Miracle-dz/UniFAR-FTUs huggingface dataset:
A cooperative strategy based on Nash bargaining solution under spectral mask constraint in cooperative relay networks

Resource allocation among multiple users interference channel has posed significant issues in cooperative communication networks. A game-theoretic approach is studied under spectral mask constraints (SMCs) in cooperative relay networks using Amplify-and-Forward as a relaying protocol. The cooperative strategy employed as game-theoretic approach is based on the Nash bargaining solution (NBS). The Nash equilibrium and Nash bargaining solutions are derived for a two-player game based on the joint time division multiplexing and frequency division multiplexing (TDM/FDM) as a mode of cooperation. The condition for the uniqueness and existence of the NBS is derived.
Can “Temps” Secure Future Employment? Investigating the Relationship Between Proactive Behaviors and Employers’ Rehiring Decision

This study examines how the adoption of proactive socialization behaviors by temporary agency workers is related to contract renewal intention of the supervisor in the client organization in which they are assigned. We propose that the adoption of such behaviors will be associated with a favorable performance evaluation from the supervisor in the client organization, and in turn, to his or her contract renewal intention through two mechanisms, namely, role clarity and leader–member exchange (LMX), which refers to the quality of the relationship between the temporary worker and his or her supervisor. Data were collected from 217 worker–supervisor dyads. Results indicate that information-seeking behavior is related to performance evaluation through role clarity. This indirect relationship is however negative, as greater role clarity relates negatively to performance evaluation. Our results also show that LMX acts as a mediator between feedback seeking, boss-relationship building, and performance. Finally, favorable performance evaluation is positively associated with contract renewal intention.
Role of the Serine-Rich Surface Glycoprotein Srr1 of Streptococcus agalactiae in the Pathogenesis of Infective Endocarditis

The binding of bacteria to fibrinogen and platelets are important events in the pathogenesis of infective endocarditis. Srr1 is a serine-rich repeat glycoprotein of Streptococcus agalactiae that binds directly to the Aα chain of human fibrinogen. To assess the impact of Srr1 on the pathogenesis of endocarditis due to S. agalactiae, we first examined the binding of this organism to immobilized human platelets. Strains expressing Srr1 had significantly higher levels of binding to human platelets in vitro, as compared with isogenic Δsrr1 mutants. In addition, platelet binding was inhibited by pretreatment with anti-fibrinogen IgG or purified Srr1 binding region. To assess the contribution of Srr1 to pathogenicity, we compared the relative virulence of S. agalactiae NCTC 10/84 strain and its Δsrr1 mutant in a rat model of endocarditis, where animals were co-infected with the WT and the mutant strains at a 1∶1 ratio. At 72 h post-infection, bacterial densities (CFU/g) of the WT strain within vegetations, kidneys, and spleens were significantly higher, as compared with the Δsrr1 mutant. These results indicate that Srr1 contributes to the pathogenesis of endocarditis due to S. agalactiae, at least in part through its role in fibrinogen-mediated platelet binding.
Stable bifunctional chelates of metals used in radiotherapy.

Monoclonal antibody technology allows the specificity of an antibody for its antigen to be used in targeting cancer cells. The conjugation of metals, particularly radionuclides such as 90Y or 67Cu, to monoclonal antibodies results in agents for radioimmunotherapy and other medical applications. Chelators that can hold radiometals with high stability under physiological conditions are essential to avoid excessive radiation damage to nontarget cells. Derivatives of polyazamacrocycles (bearing a C-substituted functional group for antibody attachment) can exhibit remarkable kinetic inertness; for example, the copper complex of the 14-membered 6-(p-nitrobenzyl)-1,4,8,11-tetraazacyclotetradecane-N,N',N'',N'''- tetraacetic acid is very stable in human serum under physiological conditions, and a conjugate of this complex with a monoclonal antibody has tested well in tumor-bearing mice. Desreux and coworkers [Loncin, M. F. , Desreux, J. F. , and Merciny, E. Inorg. Chem., 25: 2646-2648, 1986] have shown that complexes of lanthanides with 1,4,7,10-tetraazacyclododecane-N,N',N'',N'''-tetraacetic acid have formation constants that are several orders of magnitude higher than that of 1,4,8,11-tetraazacyclotetradecane-N,N',N'',N'''-tetraacetic acid; thus the 12-membered macrocycle is the favored target for binding trivalent yttrium. We have developed a new synthetic route to these macrocycles via peptide synthesis and intramolecular tosylamide ring closure. Incubation of the 88Y-(III) complex of 2-p-nitrobenzyl-1,4,7,10-tetraazacyclododecane-N,N',N'',N'''- tetraacetic acid for 18 days in serum results in loss of so little Y(III) from the complex (less than 0.5%) that the rate of loss cannot be measured under these conditions.
The social construction of ‘deafness’: explored through the experiences of Black South African mothers raising a deaf child

