Miracle-dz/UniFAR-FTUs huggingface dataset:
A High-Performance and Secure TRNG Based on Chaotic Cellular Automata Topology

As an important hardware security primitive, the true random number generator (TRNG) has been widely utilized in many critical applications. The performance and security of TRNGs are always dominant features that determine the usability of a TRNG scheme. In this article, we propose a novel TRNG design method based on a self-timed ring structure. Different from most existing self-timed circuit-based TRNGs that use ring oscillators, the basic circuit component of the proposed TRNG is a digital realization of a chaotic cellular automata topology. We utilize three different methods to validate the proposed TRNG method, including HSpice simulation, FPGA prototype, and ASIC test chips. As the proposed TRNG structure is pure digital, thus it is synthesizable with standard all-digital components. The test chips of the proposed TRNG structure are fabricated with 40nm TSMC technology node, with a hardware footprint equals to 75 NAND gates and a die area of $270\mu m^{2}$ . With a PCIe setup connecting the testchip and computer, the TRNGs achieves high throughput as 1600Mb/s. The FPGA implementation is built on a Virtex-6 family FPGA from Xilinx, which also achieves lightweight overhead: 53 look-up-table (LUT) and 22 D Flip-flops (DFF). The collected random numbers from FPGA implementations and ASIC testchips are comprehensively tested with three test suites, including NIST SP800–22, NIST SP800–90B, and AIS-31 with a high pass-rate. Further, the security of the TRNG testchips and FPGA implementations are validated by applying three different attacks, including frequency injection attacks, power attacks, and thermal attacks. The experimental results demonstrate that the proposed TRNG structure is immune to these attacks with trivial entropy loss
Corporate Bankruptcy Measurement and Prediction: Evidence from Publicly Traded US Firms

We develop a bankruptcy classification model combining financial ratio analysis, measurement theory, and logistic analysis from a sample of 258 bankrupt and non-bankrupt public companies in the United States. Transformed financial variables are developed to a bankruptcy classification measurement model using the confirmatory factor analysis, which is then refined to a four variable, logit bankruptcy model. The result shows that the model possesses high classification accuracy and relatively small differences in classification rates between in-sample and out-of-sample as compared to industry-relative analysis. As such, our findings help managers more accurately estimate bankruptcy risk and thus, have a better opportunity to take corrective actions early, enhancing corporate financial sustainability.
Distributed Hypergraph Processing Using Intersection Graphs

The advent of online applications such as social networks has led to an unprecedented scale of data and complex relationships among data. Hypergraphs are introduced to represent complex relationships that may involve more than two entities. A hypergraph is a generalized form of a graph, where edges are generalized to hyperedges. Each hyperedge may consist of any number of vertices. The flexibility of hyperedges also brings challenges in distributed hypergraph processing. In particular, a hypergraph is more difficult to be partitioned and distributed among <inline-formula><tex-math notation="LaTeX">$k$</tex-math> <alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="gu-ieq1-3022014.gif"/></alternatives></inline-formula> workers with balanced partitions. In this paper, we propose to convert a hypergraph into an intersection graph before partitioning by leveraging the inherent shared relationships among hypergraphs. We explore the intersection graph construction method and the corresponding partition strategy which can achieve the goal of evenly distributing vertices and hyperedges across workers, while yielding a significant communication reduction. We also design a distributed processing framework named <inline-formula><tex-math notation="LaTeX">$Hyraph$</tex-math> <alternatives><mml:math><mml:mrow><mml:mi>H</mml:mi><mml:mi>y</mml:mi><mml:mi>r</mml:mi><mml:mi>a</mml:mi><mml:mi>p</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="gu-ieq2-3022014.gif"/></alternatives></inline-formula> that can directly run hypergraph analysis algorithms on our intersection graphs. Experimental results on real datasets confirm the effectiveness of our techniques and the efficiency of the <inline-formula><tex-math notation="LaTeX">$Hyraph$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>H</mml:mi><mml:mi>y</mml:mi><mml:mi>r</mml:mi><mml:mi>a</mml:mi><mml:mi>p</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="gu-ieq3-3022014.gif"/></alternatives></inline-formula> framework.
The Professionalization of Honors Education

