Miracle-dz/UniFAR-FTUs huggingface dataset:
Scheduling Dags under Uncertainty

This paper introduces a parallel scheduling problem where a directed acyclic graph modeling $t$ tasks and their dependencies needs to be executed on $n$ unreliable workers. Worker $i$ executes task $j$ correctly with probability $p_{i,j}$. The goal is to find a regimen $\Sigma$, that dictates how workers get assigned to tasks (possibly in parallel and redundantly) throughout execution, so as to minimize the expected completion time. This fundamental parallel scheduling problem arises in grid computing and project management fields, and has several applications. We show a polynomial time algorithm for the problem restricted to the case when dag width is at most a constant and the number of workers is also at most a constant. These two restrictions may appear to be too severe. However, they are fundamentally required. Specifically, we demonstrate that the problem is NP-hard with constant number of workers when dag width can grow, and is also NP-hard with constant dag width when the number of workers can grow. When both dag width and the number of workers are unconstrained, then the problem is inapproximable within factor less than 5/4, unless P=NP.
Normal Forms in Combinatory Logic

Let R be a convergent term rewriting system, and let CR-equalityon (simply typed) combinatory logic terms be the equality induced by s? Requalityon terms of the (simply typed) lambda calculus under any of the standardtranslations between these two frameworks for higher-order reasoning. Wegeneralize the classical notion of strong reduction to a reduction relation whichgenerates CR-equality and whose irreducibles are exactly the translates of longsR-normal forms. The classical notion of strong normal form in combinatorylogic is also generalized, yielding yet another description of these translates.Their resulting tripartite characterization extends to the combined first-order algebraicand higher-order setting the classical combinatory logic descriptions ofthe translates of long s-normal forms in the lambda calculus. As a consequence,the translates of long sR-normal forms are easily seen to serve as canonicalrepresentatives for CR-equivalence classes of combinatory logic terms for nonempty,as well as for empty, R.
Riemannian level-set methods for tensor-valued data

We present a novel approach for the derivation of PDEs modeling curvaturedriven flows for matrix-valued data. This approach is based on the Riemannian geometry of the manifold of Symmetric Positive Definite MatricesP(n). The differential geometric attributes ofP(n) such as the bi-invariant metric, the covariant derivative and the Christoffel symbols allow us to extend scalar-valued mean curvature and snakes methods to the tensor data setting. Since the data live onP(n), these methods have the natural property of preserving positive definiteness of the initial data. Experiments on three-dimensional real DT-MRI data show that the proposed methods are highly robust.
Inactivation of Human Manganese-superoxide Dismutase by Peroxynitrite Is Caused by Exclusive Nitration of Tyrosine 34 to 3-Nitrotyrosine*

Peroxynitrite has recently been implicated in the inactivation of many enzymes. However, little has been reported on the structural basis of the inactivation reaction. This study proposes that nitration of a specific tyrosine residue is responsible for inactivation of recombinant human mitochondrial manganese-superoxide dismutase (Mn-SOD) by peroxynitrite. Mass spectroscopic analysis of the peroxynitrite-inactivated Mn-SOD showed an increased molecular mass because of a single nitro group substituted onto a tyrosine residue. Single peptides that had different elution positions between samples from the native and peroxynitrite-inactivated Mn-SOD on reverse-phase high performance liquid chromatography were isolated after successive digestion of the samples by staphylococcal serine protease and lysylendopeptidase and subjected to amino acid sequence and molecular mass analyses. We found that tyrosine 34 of the enzyme was exclusively nitrated to 3-nitrotyrosine by peroxynitrite. This residue is located near manganese and in a substrate O⨪2 gateway in Mn-SOD.
Cooperative Multiplexing in a Half Duplex Relay Network: Performance and Constraints

Previous work on relay networks has concentrated primarily on the diversity benefits of such techniques. This paper explores the possibility of also obtaining multiplexing gain in a relay network, while retaining diversity gain. Specifically, consider a network in which a single source node is equipped with one antenna and a destination is equipped with two antennas. It is shown that, in certain scenarios, by adding a relay with two antennas and using a successive relaying protocol, the diversity multiplexing tradeoff performance of the network can be lower bounded by that of a 2 by 2 MIMO channel, when the decode-and-forward protocol is applied at the relay. A distributed D-BLAST architecture is developed, in which parallel channel coding is applied to achieve this tradeoff. A space-time coding strategy, which can bring a maximal multiplexing gain of more than one, is also derived for this scenario. As will be shown, while this space-time coding strategy exploits maximal diversity for a small multiplexing gain, the proposed successive relaying scheme offers a significant performance advantage for higher data rate transmission. In addition to the specific results shown here, these ideas open a new direction for exploiting the benefits of wireless relay networks.
Cryogels and Monoliths: Promising Tools for Chromatographic Purification of Nucleic Acids

