Miracle-dz/UniFAR-FTUs huggingface dataset:
Persistent Memory in Single Node Delay-Coupled Reservoir Computing

Delays are ubiquitous in biological systems, ranging from genetic regulatory networks and synaptic conductances, to predator/pray population interactions. The evidence is mounting, not only to the presence of delays as physical constraints in signal propagation speed, but also to their functional role in providing dynamical diversity to the systems that comprise them. The latter observation in biological systems inspired the recent development of a computational architecture that harnesses this dynamical diversity, by delay-coupling a single nonlinear element to itself. This architecture is a particular realization of Reservoir Computing, where stimuli are injected into the system in time rather than in space as is the case with classical recurrent neural network realizations. This architecture also exhibits an internal memory which fades in time, an important prerequisite to the functioning of any reservoir computing device. However, fading memory is also a limitation to any computation that requires persistent storage. In order to overcome this limitation, the current work introduces an extended version to the single node Delay-Coupled Reservoir, that is based on trained linear feedback. We show by numerical simulations that adding task-specific linear feedback to the single node Delay-Coupled Reservoir extends the class of solvable tasks to those that require nonfading memory. We demonstrate, through several case studies, the ability of the extended system to carry out complex nonlinear computations that depend on past information, whereas the computational power of the system with fading memory alone quickly deteriorates. Our findings provide the theoretical basis for future physical realizations of a biologically-inspired ultrafast computing device with extended functionality.
The Relationship between Coping and Job Performance

Workers on the frontline of public service, such as teachers and social workers, cannot provide unlimited support to all their clients, because of among else scarce time and money. To deal with this, they use various coping strategies. We analyze one important coping strategy such “street-level bureaucrats” can use: prioritizing motivated clients over unmotivated clients. We study the effect of this coping strategy on job performance, as rated by their supervisors. In other words, do street-level bureaucrats who especially help motivated clients get lower or higher job performance ratings? By studying this relationship, we can test two narratives in frontline work: the state-agent narrative versus the citizen-agent narrative. If supervisors follow a state-agent narrative, they would give street-level bureaucrats that prioritize motivated clients lower performance ratings. Supervisors could stress values like legality and equality: workers should follow governmental rules and should treat all clients equally. Contrary to this, if supervisors follow a citizen-agent narrative, they would give street-level bureaucrats who especially help motivated clients higher performance ratings. Motivated clients are the “deserving clients,” worthy of investment. “Pushing” unmotivated clients may also be a very inefficient use of scarce resources. Using a multi-source study of social workers in one organization in the United States, we show that supervisors give higher job performance ratings to street-level bureaucrats who prioritize motivated clients. Implications of this finding and a future research agenda are shown.
A Genet ic Algor i thm Appr oach For Pat ter n Recogni t ion I n Biomedical Signals

This article deals with the problem of on-line wave detection in non-stationary signals, through a polynomial approximation based on genetics algorithms. This approximation is estimated by minimising a nonlinear error function, described by the circle equation. We apply this approach to detect sleep spindlewaveforms in electroencephalograms (EEG) and R wave and ectopics beats in electrocardiograms (ECG). Theproposed method makes it possible to recognise time-variant waveform with a simpledecisional parameters.
CyberPsychological Computation on Social Community of Ubiquitous Learning

Under the modern network environment, ubiquitous learning has been a popular way for people to study knowledge, exchange ideas, and share skills in the cyberspace. Existing research findings indicate that the learners' initiative and community cohesion play vital roles in the social communities of ubiquitous learning, and therefore how to stimulate the learners' interest and participation willingness so as to improve their enjoyable experiences in the learning process should be the primary consideration on this issue. This paper aims to explore an effective method to monitor the learners' psychological reactions based on their behavioral features in cyberspace and therefore provide useful references for adjusting the strategies in the learning process. In doing so, this paper firstly analyzes the psychological assessment of the learners' situations as well as their typical behavioral patterns and then discusses the relationship between the learners' psychological reactions and their observable features in cyberspace. Finally, this paper puts forward a CyberPsychological computation method to estimate the learners' psychological states online. Considering the diversity of learners' habitual behaviors in the reactions to their psychological changes, a BP-GA neural network is proposed for the computation based on their personalized behavioral patterns.
High-Speed Optical Cache Memory as Single-Level Shared Cache in Chip-Multiprocessor Architectures