Honors education in America has undergone a process that sociologist Theodore Caplow describes as professionalization . Caplow identifies four stages whereby a developing profession transitions to a professional association: organizing membership, changing the name of occupation from its previous status, developing a code of ethics, and after a period of political agitation, beginning a process by which to enforce occupational barriers . Each of these defined stages present new challenges to honors educators . This paper examines honors education in the context of specialization, considering both the origins and growth of honors education in the last century and contemporary discourse relating to certification and systematic program review . While controversy over certification has abated in recent years, Caplow’s theory suggests that the issue is likely to resurface .
Peloria and pseudopeloria in british orchids

Peloric orchids have either (a) the lateral , inner perianth segments replaced by additional labella or (b) the labellum replaced by a third, undifferentiated, inner perianth segment. Type (a) mutants have been reported for several British and Irish orchid species , but type (b) mutants are extremely rare. Aberrant orchids with poorly differentiated labella that resemble their outer perianth segments ('sepaloid') are best described as pseudopeloric. They have often been misidentified as type (b) peloric individuals or as hybrids , and are more frequent than has previously been suggested.
A Key For Hypoxia Genetic Adaptation In Alpaca Could Be A HIF1A Truncated bHLH Protein Domain

Animals exposed to hypoxia, triggers a physiological response via Hypoxia Inducible Factors (HIF1). In this study, we have evidenced the existence of genetic events that caused the loss of most of the bHLH domain in HIF1A proteins borne by Alpaca and other members of the Cetartiodactyla superorder. In these truncate domains, some stop codons are found at identical nucleotide positions in both, Artiodactyls and Cetaceans, indicating that mutations originating the truncated domains occurs before their divergence about 55 million years ago. The relevance of this findings for adaptation of Alpacas to hypoxia of high altitude conditions are discussed.
Intrinsic polarization cues interfere with pheromone gradient sensing in S. cerevisiae

Polarity decisions are central to many processes, including mitosis and chemotropism. In S. cerevisiae, budding and mating projection (MP) formation use an overlapping system of cortical landmarks that converge on the small G-protein Cdc42. However, pheromone gradient sensing must override the Rsr1-dependent internal polarity cues used for budding. Using this model system, we asked what happens when intrinsic and extrinsic spatial cues are misaligned. Is there competition, or collaboration? By live cell microscopy and microfluidics technics we uncovered three previously overlooked features of this signaling system. First, the cytokinesis-associated polarization patch serves as a polarity landmark independently of all known cues. Second, the Rax1-Rax2 complex functions as novel pheromone promoted polarity cue in the distal pole of the cells. Finally, we showed that internal cues remain active during pheromone gradient tracking and that they interfere with this process biasing the location of MPs, since yeast defective in internal cue utilization align significantly better than wild-type with artificially generated pheromone gradients.
EMF Voltage Distortion Mitigation in Fractional-Slot Permanent Magnet Machines by Varying Coil Distribution and Turn Ratio

Electromagnetic force (EMF) voltage distortion, caused by armature reaction, not only deteriorates torque ripple in constant torque region but also reduces the flux weakening operation region, especially for fractional slot permanent magnet (PM) machines. This article mitigates voltage distortion by varying coil distribution and turn ratio with employment of triple layer (TL) and quadruple layer (QL) windings with varied coil turns. Coil turn ratios of TL and QL windings are optimized to minimize voltage distortion ratio. Reduction mechanisms are exhaustively analyzed. It is found that, for the TL winding with optimal turn ratio, the armature leakage flux and PM leakage flux in tooth tips will not reverse polarities simultaneously, resulting in much lower voltage distortion. On the other hand, for the QL winding, voltage distortion reduction is realized by lowering the amplitude of armature tooth tips leakage flux. Compared with conventional double or single layer (SL) winding machines, torque ripple can be reduced and flux weakening capability is greatly enhanced. The 12-slot/10-pole prototypes with both SL and optimum TL windings are fabricated and tested to verify the analysis.
Microclima: An r package for modelling meso‐ and microclimate

