Miracle-dz/UniFAR-FTUs huggingface dataset:
Progress towards practical qubit computation using approximate Gottesman-Kitaev-Preskill codes

Encoding a qubit in the continuous degrees of freedom of an oscillator is a promising path to error-corrected quantum computation. One advantageous way to achieve this is through Gottesman-Kitaev-Preskill (GKP) grid states, whose symmetries allow for the correction of any small continuous error on the oscillator. Unfortunately, ideal grid states have infinite energy, so it is important to find finite-energy approximations that are realistic, practical, and useful for applications. In the first half of this work we investigate the impact of imperfect GKP states on computational circuits independently of the physical architecture. To this end, we analyze the behaviour of the physical and logical content of normalizable GKP states through several figures of merit, employing a recently-developed modular subsystem decomposition. By tracking the errors that enter into the computational circuit due to imperfections in the GKP states, we are able to gauge the utility of these states for NISQ (Noisy Intermediate-Scale Quantum) devices. In the second half, we focus on a state preparation approach in the photonic domain wherein photon-number-resolving measurements on some modes of Gaussian states produce non-Gaussian states in others. We produce detailed numerical results for the preparation of GKP states alongside estimating the resource requirements in practical settings and probing the quality of the resulting states with the tools we develop. Our numerical experiments indicate that we can generate any state in the GKP Bloch sphere with nearly equal resources, which has implications for magic state preparation overheads.
Simulation of the Use of the Material Handling Equipment in the Operation Process

Abstract The article presents the simulation model and simulation experiments aimed at the rationalisation of bottle necks in operations in terms of the capacity utilisation of the material handling equipment, in particular, six conveyance lines. The models and experiments were designed in the Tecnomatix simulation environment which facilitates the modelling of even more complex systems. The simulation model visualises the material flow within the production process comprising individual technological operations and workplaces. The model was used to perform simulation experiments with adjusted production times and added or removed workplaces. The presented article also contains the graphs of the utilised capacity of the machinery and operators. The obtained experimental results suggesting the partial or complete elimination of the accumulation of unfinished products.
Developing a Recommendation Benchmark for MLPerf Training and Inference

Deep learning-based recommendation models are used pervasively and broadly, for example, to recommend movies, products, or other information most relevant to users, in order to enhance the user experience. Among various application domains which have received significant industry and academia research attention, such as image classification, object detection, language and speech translation, the performance of deep learning-based recommendation models is less well explored, even though recommendation tasks unarguably represent significant AI inference cycles at large-scale datacenter fleets. To advance the state of understanding and enable machine learning system development and optimization for the commerce domain, we aim to define an industry-relevant recommendation benchmark for the MLPerf Training andInference Suites. The paper synthesizes the desirable modeling strategies for personalized recommendation systems. We lay out desirable characteristics of recommendation model architectures and data sets. We then summarize the discussions and advice from the MLPerf Recommendation Advisory Board.
Granule Cell Dispersion in Human Temporal Lobe Epilepsy: Proteomics Investigation of Neurodevelopmental Migratory Pathways

