Miracle-dz/UniFAR-FTUs huggingface dataset:
The Hidden Subgroup Problem in Affine Groups: Basis Selectio n in Fourier Sampling

Some quantum algorithms, including Shor’s celebrated fact oring and discrete log algorithms, proceed by reduction to a hidden subgroup problem , in which a subgroup H of a group G must be determined from a quantum state uniformly supported on a left coset of H. These hidden subgroup problems are then solved by Fourier sampling: the quantum Fourier transform of is computed and measured. When the underlying group is non-Abelian, two important variants of the Fourier sampling paradigm have been identified: the weak standard method, where only representation names are measured, and the strong standard method, where full measurement occurs. It has remained open whether the strong standard method is indeed stronger, that is, whether there are hidden subgroups which can be reconstructed via the strong method by not by the weak, or any other known, method. In this article, we settle this question in the affirmative. W e show that hidden subgroups of semidirect products of the formZqnZp, whereqj(p 1) andq = p=polylog(p), can be efficiently determined by the strong standard method . Furthermore, the weak standard method and the “forgetful” Abelian method are insufficient for these groups. We also give an information-theoretic solution to the the hidden conjugate problem over the affine groups, which generalizes known results for the dihedral groups. In particular, this g ives rise to an information-theoretic solution for the hidd en subgroup problem over the Affine groups for a prime p where p 1 has polylog(p) divisors. Finally, we prove a closure property for the class of groups over which the hidden subgroup problem can be solved efficiently.
A Multiple-Ring-Modulated JTE Technique for 4H-SiC Power Device With Improved JTE-Dose Window

In this paper, a multiple-ring-modulated junction termination extension (MRM-JTE) technology for large-area silicon carbide PiN rectifier rated at 4500 V is proposed and experimentally investigated using a standard two-zone JTE (TZ-JTE) process without extra process steps or masks. Multiple modulation rings embedded inside JTE region, which is similar to varied lateral doping technology widely used in silicon devices, form a gradual decrease of effective charges in the termination region. MRM combines the advantages of two termination techniques, namely, ring-assisted JTE and space-modulated JTE, to enlarge the optimum JTE-dose window with high reverse blocking voltage in comparison with conventional TZ-JTE. A breakdown voltage of 4940 V at a leakage current of <inline-formula> <tex-math notation="LaTeX">$1~\mu \text{A}$ </tex-math></inline-formula> is achieved from fabricated MRM-JTE rectifiers with a 36-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>-thick N<sup>−</sup> epilayer doped to <inline-formula> <tex-math notation="LaTeX">${1.8} \times {10}^{{15}}$ </tex-math></inline-formula> cm<sup>−3</sup>, which is 92% of the ideal parallel-plane value. A forward current of 50 A has been measured at a forward voltage drop of 3.9 V for devices with an active anode area of 9.2 mm<sup>2</sup>, corresponding to low differential on-resistance of 1.8 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega \cdot \text {cm}^{ {2}}$ </tex-math></inline-formula>. The simulation and experimental results show that the proposed device exhibits approximately 2 times JTE dose tolerance window improvement with respect to the breakdown voltage of 4500 V.
The Neuroeconomics of Distrust: Sex Differences in Behavior and Physiology.

Trust is an essential component of transactions that occur over time. The amount of trust accorded to others is typically conditioned on multiple factors, including knowledge of the other party, the history of interactions, and the context of exchange. Determining who to trust and who to distrust is especially important in modern societies with largely impersonal exchange (Vernon L. Smith, 2003). In fact, trust is among the strongest predictors of whether a country will successfully develop: poor countries are by-and-large low-trust countries. This occurs because low trust inhibits investment and thereby the creation of wealth (Zak and Stephen Knack, 2001). Unfortunately, subjects in laboratory settings are unable to articulate clearly why they decide to trust or distrust a trading partner. In order to discover why human beings trust or distrust others, economists have begun obtaining physiologic measurements during trust experiments (Zak, 2005). This new transdisciplinary field is called neuroeconomics (Kevin McCabe et al., 2001; Zak, 2004; Colin F. Camerer et al., 2005). Recently, Zak et al. (2004, 2005) reported that people who received a signal of trust in an experimental game had higher levels of the neuroactive hormone oxytocin (OT) than those who received similar amounts of money absent a trust signal. In addition, higher OT levels were associated with an increased reciprocation of trust (i.e., greater trustworthiness). Animal models have shown that OT promotes prosocial behaviors by producing a pleasurable sensation. Because humans are highly social creatures, there may be both positive and negative physiologic controls over social behaviors, as there are for other important behaviors. For example, the hormones ghrelin and leptin are primary regulators of nourishment, promoting food intake and signaling satiety to terminate food consumption, respectively. This paper provides evidence for a hormone that is associated with a negative social interaction, distrust. We test two hypotheses:
Improving Food Safety in the Domestic Environment: The Need for a Transdisciplinary Approach