Climate is of fundamental importance to the ecology and evolution of all organisms. However, studies of climate–organism interactions usually rely on climate variables interpolated from widely spaced measurements or modelled at coarse resolution, whereas the conditions experienced by many organisms vary over scales from millimetres to metres. To help bridge this mismatch in scale, we present models of the mechanistic processes that govern fine‐scale variation in near‐ground air temperature. The models are flexible (enabling application to a wide variety of locations and contexts), can be run using freely available data and are provided as an R package. We apply a mesoclimate model to the Lizard Peninsula in Cornwall to provide hourly estimates of air temperature at resolution of 100 m for the period Jan‐Dec 2010. A microclimate model is then applied to a 1 km2 region of the Lizard Peninsula, Caerthillean Valley (49.969°N, 5.215°W), to provide hourly estimates of near‐ground air temperature at resolution of 1 m2 during May 2010. Our models reveal substantial spatial variation in near‐ground temperatures, driven principally by variation in topography and, at the microscale, by vegetation structure. At the meso‐scale, hours of exposure to air temperatures at 1 m height in excess of 25°C ranged from 23 to 158 hr, despite this temperature never being recorded by the weather station within the study area during the study period. At the micro‐scale, steep south‐facing slopes with minimal vegetation cover experienced temperatures in excess of 40°C. The microclima package is flexible and efficient and provides an accurate means of modelling fine‐scale variation in temperature. We also provide functions that facilitate users to obtain and process a variety of freely available datasets needed to drive the model.
Development of a data-driven model for turbulent heat transfer in turbomachinery

Machine Learning (ML) algorithms have become popular in many fields, including applications related to turbomachinery and heat transfer. The key properties of ML are the capability to partially tackle the problem of slowing down of Moore’s law and to dig-out correlations within large datasets like those available on turbomachinery. Data come from experiments and simulations with different degree of accuracy, according to the test-rig or the CFD approach. When dealing with modelling of turbulent flows in turbomachinery there is a constant trade-off between accuracy and computational costs, but starting from the large amount of data on turbomachinery performance, with ML it is possible to train a learner to correct and improve CFD. The aim of this work is to investigate an innovative data-driven approach that could lead to a significant improvement in the analysis of heat transfer in turbulent flows. The effects of Reynolds number and wall temperature on heat transfer for a double forward-facing step with two squared obstacles were investigated by numerical simulations carried out in OpenFOAM. Then a machine-learnt model was derived using a regression algorithm. The results of regressor showed that a data-driven approach can effectively predict results of the RANS model.
Los criaderos de esclavos: medicina, cuerpos y sexualidad en los ingenios de Cuba

Abstract:In the following essay I explore the control of slave’s bodies and sexuality in Cuba during 19th century. I focus my attention on the medical manuals that were written by doctors such as Honorato Bernard Chateausalins, Justino Valdés Castro and Henri Dumont, and the practice of slave breeding in the plantations. I analyze the architecture and space design of the “barracón”, the reasons for the control of the slave’s reproduction, and the methods of surveillance, punishment and persuasion that were used to ensure their compliance. The idea is to show how science and slave power joined forces to guarantee the slaves’ maximum performance in the ingenious which is something few articles have explored before.
A data-driven agent-based simulation to predict crime patterns in an urban environment