Granule cell dispersion (GCD) is a common pathological feature observed in the hippocampus of patients with Mesial Temporal Lobe Epilepsy (MTLE). Pathomechanisms underlying GCD remain to be elucidated, but one hypothesis proposes aberrant reactivation of neurodevelopmental migratory pathways, possibly triggered by febrile seizures. This study aims to compare the proteomes of basal and dispersed granule cells in the hippocampus of eight MTLE patients with GCD to identify proteins that may mediate GCD in MTLE. Quantitative proteomics identified 1,882 proteins, of which 29% were found in basal granule cells only, 17% in dispersed only and 54% in both samples. Bioinformatics analyses revealed upregulated proteins in dispersed samples were involved in developmental cellular migratory processes, including cytoskeletal remodeling, axon guidance and signaling by Ras homologous (Rho) family of GTPases (P < 0.01). The expression of two Rho GTPases, RhoA and Rac1, was subsequently explored in immunohistochemical and in situ hybridization studies involving eighteen MTLE cases with or without GCD, and three normal post mortem cases. In cases with GCD, most dispersed granule cells in the outer-granular and molecular layers have an elongated soma and bipolar processes, with intense RhoA immunolabeling at opposite poles of the cell soma, while most granule cells in the basal granule cell layer were devoid of RhoA. A higher percentage of cells expressing RhoA was observed in cases with GCD than without GCD (P < 0.004). In GCD cases, the percentage of cells expressing RhoA was significantly higher in the inner molecular layer than the granule cell layer (P < 0.026), supporting proteomic findings. In situ hybridization studies using probes against RHOA and RAC1 mRNAs revealed fine peri- and nuclear puncta in granule cells of all cases. The density of cells expressing RHOA mRNAs was significantly higher in the inner molecular layer of cases with GCD than without GCD (P = 0.05). In summary, our study has found limited evidence for ongoing adult neurogenesis in the hippocampus of patients with MTLE, but evidence of differential dysmaturation between dispersed and basal granule cells has been demonstrated, and elevated expression of Rho GTPases in dispersed granule cells may contribute to the pathomechanisms underpinning GCD in MTLE.
Long-Term Fire Regime Modifies Carbon and Nutrient Dynamics in Decomposing Eucalyptus pilularis Leaf Litter

The changes in fire regimes expected under climate change are likely to disrupt the biogeochemical cycling of carbon (C) and nutrients in forest ecosystems. Plant litter decomposition is a critical step in the terrestrial biogeochemical cycle, and is an important determinant of fire fuel load and forest C balance. We conducted a 277-day leaf litter decomposition experiment in an Australian eucalypt forest to test whether three contrasting, long-term fire regimes (no burning [NB], 4-yearly burning, and 2-yearly burning) were associated with different C and nutrient dynamics during litter decomposition. Fire regime had strong effects on many litter properties, including overall rates of decomposition and C loss, which were greatest in the NB treatment, suggesting that fire regime can modify the rate at which C is returned from litter to soil or the atmosphere. This has potentially important implications for soil C storage and atmospheric CO2 concentrations under a changing climate. High-frequency fire was associated with litter nutrient depletion and high microbial nutrient demand, but did not affect nutrient loss rates from decomposing litter, suggesting conservative use and retention of nutrients by the litter microbial biomass. These effects differed qualitatively between 2- and 4-yearly burning regimes, and they show how decadal-scale increases in fire frequency might contribute to soil nutrient depletion by disrupting decomposition. Many effects of fire regime on litter properties throughout decomposition were sensitive to litter bag re-collection date, suggesting that seasonal factors moderate the effects of fire regime, and that the role of fire regime-altered litter chemistry in shaping decomposition may be secondary to that of fire regime-altered environmental variables. Together, our findings highlight the potential consequences of long-term increases in prescribed fire frequency for litter decomposition and the storage and cycling of C and nutrients in eucalypt forests, and reveal the specific importance of average burn frequency in this context.
Run-Time Power Modelling in Embedded GPUs with Dynamic Voltage and Frequency Scaling

This paper investigates the application of a robust CPU-based power modelling methodology that performs an automatic search of explanatory events derived from performance counters to embedded GPUs. A 64-bit Tegra TX1 SoC is configured with DVFS enabled and multiple CUDA benchmarks are used to train and test models optimized for each frequency and voltage point. These optimized models are then compared with a simpler unified model that uses a single set of model coefficients for all frequency and voltage points of interest. To obtain this unified model, a number of experiments are conducted to extract information on idle, clock and static power to derive power usage from a single reference equation. The results show that the unified model offers competitive accuracy with an average 5% error with four explanatory variables on the test data set and it is capable to correctly predict the impact of voltage, frequency and temperature on power consumption. This model could be used to replace direct power measurements when these are not available due to hardware limitations or worst-case analysis in emulation platforms.
Reactivity of 1-allylsilatrane in ruthenium-catalyzed silylative coupling with olefins – mechanistic considerations