Microbial food safety has been the focus of research across various disciplines within the risk analysis community. Natural scientists involved in food microbiology and related disciplines work on the identification of health hazards, and the detection of pathogenic microorganisms. To perform risk assessment, research activities are increasingly focused on the quantification of microbial contamination of food products at various stages in the food chain, and modeling the impact of this contamination on human health. Social scientists conduct research into how consumers perceive food risks, and how best to develop effective risk communication with consumers in order to improve public health through improved food handling practices. The two approaches converge at the end of the food chain, where the activities regarding food preparation and food consumption are considered. Both natural and social sciences may benefit from input and expertise from the perspective of the alternative discipline, although, to date, the integration of social and natural sciences has been somewhat limited. This article therefore explores the potential of a transdisciplinary approach to food risk analysis in terms of delivering additional improvements to public health. Developing knowledge arising from research in both the natural and social sciences, we present a novel framework involving the integration of the two approaches that might provide the most effective way to improve the consumer health associated with food‐borne illness.
Remote Voice Acquisition in Multimodal Surveillance

Multimodal surveillance systems using visible/IR cameras and other sensors are widely deployed today for security purpose, particularly when subjects are at a large distance. However, audio information as an important data source has not been well explored. One of the reasons is because audio detection using microphones needs installation close to the subjects in monitoring. In this paper, we investigate a novel "optical" sensor, called laser Doppler vibrometer (LDV), for capturing voice signals in a very large range to realize a truly remote and multimodal surveillance system. Speech enhancement approaches are studied based on the characteristics of LDV audio. Experimental results show that remote voice detection via an LDV is promising when choosing appropriate targets close to human subjects in the environment
OpenFlow versus Commercial Load Balancers in a Campus Network

The production network at Arizona State University uses a commercial load balancer for syslog messages from the firewalls at the data center perimeter. The logs are carried in UDP packets and the load balancer divides the load among servers based on the source IP address. This may cause a server to be overwhelmed due to the high incoming data rate, and create log files unequal in size. These logs are processed by splunk, an application for analyzing the massive streams of data generated by IT systems. In a testbed with an OpenFlow switch and three rsyslog servers, fed by a mirror of the firewall rsyslog traffic, we study controllers implementing round-robin, random, and load-based load balancing policies. All policies achieve delivery ratios comparable with the commercial load balancer. When servers fail or go down for maintenance, the load-based policy produces log files the most balanced in size. The results suggest that an OpenFlow load balancer has performance competitive with a commercial load balancer, and can equalize the log file size at servers which improves the efficiency of splunk.
Measurement of evapotranspiration of mixed bush and grass in headwater region of North China plain

Bowen ratio and energy balance (BREB) method is used to measure the evapotranspiration of mixed bush and grass in headwater re,lion of North China Plain. The results show that it is hard to observe the long-term data by BREB. To analyze the diurnal variation in the energy balance, three days (DOY 225, clear day. DOY 228, cloudy, fog in the morning, and 0.2mm of Precipitation, DOY 236, cloudy) in 2001 are selected from the growing period of vegetation. Actual evapotranspiration is 4.13, 0.95, and 3.38 mm/day, with mean value of 2.82 mm/day. Peak hourly ET is 0.66 mm/hour. Bowen ratio is 0.28, 0.30, and 0.35 on DOY 225, 228, and 236, respectively. The daily ET0 calculated by hourly Penmen-Monteith mothod on DOY 225, 228, and 236 is 4.68, 1.04, and 3.54 mm/day. Crop coefficient (Kc) is 0.88, 0.92, and 0.96, respectively. The variation of Kc is in agreement with the ET in clear day.
Comprehensive methylome analysis of ovarian tumors reveals hedgehog signaling pathway regulators as prognostic DNA methylation biomarkers

Women with advanced stage ovarian cancer (OC) have a five-year survival rate of less than 25%. OC progression is associated with accumulation of epigenetic alterations and aberrant DNA methylation in gene promoters acts as an inactivating “hit” during OC initiation and progression. Abnormal DNA methylation in OC has been used to predict disease outcome and therapy response. To globally examine DNA methylation in OC, we used next-generation sequencing technology, MethylCap-sequencing, to screen 75 malignant and 26 normal or benign ovarian tissues. Differential DNA methylation regions (DMRs) were identified, and the Kaplan–Meier method and Cox proportional hazard model were used to correlate methylation with clinical endpoints. Functional role of specific genes identified by MethylCap-sequencing was examined in in vitro assays. We identified 577 DMRs that distinguished (p < 0.001) malignant from non-malignant ovarian tissues; of these, 63 DMRs correlated (p < 0.001) with poor progression free survival (PFS). Concordant hypermethylation and corresponding gene silencing of sonic hedgehog pathway members ZIC1 and ZIC4 in OC tumors was confirmed in a panel of OC cell lines, and ZIC1 and ZIC4 repression correlated with increased proliferation, migration and invasion. ZIC1 promoter hypermethylation correlated (p < 0.01) with poor PFS. In summary, we identified functional DNA methylation biomarkers significantly associated with clinical outcome in OC and suggest our comprehensive methylome analysis has significant translational potential for guiding the design of future clinical investigations targeting the OC epigenome. Methylation of ZIC1, a putative tumor suppressor, may be a novel determinant of OC outcome.
Adaptive Dynamics of Pathogen–Host Interactions