Spatial crime simulations contribute to our understanding of the mechanisms that drive crime and can support decision-makers in developing effective crime reduction strategies. Agent-based models that integrate geographical environments to generate crime patterns have emerged in recent years, although data-driven crime simulations are scarce. This article (1) identifies numerous important drivers of crime patterns, (2) collects relevant, openly available data sources to build a GIS-layer with static and dynamic geographical, as well as temporal features relevant to crime, (3) builds a virtual urban environment with these layers, in which individual offender agents navigate, (4) proposes a data-driven decision-making process using machine-learning for the agents to decide whether to engage in criminal activity based on their perception of the environment and, finally, (5) generates fine-grained crime patterns in a simulated urban environment. The novelty of this work lies in the various large-scale data layers, the integration of machine learning at individual agent level to process the data layers, and the high resolution of the resulting predictions. The results show that the spatial, temporal, and interaction layers are all required to predict the top street segments with the highest number of crimes. In addition, the spatial layer is the most informative, which means that spatial data contributes most to predictive performance. Thus, these findings highlight the importance of the inclusion of various open data sources and the potential of theory-informed, data-driven simulations for the purpose of crime prediction. The resulting model is applicable as a predictive tool and as a test platform to support crime reduction.
Age-of-Information-Based Host Selection for Mobile User Provided Networks

The concept of user-provided networks (UPNs) have been investigated to utilize the power of citizens in the deployment of mobile networks. In addition to conventional fixed host models, mobile models have been proposed to utilize movable hosts including mobile phone users. When mobile UPNs are considered, it becomes difficult for clients to establish a stable connection to hosts because they can be in moving environments, such as public buses and taxis. To address this problem, this article focuses on Age of Information (AoI) which a measure of the freshness of a continually updated piece of information. Thus, this article proposes an AoI-based host-selection algorithm for mobile UPNs for establishing stable connection. It determines the host device to establish a wireless link based on the history of values and AoI of received signal strength indicator (RSSI). The performance of the proposed scheme is confirmed through theoretical analysis and computer simulations.
Population biology and reproductive characteristics of the hermit crab Pagurus minutus Hess, 1865 (Decapoda: Anomura: Paguridae) in the northern part of the species range (Peter the Great Bay, the Sea of Japan)

ABSTRACT Population and reproductive characteristics of the hermit crab Pagurus minutus Hess, 1865 were examined in the northern part of the species range (Vostok Bay, Peter the Great Bay, the Sea of Japan). The average population density was 13 ± 4 SE ind/m2 and did not change during the year; the average biomass was 12.56 ± 3.43 SE g/m2. Hermit crabs were found at a depth of 0–6 m, but were most numerous at 0–1 m in summer and at 2–3 m in winter, hence this species showed distinct seasonal migrations. The shield length of males varied from 1.0 to 7.0 mm and females from 1.1 to 4.7 mm. The population of P. minutus was signiﬁcantly female-biased. Females attained sexual maturity at a shield length of more than 1.1 mm. Ovigerous females were found from early May to early September. Pagurus minutus has no embryonic diapause, with the incubation period lasting about a month. The increase in the number of females with eggs at early developmental stages in July–August suggested that repeated ovipositions took place in summer. The prevalence of females in the population and their early maturation, multiple spawning, the release of larvae during the most favourable time for their development, and distinct vertical migrations allowing avoidance of the negative effect of the ice-cover, ensure high abundance of P. minutus in the northern part of the species range.
Compensation Network Optimal Design Based on Evolutionary Algorithm for Inductive Power Transfer System

Conventional design and optimization of passive compensation network (PCN) for inductive power transfer (IPT) system are based on specific topologies. The demerits of this design method are: i) The topology is mostly chosen by experience; ii) The design parameters are not multi-objective optimal. Aiming at these issues, this paper proposes an optimal PCN design scheme based on evolutionary algorithm (EA) to synchronously optimize the topology and parameters of PCN for IPT system. Firstly, a unified mathematical model of the PCN is presented and derived by transmission matrix. Then, according to the mathematical model, the multi-objective functions (such as output fluctuation and efficiency) as well as the constraints (such as load and coupling coefficient) for the optimal PCN design are established. The EA based multi-objective optimal PCN design algorithm is further constructed. Six optimal results are obtained using the algorithm, and one optimized PCN having minimum output current fluctuation and high-efficiency is chosen to validate the effectiveness of the proposed design scheme in experiment. For the given IPT system with the optimized PCN, the maximum fluctuation of output current is no more than 11%, within 200% of load variation and about 77% of coupling variation.
Grand Challenges in Central Europe: The Relationship of Food Security, Climate Change, and Energy Use