Abstract We have developed a new effective route for obtaining (E)-vinyl-substituted silatranes via ruthenium-catalyzed silylative coupling of 1-allylsilatrane with olefins. Experimental research allowed us also to propose the mechanism of the process based on stoichiometric reactions.
Longitudinal Changes in Whole-Brain Functional Connectivity Strength Patterns and the Relationship With the Global Cognitive Decline in Older Adults

Aging is associated with changes in brain functional patterns as well as cognition. The present research sought to investigate longitudinal changes in whole brain functional connectivity strength (FCS) and cognitive performance scores in very old cognitively unimpaired individuals. We studied 34 cognitively normal elderly individuals at both baseline and 4-year follow-up (baseline age = 78 ± 3.14 years) with resting-state functional magnetic resonance imaging (r-fMRI), structural MRI scans, and neuropsychological assessments conducted. Voxel-based whole brain FCS was calculated and we found that bilateral superior parietal and medial frontal regions showed decreased FCS, while the supplementary motor area (SMA) and insula showed increased FCS with age, along with a decrease in bilateral prefrontal cortical thickness. The changes of FCS in left precuneus were associated with an aging-related decline in global cognition. Taken together, our results suggest changes in FCS with aging with the precuneus as a hub and this may underlie changes in global cognition that accompany aging. These findings help better understand the normal aging mechanism.
Bottom-Up Effects of Organic Fertilizers on Plutella xylostella (L) with Selected Cruciferous Crop Plants

ABSTRACT. The diamondback moth, Plutella xylostella (L.) (Lep.: Plutellidae), is a key pest of cruciferous vegetables, throughout the world including Iran. In this study, the effects of vermicompost and humic acid fertilizers on the population growth of P. xylostella were investigated and, furthermore, root system development in the host plants, canola, white cabbage, and red cabbage, was evaluated under the same experimental conditions. Our results showed that brassicaceous plants fertilized by these fertilizers significantly reduced the immature survival rate of P. xylostella. Reared adults from larvae on the treated plants also showed statistically significant reductions in the longevity, oviposition period, and fecundity compared with the control. On canola, white cabbage and red cabbage, the values of R0 (35.4, 10.2 and 6.2 offspring/individual, respectively), r (0.151, 0.098 and 0.071 day-1, respectively), and λ (1.163, 1.103 and 1.074 day-1, respectively) on the plants fertilized with both humic acid and vermicompost were significantly lower than control plants. We also found that on the plants fertilized with humic acid and vermicompost, the number of eggs laid was lower than control plants. The tested fertilizers improved the development of root systems and subsequently enhanced the plant resistance to P. xylostella. Our results show that humic acid and vermicompost root application induces systemic defenses in canola, white cabbage, and red cabbage that have negative effects on the performance of P. xylostella. These findings will help to develop our knowledge regarding the effects of humic acid and vermicompost fertilizers on the resistance of plants to P. xylostella.
Exploring RDF Graphs through Summarization and Analytic Query Discovery

Graph data is central to many applications, ranging from social networks to scientific databases. Graph formats maximize the flexibility offered to data designers, as they are mostly schema-less and thus can be used to capture very heterogeneous-structure content. RDF, the W3C’s format for sharing open (linked) data, addsthepossibilityto attach semantics todata,describingapplication-domain constraints by means of ontologies; in turn, this leads to implicit data that is also part of a graph even if it is not explicitly in it. In this paper, we present a structured walk through the problem of analyzing and exploring RDF graphs by finding groups of structurally similar nodes , and by automatically identifying interesting aggregates theirein . We outline the challenges raised by such processing in large, complex RDF graphs, outline the basic principles behind existing solutions, and highlight opportunities for future research.
Synthesis of Porous ZnO Films on Quartz Substrates by Thermal Oxidation and the Oxidant Atmosphere Effect

