Miracle-dz/UniFAR-FTUs huggingface dataset:
Animal model of methylphenidate's long-term memory-enhancing effects

Methylphenidate (MPH), introduced more than 60 years ago, accounts for two-thirds of current prescriptions for attention deficit hyperactivity disorder (ADHD). Although many studies have modeled MPH's effect on executive function, almost none have directly modeled its effect on long-term memory (LTM), even though improvement in LTM is a critical target of therapeutic intervention in ADHD. We examined the effects of a wide range of doses of MPH (0.01–10 mg/kg, i.p.) on Pavlovian fear learning, a leading model of memory. MPH's effects were then compared to those of atomoxetine (0.1–10 mg/kg, i.p.), bupropion (0.5–20 mg/kg, i.p.), and citalopram (0.01–10 mg/kg, i.p.). At low, clinically relevant doses, MPH enhanced fear memory; at high doses it impaired memory. MPH's memory-enhancing effects were not confounded by its effects on locomotion or anxiety. Further, MPH-induced memory enhancement seemed to require both dopamine and norepinephrine transporter inhibition. Finally, the addictive potential of MPH (1 mg/kg and 10 mg/kg) was compared to those of two other psychostimulants, amphetamine (0.005 mg/kg and 1.5 mg/kg) and cocaine (0.15 mg/kg and 15 mg/kg), using a conditioned place preference and behavioral sensitization paradigm. We found that memory-enhancing effects of psychostimulants observed at low doses are readily dissociable from their reinforcing and locomotor activating effects at high doses. Together, our data suggest that fear conditioning will be an especially fruitful platform for modeling the effects of psychostimulants on LTM in drug development.
Towards robot audition

We discuss biologically inspired techniques for processing sound with a view to solving the what and where tasks. Starting from an auditorynerve like representation, we discuss an onset grouping technique for determining when to compute interaural time and intensity differences, and hence find the direction (where) of the source even in the presence of reflections. We have not yet implemented the what task, and we discuss some possible strategies.
Cytomegalovirus prevents antigen presentation by blocking the transport of peptide-loaded major histocompatibility complex class I molecules into the medial-Golgi compartment

Selective expression of murine cytomegalovirus (MCMV) immediate-early (IE) genes leads to the presentation by the major histocompatibility complex (MHC) class I molecule Ld of a peptide derived from MCMV IE protein pp89 (Reddehase, M.J., J. B. Rothbard, and U.H. Koszinowski. 1989. Nature (Lond.). 337:651). Characterization of endogenous antigenic peptides identified the pp89 peptide as the nonapeptide 168YPHFMPTNL176 (del Val, M., H.-J. Schlicht, T. Ruppert, M.J. Reddehase, and U.H. Koszinowski. 1991. Cell. 66:1145). Subsequent expression of MCMV early genes prevents presentation of pp89 (del Val, M., K. Munch, M.J. Reddehase, and U.H. Koszinowski. 1989. Cell. 58:305). We report on the mechanism by which MCMV early genes interfere with antigen presentation. Expression of the IE promoter-driven bacterial gene lacZ by recombinant MCMV subjected antigen presentation of beta-galactosidase to the same control and excluded antigen specificity. The Ld-dependent presence of naturally processed antigenic peptides also in nonpresenting cells located the inhibitory function subsequent to the step of antigen processing. The finding that during the E phase of MCMV gene expression the MHC class I heavy chain glycosylation remained in an Endo H-sensitive form suggested a block within the endoplasmic reticulum/cis-Golgi compartment. The failure to present antigenic peptides was explained by a general retention of nascent assembled trimolecular MHC class I complexes. Accordingly, at later stages of infection a significant decrease of surface MHC class I expression was seen, whereas other membrane glycoproteins remained unaffected. Thus, MCMV E genes endow this virus with an effective immune evasion potential. These results also indicate that the formation of the trimolecular complex of MHC class I heavy chain, beta 2-microglobulin, and the finally trimmed peptide is completed before entering the medial-Golgi compartment.
Multi-failure restoration with minimal flow operations in software defined networks

We consider dynamic flow restoration in multi-failure scenarios in OpenFlow-based Software-Defined Networks (SDNs). Flexibility of network configuration in these networks makes it possible to dynamically restore flows in case of multilink failures. To re-route the failed flows, network devices such as switches carry out flow operations, i.e., add new flow-entries to the flow-tables. In disaster scenarios where thousands of flows must be restored in a short time, the time required to perform such operations is significant and must be minimized to maintain a carrier-grade network. Shortest-path based techniques do not take into account the number of flow operations (namely, operation cost) and therefore are inefficient for disaster scenarios. We incorporate the operation cost into the flow restoration problem, and formulate the problem of finding a path 1) with the lowest path cost with capped operation cost, 2) with the lowest possible operation cost, and 3) with the minimum operation cost amongst all the paths with a Dijkstra-like path cost. We propose optimal algorithms with Dijkstra-like complexity for the second and third problems. The simulation results with European Reference Network (ERnet) show that our proposed methods on average can reduce the number of required flow operations up to 15% while the path cost rises less than 3%.
Network coding-based protection scheme for Elastic Optical Networks

