Miracle-dz/UniFAR-FTUs huggingface dataset:
Entanglement consumption of instantaneous nonlocal quantum measurements

Relativistic causality has dramatic consequences on the measurability of nonlocal variables and poses the fundamental question of whether it is physically meaningful to speak about the value of nonlocal variables at a particular time. Recent work has shown that by weakening the role of the measurement in preparing eigenstates of the variable, it is in fact possible to measure all nonlocal observables instantaneously by exploiting entanglement. However, for these measurement schemes to succeed with certainty, an infinite amount of entanglement must be distributed initially and all this entanglement is necessarily consumed. In this work, we sharpen the characterization of instantaneous nonlocal measurements by explicitly devising schemes in which only a finite amount of the initially distributed entanglement is ever utilized. This enables us to determine an upper bound to the average consumption for the most general cases of nonlocal measurements. This includes the tasks of state verification, where the measurement verifies if the system is in a given state, and verification measurements of a general set of eigenstates of an observable. Despite its finiteness, the growth of entanglement consumption is found to display an extremely unfavourable exponential of an exponential scaling with either the number of qubits needed to contain the Schmidt rank of the target state or the total number of qubits in the system for an operator measurement. This scaling is seen to be a consequence of the combination of the generic exponential scaling of unitary decompositions combined with the highly recursive structure of our scheme required to overcome the no-signalling constraint of relativistic causality.
Training RBMs based on the signs of the CD approximation of the log-likelihood derivatives

Contrastive Divergence (CD) learning is frequently applied to Restricted Boltzmann Machines (RBMs), the building blocks of deep believe networks. It relies on biased approximations of the log-likelihood gradient. This bias can deteriorate the learning process. It was claimed that the signs of most components of the CD update are equal to the corresponding signs of the log-likelihood gradient. This suggests using optimization techniques only depending on the signs. Resilient backprop- agation is such a method and we combine it with CD learning. However, it does not prevent divergence caused by the approximation bias.
Research on Engineering Teaching Mode of "Introduction" Course in Engineering Colleges under the Background of Big Data

The development of information technology, such as Internet, Internet of things and cloud computing, has brought the storage of massive information resources for human beings, and human beings have entered the era of big data.Big data has changed people's values, lifestyles and ways of thinking.The big data brings challenges to the teaching of "Introduction"course, but also brings opportunities to the teaching of "Introduction"course.For the teaching of "Introduction"course of engineering colleges, we must deepen teaching reform, actively meet the challenges and opportunities brought by big data, and innovate engineering teaching mode, so as to continuously improve teaching quality.
Managed Fire Frequency Significantly Influences the Litter Arthropod Community in Longleaf Pine Flatwoods

Abstract Frequent prescribed burns are essential to pine forest restoration and management. Research studies have assessed effects of prescribed fire and burn frequency on plants and vertebrates, but impacts of fire on terrestrial invertebrate communities are still poorly understood. This case study investigated effects of burning frequency on species richness and community composition of social insects (ants, Hymenoptera: Formicidae and termites, Blattodea: Isoptera) in fire-managed Southern longleaf pine flatwoods in central Florida. Community response to different fire frequencies was assessed: burned annually, every 2 yr, or every 3 yr, 30 yr unburned and 75 yr unburned. Richness was similar across all treatments, but ant community composition and species density significantly differed between frequently burned (1, 2, and 3 yr) and long-unburned (30 and 75 yr) treatments. Long-unburned treatments had higher ant abundance, but the species present were less characteristic of open canopy longleaf pine habitat than ants in frequently burned treatments. The annual burn treatment differed from 2-yr burn in species density, but to a lesser degree. Exotic species abundance was highest in frequently burned sites; only native species were detected in the 75-yr unburned plot. The red imported fire ant, Solenopsis invicta Buren (Hymenoptera: Formicidae), was detected in all regularly burned plots but not in long-unburned sites. Frequent burning at this site increased habitat suitability for ant species adapted to the sunny, open canopy, and diverse niches characteristic of longleaf pine forest; however, regular fire disturbance also increased the likelihood of exotic ant species establishment.
PlanHab*: hypoxia does not worsen the impairment of skeletal muscle oxidative function induced by bed rest alone

Superposition of hypoxia on 21 day bed rest did not worsen the impairment of skeletal muscle oxidative function induced by bed rest alone. A significant impairment of maximal oxidative performance was identified downstream of cardiovascular O2 delivery, involving both the intramuscular matching between O2 supply and utilization and mitochondrial respiration. These chronic adaptations appear to be relevant in terms of exposure to spaceflights and reduced gravity habitats (Moon or Mars), as characterized by low gravity and hypoxia, in patients with chronic diseases characterized by hypomobility/immobility and hypoxia, as well as in ageing.
A rare case report

