Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFICIENT GENETIC BASED METHODS FOR OPTIMIZING THE REVERSIBLE AND QUANTUM LOGIC CIRCUITS

Various synthesis methods have been proposed in the literature for reversible and quantum logic circuits. However, there are few algorithms to optimize an existing circuit with multiple constraints simultaneously. In this paper, some heuristics in genetic algorithms (GA) to optimize a given circuit in terms of quantum cost, number of gates, location of garbage outputs, and delay, are proposed. The proposed methods can optimize an existing circuit with a given truth table, including don’t care values, for different aspects of optimality. The results show good enhancements in the optimization of benchmark circuits compared to the previously published methods.
The Bystander Approach to Violence Prevention: Considerations for Implementation in Europe

Objective: In recent years there has been a growing awareness of the prevalence of sexual violence in U.K. university student populations, yet prevention efforts are in their infancy. Evidence from the United States shows that empowering bystanders to intervene to prevent violence rather than focusing on perpetrators or victims is a promising strategy particularly suited to university settings. Public Health England commissioned a bystander program, The Intervention Initiative, for U.K. universities. This paper discusses the theoretical underpinnings of the bystander approach and the challenges for practical implementation in Europe. Method: We review findings from research relating to bystander theories, social norms theory, and effective prevention programming that inform the development of maximally effective bystander programs. Results: Bystander programs are complex, multifaceted interventions based on taking participants through the different stages required for an individual to move from inaction to action as described by Latané and Darley, 1969, 1970 in their organizing framework for bystander intervention and incorporating a social norms element. Programs that adhere to the principles for effective prevention as set out by Nation et al. (2003) are most likely to be effective. We demonstrate how these criteria informed the cultural specificity of The Intervention Initiative to U.K. university settings and the challenges in adapting the approach for European settings. Conclusion: More research is needed to develop and test bystander programs in different European countries to build an evidence base for effective prevention programming.
A Cross-Layer Data Dissemination Protocol for Energy Efficient Sink Discovery in Wireless Sensor Networks

This paper proposes a cross-layer protocol for energy-aware routing in wireless sensor networks. The protocol combines the energy depreciation rate, node distance and neighbourhood information from physical layer together with TDMA schedules from the MAC layer and also network life requirements from the application layer, to effectively determine the most efficient routes to the base station. This cross layer efficiency measure is then used to form dynamic clusters that adapt to changing traffic and energy conditions so assisting to both control the transmission power and schedule the sleep-wake cycles of nodes for better energy utilisation. The proposed protocol is a recursive aggregation scheme that transforms a network-wide routing dissemination problem into a single-hop query protocol that aids nodes in making multi-hop routing decisions based solely upon the information provided by their single-hop neighbours. Results confirm that the proposed technique balances the load on forwarding nodes, adapts the MAC layer precisely to the routing layer and minimizes data delivery time for increased traffic and large scale networks.
Combining Measures of Dispersal to Identify Conservation Strategies in Fragmented Landscapes

Abstract: Understanding the way in which habitat fragmentation disrupts animal dispersal is key to identifying effective and efficient conservation strategies. To differentiate the potential effectiveness of 2 frequently used strategies for increasing the connectivity of populations in fragmented landscapes—corridors and stepping stones—we combined 3 complimentary methods: behavioral studies at habitat edges, mark‐recapture, and genetic analyses. Each of these methods addresses different steps in the dispersal process that a single intensive study could not address. We applied the 3 methods to the case study of Atrytonopsis new species 1, a rare butterfly endemic to a partially urbanized stretch of barrier islands in North Carolina (U.S.A.). Results of behavioral analyses showed the butterfly flew into urban and forested areas, but not over open beach; mark‐recapture showed that the butterfly dispersed successfully through short stretches of urban areas (<500 m); and genetic studies showed that longer stretches of forest (>5 km) were a dispersal barrier, but shorter stretches of urban areas (≤5 km) were not. Although results from all 3 methods indicated natural features in the landscape, not urbanization, were barriers to dispersal, when we combined the results we could determine where barriers might arise: forests restricted dispersal for the butterfly only when there were long stretches with no habitat. Therefore, urban areas have the potential to become a dispersal barrier if their extent increases, a finding that may have gone unnoticed if we had used a single approach. Protection of stepping stones should be sufficient to maintain connectivity for Atrytonopsis new species 1 at current levels of urbanization. Our research highlights how the use of complementary approaches for studying animal dispersal in fragmented landscapes can help identify conservation strategies.
Towards the Verification of The AKS Primality Test in ACL 2