The increasing demand for highly pure biopharmaceuticals has put significant pressure on the biotechnological industry to innovate in production and purification processes. Nucleic acid purification, crucial for gene therapy and vaccine production, presents challenges due to the unique physical and chemical properties of these molecules. Meeting regulatory standards necessitates large quantities of biotherapeutic agents of high purity. While conventional chromatography offers versatility and efficiency, it suffers from drawbacks like low flow rates and binding capacity, as well as high mass transfer resistance. Recent advancements in continuous beds, including monoliths and cryogel-based systems, have emerged as promising solutions to overcome these limitations. This review explores and evaluates the latest progress in chromatography utilizing monolithic and cryogenic supports for nucleic acid purification.
Scientific Collaboratories as Socio-Technical Interaction Networks: A Theoretical Approach

Collaboratories refer to laboratories where scientists can work together while they are in distant locations from each other and from key equipment. They have captured the interest both of CSCW researchers and of science funders who wish to optimize the use of rare scientific equipment and expertise. We examine the kind of CSCW conceptions that help us best understand the character of working relationships in these scientific collaboratories. Our model, inspired by actor-network theory, considers technologies as Socio-technical Interaction Networks (STINs). This model provides a rich understanding of the scientific collaboratories, and also a more complete understanding of the conditions and activities that support collaborative work in them. We illustrate the significance of STIN models with several cases drawn from the fields of high energy physics and materials science.
Towards the integration of mental practice in rehabilitation programs. A critical review

Many clinical studies have investigated the use of mental practice (MP) through motor imagery (MI) to enhance functional recovery of patients with diverse physical disabilities. Although beneficial effects have been generally reported for training motor functions in persons with chronic stroke (e.g., reaching, writing, walking), attempts to integrate MP within rehabilitation programs have been met with mitigated results. These findings have stirred further questioning about the value of MP in neurological rehabilitation. In fact, despite abundant systematic reviews, which customarily focused on the methodological merits of selected studies, several questions about factors underlying observed effects remain to be addressed. This review discusses these issues in an attempt to identify factors likely to hamper the integration of MP within rehabilitation programs. First, the rationale underlying the use of MP for training motor function is briefly reviewed. Second, three modes of MI delivery are proposed based on the analysis of the research protocols from 27 studies in persons with stroke and Parkinson's disease. Third, for each mode of MI delivery, a general description of MI training is provided. Fourth, the review discusses factors influencing MI training outcomes such as: the adherence to MI training, the amount of training and the interaction between physical and mental rehearsal; the use of relaxation, the selection of reliable, valid and sensitive outcome measures, the heterogeneity of the patient groups, the selection of patients and the mental rehearsal procedures. To conclude, the review proposes a framework for integrating MP in rehabilitation programs and suggests research targets for steering the implementation of MP in the early stages of the rehabilitation process. The challenge has now shifted towards the demonstration that MI training can enhance the effects of regular therapy in persons with subacute stroke during the period of spontaneous recovery.
Ontology-supported and ontology-driven conceptual navigation on the World Wide Web

This paper presents the principles of ontology-supported and ontology-driven conceptual navigation. Conceptual navigation realizes the independence between resources and links to facilitate interoperability and reusability. An engine builds dynamic links, assembles resources under an argumentative scheme and allows optimization with a possible constraint, such as the user's available time. Among several strategies, two are discussed in detail with examples of applications. On the one hand, conceptual specifications for linking and assembling are embedded in the resource meta-description with the support of the ontology of the domain to facilitate meta-communication. Resources are like agents looking for conceptual acquaintances with intention. On the other hand, the domain ontology and an argumentative ontology drive the linking and assembling strategies.
Using Learning-based Filters to Detect Rule-based Filtering Obsolescence

For years, Caisse des Depots et Consignations has produced information filtering applications. To be operational, these applications require high filtering performances which are achieved by using rule-based filters. With this technique, an administrator has to tune a set of rules for each topic. However, filters become obsolescent over time. The decrease of their performances is due to diachronic polysemy of terms that involves a loss of precision and to diachronic polymorphism of concepts that involves a loss of recall. To help the administrator to maintain his filters, we have developed a method which automatically detects filtering obsolescence. It consists in making a learning-based control filter using a set of documents which have already been categorised as relevant or not relevant by the rule-based filter. The idea is to supervise this filter by processing a differential comparison of its outcomes with those of the control one. This method has many advantages. It is simple to implement since the training set used by the learning is supplied by the rule-based filter. Thus, both the making and the use of the control filter are fully automatic. With automatic detection of obsolescence, learning-based filtering finds a rich application which offers interesting prospects.
Methods and Tools for Building the Catalan WordNet