We present an optical bus-based Chip Multiprocessor architecture where the processing cores share an optical single-level cache unit. Physically, the optical cache is implemented externally in a separate chip located next to the CPU die. The cache interconnection system is realized through WDM optical interfaces that connect the shared cache module with the processing cores and the Main Memory via spatial-multiplexed optical waveguides; hence, the CPU-DRAM communication completely takes place in the optical domain. To evaluate the shared optical cache approach, we carry out system-level simulations of 6 realistic processor parallel workloads via the Gem5 platform. The optical cache architecture is compared against the conventional electronic Chip Multiprocessor topology that uses dedicated on-chip L1 electronic caches and a shared L2 cache. The results show significant reduction in the L1 miss rate of up to 96% for certain cases; on average, a performance speed-up of up to 20.53% or a reduction of up to 65.8% in cache capacity requirements is attained. Combined with high-bandwidth CPU-DRAM bus solutions based on optical interconnects, the proposed design is a quite promising system architecture that bridges the gap between high-speed optically connected CPU-DRAM schemes and high-speed optical memory technologies.
Adaptive-based distributed cooperative multi-robot coverage

In this paper we present a solution to the problem of positioning a team of mobile robots for a surveillance task in a non-convex environment with unknown obstacles. The problem is addressed taking into account several physical and environmental constraints like limited sensor capabilities, obstacle-avoidance, etc. The goal is to maximize the area monitored by the team, by identifying the best configuration of the team members. Due to the non-convex nature of the problem, an analytical solution cannot be obtained. The proposed method is based on a new cognitive-based, adaptive optimization algorithm (CAO). This method allows getting coordinated and scalable controls to accomplish the task. Furthermore, we propose a different formulation of the problem in order to obtain a distributed solution which allows us to consider also limited communication capabilities. Extensive simulations are presented to evaluate the efficiency of the proposed algorithm and to compare centralized and distributed approach.
Detecting Brain Activation in FMRI Data without Prior Knowledge of Mental Event Timing

Almost all methods of detecting brain activation in fMRI data depend on prior knowledge of mental event timing. For example, the investigator may be required to stipulate the short time intervals during which mental activity occurs. In addition, the hemodynamic response to mental activity is often assumed to be linearly additive, and the shape of that response is frequently estimated or modeled. Analysis methods that do not make these assumptions still require prior knowledge of characteristics of the spatial distribution of neural activity. This paper describes a new method of analyzing fMRI data that does not rely on any of these assumptions. Instead, our approach is based on the following simple premise: the time course of signal in activated voxels will not vary significantly when an entire task protocol is repeated by the same individual. The model-independence of this approach makes it suitable for "screening" fMRI data for brain activation that may have unanticipated timing. Retrospective examination of the time course of the detected signals may be used to understand the nature of the activity. We demonstrate the method by using it to detect brain activation in two subjects who performed hand sensorimotor tasks according to block and single-trial designs.
Two-map protocols guarantee to reobtain a maximally entangled pure state all by the standard purification LOCC operations

An idea of hybrid maps is proposed to establish entanglement purification protocols which guarantee to purify any distillable state to a desired maximally entangled pure state all by the standard purification local operations and classical communications. The two-map protocols proposed in this work perform much better than the IBM and Oxford protocols in the sense that they require significantly fewer operation times in yielding a same amount of the desired pure state. Besides, the proposed protocols in fact can induce higher yields than those obtained when the Oxford protocol is used.
Mixed evidence for reduced local adaptation in wild salmon resulting from interbreeding with escaped farmed salmon: complexities in hybrid fitness

Interbreeding between artificially‐selected and wild organisms can have negative fitness consequences for the latter. In the Northwest Atlantic, farmed Atlantic salmon recurrently escape into the wild and enter rivers where small, declining populations of wild salmon breed. Most farmed salmon in the region derive from an ancestral source population that occupies a nonacidified river (pH 6.0–6.5). Yet many wild populations with which escaped farmed salmon might interbreed inhabit acidified rivers (pH 4.6–5.2). Using common garden experimentation, and examining two early‐life history stages across two generations of interbreeding, we showed that wild salmon populations inhabiting acidified rivers had higher survival at acidified pH than farmed salmon or F1 farmed‐wild hybrids. In contrast, however, there was limited evidence for reduced performance in backcrosses, and F2 farmed‐wild hybrids performed better or equally well to wild salmon. Wild salmon also survived or grew better at nonacidified than acidified pH, and wild and farmed salmon survived equally well at nonacidified pH. Thus, for acid tolerance and the stages examined, we found some evidence both for and against the theory that repeated farmed‐wild interbreeding may reduce adaptive genetic variation in the wild and thereby negatively affect the persistence of depleted wild populations.
A Study of the Discrete-Time Switched LQR Problem