Optical technologies are the foundations supporting the current telecommunication network backbones due to the high speed transmissions achieved in fiber optical networks. Traditional optical networks consist of a fixed 50 GHz grid, resulting in a low optical spectrum (OS) utilization, specifically with transmission rates above 100 Gbps. This issue is magnified when network resilience capabilities are required. For instance, proactive protection solutions such as Dedicated Protection (DP) are widely used because of their low recovery time. However, a significant drawback of DP is its high utilization of optical bandwidth. Recently, optical networks are undergoing significant changes with the purpose of providing a flexible grid that can fully exploit the potential of optical networks. This has led to a new network paradigm termed as Elastic Optical Networks (EON). Moreover, a novel strategy referred to as network coding (NC) has been proposed with the aim of improving network throughput. In this paper, we propose a proactive protection scheme so-called E-DPNC* that combines both the advantages concerning network throughput offered by EON and NC, and the low recovery time of a DP scheme, in order to enable network resilience against optical link failures while also reducing the optical spectrum utilization. Our evaluation results show that our solution reduces the OS utilization by 41% compared with conventional protection schemes deployed on fixed grid scenarios.
Experimental Results in Approximation of Max-Min Resource Sharing ?

We implement the algorithm for the max-min resource sharing problem described in [12], using a new line search technique for determining a suitable step length. Our line search technique uses a modified potential function that is less costly to evaluate, thus heuristically simplifying the computation. Observations concerning the quality of the dual solution and oscillating behavior of the algorithm are made. First numerical observations are briefly discussed. In particular we study a certain class of linear programs, namely the computational bottleneck of an algorithm from [15] for solving strip packing with an approach from [18, 22]. For these, we obtain practical running times. Our implementation is able to solve instances for small accuracy parameters for which the methods proposed in theory are out of practical interest. More precisely, the technique from [15] improves the known runtime bound of O(M ln(Mn/(at)) + Mn/t + ln(Mn/(at))) to the more favourable bound O(M( −3( −2 + ln M) + M( −2 + ln M))), where n denotes the number of items, M the number of distinct item widths, a the width of the narrowest item and t is a desired additive tolerance.
Fair Value of Liabilities: The Financial Economics Perspective

Abstract In this paper we present the fundamental approaches of financial economics to valuation. Three methods are demonstrated by which financial economists account for risk. We illustrate how these methods relate to one another and how they can be applied in the valuation of risky corporate bonds, guaranteed investment contracts (GICs) with and without interest rate contingencies, and whole life insurance. Next, we discuss how these models treat orthogonal risks, such as the kind often covered by insurance contracts. Demand side and supply side diversification are treated, and liquidity risk is then considered. We conclude with a summary of the benefits of decomposition and transparency.
Finite-sensor fault-diagnosis simulation study of gas turbine engine using information entropy and deep belief networks

Precise fault diagnosis is an important part of prognostics and health management. It can avoid accidents, extend the service life of the machine, and also reduce maintenance costs. For gas turbine engine fault diagnosis, we cannot install too many sensors in the engine because the operating environment of the engine is harsh and the sensors will not work in high temperature, at high rotation speed, or under high pressure. Thus, there is not enough sensory data from the working engine to diagnose potential failures using existing approaches. In this paper, we consider the problem of engine fault diagnosis using finite sensory data under complicated circumstances, and propose deep belief networks based on information entropy, IE-DBNs, for engine fault diagnosis. We first introduce several information entropies and propose joint complexity entropy based on single signal entropy. Second, the deep belief networks (DBNs) is analyzed and a logistic regression layer is added to the output of the DBNs. Then, information entropy is used in fault diagnosis and as the input for the DBNs. Comparison between the proposed IE-DBNs method and state-of-the-art machine learning approaches shows that the IE-DBNs method achieves higher accuracy.
Influence of match playing time and the length of the between-match microcycle in Spanish professional soccer players’ perceived training load