The synthesis and characterization of porous ZnO films using a two stage process by thermal oxidation using a bilayer precursor film (ZnO/Zn) consisting of a Zn film covered with a ZnO nanofilm formed on quartz substrates is reported. The Zn films of 50 nm were grown by DC sputtering method at 300K. In the first stage bilayer precursor films (BPF) of ZnO/Zn were produced by growing a ZnO nanofilm on Zn films by thermal oxidation at 350 °C by 30 min in N2 atmosphere containing 5 ppm of O2, and in the second stage the BPFs were oxidized at 800 °C for one hour either in dry N2, dry or wet air with 42% of humidity. The produced porous ZnO films were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) and UV-Vis spectroscopy measurements. The results revealed the role of the oxygen content and the relevance of the humidity content in the processing atmosphere. When the BPF was oxidized in N2 with low oxygen content nanoporous ZnO films of wurtzite phase with its c-axis perpendicular to the plane of the substrate were produced. When rich oxygen content oxidation atmospheres were used, either in dry or wet air, nanoporous ZnO films with three main crystallite orientations (100), (002) and (101), were produced. The optical transmittance characteristics at the band edge region were strongly influenced by the humidity content but induce the formation of reproducible nanoporous ZnO films with sizes of ≈ 10 nm.
Development of a Composite Material Based on Polymers Polydimethylsiloxane and Polytetrafluoroethylene Use in Human Prosthetic Coatings

The purpose of this study is the development of a composite material composed of a main layer of polydimethylsiloxane (PDMS) and a reinforcement of polytetrafluoroethylene (PTFE), to be used later in human prosthesis coatings. A mass ratio of the main layer consisting of PDMS:Tetraethyl orthosilicate (TEOS):Di-n-butyl tin dilaurate (DBTL) in the range of 33:1:0.5; 25:1:0.5; 10:1:0.5, and the mass ratio of the composite material (PTFE:PDMS) with a range was evaluated of 1:9; 1:1; 2:3. Obtaining the following results: Tensile strength of 0.085 MPa based on the ratio of 33:1:0.5 - 1:9 and 0.59 MPa with respect to the ratio of 10:1:0.5 - 2:3, evidencing an increase in tensile strength by decreasing the weight of PDMS and increasing the weight of PTFE. On the other hand, the composite material obtained is hydrophobic, insoluble in ethanol and water, has a cross-linking percentage of 98.74 % and 99.66 % respectively, also has a minimum permeance of 5.24x10-7 (g Pa-1 s-1 m-2). With which it is concluded that the treatment whose properties resemble the human skin is the combination 10:1:0.5 - 1:1 that allowed to obtain an average tensile strength of 0.66 MPa, average modulus of elasticity of 6.56 MPa, similar to the dermis of a 43 year old person.
Preface

In this issue, we have selected several interesting papers, dealing with arrhythmias. Sarquella-Brugada et al. summarized the clinical value and cost-effectiveness of electrocardiogram screening and genetic analysis in the neonatal population, while AlGhamdi provided a nice overview of the principles for the subcutaneous implantable cardioverter defibrillators system, covering very basic components, such as implantation procedure, clinical indications, safety, efficacy, and future directions. Immunology is such a hot topic for the past year in medicine, Chera et al. has kept us warm during the winter season by elucidating the same topic about the relationship between anti-cardiomyocyte autoantibodies and sudden cardiac death which is related to the area of autoimmune disorders. Another interesting concept reviewed by Marinheiro et al. demonstrated that ventricular arrhythmias could be secondary, caused by, Obstructive Sleep Apnea (OSA), and this explains why treatment of OSA with continuous positive airway pressure therapy could potentially prevent the occurrence of VA.
On Evaluating Performance of Balanced Optimization of ETL Processes for Streaming Data Sources

A core component of each data warehouse architecture is the Extract-Transform-Load (ETL) layer. The processes in this layer integrate, transform, and move large volumes of data from data sources into a data warehouse (or data lake). For this reason, efficient execution of ETL processes is of high practical importance. Research approaches propose task re-ordering and parallel processing. In practice, companies apply scale-up or scale-out techniques to increase performance of the ETL layer. ETL tools existing on the market support mainly parallel processing of ETL tasks. Additionally, the IBM DataStage ETL engine applies the so-called balanced optimization that allows to execute ETL tasks either in a data source or in a data warehouse, thus optimizing overall resource consumption. With the widespread of non-relational technologies for storing big data, it is essential to apply ETL processes to this type of data. In this paper, we present an initial assessment of the balanced optimization applied to IBM DataStage, ingesting data from Kafka streaming data source.
Sampled-data state-estimation of delayed complex-valued neural networks