This chapter explains why the traditional approach of predicting evolutionary outcomes by maximizing the basic reproduction ratio of a disease is not always appropriate. Since pathogens tend to affect their host environment in radical ways, selection pressures usually depend on the types of pathogens and hosts that are established in an infected population. After outlining the theory of adaptive dynamics as a versatile toolbox for investigating the evolution and coevolution of pathogen-host interactions under conditions of frequency-dependent selection, examples illustrate how classic methods and the new models presented here result in different predictions about the evolution of infectious diseases.
A new softening agent for melt spinning of softwood kraft lignin

Kraft lignin obtained from the pulping of wood is an interesting new precursor material for carbon fiber production because of its high carbon content and ready availability. However, continuous spinning of softwood kraft lignin (SKL) has been impossible because of its insufficient softening characteristics and neat hardwood kraft lignin (HKL) has required extensive pretreatments to enable fiber formation. Softwood kraft lignin permeate (SKLP) and hardwood kraft lignin permeate (HKLP), fractionated by membrane filtration, were continuously melt spun into fibers. To improve the spinnability of SKL and HKL, HKLP was added as a softening agent. SKL- and HKL-based fibers were obtained by adding 3–98 wt % HKLP. A suitable temperature range for spinning was 20–85°C above the Tg of the lignin samples, and this range gave a flawless appearance according to the SEM analysis. Smooth, homogeneous fibers of SKLP, HKLP, and SKL with HKLP were successfully processed into solid carbon fibers. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013
Double Emulsion‐Templated Nanoparticle Colloidosomes with Selective Permeability

Colloidosomes are microcapsules whose shell consists of densely packed colloidal particles. Their physical properties such as permeability, mechanical strength, and biocompatibility can be precisely controlled through the proper choice of colloids and preparation conditions for their assembly. The high degree of control over their physical properties makes colloidosomes attractive structures for encapsulation and controlled release of materials ranging from fragrances and active ingredients to molecules produced by living cells. The conventional approach to preparing colloidosomes involves two steps: first, colloidal particles are introduced into the continuous phase of a water-in-oil emulsion. The particles self-assemble at the interface between the two immiscible liquid phases and form a colloidal shell structure. Subsequently, the colloidal shell structures, templated by the water droplets dispersed in the oil phase, are transferred to an aqueous phase either by centrifugation or repeated washing. This strategy has been used to create colloidosomes with particles ranging from 5 nm to several microns in diameter. The colloidosome structures must be mechanically stabilized to successfully transfer them into an aqueous phase; however, even with this stabilization, the transfer processes are not efficient, often leading to damage of the original structure. In addition, the size distribution of colloidosomes prepared by the conventional technique is rather broad, as polydisperse emulsions are typically used as templates. A narrow size distribution for colloidosomes is especially important for the encapsulation of active pharmaceutical ingredients where delivery of an exact amount is critical. Thus, it is important to develop a more convenient and direct means to create monodisperse and fully controllable colloidosomes, in particular, eliminating the transfer step. In this work, we demonstrate that nanoparticle colloidosomes with selective permeability can be prepared from monodisperse double emulsions as templates. Monodisperse
Fabrication and electrical characterization of bamboo fiber-reinforced polypropylene composite

The objective of this research is to characterize the bamboo fiber extracted from bamboo culms and to use these fibers as reinforcement in polypropylene-based composites. In the experiment, the fiber was characterized using wet chemical analysis, scanning electron microscope, and Fourier transform infrared spectroscopy. Chemical analysis showed that bamboo fiber had high amount of holocellulose containing alpha cellulose with compact sclerenchyma sheath. Bamboo fiber showed significant wave length at 3420 cm−1 for –OH stretching, 2921 cm−1 for –CH stretching, and 896 cm−1 for 1,4-β linkage for cellulose units in Fourier transform infrared spectroscopy spectrum. This paper also illustrates the electrical properties of the fabricated composite that consist of bamboo fiber-reinforced polypropylene. At constant temperature (about 30℃), frequency dependence of capacitance, resistivity, and dielectric constant was observed. There were approximately 19.35%, 90.38%, and 66.67% decrease in capacitance, resistance, and dielectric constant, respectively, over a range of frequency from 1 kHz to 10 kHz. This research is focused to use the polymeric composite as an insulating material while observing its electrical properties.
Study of service innovation on School Bullying treatment with inviting Knowledge Management features