Individual realities and perceptions are embedded in a web of dominant social and cultural views which shape the individual. Mothers of deaf children therefore cannot be understood in isolation, and neither can their experiences, perceptions, and well-being. The present research investigated the construction of deafness through the experiences of mothers raising a deaf child and considered the manner in which these constructions impacted their well-being and relationship with the child. The study explored the experiences of six Black South African hearing mothers of a deaf child between the ages of 3 and 8 years. Individual semi-structured interviews were conducted. Through an interpretive theoretical framework, this qualitative study noted seven main themes, namely, deafness is foreign and unknown, increased awareness and normalising of deafness, religious and Traditional African beliefs about disability/deafness, external pity and the mother’s rejection of it, the discourse of the ‘superiority’ of speech and encouragement of speech and hearing, barriers to communicating with her child, and ‘Why me? ’ attribution of cause and the emergence of blame. Extrapolation of the data suggests that there is a lack of awareness regarding the deaf individual, and this creates misinformed perceptions about deafness which impact negatively both mother and child.
Inter-Document Similiarities, Language Models, and Ad Hoc Information Retrieval

Search engines have become a crucial tool for finding information in repositories containing large amounts of textual data in unstructured form (e.g., the Web). However, the task of ad hoc information retrieval, that is, finding documents within a corpus that are relevant to an information need specified using a query, remains a hard challenge. The language modeling approach to information retrieval provides an effective framework for approaching various problems and has yielded impressive empirical performance. However, most previous work on language models for information retrieval focuses on document-specific characteristics to estimate documents' language models, and therefore does not take into account the structure of the surrounding corpus, a potentially rich source of additional information. We present a novel perspective for approaching the task of ad hoc retrieval: information provided by document-based language models can be enhanced by the incorporation of information drawn from clusters of similar documents that are created offline. We present several retrieval algorithms that are natural instantiations of this idea and that post performance that is substantially better than that of the standard language modeling approach. We also show that the best performing of these algorithms posts state-of-the-art performance for structural re-ranking, wherein an initially retrieved subset of the documents is re-ranked to obtain high precision specifically among the first few documents, using inter-document similarities within the list as an extra information source. As further exploration of the re-ranking approach just described, and inspired by the PageRank and HITS (hubs and authorities) algorithms for Web search, we propose a graph-based framework that applies to document collections lacking hyperlink information. Specifically, centrality induced over graphs wherein links represent asymmetric language-model-based inter-document similarities constitutes the basis of effective re-ranking algorithms. Combining our two paradigms for similarity representation---i.e., clusters of documents and links representing language-model-based inter-item similarities---helps to improve the effectiveness of centrality-based approaches. For example, document "authoritativeness" as induced by the HITS algorithm over cluster-document graphs is a highly effective re-ranking criterion. Furthermore, "authoritative" clusters are shown to contain a high percentage of relevant documents.
Integration of Biological Data with Semantic Networks

