category) of development factors that add value to Etna Rosso DOC wine and provide interesting food for thought for wine-producing companies in other wine-growing areas with unique pedoclimatic characteristics that determine a strong brand–land link between wine and its territory or origin. Practical implications encompass new elements for winemakers, as well as for local decision-makers and stakeholders, for the formulation of more effective communication strategies and territorial revitalization strategies to enhance the competitiveness and appreciation of wines with strong geographical identity traits. To highlight these elements, a new theoretical model was designed that includes the experience of the territory and the product in the value chain of iconic wines.
Integrated production-distribution planning optimization models: A review in collaborative networks context

Researchers in the area of collaborative networks are more and more aware of proposing collaborative approaches to address planning processes, due to the advantages associated when enterprises perform integrated planning models. Collaborative production-distribution planning, among the supply network actors, is considered a proper mechanism to support enterprises on dealing with uncertainties and dynamicity associated to the current markets. Enterprises, and especially SMEs, should be able to overcome the continuous changes of the market by increasing their agility. Carrying out collaborative planning allows enterprises to enhance their readiness and agility for facing the market turbulences. However, SMEs have limited access when incorporating optimization tools to deal with collaborative planning, reducing their ability to respond to the competition. The problem to solve is to provide SMEs affordable solutions to support collaborative planning. In this regard, new optimisation algorithms are required in order to improve the collaboration within the supply network partners. As part of the H2020 Cloud Collaborative Manufacturing Networks (C2NET) research project, this paper presents a study on integrated production and distribution plans. The main objective of the research is to identify gaps in current optimization models, proposed to address integrated planning, taking into account the requirements and needs of the industry. Thus, the needs of the companies belonging to the industrial pilots, defined in the C2NET project, are identified; analysing how these needs are covered by the optimization models proposed in the literature, to deal with the integrated production-distribution planning.
Nanoionics‐Enabled Memristive Devices: Strategies and Materials for Neuromorphic Applications

Memristors have been intensively studied in recent years as potential building blocks for the construction of versatile neuromorphic architectures. The prevalent developments focus on nanoionic devices due to their ease of reversible regulation, non‐volatility, scalability down to the atomic level, low‐energy consumption, nanosecond response times, sufficient yield and reliability, as well as their homologous regulatory mechanisms with biological synapses. However, despite their desirable structures and excellent properties, the previously reported properties of nanoionic devices are rather diverse and dissatisfactory for the delivery of reliable analog properties, which is the precondition for the imitation of brain‐like functions. Here, the general requirements of neuromorphic engineering in terms of device structure, characterization parameters, synaptic functions and device engineering are introduced. A critical overview of the proposed nanoionic mechanisms for memristive switching is given, focusing particularly on providing fundamental insights into the strategies for regulating the adaptive memristive characteristics of devices that resemble the behaviors of biological synapses, which is an element of neural networks. In addition, the research progress in active materials (e.g., oxides, polymers, small molecules, bio‐macromolecules and dopants), especially regarding the correlation among materials, is elaborated and a rational approach to provide insight into new design strategies is considered, and how to understand the memristor mechanisms and further achieve excellent memristive devices is discussed. Finally, the current challenges and several possible future research directions in this area are discussed.
Integrin alpha(4)beta(7) expression on peripheral blood CD4(+) T cells predicts HIV acquisition and disease progression outcomes

The gastrointestinal (GI) mucosa is central to HIV pathogenesis, and the integrin α 4 β 7 promotes the homing of immune cells to this site. Data from SIV animal models suggest that α 4 β 7 blockade provides prophylactic and therapeutic benefits. Here we show that pre-HIV infection levels of α 4 β 7+ peripheral blood CD4 + T cells, independent of other T cell phenotypes and genital inflammation, were associated with increased rates of HIV acquisition in South African women. This association was stronger when infection was caused by HIV strains containing V2 Env motifs with a preference for α 4 β 7 binding. A similar acquisition effect was observed in a Kenyan cohort, and in non-human primates (NHPs) following intravaginal SIV challenge. In addition, pre-HIV α 4 β 7+ CD4 + T cells predicted higher set point viral load and a >2-fold increased rate of CD4 decline. These results were confirmed in SIV-infected NHPs. Increased frequencies of pre-HIV α 4 β 7+ CD4 + T cells were also associated with higher post-infection expression of LPS binding protein, a microbial translocation marker, suggestive of more extensive gut damage. CD4 + T cells expressing α 4 β 7 were rapidly depleted very early in HIV infection, particularly from the GI mucosa, and were not restored by early antiretroviral therapy (ART). This study provides a link between α 4 β 7 expression and HIV clinical outcomes in humans, in line with observations made in NHPs. Given the availability of a clinically approved anti- α 4 β 7 monoclonal antibody for treatment of inflammatory bowel disease, these data support further evaluation of targeting α 4 β 7 integrin as a clinical intervention during HIV infection. Linear mixed models were used to compare LBP levels over time. Spearman correlation was used to analyze associations between the two variables.
A Proof of Propositions