Abstract In this paper we introduce the methodology used andthe basic phases we followed to develop the Catalan WordNet,and which lexical resources have been employed in its building.This methodology, as well as the tools we made use of, havebeen thought in a general way so that they could be applied toany other language. 1. Introduction In recent years the research in the NaturalLanguage Processing (NLP) field has proved the need forextensive and complete Lexical Knowledge Bases(LKBs). Acquiring such lexical/semantic structures is ahard problem and has been usually approached byreusing, merging and tuning existing lexical material.While in English the "lexical bottleneck" problem seemsto be softened (e.g. WordNet (Miller, 1990), AlveyLexicon (Grover et al. 1993), COMLEX (Grishman et al.1994) and so on) there are no available wide rangelexicons for NLP for other languages. Manualconstruction of lexicons is the most reliable technique forobtaining structured lexicons but it is costly and highlytime-consuming. This is the reason for many researchersto have focussed on the massive acquisition of lexicalknowledge and semantic information from pre-existingstructured lexical resources as automatically as possible.The work presented here shows a fast productionmethodology to build large scale multilingual LKBs fromconventional dictionaries.In this way, the English WordNet developed atPrinceton University is consolidating as a standard defacto for the lexical-semantic representation of the naturallanguage for English. Considering this English WordNetas a reference, new WordNets in some other languages arebeing built, such as the ones for Galician, Basque, Spanishand Catalan, for instance. Besides, multilingual links areestablished automatically through the connections withthe English WordNet.The research we are now presenting is embeddedin the Catalan WordNet project, which is being carried outby the Grup de Tractament de Llenguatge Natural(GTLN-UPC-UB) of the Centre de Referencia enEnginyeria Linguistica (CREL)
Rahasia kesetiaan konsumen: Hubungan kepuasan, kepercayaan, nilai, kualitas, dan harga pada pembelian ulang teh organik

: This study aims to analyze the effect of product satisfaction, customer trust in the product, value, tea quality, and price fairness on repurchase intention in the organic tea industry in Indonesia. The sample in this study were organic tea consumers with predetermined standard criteria. These criteria were determined using a purposive sampling method. The number of samples is 225 respondents. The data analysis technique used is structural equation modeling (SEM). The results of this study indicate that product satisfaction, brand trust, perceived value, perceived quality, perceived price fairness have a positive effect on repurchase intention.
Quality of care.

Editor – I am moved by Professor Allan's elegy to bygone NHS virtues of ‘calm caring and gentle pace of clinical life… and all the time in the world to deliver compassionate care' ( Clin Med October 2009 p 407). One's immediate instinct would be to say ‘Ah, but times have changed' – only
Knowledge Representation Issues in Semantic Graphs for Relationship Detection

Biodefense Knowledge Center, Lawrence Livermore National Laboratory.An important task for Homeland Security is the prediction of threat vulnerabilities, such as through the de-tection of relationships between seemingly disjoint entities. A structure used for this task is a semantic graph,also known as a relational data graph or an attributed relational graph. These graphs encode relationships astyped links between a pair of typed nodes. Indeed, semantic graphs are very similar to semantic networks usedin AI. The node and link types are related through an ontology graph (also known as a schema). Furthermore,each node has a set of attributes associated with it (e.g., “age” may be an attribute of a node of type “person”). Unfortunately, the selection of types and attributes for both nodes and links depends on human expertise and issomewhat subjective and even arbitrary. This subjectiveness introduces biases into any algorithm that operateson semantic graphs. Here, we raise some knowledge representation issues for semantic graphs and providesome possible solutions using recently developed ideas in the ﬁeld of complex networks. In particular, we usethe concept of transitivity to evaluate the relevance of individual links in the semantic graph for detecting rela-tionships. We also propose new statistical measures for semantic graphs and illustrate these semantic measureson graphs constructed from movies and terrorism data.I. INTRODUCTION
A Bayesian Network Model for Predicting Insider Threats

This paper introduces a Bayesian network model for the motivation and psychology of the malicious insider. First, an initial model was developed based on results in the research literature, highlighting critical variables for the prediction of degree of interest in a potentially malicious insider. Second, a survey was conducted to measure these predictive variables in a common sample of normal participants. Third, a structural equation model was constructed based on the original model, updated based on a split-half sample of the empirical survey data and validated against the other half of the dataset. Fourth, the Bayesian network was adjusted in light of the results of the empirical analysis. Fifth, the updated model was used to develop an upper bound on the quality of model predictions of its own simulated data. When empirical data regarding psychological predictors were input to the model, predictions of counterproductive behavior approached the upper bound of model predictiveness.
Searching for Plannable Domains can Speed up Reinforcement Learning