In recent years, the broad utilization of high-throughput experimental techniques resulted in a vast amount of expression and interaction data, accompanied by information on metabolic, cell signaling and gene regulatory pathways accumulated in the literature and databases. Thus, one of the major goals of modern bioinformatics is to process and integrate heterogeneous biological data to provide an insight into the inner workings of a cell governed by complex interaction networks. The paper reviews the current development of semantic network (SN) technologies and their applications to the integration of genomic and proteomic data. We also elaborate on our own work that applies a semantic network approach to modeling complex cell signaling pathways and simulating the cause-effect of molecular interactions in human macrophages. The review is concluded with a discussion of the prospective use of semantic networks in bioinformatics practice as an efficient and general language for data integration, knowledge representation and inference.
Golden gaskets: variations on the Sierpiński sieve

We consider the iterated function systems (IFSs) that consist of three general similitudes in the plane with centres at three non-collinear points, with a common contraction factor λ in (0, 1). As is well known, for λ = 1/2 the attractor, S_λ, is a fractal called the Sierpinski sieve and for λ < 1/2 it is also a fractal. Our goal is to study S_λ for this IFS for 1/2 < λ < 2/3 , i.e. when there are ‘overlaps’ in S_λ as well as ‘holes’. In this introductory paper we show that despite the overlaps (i.e. the breaking down of the open set condition (OSC)), the attractor can still be a totally self-similar fractal, although this happens only for a very special family of algebraic λ (so-called multinacci numbers). We evaluate the ausdorff dimension of S_λ for these special values by showing that S_λ is essentially the attractor for an infinite IFS that does satisfy the OSC. We also show that the set of points in the attractor with a unique ‘address’ is self-similar and compute its dimension. For non-multinacci values of λ we show that if λ is close to 2/3 , then S_λ has a non-empty interior. Finally we discuss higher-dimensional analogues of the model in question.
Computing Distance Histograms Efficiently in Scientific Databases

Particle simulation has become an important re- search tool in many scientific and engineering fields. Data gen- erated by such simulations impose great challenges to database storage and query processing. One of the queries against particle simulation data, the spatial distance histogram (SDH) query, is the building block of many high-level analytics, and requires quadratic time to compute using a straightforward algorithm. In this paper, we propose a novel algorithm to compute SDH based on a data structure called density map, which can be easily implemented by augmenting a Quad-tree index. We also show the results of rigorous mathematical analysis of the time complexity of the proposed algorithm: our algorithm runs on Θ(N 3 2 ) for two-dimensional data and Θ(N 5 3 ) for three-dimensional data, respectively. We also propose an approximate SDH processing algorithm whose running time is unrelated to the input size N. Experimental results confirm our analysis and show that the approximate SDH algorithm achieves very high accuracy.
Severe Hypoglycemia and Mortality After Cardiovascular Events for Type 1 Diabetic Patients in Sweden

OBJECTIVE To examine whether previous severe hypoglycemic events were associated with the risk of all-cause mortality after major cardiovascular events (myocardial infarction [MI] or stroke) in patients with type 1 diabetes. RESEARCH DESIGN AND METHODS This study is based on data from the Swedish National Diabetes Register linked to patient-level hospital records, prescription data, and death records. We selected patients with type 1 diabetes who visited a clinic during 2002–2010 and experienced a major cardiovascular complication after their clinic visit. We estimated a two-part model for all-cause mortality after a major cardiovascular event: logistic regression for death within the first month and a Cox proportional hazards model conditional on 1-month survival. At age 60 years, 5-year cumulative mortality risk was estimated from the models for patients with and without prior diabetes complications. RESULTS A total of 1,839 patients experienced major cardiovascular events, of whom 403 had previously experienced severe hypoglycemic events and 703 died within our study period. A prior hypoglycemic event was associated with a significant increase in mortality after a cardiovascular event, with hazard ratios estimated at 1.79 (95% CI 1.37–2.35) within the first month and 1.25 (95% CI 1.02–1.53) after 1 month. Patients with prior hypoglycemia had an estimated 5-year cumulative mortality risk of 52.4% (95% CI 45.3–59.5) and 39.8% (95% CI 33.4–46.3) for MI and stroke, respectively. CONCLUSIONS We have found evidence that patients with type 1 diabetes in Sweden with prior severe hypoglycemic events have increased risk of mortality after a cardiovascular event.
A Modular Re-programmable Digital Receiver Architecture