Pursuing various sustainable development goals is posing new challenges for societies, policymakers, and researchers alike. This study implements an exploratory approach to address the complexity of food security and nuance its relationship with other grand challenges, such as energy use and climate change, in Central European countries. A multiple factor analysis (MFA) suggests that the three pillars of food security relate differently to climate change: food affordability and food accessibility positively correlate with climate change, while food quality has a negative association with temperature rise. However, if countries switched to renewable energy resources, all three pillars of food security could be achieved simultaneously. The study also underlines regional inequalities regarding grand challenges and emphasizes the need for innovative local solutions, i.e., advances in agriculture systems, educational programs, and the development of environmental technologies that consider social and economic issues.
A Generic Global-to-Local Quantitative Transformation Model (GGTLQTM) for Modeling Socioeconomic Indicators From DMSP-OLS Nighttime Light Imagery

To expand the application range of Defense Meteorological Satellite Program Operational Line Scanner (DMSP-OLS) nighttime light (NTL) imagery and fully utilize the value of such imagery in this letter of socioeconomic indicators on a small scale, this letter proposes a novel model, that is, the generic global-to-local quantitative transformation model (GGTLQTM), and a new comprehensive indicator of accuracy, that is, adjusted normalized cross-validation (ANCV). The consistency of the GGTLQTM, from its fundamental hypotheses to conclusions, was studied to discuss the reliability of the model. Thirteen county-level administrative regions of Wuhan, China, were selected as the research areas. It was found that the GGTLQTM can 1) obtain a relatively high accuracy by selecting an optimal linear or nonlinear model, that the quadratic polynomial is the optimal GGTLQTM assessed by ANCV; 2) be established without relying on strict hypotheses; and 3) detect universal (global) homogeneity and individual (local) heterogeneity. In letter, the proposed GGTLQTM not only expands the application range of DMSP-OLS NTL remote sensing but also proposes a new idea on the geoscience-scale transformation, which helps integrate geoscience and remote sensing.
Advances in Deep Learning through Gradient Amplification and Applications

Deep neural networks currently play a prominent role in solving problems across a wide variety of disciplines. Improving performance of deep learning models and reducing their training times are some of the ongoing challenges. Increasing the depth of the networks improves performance but suffers from the problem of vanishing gradients and increased training times. In this research, we design methods to address these challenges in deep neural networks and demonstrate deep learning applications in several domains. We propose a gradient amplification based approach to train deep neural networks, which improves their training and testing accuraries, addresses vanishing gradients, as well as reduces the training time by reaching higher accuracies even at higher learning rates. We also develop an integrated training strategy to enable/disable amplification at certain epochs. Detailed analysis is performed on different neural networks using random amplification, where the layers to be amplified are selected randomly. The implications of gradient amplification on the number of layers, types of layers, amplification factors, training strategies and learning rates are studied in detail. With this knowledge, effective ways to update gradients are designed to perform amplification at layer-level and also at neuron-level. Lastly, we provide applications of deep learning methods to some of the challenging problems in the areas of smartgrids and bioinformatics. Deep neural networks with feed forward architectures are used to solve data integrity attacks in smart grids. We propose an image based preprocessing method to convert heterogenous genomic sequences into images which are then classified to detect Hepatitis C virus(HCV) infection stages. In summary, this research advances deep learning techniques and their applications to real world problems. INDEX WORDS: Deep learning, Gradient amplification, Learning rate, Vanishing gradient, Smart grid, Sequence image normalization ADVANCES IN DEEP LEARNING THROUGH GRADIENT AMPLIFICATION AND APPLICATIONS