Rationale:Comamonas species are rarely associated with human infections. Recent reports found thatComamonas kerstersiiwas associated with severe diseases such as abdominal infection and bacteremia. However, C. kerstersii maybe be confused with Comamonas testosteroni using the automatic bacterial identification systems currently available. Patientconcerns:A 31-year-old man who had onset of left upper abdominal pain developed clinical manifestations of right lower abdominal pain and classic migration of pain at the temperature of 39°C. The positive strain of aerobic and anaerobic bottles of blood cultures was identified. Diagnoses: The patient was diagnosed as acute peritonitis and perforated appendix with abdominal abscess. Interventions: The bacterium was identified by routine methods, MALDI-TOF-MS and PCR amplification of the 16S rRNA. The patient was treated with exploratory laparotomy, appendectomy, tube drainage, and prescribing antibiotic treatment. Outcomes: The patients were discharged with complete recovery. The organisms were confirmed as C. kerstersii by MALDI-TOFMS and a combination of the other results. Lessons: Our findings suggest that C. kerstersii infection occurs most often in association with perforated appendix and bacteremia. We presume thatC. kerstersii is an opportunistic pathogen or commensal with the digestive tract and appendix bacteria. Abbreviations: C. kerstersii =Comamonas kerstersii, MALDI-TOF-MS =matrix-assisted laser desorption ionization–time of flight mass spectrometry, MIC = minimum inhibitory concentration, PCR = polymerase chain reaction.
Modelling the impact of climate change on the atmospheric transport and the fate of persistent organic pollutants in the Arctic

Abstract. The Danish Eulerian Hemispheric Model (DEHM) was applied to investigate how projected climate changes will affect the atmospheric transport of 13 persistent organic pollutants (POPs) to the Arctic and their environmental fate within the Arctic. Three sets of simulations were performed, one with present day emissions and initial environmental concentrations from a 20-year spin-up simulation, one with present day emissions and with initial environmental concentrations set to zero and one without emissions but with initial environmental concentrations from the 20-year spin-up simulation. Each set of simulations consisted of two 10-year time slices representing the present (1990–2000) and future (2090–2100) climate conditions. DEHM was driven using meteorological input from the global circulation model, ECHAM/MPI-OM, simulating the SRES (Special Report on Emissions Scenarios) A1B climate scenario. Under the applied climate and emission scenarios, the total mass of all compounds was predicted to be up to 55 % lower across the Northern Hemisphere at the end of the 2090s than in the 1990s. The mass of HCHs within the Arctic was predicted to be up to 38 % higher, whereas the change in mass of the PCBs was predicted to range from 38 % lower to 17 % higher depending on the congener and the applied initial environmental concentrations. The results of this study also indicate that contaminants with no or a short emission history will be more rapidly transported to and build up in the arctic environment in a future warmer climate. The process that dominates the environmental behaviour of POPs in the Arctic under a future warmer climate scenario is the shift in mass of POPs from the surface media to the atmosphere induced by the higher mean temperature. This is to some degree counteracted by higher degradation rates also following the higher mean temperature. The more dominant of these two processes depends on the physical-chemical properties of the compounds. Previous model studies have predicted that the effect of a changed climate on the transport of POPs to the Arctic is moderate relative to the effect of proposed changes in emissions, which is confirmed in this study. However, the model studies do not agree on whether climate change acts to reduce or increase environmental concentrations of POPs in the Arctic, and further work is needed to resolve this matter.
A Genome-Wide Linkage Study for Chronic Obstructive Pulmonary Disease in a Dutch Genetic Isolate Identifies Novel Rare Candidate Variants