This paper studies the sampled-data state-estimation problem of delayed complex-valued neural networks (CVNNs). By using Lyapunov–Krasovskii functional (LKF), standard integral inequality together with the reciprocal convex approach, a delay-dependent condition is established in terms of the solution to linear matrix inequalities (LMIs) such that the consider CVNNs is asymptotically stable. As a result, an estimator gain matrix can be obtained through sampling instant. Finally, a simulation example is given to illustrate the theoretical analysis.
NTT Communication Science Laboratories and National Institute of Informatics at TRECVID 2017 Instance Search

We describe our approaches that were tested in the TRECVID 2017 Instance Search (INS) task. In this year’s INS, shots including a person at a location were to be retrieved after a location query and person query were given. We used ASMK for the location search and OpenFace for the person search. The results of the location search and person search were fused according to the ranks in the two searches or with the weighted sum of the z-scores of these searches. We also applied human region removal to the sample images for the location search. We submitted four runs as follows for task A in which no video and only the provided sample images of the queries were used.
Applications of Tolerance Rough Set Model Semantic Text Analysis

Tolerance Rough Set Model (TRSM) is an extension of Rough Set theory and can be used as a tool for approximation of hidden concepts in collections of documents. In recent years, numerous successful applications of TRSM in web intelligence including text classiﬁcation, clustering, thesaurus generation, semantic indexing
Prescribed performance fault tolerant control for uncertain nonlinear systems with input saturation

This paper investigates the problem of adaptive fault tolerant tracking control for uncertain nonlinear systems in the event of external disturbances, actuator faults and input saturation. In order to guarantee the transient and steady-state control performance, the prescribed performance function is employed to impose the anticipant performance criterion on the output tracking error. By using the error transformation, the original inequality error constraint is converted to an unconstrained issue. For the conversion systems, the negative effect resulting from the external disturbances is restrained via the adaptive control approach. The radial basis function neural network (RBFNN) is adopted to handle the unknown nonlinear functions. The Nussbaum gain technique is utilised to deal with the nonlinear terms arising from the actuator faults and input saturation. Combining with the backstepping control strategy, an adaptive neural fault tolerant control (FTC) scheme is developed for the uncertain nonlinear systems. It is rigorously proved that the proposed controller is capable of ensuring the boundness of all closed-loop signals as well as the specified tracking performance. Simulation results on two examples are given to illustrate the effectiveness of the presented control approach.
Finite-time stabilisation of discrete networked cascade control systems under transmission delay and packet dropout via static output feedback control

ABSTRACT Delay and packet dropout are two inherent sources of the performance degradation or instability in the networked control systems. This paper addresses the stochastic finite time stability analysis of a class of discrete-time network-based cascade control systems against varying delay and random packet dropout modelled by the Bernoulli distributed sequence. Based on the Finsler lemma, sufficient conditions are derived to stabilise such systems by designing static output feedback controller with the help of linear matrix inequalities (LMIs). This LMI-based conditions are provided to ensure the stochastic finite-time stability with a given disturbance attenuation level. Efficiency of the proposed controller is verified by numerical examples.
A Fuzzy Set Tool in the Classification and Prediction Software System (CLAPSS) (short paper)

In the paper, we give the outline of a fuzzy set tool implemented in the Classiﬁcation and Prediction Software System (CLAPSS). CLAPSS is being developed for solving diﬀerent classiﬁcation and prediction problems using, among others, some specialized approaches based mainly on fuzzy sets and rough sets which are not available in other machine learning software systems. Theoretical background as well as the module embedded in CLPASS, for fuzziﬁcation of attribute values in information/decision systems, are described. Moreover, possible further steps in the usage of CLAPSS (generation of fuzzy decision trees as well as fuzzy ﬂow graphs) are mentioned.