A.2 Proof of Proposition 2 Proposition 2 (rank-1 condition on θ). For any positive tensor P , rank(P) = 1 if and only if its all many-body θ parameters are 0. Proof. First, we show that rank(P) = 1 implies all many-body θ-parameters are 0. From the assumption of rank(P) = 1, the m-th row of the mode-k expansion of P have to be a constant multiple of the (m− 1)-th row for all m = {2, . , Ik} and k ∈ [d]. That is, P m,j P m−1,j = Pi1,. ,ik−1,m,ik+1,. ,id Pi1,. ,ik−1,m−1,ik+1,. ,id = exp  i1 ∑ i1=1 · · · ik−1 ∑ ik−1=1 ik+1 ∑ ik+1=1 · · · id ∑ id=1 θi1,. ,ik−1,m,ik+1,. ,id  can depend on only m. If any many-body parameter θi1,. ,m,. id is not 0 for i ′ 1 + · · ·+ ik−1 + ik+1 + · · ·+ id 6= d− 1, the left side of the above equation depends on indices other than m. For example, if a many-body parameter θ2,1,. ,1,m,1,. 1 is not 0, the equation depends on the value of i1. Therefore, all many-body parameters of rank-1 tensor are 0. Next, we show that rank(P) = 1 if all many-body θ-parameters are 0. If all many-body θ-parameters are 0, we have
Caulk: Lookup Arguments in Sublinear Time

We present position-hiding linkability for vector commitment schemes: one can prove in zero knowledge that one or m values that comprise commitment \cm all belong to the vector of size N committed to in \com. Our construction \textsfCaulk can be used for membership proofs and lookup arguments and outperforms all existing alternatives in prover time by orders of magnitude. For both single- and multi-membership proofs the \textsfCaulk protocol beats SNARKed Merkle proofs by the factor of 100 even if the latter is instantiated with Poseidon hash. Asymptotically our prover needs O(m^2 + młog N) time to prove a batch of m openings, whereas proof size is O(1) and verifier time is O(łog(łog N)). As a lookup argument, \textsfCaulk is the first scheme with prover time sublinear in the table size, assuming O(Nłog N) preprocessing time and O(N) storage. It can be used as a subprimitive in verifiable computation schemes in order to drastically decrease the lookup overhead. Our scheme comes with a reference implementation and benchmarks.
Human Skeletal Muscle Hypertrophy

It is well known that resistance training can induce substantial muscle hypertrophy. Longitudinal studies of four weeks to two years have been done to assess the effects of various resistance training protocols on muscle size. The majority of studies have been short-term (<20 weeks) and have used untrained subjects. Models of training commonly involve a single exercise using a high-repetition (>8), low-moderate loads, and multiple sets (usually 3-5). The muscles most often examined are the elbow flexors and knee extensors. It is not clear that a best or optimal method of training exists. It is difficult to draw comparisons between many of these studies due to differences in training protocol, the subjects’ training status, the muscle examined, and the method used to derive muscle size changes. Because of the paucity of data on trained subjects, future work should examine the adaptive response in these trained individuals. The use of high weight, low repetition as well as periodized training protocols need further examination. Furthermore, the existence of intermuscular differences in training response would suggest that not all training protocols elicit the same effect in different muscles.
Multimodal Sentiment and Personality Perception Under Speech: A Comparison of Transformer-based Architectures

Human-machine, human-robot interaction, and collaboration appear in diverse fields, from homecare to Cyber-Physical Systems. Technological development is fast, whereas real-time methods for social communication analysis that can measure small changes in sentiment and personality states, including visual, acoustic and language modalities are lagging, particularly when the goal is to build robust, appearance invariant, and fair methods. We study and compare methods capable of fusing modalities while satisfying real-time and invariant appearance conditions. We compare state-of-the-art transformer architectures in sentiment estimation and introduce them in the much less explored field of personality perception. We show that the architectures perform differently on automatic sentiment and personality perception, suggesting that each task may be better captured/modeled by a particular method. Our work calls attention to the attractive properties of the linear versions of the transformer architectures. In particular, we show that the best results are achieved by fusing the different architectures’ preprocessing methods. However, they pose extreme conditions in computation power and energy consumption for real-time computations for quadratic transformers due to their memory requirements. In turn, linear transformers pave the way for quantifying small changes in sentiment estimation and personality perception for real-time social communications for machines and robots.
A Step Forward in Human-Robot Collaboration – The Project CollRob ∗