In this paper we present a recursive implementation of the Agrawal, Kayal and Saxena primality testing algorithm, which is the first unconditional deterministic polynomial time primality testing algorithm. Since the algorithm’s proof of correctness makes use of informal notation and omits many key steps, we present the progress made toward using ACL2 to verify the algorithm’s correctness. In particular, we focus on verifying that if n is prime, then the AKS algorithm will return PRIME.
Increasing anomaly handling efficiency in large organizations using applied machine learning

Maintenance costs can be substantial for large organizations (several hundreds of programmers) with very large and complex software systems. By large we mean lines of code in the range of hundreds of thousands or millions. Our research objective is to improve the process of handling anomaly reports for large organizations. Specifically, we are addressing the problem of the manual, laborious and time consuming process of assigning anomaly reports to the correct design teams and the related issue of localizing faults in the system architecture. In large organizations, with complex systems, this is particularly problematic because the receiver of an anomaly report may not have detailed knowledge of the whole system. As a consequence, anomaly reports may be assigned to the wrong team in the organization, causing delays and unnecessary work. We have so far developed two machine learning prototypes to validate our approach. The latest, a re-implementation and extension, of the first is being evaluated on four large systems at Ericsson AB. Our main goal is to investigate how large software development organizations can significantly improve development efficiency by replacing manual anomaly report assignment and fault localization with machine learning techniques. Our approach focuses on training machine learning systems on anomaly report databases; this is in contrast to many other approaches that are based on test case execution combined with program sampling and/or source code analysis.
Properties of the Second Type of Covering-Based Rough Sets

To deal with the vagueness and granularity in information systems, researchers proposed several methods such as fuzzy theory and rough set theory. This paper studies a class of covering-based generalized rough sets. In this setting, a covering can generate a lower approximation operation and an upper approximation operation, but some of common properties of classical lower and upper approximation operations are no longer satisfied. We investigate conditions for a covering under which these properties hold for the second type of covering-based lower and upper approximation operations
AnDarwin: Scalable Detection of Android Application Clones Based on Semantics

Smartphones rely on their vibrant application markets; however, plagiarism threatens the long-term health of these markets. We present a scalable approach to detecting similar Android apps based on their semantic information. We implement our approach in a tool called AnDarwin and evaluate it on 265,359 apps collected from 17 markets including Google Play and numerous third-party markets. In contrast to earlier approaches, AnDarwin has four advantages: it avoids comparing apps pairwise, thus greatly improving its scalability; it analyzes only the app code and does not rely on other information-such as the app's market, signature, or description-thus greatly increasing its reliability; it can detect both full and partial app similarity; and it can automatically detect library code and remove it from the similarity analysis. We present two use cases for AnDarwin: finding similar apps by different developers (“clones”) and similar apps from the same developer (“rebranded”). In 10 hours, AnDarwin detected at least 4,295 apps that are the victims of cloning and 36,106 rebranded apps. Additionally, AnDarwin detects similar code that is injected into many apps, which may indicate the spread of malware. Our evaluation demonstrates AnDarwin's ability to accurately detect similar apps on a large scale.
Memory and executive functions in adults with Gilles de la Tourette syndrome and chronic tic disorder