This paper studies the discrete-time switched LQR (DSLQR) problem based on a dynamic programming approach. One contribution of this paper is the analytical characterization of both the value function and the optimal hybridcontrol strategy of the DSLQR problem. Their connections to the Riccati equation and the Kalman gain of the classical LQR problem are also discussed. Several interesting properties of the value functions are derived. In particular, we show that under some mild conditions, the family of finite-horizon value functions of the DSLQR problem is homogeneous (of degree 2), uniformly bounded over the unit ball, and converges exponentially fast to the infinitehorizon value function. Based on these properties, efficient algorithms are proposed to solve the finite-horizon and infinite-horizon DSLQR problems. More importantly, we establish conditions under which the strategies generated by the algorithms are stabilizing and suboptimal. These conditions are derived explicitly in terms of subsystem matrices and are thus very easy to verify. The proposed algorithms and the analysis provide a systematic way of solving the DSLQR problem with guaranteed closed-loop stability and suboptimal performance. Simulation results indicate that the proposed algorithms can efficiently solve not only specific but also randomly generated DSLQR problems, making the NP-hard problems numerically tractable.
Down-Regulation of Sox11 Is Required for Efficient Osteogenic Differentiation of Adipose-Derived Stem Cells

Adipose-derived stem cells represent a type of mesenchymal stem cells with the attendant capacity to self-renew and differentiate into multiple cell lineages. We have performed a microarray-based gene expression profiling of osteogenic differentiation and found that the transcription factor Sox11 is down-regulated during the process. Functional assays demonstrate that down-regulation of Sox11 is required for an efficient differentiation. Furthermore, results from forced expression of constitutively-active and dominant-negative derivatives of Sox11 indicate that Sox11 functions as a transcriptional activator in inhibiting osteogenesis. Sox11 thus represents a novel regulator of osteogenesis whose expression and activity can be potentially manipulated for controlled differentiation.
Cytotoxicity of naphthoquinones and their capacity to generate reactive oxygen species is quenched when conjugated with gold nanoparticles

Several reports have demonstrated the anticancer activities of plumbagin, a naphthoquinone derivative isolated from plants belonging to Plumbaginaceae family. However, to the best of our knowledge, there are no reports which describe gold nanoconjugation with plumbagin, even though plumbagin is considered to be a promising therapeutic agent. In this report, we demonstrate the fabrication and characterization of gold nanoparticles conjugated with plumbagin (AuPB) that can reduce the toxicity of the latter, and their capacity for cellular localization and generation of reactive oxygen species. The anticancer activity and ability of plumbagin to produce reactive oxygen species was studied and compared with that of bromoderivatives of 1,4 naphthoquinones such as 2-bromo-1,4-naphthoquinone (2-BNQ) and 2,3-dibromo-1, 4-naphthoquinone (2,3-DBNQ) and their gold nanoconjugates. Plumbagin and bromoderivatives of 1,4 naphthoquinones in the form of gold nanoconjugates showed reduced cytotoxicity and apoptosis compared with the pristine compounds, ie, plumbagin, 2-BNQ, and 2,3-DBNQ. Interestingly, we observed that the gold nanoparticles could quench the reactive oxygen species-generating capacity of plumbagin, 2-BNQ, and 2,3-BNQ, which is one of the main mechanisms of action of the naphthoquinones. Therefore, it can be concluded that conjugation with gold nanoparticles can reduce the cytotoxicity of these compounds.
Sharing-based Privacy and Availability of Cloud Data Warehouses

Cloud computing can help reduce costs, increase business agility and deploy applications with a high return on investment such as data warehouses. However, storing and managing data in the cloud may not be fully trustworthy. In this article, we focus on both data security (data privacy, availability and integrity) and data analysis in the cloud. To solve the data security issue, we propose a new (m,n, t) multi secret sharing scheme based on block cryptography, secret sharing and hash functions. Moreover, we apply this solution onto a cloud data warehouse such that data security and data analysis are addressed. An extensive security and performance analysis shows that the proposed schemes can prevent most attacks, guarantee data availability and integrity, and allow analyzing data at low costs (data storage, data transfer and time computation) in the pay-as-you-go economic model in the cloud.
Concatamerization of Adeno-Associated Virus Circular Genomes Occurs through Intermolecular Recombination

ABSTRACT Long-term recombinant AAV (rAAV) transgene expression in muscle has been associated with the molecular conversion of single-stranded rAAV genomes to high-molecular-weight head-to-tail circular concatamers. However, the mechanisms by which these large multimeric concatamers form remain to be defined. To this end, we tested whether concatamerization of rAAV circular intermediates occurs through intra- or intermolecular mechanisms of amplification. Coinfection of the tibialis muscle of mice with rAAV alkaline phosphatase (Alkphos)- and green fluorescent protein (GFP)-encoding vectors was used to evaluate the frequency of circular concatamer formation by intermolecular recombination of independent viral genomes. The GFP shuttle vector also encoded ampicillin resistance and contained a bacterial origin of replication to allow for bacterial rescue of circular intermediates from Hirt DNA of infected muscle samples. The results demonstrated a time-dependent increase in the abundance of rescued plasmids encoding both GFP and Alkphos, which reached 33% of the total circular intermediates by 120 days postinfection. Furthermore, these large circular concatamers were capable of expressing both GFP- and Alkphos-encoding transgenes following transient transfection in cell lines. These findings demonstrate that concatamerization of AAV genomes in vivo occurs through intermolecular recombination of independent monomer circular viral genomes and suggest new viable strategies for delivering multiple DNA segments at a single locus. Such developments will expand the utility of rAAV for splicing large gene inserts or large promoter-gene combinations carried by two or more independent rAAV vectors.
‘‘Covert Coping’’ in Extreme Environments: Insights from South African Submarines