Reinforcement learning (RL) involves sequential decision making in uncertain environments. The aim of the decision-making agent is to maximize the benefit of acting in its environment over an extended period of time. Finding an optimal policy in RL may be very slow. To speed up learning, one often used solution is the integration of planning, for example, Sutton's Dyna algorithm, or various other methods using macro-actions. Here we suggest to separate plannable, i.e., close to deterministic parts of the world, and focus planning efforts in this domain. A novel reinforcement learning method called plannable RL (pRL) is proposed here. pRL builds a simple model, which is used to search for macro actions. The simplicity of the model makes planning computationally inexpensive. It is shown that pRL finds an optimal policy, and that plannable macro actions found by pRL are near-optimal. In turn, it is unnecessary to try large numbers of macro actions, which enables fast learning. The utility of pRL is demonstrated by computer simulations.
Calcium Carbonate-Based Nanoplatforms for Cancer Therapeutics: Current State of Art and Future Breakthroughs

With the rapid development of nanotechnology, nanomaterials have shown immense potential for antitumor applications. Nanosized calcium carbonate (CaCO3) materials exhibit excellent biocompatibility and degradability, and have been utilized to develop platform technologies for cancer therapy. These materials can be engineered to carry anticancer drugs and functional groups that specifically target cancer cells and tissues, thereby enhancing therapeutic efficacy. Additionally, their physicochemical properties can be tailored to enable stimuli-responsive therapy and precision drug delivery. This Review consolidates recent literatures focusing on the synthesis, physicochemical properties, and multimodal antitumor therapies of CaCO3-based nanoplatforms (CBN). We also explore the current challenges and potential breakthroughs in the development of CBN for antitumor applications, providing a valuable reference for researchers in the field.
An institution for processes and data

Csp-Casl [7] is a comprehensive specification language which combines processes written in the process algebra Csp [2, 8] with the specification of data types formulated in algebraic specification language Casl [5]. Recent developments on Csp-Casl cover tool support [6] as well as testing from Csp-Casl specifications [3]. In this talk we address the question of how to formulate Csp-Casl as an institution [1]. The Csp-Casl semantics follows a two-step approach: in its first step, the data specified in Casl is turned into an alphabet of communications, in its second step, the Csp process semantics is applied. This allows us to base our new formulation of a Csp-Casl institution on our previous work concerning Csp alone [4]. Solving this fundamental question of semantic nature has impact on the specification practice: the institution independent structuring mechanisms of Casl [5] become available within Csp-Casl specifications; furthermore, as projecting from Csp-Casl institution into Casl institution yields an institution morphism, it is also possible to use Csp-Casl within heterogeneous specifications.
A Series-Parallel Compensated IPT System with Load-Independent Voltage Gain and Efficiency

In this paper, by first analyzing the circuit model of series-parallel compensated inductive power transfer (IPT) system, the relation among voltage gain, transfer efficiency, operation frequency and load resistance is obtained. According to the analysis, a novel design of series-parallel compensated IPT system realizing optimized transfer efficiency and voltage gain regardless of load resistance is then proposed. Simulation and experiment verification are carried out with various load resistances. An almost load-independent voltage gain and efficiency up to 96.8% are achieved when the air gap between coupled coils is 17.91mm.
Personalizing the GAMS Cross-Index

The NIST Guide to Available Mathematical Software (GAMS)system at http://gams.nist. gov serves as the gateway to thousands of scientific codes and modules for numerical com-putation.We describe the PIPE personalization facility for GAMS,whereby content from the cross-index is specialized for a user desiring software recommendations for a specific problem instance.The key idea is to (i)mine structure,and (ii)exploit it in a programmatic manner to generate personalized web pages.Our approach supports both content based and collaborative personalization and enables information integration from multiple (and complementary)web resources.We present case studies for the domain of linear,second-order,elliptic partial differential equations that indicate strong empirical evidence for the usefulness of our semi-automatic approach.
Metaheuristic-Based Feature Selection Methods for Diagnosing Sarcopenia with Machine Learning Algorithms

This study explores the efficacy of metaheuristic-based feature selection in improving machine learning performance for diagnosing sarcopenia. Extraction and utilization of features significantly impacting diagnosis efficacy emerge as a critical facet when applying machine learning for sarcopenia diagnosis. Using data from the 8th Korean Longitudinal Study on Aging (KLoSA), this study examines harmony search (HS) and the genetic algorithm (GA) for feature selection. Evaluation of the resulting feature set involves a decision tree, a random forest, a support vector machine, and naïve bayes algorithms. As a result, the HS-derived feature set trained with a support vector machine yielded an accuracy of 0.785 and a weighted F1 score of 0.782, which outperformed traditional methods. These findings underscore the competitive edge of metaheuristic-based selection, demonstrating its potential in advancing sarcopenia diagnosis. This study advocates for further exploration of metaheuristic-based feature selection’s pivotal role in future sarcopenia research.