Chronic obstructive pulmonary disease (COPD) is a complex and heritable disease, associated with multiple genetic variants. Specific familial types of COPD may be explained by rare variants, which have not been widely studied. We aimed to discover rare genetic variants underlying COPD through a genome-wide linkage scan. Affected-only analysis was performed using the 6K Illumina Linkage IV Panel in 142 cases clustered in 27 families from a genetic isolate, the Erasmus Rucphen Family (ERF) study. Potential causal variants were identified by searching for shared rare variants in the exome-sequence data of the affected members of the families contributing most to the linkage peak. The identified rare variants were then tested for association with COPD in a large meta-analysis of several cohorts. Significant evidence for linkage was observed on chromosomes 15q14–15q25 [logarithm of the odds (LOD) score = 5.52], 11p15.4–11q14.1 (LOD = 3.71) and 5q14.3–5q33.2 (LOD = 3.49). In the chromosome 15 peak, that harbors the known COPD locus for nicotinic receptors, and in the chromosome 5 peak we could not identify shared variants. In the chromosome 11 locus, we identified four rare (minor allele frequency (MAF) <0.02), predicted pathogenic, missense variants. These were shared among the affected family members. The identified variants localize to genes including neuroblast differentiation-associated protein (AHNAK), previously associated with blood biomarkers in COPD, phospholipase C Beta 3 (PLCB3), shown to increase airway hyper-responsiveness, solute carrier family 22-A11 (SLC22A11), involved in amino acid metabolism and ion transport, and metallothionein-like protein 5 (MTL5), involved in nicotinate and nicotinamide metabolism. Association of SLC22A11 and MTL5 variants were confirmed in the meta-analysis of 9,888 cases and 27,060 controls. In conclusion, we have identified novel rare variants in plausible genes related to COPD. Further studies utilizing large sample whole-genome sequencing should further confirm the associations at chromosome 11 and investigate the chromosome 15 and 5 linked regions.
Using Common Table Expressions to Build a Scalable Boolean Query Generator for Clinical Data Warehouses

We present a custom, Boolean query generator utilizing common-table expressions (CTEs) that is capable of scaling with big datasets. The generator maps user-defined Boolean queries, such as those interactively created in clinical-research and general-purpose healthcare tools, into SQL. We demonstrate the effectiveness of this generator by integrating our study into the Informatics for Integrating Biology and the Bedside (i2b2) query tool and show that it is capable of scaling. Our custom generator replaces and outperforms the default query generator found within the Clinical Research Chart cell of i2b2. In our experiments, 16 different types of i2b2 queries were identified by varying four constraints: date, frequency, exclusion criteria, and whether selected concepts occurred in the same encounter. We generated nontrivial, random Boolean queries based on these 16 types; the corresponding SQL queries produced by both generators were compared by execution times. The CTE-based solution significantly outperformed the default query generator and provided a much more consistent response time across all query types (M = 2.03, SD = 6.64 versus M = 75.82, SD = 238.88 s). Without costly hardware upgrades, we provide a scalable solution based on CTEs with very promising empirical results centered on performance gains. The evaluation methodology used for this provides a means of profiling clinical data warehouse performance.
Introducing automation to the molecular diagnosis of Trypanosoma cruzi infection: A comparative study of sample treatments, DNA extraction methods and real-time PCR assays

Background Polymerase chain reaction (PCR) has become a useful tool for the diagnosis of Trypanosoma cruzi infection. The development of automated DNA extraction methodologies and PCR systems is an important step toward the standardization of protocols in routine diagnosis. To date, there are only two commercially available Real-Time PCR assays for the routine laboratory detection of T. cruzi DNA in clinical samples: TCRUZIDNA.CE (Diagnostic Bioprobes Srl) and RealCycler CHAG (Progenie Molecular). Our aim was to evaluate the RealCycler CHAG assay taking into account the whole process. Methodology/Principal findings We assessed the usefulness of an automated DNA extraction system based on magnetic particles (EZ1 Virus Mini Kit v2.0, Qiagen) combined with a commercially available Real-Time PCR assay targeting satellite DNA (SatDNA) of T. cruzi (RealCycler CHAG), a methodology used for routine diagnosis in our hospital. It was compared with a well-known strategy combining a commercial DNA isolation kit based on silica columns (High Pure PCR Template Preparation Kit, Roche Diagnostics) with an in-house Real-Time PCR targeting SatDNA. The results of the two methodologies were in almost perfect agreement, indicating they can be used interchangeably. However, when variations in protocol factors were applied (sample treatment, extraction method and Real-Time PCR), the results were less convincing. A comprehensive fine-tuning of the whole procedure is the key to successful results. Guanidine EDTA-blood (GEB) samples are not suitable for DNA extraction based on magnetic particles due to inhibition, at least when samples are not processed immediately. Conclusions/Significance This is the first study to evaluate the RealCycler CHAG assay taking into account the overall process, including three variables (sample treatment, extraction method and Real-Time PCR). Our findings may contribute to the harmonization of protocols between laboratories and to a wider application of Real-Time PCR in molecular diagnostic laboratories associated with health centers.
A hidden Markov model-based technique for gait segmentation using a foot-mounted gyroscope