School Bullying (SB) has substantially received great importance of global attention. SB is a complicated matter with various factors involved. In the past decades, the traditional SB treatment (SBT) approaches are getting invalid due to the environment changing on social, economical, technological, political, and cultural aspects. In such rapid changing world, innovation is perceived as an ideal means of reactivating declining affairs. This paper presents an innovation study aiming at inviting Knowledge Management (KM) to support SBT. With operating a technology foresight (TF) based process, a vision network implicating eight essential KM-supported SBT initiatives are determined. The introduced study may not only contribute to designing new SBT functions, but sketch a considerable business model for stakeholders to modernize SBT services.
Students behavioural analysis in an online learning environment using data mining

The focus of this research was to use Educational Data Mining (EDM) techniques to conduct a quantitative analysis of students interaction with an e-learning system through instructor-led non-graded and graded courses. This exercise is useful for establishing a guideline for a series of online short courses for them. A group of 412 students' access behaviour in an e-learning system were analysed and they were grouped into clusters using K-Means clustering method according to their course access log records. The results explained that more than 40% from the student group are passive online learners in both graded and non-graded learning environments. The result showed that the difference in the learning environments could change the online access behaviour of a student group. Clustering divided the student population into five access groups based on their course access behaviour. Among these groups, the least access group (NG-41% and G-42%) and the highest access group (NG-9% and G-5%) could be identified very clearly due to their access variation from the rest of the groups.
Stillbirth risk with social class and deprivation : no evidence for increasing inequality

0895-4356/00/$ – see front matter © 2000 Elsevier Science Inc. All rights reserved. PII: S0895-4356(99)00169-9 Stillbirth risk with social class and deprivation: no evidence for increasing inequality T.J.B. Dummera, H.O. Dickinsona,*, M.S. Pearcea, M.E. Charltonb, L. Parkera aNorth of England Children’s Cancer Research Unit, Department of Child Health, University of Newcastle upon Tyne, Royal Victoria Infirmary, Queen Victoria Road, Newcastle upon Tyne, NE1 4LP, England, UK bDepartment of Geography, University of Newcastle upon Tyne, Daysh Building, Newcastle upon Tyne, NE1 7RU, England, UK Received 2 December 1998; received in revised form 2 August 1999; accepted 23 August 1999
Hitmachine: Collective Musical Expressivity for Novices

This paper presents a novel platform for expressive music making called Hitmachine. Hitmachine lets you build and play your own musical instruments from Legos and sensors and is aimed towards empowering everyone to engage in rich music making despite prior musical experience. The paper presents findings from a 4-day workshop where more that 150 children from ages 3-13 built and played their own musical instruments. The children used different sensors for playing and performed with their instruments on stage. The findings show how age influenced the children’s musical understanding and expressivity, and gives insight into important aspects to consider when designing for expressive music for novices.
Road Detection by One-Class Color Classification: Dataset and Experiments

Detecting traversable road areas ahead a moving vehicle is a key process for modern autonomous driving systems. A common approach to road detection consists of exploiting color features to classify pixels as road or background. These algorithms reduce the effect of lighting variations and weather conditions by exploiting the discriminant/invariant properties of different color representations. Furthermore, the lack of labeled datasets has motivated the development of algorithms performing on single images based on the assumption that the bottom part of the image belongs to the road surface. In this paper, we first introduce a dataset of road images taken at different times and in different scenarios using an onboard camera. Then, we devise a simple online algorithm and conduct an exhaustive evaluation of different classifiers and the effect of using different color representation to characterize pixels.
Tribological Behaviors of Fluorinated Polyimides at Different Temperatures

4,4′-(hexafluoroisopropylidene) diphthalic anhydride (6FDA)-based copolyimides were synthesized and the tribological properties of the copolyimides with different heat histories were studied at different temperatures. Fluoride-containing polyimide (PI) showed better thermal stability, decreased friction coefficients, and postponed the consequence of friction variation, which depended on temperature, than nonfluorinated PI. Thermal treatments seemed to increase the friction coefficients of copolyimides, and reduced the tensile strengths of the materials. The effects of applied load (P) and sliding speed (V) on tribological behaviors of thermally treated copolyimides were also examined and the variations of friction coefficient depending on PV values were investigated for clear understanding of its relationship between PV value and friction coefficient with different thermal treating time. Distortions of net structures of the chains and molecular motion contributed to variations of tribological properties of thermal treated copolyimides.