ABSTRACT Objectives: To analyse the weekly differentiated perceived training load (dRPE-TL) accumulated during the in-season competitive period by professional soccer players depending on: (1) their participation during the previous official match (i.e., ≥45 min; <45 min; 0 min) and (2) the length of the between-match microcycle (8, 7, and 6 days). Methods: Twenty-one Spanish Second Division professional soccer players (age = 26.7 ± 3.1 years; body height = 182.1 ± 4.5 cm; body mass = 76.6 ± 5.6 kg) participated in this study. Weekly dRPE-TL data were collected over a 16-week in-season period during the 2015±2016 season from August to December. After each session, players reported their rating of respiratory (sRPEres) and muscular (sRPEmus) perceived exertions (training load [TL] = sRPE·volume). Results: After the application of the mixed linear model, significant differences (P< 0.01) were only found in the differentiated perceived TL measured each week for the groups specified regarding match participation time. Conclusions: Previous match participation had a substantial effect on players’ accumulated weekly TLs, with greater match participation associated with higher weekly loads regardless of compensatory training strategies. However, the length of the between-match microcycle during 1-match weeks had no effecton players’ accumulated weekly workloads.
City Research Online

Visualizing ﬂows between origins and destinations can be straightforward when dealing with small numbers of journeys or simple geographies. Representing ﬂows as lines embedded in geographic space has commonly been used to map transport ﬂows, especially when geographic patterns are important as they are when characterising cities or managing transportation. However, for larger numbers of ﬂows, this approach requires careful design to avoid problems of occlusion, salience bias and information overload. Driven by the requirements identiﬁed by users and managers of the London Bicycle Hire scheme we present three methods of representation of bicycle hire use and travel patterns. Flow maps with curved ﬂow symbols are used to show overviews in ﬂow structures. Gridded views of docking station location that preserve geographic relationships are used to explore docking station status over space and time in a graphically eﬃcient manner. Origin-Destination maps that visualise the OD matrix directly while maintaining geographic context are used to provide visual details on demand. We use these approaches to identify changes in travel behaviour over space and time, to aid station rebalancing and to provide a framework for incorporating travel modelling and simulation.
Clinical and radiographic evaluation of soft and hard tissue changes around implants: a pilot study.

BACKGROUND The aim of this study was to evaluate the clinical and radiographic changes in the peri-implant tissues around one-stage implants with different smooth neck portion lengths before and after functional prosthetic loading. METHODS Twelve one-stage implants were placed in adult patients with bilateral edentulous posterior mandibular ridges. The sites were randomly assigned into two groups of six each: group 1: 2.8 mm neck implants and group 2: neck implants. The parameters plaque index (PI), gingival index (GI), probing depth (PD), gingival margin level (GML), relative clinical attachment level (r-CAL), and optical density (OD) were measured at loading (4 months) and 12 months after implant placement. The radiographic parameter osseous level (OL) was measured at implant placement, loading, and at 12 months. Analysis of variance and the paired Student t test were used to detect difference over time and between groups. RESULTS The results showed significant differences (P<0.05) for both groups for PD, r-CAL, and OL for intragroup comparisons over time. However, no significant differences were found for PI, GI, PD, GML, OD, and OL for between-group comparisons. CONCLUSION Bony loss occurred before loading, supporting the soft tissues and maintaining the biologic width irrespective of the smooth portion length.
Robust synchronization of directed networks with coprime factor perturbed agent dynamics

This paper deals with robust synchronization of directed and undirected multi-agent networks with uncertain agent dynamics. Given a network with identical nominal dynamics, we allow uncertainty in the form of coprime factor perturbations of the transfer matrix of the agent dynamics. These perturbations are assumed to be stable and have H∞norm that is bounded by an a priori given desired tolerance. We derive state space equations for dynamic observer based protocols that achieve robust synchronization in the presence of such uncertainty. We show that this robust synchronization of the network by the dynamic protocol is equivalent to robust stabilization of a single linear system by all controllers from a given set of feedback controllers. The synchronizing protocols are expressed in terms of real symmetric solutions to algebraic Riccati equations related to the nominal agent dynamics, and contain a weighting factor depending on the eigenvalues of the graph Laplacian. We obtain an achievable interval, i.e. an interval such that for each value of the tolerance contained in this interval there exists a robustly synchronizing protocol. For undirected networks, the supremum of this interval is proportional to the square root of the quotient of the smallest and the largest eigenvalue of the graph Laplacian.
Computational complexity and implementation aspects of the incremental hash function

This paper gives the computational complexity and practical implementation aspects of a newly introduced incremental hash function called pair chaining & modular arithmetic combining incremental hash function (PCIHF). The boundary conditions of the parameters involved in the update process of PCIHF are examined in depth. It is proved that these basic requirements can be fulfilled easily. It is shown that in applications involving more than one update process if the number of blocks to be replaced is no more than 1/4 of the message blocks needed, the PCIHF is more efficient than standard SHA-1. Finally, it is observed that the computational complexity of combining operation is also important to ensure the practicability of PCIHF.
Drosophila Embryo Stage Annotation Using Label Propagation