In this paper, we describe an application of hidden Markov models (HMMs) to the problem of time-locating specific events in normal gait movement patterns. The use of HMMs in this paper is mainly related to the opportunity they offer to segment gait data collected at different walking speeds and inclinations of the walking surface. A simple four-state left-right HMM is trained on a dataset of signals collected from a mono-axial gyro during treadmill walking trials performed at different speed and incline values. The gyro is mounted at the foot instep, with its sensitivity axis oriented in the medio-lateral direction. A rule based method applied to gyro signals is used for data annotation. Sensitivity and specificity of phase classification detection higher than 95% are obtained. The estimation accuracy of heel strike, flat foot, heel off and toe off events is about 35 ms on average.
Reach-Avoid Problems via Sum-or-Squares Optimization and Dynamic Programming

Reach-avoid problems involve driving a system to a set of desirable configurations while keeping it away from undesirable ones. Providing mathematical guarantees for such scenarios is challenging but have numerous potential practical applications. Due to the challenges, analysis of reach-avoid problems involves making trade-offs between generality of system dynamics, generality of problem setups, optimality of solutions, and computational complexity. In this paper, we combine sum-of-squares optimization and dynamic programming to address the reach-avoid problem, and provide a conservative solution that maintains reaching and avoidance guarantees. Our method is applicable to polynomial system dynamics and to general problem setups, and is more computationally scalable than previous related methods. Through a numerical example involving two single integrators, we validate our proposed theory and compare our method to Hamilton-Jacobi reachability. Having validated our theory, we demonstrate the computational scalability of our method by computing the reach-avoid set of a system involving two kinematic cars.
Gesture recognition in upper-limb prosthetics: A viability study using dynamic time warping and gyroscopes

One of the significant challenges in the upper-limb-prosthetics research field is to identify appropriate interfaces that utilize the full potential of current state-of-the-art neuroprostheses. As the new generation of such prostheses paces towards approximating the human physiological performance in terms of movement dexterity and sensory feedback, it is clear that current non-invasive interfaces are still severely limited. Surface electromyography, the interface ubiquitously used in the field, is riddled with several shortcomings. Gesture recognition, an interface pervasively used in wearables and mobile devices, shows a strong potential as a non-invasive upper-limb prosthetic interface. This study aims at showcasing its potential in the field by using gyroscope sensors. To this end, we (1) explore the viability of Dynamic Time Warping as a classification method for upper-limb prosthetics and (2) look for appropriate sensor locations on the body. Results indicate an optimal classification rate of 97.53%, σ = 8.74 using a sensor located proximal to the endpoint performing a gesture.
Approximations of the Generalized Inverse of the Graph Laplacian Matrix

We devise methods for finding approximations of the generalized inverse of the graph Laplacian matrix, which arises in many graph-theoretic applications. Finding this matrix in its entirety involves solving a matrix inversion problem, which is resource-demanding in terms of consumed time and memory and hence impractical whenever the graph is relatively large. Our approximations use only a few eigenpairs of the Laplacian matrix and are parametric with respect to this number, so that the user can compromise between effectiveness and efficiency of the approximate solution. We apply the devised approximations to the problem of computing current-flow betweenness centrality on a graph. However, given the generality of the Laplacian matrix, many other applications can be sought. We experimentally demonstrate that the approximations are effective already with a constant number of eigenpairs. These few eigenpairs can be stored with a linear amount of memory in the number of nodes of the graph, and in the realistic case of sparse networks, they can be efficiently computed using one of the many methods for retrieving a few eigenpairs of sparse matrices that abound in the literature.
Verification of spent nuclear fuel in sealed dry storage casks via measurements of cosmic ray muon scattering

Most of the plutonium in the world resides inside spent nuclear reactor fuel rods. This high-level radioactive waste is commonly held in long-term storage within large, heavily shielded casks. Currently, international nuclear safeguards inspectors have no stand-alone method of verifying the amount of reactor fuel stored within a sealed cask. Here we demonstrate experimentally that measurements of the scattering angles of cosmic ray muons which pass through a storage cask can be used to determine if spent fuel assemblies are missing without opening the cask. This application of technology and methods commonly used in high-energy particle physics provides a potential solution to this long-standing problem in international nuclear safeguards.
Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification

Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions: healthy panicles, empty panicles caused by Nilaparvata lugens St&Oal, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis (PCA) was performed to obtain the principal component spectra (PCS) of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification (SVC) was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the independent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.