Introduction. The main aim of the current study was to assess whether adults with either Tourette syndrome (TS) or chronic tic disorder (CTD) show a similar neuropsychological profile. Neuropsychological investigations of tic disorders have been mostly focused on children, mainly because symptoms peak during that period. Little has been carried out on adults, even if a significant proportion of the tic population experience moderate or marked levels of tic frequency throughout adulthood. Still, it is not clear whether neuropsychological performances are affected to the same degree in adults with TS and CTD. Method. Patients diagnosed with TS were compared with a CTD group and a control group free of psychiatric or neurological diagnosis, comparable in terms of age, gender, and intelligence. All participants completed two tests of memory (Rey-Osterreich Complex Figure, California Verbal Learning Test), one test of motor dexterity (Purdue pegboard), and four tasks of executive function (Stroop, Color Trail Test, Tower of London, Wisconsin Card Sorting Test). Results. TS and CTD patients showed nonverbal memory impairments while verbal memory and executive functioning remained intact. Results also indicated that nonverbal memory performances decrease as a function of tic severity. Conclusion. Both TS and CTD patients present a specific nonverbal deficit whilst the executive and motor functions are relatively intact. The two tic disorder subgroups might be part of a spectrum implicating mainly nonverbal memory.
Non-linear prediction with LSTM recurrent neural networks for acoustic novelty detection

Acoustic novelty detection aims at identifying abnormal/novel acoustic signals which differ from the reference/normal data that the system was trained with. In this paper we present a novel approach based on non-linear predictive denoising autoencoders. In our approach, auditory spectral features of the next short-term frame are predicted from the previous frames by means of Long-Short Term Memory (LSTM) recurrent denoising autoencoders. We show that this yields an effective generative model for audio. The reconstruction error between the input and the output of the autoencoder is used as activation signal to detect novel events. The autoencoder is trained on a public database which contains recordings of typical in-home situations such as talking, watching television, playing and eating. The evaluation was performed on more than 260 different abnormal events. We compare results with state-of-the-art methods and we conclude that our novel approach significantly outperforms existing methods by achieving up to 94.4% F-Measure.
Long-Term Outcome after Lithium Augmentation in Unipolar Depression: Focus on HPA System Activity

Background: Lithium augmentation is a first-line strategy for depressed patients resistant to antidepressive therapy, but little is known about patients’ subsequent long-term course or outcome predictors. We investigated long-term outcomes of unipolar depressed patients who had participated in a study on the effects of lithium augmentation on the hypothalamic-pituitary-adrenocortical system using the combined dexamethasone/corticotrophin-releasing hormone (DEX/CRH) test. Methods: Twelve to 28 months (mean 18.6 ± 4.6 months) after lithium augmentation, 23 patients were assessed with a standardized interview, of which 18 patients had complete DEX/CRH test results. Relapse was diagnosed by DSM-IV criteria (Structured Clinical Interview for DSM-IV; SCID I). Results: Only 11 patients (48%) had a favorable follow-up, defined as absence of major depressive episodes during the observation period. Patients with a favorable and an unfavorable course did not differ in clinical or sociodemographic parameters, endocrinological results or continuation of lithium. However, fewer previous depressive episodes tended to correlate (p = 0.09) with a favorable course. Conclusion: Results from studies using the DEX/CRH test to predict relapse in depressed patients treated with antidepressants were not replicated for lithium augmentation. Our finding could reflect the elevation of DEX/CRH results by lithium, independent of clinical course. Limitations of the study are its small sample size, the heterogeneous clinical baseline conditions and the lack of lithium serum levels. The fact that lithium continuation did not predict the course might be related to the difference between the efficacy of lithium in controlled studies and its effectiveness in naturalistic settings.
An Augmented Automatic Choosing Control Designed by Extremizing a Combination of Hamiltonian and Lyapunov Functions for Nonlinear Systems with Constrained Input