Human-robot collaboration is a novel, hot topic in the ﬁeld of industrial and service robotics with considerable potential. It offers the possibility to combine human cognitive abilities with the strengths of robot technology in terms of precision and performance, thus opening up a wide range of possibilities beyond the traditional application of robots. The research project ” Collaborative Robotics” (CollRob) is an initiative focusing on the conceptualization, research, development, and evaluation of novel methods and tools for collaborative and cooperative robots. This article aims at giving an overview about this project in terms of its backgrounds, objectives, and the current status of research covering topics such as machine perception, sensitive redundant kinematic manipulation, dynamic adaptive planning, human-robot interaction and information exchange, human factors, and safety.
EFFECT OF SHORT-TERM EQUAL-VOLUME RESISTANCE TRAINING WITH DIFFERENT WORKOUT

Candow, D.G. , and D.G. Burke. Effect of short-term equal-volume resistance training with different workout frequency on muscle mass and strength in untrained men and women. J. Strength Cond. Res. 21(1):204–207. 2007. —Changes in muscle mass and strength will vary, depending on the volume and frequency of training. The purpose of this study was to determine the effect of short-term equal-volume resistance training with different workout frequency on lean tissue mass and muscle strength. Twenty-nine untrained volunteers (27–58 years; 23 women, 6 men) were assigned randomly to 1 of 2 groups: group 1 (n 15; 12 women, 3 men) trained 2 times per week and performed 3 sets of 10 repetitions to fatigue for 9 exercises, group 2 (n 14; 11 women, 3 men) trained 3 times per week and performed 2 sets of 10 repetitions to fatigue for 9 exercises. Prior to and following training, whole-body lean tissue mass (dual energy x-ray absorptiometry) and strength (1 repetition maximum squat and bench press) were measured. Both groups increased lean tissue mass (2.2%), squat strength (28%), and bench press strength (22–30%) with training (p 0.05), with no other differences. These results suggest that the volume of resistance training may be more important than frequency in developing muscle mass and strength in men and women initiating a resistance training program.
Copy Number Variation (CNV): A New Genomic Insight in Horses

Simple Summary This study aimed to contribute to our knowledge of CNVs, a type of genomic marker in equines, by producing, for the first time, a fine-scale characterization of the CNV regions (CNVRs) in the Pura Raza Española horse breed. We found not only the existence of a unique pattern of genomic regions enriched in CNVs in the PRE in comparison with the data available from other breeds but also the incidence of CNVs across the entire genome. Since these regions could affect the structure and dose of the genes involved, we also performed a gene ontology analysis which revealed that most of the genes overlapping in CNVRs were related to the olfactory pathways and immune response. Abstract Copy number variations (CNVs) are a new-fangled source of genetic variation that can explain changes in the phenotypes in complex traits and diseases. In recent years, their study has increased in many livestock populations. However, the study and characterization of CNVs in equines is still very limited. Our study aimed to investigate the distribution pattern of CNVs, characterize CNV regions (CNVRs), and identify the biological pathways affected by CNVRs in the Pura Raza Española (PRE) breed. To achieve this, we analyzed high-density SNP genotyping data (670,804 markers) from a large cohort of 654 PRE horses. In total, we identified 19,902 CNV segments and 1007 CNV regions in the whole population. The length of the CNVs ranged from 1.024 kb to 4.55 Mb, while the percentage of the genome covered by CNVs was 4.4%. Interestingly, duplications were more abundant than deletions and mixed CNVRs. In addition, the distribution of CNVs across the chromosomes was not uniform, with ECA12 being the chromosome with the largest percentage of its genome covered (19.2%), while the highest numbers of CNVs were found in ECA20, ECA12, and ECA1. Our results showed that 71.4% of CNVRs contained genes involved in olfactory transduction, olfactory receptor activity, and immune response. Finally, 39.1% of the CNVs detected in our study were unique when compared with CNVRs identified in previous studies. To the best of our knowledge, this is the first attempt to reveal and characterize the CNV landscape in PRE horses, and it contributes to our knowledge of CNVs in equines, thus facilitating the understanding of genetic and phenotypic variations in the species. However, further research is still needed to confirm if the CNVs observed in the PRE are also linked to variations in the specific phenotypical differences in the breed.