The many diverse applications of GPS have led to the development of a wide variety of specialized GPS receiver products. However, any custom receiver design results in significantly higher priced equipment than the conventional OEM receiver due to design changes, equipment changes, and the loss of economies of scale. In this paper, a modular re-programmable digital and software based GPS receiver architecture is described that can be easily and cost-effectively adapted for a variety of advanced GPS applications for military, commercial and space. NAVSYS’ re-programmable Advanced GPS Receiver (AGR) is a special purpose GPS receiver system designed to provide enhanced signal processing for special-purpose GPS applications and advanced test capability. The AGR signal processing provides highly accurate TSPI data even in stressed environments (e.g. high dynamics or low signal-to-noise) and allows low-level access to the GPS tracking loop parameters which can be used to optimize performance. Eight channels are provided with data output rates from 1 Hz to 1kHz. The system utilizes NAVSYS’ GPS digital front-end (DFE) sensor technology. NAVSYS’ GPS DFE sensor technology is adaptable to custom requirements. The DFE can be a single element or it can be implemented as a phased array. Coherency between elements is maintained with common clock circuitry. The DFE can provide 1-bit to 8-bit data at sample rates from 2 MHz to 60 MHz. Multi-frequency DFE inputs are able to track GPS (L1 or L2), Pseudolite (Lx), L5, etc. The AGR is a flexible system accommodating a variety of front ends, data transmission methods, and allows realtime processing or data storage and playback for post-test processing. The AGR can be configured to fit specific requirements with “Key-Word” changes or software modifications. The AGR is supported by NAVSYS’ Matlab GPS signal simulation toolbox to assist in system optimization. The signal simulation toolbox can be used as an analysis aid to assist in test and evaluation of GPS receivers. Data output from the AGR can be used as inputs to the toolbox to optimize receiver parameters that can then be adjusted in the AGR for optimum performance.
Project design and management based on a co-innovation framework: towards more effective research intervention for sustainable development of farming systems.

Most of the budget for agricultural research-for-development is spent through projects and therefore project impact is important for funding agencies. Impact assessment is commonly performed ex-post for the purpose of accountability and has provided little guidance to more effective project design and management. Here we present a project design that bases its project theory on co-innovation, seen as an approach that combines farming systems theory, social learning and dynamic project monitoring and evaluation. The design is implemented in the EULACIAS project, focused on stimulating strategic re-orientation of family farm strategies in three case studies in Latin America. The paper sketches the conceptual basis of the co-innovation approach and the program theory of the project. Following a description of the case studies illustrations of the implementation of the approach are provided. The paper ends with first impressions of the outcome of the approach, pending full analytical results in the cases.
The Dynamic Analysis and Prediction of Stock Markets through the Latent Markov Model

In this paper we show how the latent Markov model can be used to define different conditions in the stock market, called market- regimes. Changes in regimes can be used to detect financial crises, pinpoint the end of a crisis and predict future developments in the stock market, to some degree. The model is applied to changes in monthly price indexes of the Italian and US stock market in the period from January 2000 to July 2009.
Enforcing Location and Time-Based Access Control on Cloud-Stored Data

Recent incidents of data-breaches from the cloud suggest that users should not trust the cloud provider to enforce access control on their data. We focus on mitigating trust to the cloud in scenarios where granting access to data not only considers user identities (as in conventional access policies), but also contextual information such as the user's location and time of access. Previous work in this context assumes a fully trusted cloud that is further capable of locating users. We introduce LoTAC, a novel framework that seamlessly integrates the operation of a cloud provider and a localization infrastructure to enforce location- and time-based access control to cloud-stored data. In LoTAC, the two entities operate independently and are only trusted to offer their basic services: the cloud provider is used and trusted only to reliably store data, the localization infrastructure is used and trusted only to accurately locate users. Furthermore, neither the cloud provider nor the localization infrastructure can access the data, even if they collude. LoTAC protocols require no changes to the cloud provider and minimal changes to the localization infrastructure. We evaluate our protocols using a cellular network as the localization infrastructure and show that they incur in low communication and computation costs and scale well with a large number of users and policies.
Using Deep Learning for Detecting Spoofing Attacks on Speech Signals