In this paper we consider a nonlinear feedback control called augmented automatic choosing control (AACC) for nonlinear systems with constrained input. Constant terms which arise from sectionwise linearization of a given nonlinear system are treated as coefficients of a stable zero dynamics. Parameters included in the control are suboptimally selected by extremizing a combination of Hamiltonian and Lyapunov functions with the aid of the genetic algorithm. This approach is applied to a field excitation control problem of power system to demonstrate the splendidness of the AACC. Simulation results show that the new controller can improve performance remarkably well. Keywords—Augmented Automatic Choosing Control, Nonlinear Control, Genetic Algorithm, Hamiltonian, Lyapunov function
A Novel Method to Handle the Partial Occlusion and Shadow Detection of Highway Vehicles

This study proposes a novel method for resolving the partial occlusion and shadow of moving vehicles seen in a sequence of highway traffic images captured from a single roadside fixed camera position. The proposed method detects the shadow regions of foreground/moving vehicles in any direction with no human intervention. Also, it handles the partially occluded vehicles in three different states on the highway traffic scene. The moving vehicles are detected using the background subtraction method followed by using the dilation and erosion operations. In this step, every segmented moving vehicle has an extracted Feature Vector (FV) which is used to initialize the Shadow Searching Window (SSW). Then, a chromatic based analysis technique uses the semi mean value of RGB colour space of moving vehicle region pixels as a threshold value to discriminate the shadow of moving vehicles despite the direction of movement. Finally, the partially occluded vehicles are handled using a new training procedure based on previous estimations to calculate the new moving vehicle sizes. These sizes are employed to separate and highlight the partially occluded vehicles by drawing the bounding boxes for each moving vehicle.
Visual Discovery of Patterns in Sparse Event Data Using Sentinel Event Alignment , Ranking , and Filtering

Temporal events are typically visualized on a time line indicating the date they occurred. This perspective is useful when the questions one wishes to answer are related to the date of occurrence. This view, however, makes it difficult to identify patterns related to the relative duration between events or the sequential order. We demonstrate how the simple notion of temporal alignment by some sentinel event can be a powerful method for visually identifying temporally correlated events. We put this idea into practice with the implementation of LifeTimes, a tool targeted at viewing personal event histories, such as patient medical histories. We demonstrate the power of this method on a dataset of sports injury reports.
Bridging the gap between clinical neuroscience and cognitive rehabilitation: the role of cognitive training, models of neuroplasticity and advanced neuroimaging in future brain injury rehabilitation.

BACKGROUND Magnetic resonance imaging (MRI) has brought about advances in the fields of brain plasticity and lifespan brain change, that might be of special interest for cognitive rehabilitation research and, eventually, in clinical practice. Parallel, intensive cognitive training studies show promising results for the prospect of retraining some of the impaired functioning following acquired brain injury. OBJECTIVES However, cognitive training research is largely performed without concurrent assessments of brain structural change and reorganization, which could have addressed possible mechanisms of training-related neuroplasticity. METHODS Criticism of cognitive training studies is often focused on lack of ecologically valid, daily-living assessments of treatment effect, and on whether the applied cognitive measures overlap too much with the training exercises. Yet, the present paper takes another point of view, where the relevance of recent MRI research of brain plasticity to the field of cognitive rehabilitation is examined. RESULTS Arguably, treatment ought to be measured at the same level of the International Classification of Functioning, Disability and Health model, as it is targeting. In the case of cognitive training that will be the "body structure" and "body function" levels. CONCLUSIONS MRI has shown promise to detect macro- and microstructural activity-related changes in the brain following intensive training.
Adaptive Chains