Submariners are a group of people who live and work in enclosed habitats. In this confined environment, they need to balance the emotional needs of individuals with the resources of the group, to ensure effective coping for all. This study explored the mechanisms of maintaining this balance. Observations and a self-report measure (a modified Brief COPE) were used to describe the coping of submariners during deployment. Findings from the thematic analysis and self-report instrument are briefly described. An apparent contrast between the qualitative observations and quantitative data was noted, and when closely examined reveal two mechanisms whereby submariners managed their individual psychological needs without unbalancing the psychological state of the collective group. These were, firstly, the use of humor and talking nonsense as sublimated—or covert—way of venting emotions, and secondly, the use of rituals such as mealtimes as covert opportunities to provide and receive psychological support. This study illustrates how groups living in enclosed habitats may use covert mechanisms to cope with emotional needs, in order to maintain the emotional balance in the confined environment.
On interaction between loss characterization and forward error correction in wireless multimedia communication

With the steadily growing synergy between existing heterogeneous networks, the wireless LAN appears as the de-facto wireless access network in the end-to-end multimedia services distribution chain. Unlike in the traditional wired multi-hop networks (Internet) where congestions increase persistently both delays and losses, wireless packet losses are often location- and time-varying. Particularly, WLAN communication is characterized by high bit error rates that translates into tight loss dependency. The loss process may rapidly shift between different loss correlations levels, resulting in poor forward error correction (FEC) recovery capabilities. In this paper, we address this issue by providing a combined loss model to accurately characterize the wireless loss distribution features. We use control theory guided parameter tuning in order to urge the convergence of the loss models towards seizing the instantaneous loss distribution trends. Finally, we derive a new loss-specific QoS metrics for new FEC block allocation scheme.
Model-Based Unsupervised Spoken Term Detection with Spoken Queries

We present a set of model-based approaches for unsupervised spoken term detection (STD) with spoken queries that requires neither speech recognition nor annotated data. This work shows the possibilities in migrating from DTW-based to model-based approaches for unsupervised STD. The proposed approach consists of three components: self-organizing models, query matching, and query modeling. To construct the self-organizing models, repeated patterns are captured and modeled using acoustic segment models (ASMs). In the query matching phase, a document state matching (DSM) approach is proposed to represent documents as ASM sequences, which are matched to the query frames. In this way, not only do the ASMs better model the signal distributions and time trajectories of speech, but the much-smaller number of states than frames for the documents leads to a much lower computational load. A novel duration-constrained Viterbi (DC-Vite) algorithm is further proposed for the above matching process to handle the speaking rate distortion problem. In the query modeling phase, a pseudo likelihood ratio (PLR) approach is proposed in the pseudo relevance feedback (PRF) framework. A likelihood ratio evaluated with query/anti-query HMMs trained with pseudo relevant/irrelevant examples is used to verify the detected spoken term hypotheses. The proposed framework demonstrates the usefulness of ASMs for STD in zero-resource settings and the potential of an instantly responding STD system using ASM indexing. The best performance is achieved by integrating DTW-based approaches into the rescoring steps in the proposed framework. Experimental results show an absolute 14.2% of mean average precision improvement with 77% CPU time reduction compared with the segmental DTW approach on a Mandarin broadcast news corpus. Consistent improvements were found on TIMIT and MediaEval 2011 Spoken Web Search corpus.
Optimizing Feature Selection Techniques for Sentiment Classification

A hybrid feature selection method is proposed to distinguish the salient features that allow identifying the viewpoint underlying a text review, that is, to determine its sentiment polarity. This method makes use of fundamental pre-processing tasks known as filter and wrapper techniques. The effectiveness of this approach is demonstrated on a data set where each document is represented by two distinct feature vectors based on two different sets of rules.
Deriving rhetorical relationships from semantic content

This paper investigates to what extent rhetorical relations can be assigned purely on the basis of propositional content, without any reference to speaker goals or other pragmatic information. This task confronts any NLG system designed to generate coherent text from a set of formally represented statements; we consider it here in the context of an ontology verbaliser, for which the input is a set of axioms encoded in the web ontology language OWL. A simple set-theoretical model of the possible semantic relationships between two statements is proposed; this model allows 46 logically consistent relationships, of which we hypothesise that 11 are rhetorically coherent. This hypothesis is tested through an empirical survey which also provides evidence on how the coherent patterns are expressed linguistically.