In this work we propose a system for automatic classification of Drosophila embryos into developmental stages. While the system is designed to solve an actual problem in biological research, we believe that the principle underlying it is interesting not only for biologists, but also for researchers in computer vision. The main idea is to combine two orthogonal sources of information: one is a classifier trained on strongly invariant features, which makes it applicable to images of very different conditions, but also leads to rather noisy predictions. The other is a label propagation step based on a more powerful similarity measure that however is only consistent within specific subsets of the data at a time. In our biological setup, the information sources are the shape and the staining patterns of embryo images. We show experimentally that while neither of the methods can be used by itself to achieve satisfactory results, their combination achieves prediction quality comparable to human performance.
PLAG1 and NCAPG‐LCORL in livestock

Abstract A recent progress on stature genetics has revealed simple genetic architecture in livestock animals in contrast to that in humans. PLAG1 and/or NCAPG‐LCORL, both of which are known as a locus for adult human height, have been detected for association with body weight/height in cattle and horses, and for selective sweep in dogs and pigs. The findings indicate a significant impact of these loci on mammalian growth or body size and usefulness of the natural variants for selective breeding. However, association with an unfavorable trait, such as late puberty or risk for a neuropathic disease, was also reported for the respective loci, indicating an importance to discriminate between causality and association. Here I review the recent findings on quantitative trait loci (QTL) for stature in livestock animals, mainly focusing on the PLAG1 and NCAPG‐LCORL loci. I also describe our recent efforts to identify the causative variation for the third major locus for carcass weight in Japanese Black cattle.
The Current Situation and Prevention Strategy for the Internet Addiction Disorder of College Students

The Internet addiction disorder among college students has become a vital social issue that impacts the overall development of college students. Thus, this paper introduces the specific current situation of the Internet addiction disorder on campus, and suggests that we should strengthen the Education for Humane Mental Quality; that we should flourish campus culture; that we should reinforce the management of network on campus, and implement the “Green Surfing” program; that we should fortify psychological instruction, and offer prompt psychotherapy to the addicted students; that we should adopt the prevention strategy such as rectifying the surrounding environment of campus and establishing interaction with parents and so on. With the large-scale application of the Internet in both higher education and university life, the Internet has become almost a normal and habit in daily life. In addition to its convenience, it also gives rise to many negative impact, bringing its impact on collegiate psyche to the public concern. Since Young [2] put forward PIU (Pathological Internet Use) in 1996, scholars from home and abroad conducted numerous research on this issue. Some scholars tend to describe the phenomenon of psychological addiction of the Internet as IAD (Internet Addiction Disorder), which, however, has not been universally recognized but its consequent symptom has been intensively studied. Undoubtedly, the Internet addiction disorder inflicts huge impacts on college students who are the pillar of society in future and are still growing. Thus, the current situation and prevention strategy ranks the first in all subjects of higher education research.
The formation of financial industries in USA, France and Russia

Despite the multiplicity of research on the surge of the financial industry the world over, little is done to understand the national context shaping the objectives and designs of this economic sector. The overall image that emerges from the literature is that globalisation and liberalisation of economies make the expansion of this sector indispensable for further development. This paper stresses the heterogeneity of the socio-psychological origins of the need for saving and contribution management, as well as the heterogeneity of the sources of savings and loan funding. In particular this paper discusses the following national characteristics: 1) the social belief in trust that smoothes the progress of insurance and pension fund business in the USA, 2) the traditional preference for saving and lending in France that explains the advance of banks and credit institutions for analysis of financial risks, and 3) the natural resources ground rent of Russian State provides the formation of sovereign-wealth funds, which require an up to date knowledge based investment management. This paper serves as a road sign indicating that the path of uniform financial industry formation is a waste of time and may bring about social and economic troubles.
Non-negative matrix deconvolution in noise robust speech recognition

High noise robustness has been achieved in speech recognition by using sparse exemplar-based methods with spectrogram windows spanning up to 300 ms. A downside is that a large exemplar dictionary is required to cover sufficiently many spectral patterns and their temporal alignments within windows. We propose a recognition system based on a shift-invariant convolutive model, where exemplar activations at all the possible temporal positions jointly reconstruct an utterance. Recognition rates are evaluated using the AURORA-2 database, containing spoken digits with noise ranging from clean speech to −5 dB SNR. We obtain results superior to those, where the activations were found independently for each overlapping window.