Adaptive chains are chains that are able to learn from all previous elements in the chain. It is an extension of Markov chains. It is proved convergence of adaptive chains that satisses a strong Doeblin condition (i.e., the transition density r from x i to y satisses r(y; x 1 ; x 2 ; : : : ; x i) a i (y) for all x 1 ; : : : ; x i ; y in the state space. ) By using the previous iterations of the adaptive chain, it is possible to increase a i which will improve convergence compared with Markov chains. It is also proved a decreasion rate in the covariance between element x i and x i+j as j increases. The results may also be applied on regeneration chains where only the history before the last regeneration is used. Particularly interesting is the adaptive Metropolis-Hastings algorithm. Adaptive simulated annealing is also described and convergence is proved when the temperature decreases proportional with M= log i. The convergence is due to contraction properties of integral operators with a stationary distribution and that satisses a strong Doeblin condition. The algorithm is particularly useful when it is necessary with many samples from the same distribution like in Bayesian estimation, and in applications where it is very expensive to calculate the limiting density like inverse problems and optimisation. Markov chains are widely used as models and computational devices in areas ranging from statistics to physics. The theory and applications of Markov chains are very active elds of research: see, for example, Meyn and Tweedie (1993), Gilks et al. (1996) and Geyer (1992). In many Markov chains, one gains knowledge about the limiting distribution as the number of iterations increases. This knowledge may be used to adapt the transition densities in order to improve the convergence of the chain. Since the chain is adaptive, it is not a Markov chain. Part of Markov chain theory may be adapted to adaptive chains. There has been a large number of papers on adaptive chains the last years. Some paper focus on regeneration; Roberts and Tweedie (1998) and Gilks et al. (1998). At the regeneration time the present state is independent of the previous states. States before the last regeneration may then be used in the transition density. The chain is a Markov chain between each regeneration time. In Haario et al A (1998) …
The Kyutech corpus and topic segmentation using a combined method

Summarization of multi-party conversation is one of the important tasks in natural language processing. In this paper, we explain a Japanese corpus and a topic segmentation task. To the best of our knowledge, the corpus is the first Japanese corpus annotated for summarization tasks and freely available to anyone. We call it “the Kyutech corpus. ” The task of the corpus is a decision-making task with four participants and it contains utterances with time information, topic segmentation and reference summaries. As a case study for the corpus, we describe a method combined with LCSeg and TopicTiling for a topic segmentation task. We discuss the effectiveness and the problems of the combined method through the experiment with the Kyutech corpus.
The CIN4 Chromosomal Instability qPCR Classifier Defines Tumor Aneuploidy and Stratifies Outcome in Grade 2 Breast Cancer

Purpose Quantifying chromosomal instability (CIN) has both prognostic and predictive clinical utility in breast cancer. In order to establish a robust and clinically applicable gene expression-based measure of CIN, we assessed the ability of four qPCR quantified genes selected from the 70-gene Chromosomal Instability (CIN70) expression signature to stratify outcome in patients with grade 2 breast cancer. Methods AURKA, FOXM1, TOP2A and TPX2 (CIN4), were selected from the CIN70 signature due to their high level of correlation with histological grade and mean CIN70 signature expression in silico. We assessed the ability of CIN4 to stratify outcome in an independent cohort of patients diagnosed between 1999 and 2002. 185 formalin-fixed, paraffin-embedded (FFPE) samples were included in the qPCR measurement of CIN4 expression. In parallel, ploidy status of tumors was assessed by flow cytometry. We investigated whether the categorical CIN4 score derived from the CIN4 signature was correlated with recurrence-free survival (RFS) and ploidy status in this cohort. Results We observed a significant association of tumor proliferation, defined by Ki67 and mitotic index (MI), with both CIN4 expression and aneuploidy. The CIN4 score stratified grade 2 carcinomas into good and poor prognostic cohorts (mean RFS: 83.8±4.9 and 69.4±8.2 months, respectively, p = 0.016) and its predictive power was confirmed by multivariate analysis outperforming MI and Ki67 expression. Conclusions The first clinically applicable qPCR derived measure of tumor aneuploidy from FFPE tissue, stratifies grade 2 tumors into good and poor prognosis groups.