It is well known that speaker verification systems are subject to spoofing attacks. The Automatic Speaker Verification Spoofing and Countermeasures Challenge -- ASVSpoof2015 -- provides a standard spoofing database, containing attacks based on synthetic speech, along with a protocol for experiments. This paper describes CPqD's systems submitted to the ASVSpoof2015 Challenge, based on deep neural networks, working both as a classifier and as a feature extraction module for a GMM and a SVM classifier. Results show the validity of this approach, achieving less than 0.5\% EER for known attacks.
Using heterogeneous sensory measurements in a compliant magnetic localization system for medical intervention

In many medical intervention procedures, passive magnetic tracking technology has found favor in continuous localization of medical instruments and tools inside the human body. By utilizing a small permanent magnet as a passive source, it requires no dedicated power supply or wire connection into the body. Past researches usually adopt rigid structures to restrict the movement of sensors, as the precise positional information of the homogeneous magnetic sensors play an important role in the accuracy of traditional inverse optimization algorithms. In this paper, we investigate methods to enable the sensing system to be used for the nasogastric (NG) tube localization in a compliant setting, such that the device can conform around the patient for improved ergonomics and comfort. Such a system, which now contains additional sensors required to sense the active compliance, will contain a non-homogeneous sensor assembly producing heterogeneous sensory information. Two methods are proposed and evaluated: one is a modified inverse optimization method using a deformation model in series with the magnetic field model; the other is a direct forward Artificial Neural Network (ANN) method. The efficacy of both methods were evaluated and compared by numerical simulation and experiments. Advantages and disadvantages of both methods were discussed at the end.
Cadherins Interact With Synaptic Organizers to Promote Synaptic Differentiation

Classical cadherins, a set of ~20 related recognition and signaling molecules, have been implicated in many aspects of neural development, including the formation and remodeling of synapses. Mechanisms underlying some of these steps have been studied by expressing N-cadherin (cdh2), a Type 1 cadherin, in heterologous cells, but analysis is complicated because widely used lines express cdh2 endogenously. We used CRISPR-mediated gene editing to generate a Human embryonic kidney (HEK)293 variant lacking Cdh2, then compared the behavior of rodent cortical and hippocampal neurons co-cultured with parental, cdh2 mutant and cdh2-rescued 293 lines. The comparison demonstrated that Cdh2 promotes neurite branching and that it is required for three synaptic organizers, neurologin1 (NLGL1), leucine-rich repeat transmembrane protein 2 (LRRtm2), and Cell Adhesion Molecule 1 (Cadm1/SynCAM) to stimulate presynaptic differentiation, assayed by clustering of synaptic vesicles at sites of neurite-293 cell contact. Similarly, Cdh2 is required for a presynaptic organizing molecule, Neurexin1β, to promote postsynaptic differentiation in dendrites. We also show that another Type I cadherin, Cdh4, and a Type II cadherin, Cdh6, can substitute for Cdh2 in these assays. Finally, we provide evidence that the effects of cadherins require homophilic interactions between neurites and the heterologous cells. Together, these results indicate that classical cadherins act together with synaptic organizers to promote synaptic differentiation, perhaps in part by strengthening the intracellular adhesion required for the organizers to act efficiently. We propose that cadherins promote high affinity contacts between appropriate partners, which then enable synaptic differentiation.
Cadmium Toxicity and Treatment

Cadmium is a heavy metal of considerable toxicity with destructive impact on most organ systems. It is widely distributed in humans, the chief sources of contamination being cigarette smoke, welding, and contaminated food and beverages. Toxic impacts are discussed and appear to be proportional to body burden of cadmium. Detoxification of cadmium with EDTA and other chelators is possible and has been shown to be therapeutically beneficial in humans and animals when done using established protocols.