Miracle-dz/UniFAR-FTUs huggingface dataset:
Including nuclear degrees of freedom in a lattice Hamiltonian

Motivated by many observations of anomalies in condensed matter systems, we consider a new fundamental Hamiltonian in which condensed matter and nuclear systems are described initially on the same footing. Since it may be possible that the lattice will respond to the mass change associated with a excited nuclear state, we adopt a relativistic description throughout based on a many-particle Dirac formalism. This approach has not been used in the past, perhaps due to the difficulty in separating the center of mass and relative degrees of freedom of the nuclear system, or perhaps due to an absence of applications for such a model. In response to some recent ideas about how to think about the center of mass and relative separation, we obtained from the Dirac model a new fundamental Hamiltonian in which the lattice couples to different states within the composite nuclei within the lattice. In this description the different nuclear states have different mass energies and kinetic energies, as we had expected. In addition there appear new terms which provide for nuclear excitation as a result of coupling to the composite momentum. This new effect comes about because of changes in the composite nuclear state as a result of the dynamical Lorentz boost in the lattice.
Large-amount Blockchain Transaction Conflict Resolution Technology Based on Dependency Graph for Multi-property Assets

The application scenarios of blockchain are gradually expanding, and the assets in the blockchain have also extended to complex digital assets, such as drawings and identity records. Many existing parallelization schemes primarily focus on simple assets like native tokens, are unsuitable for complex assets. Complex assets often require collaborative processing among multiple applications, and their multiple states, properties, and complex interrelationships result in constant changes. This complexity poses challenges for ensuring the correctness of conflict expression in existing solutions. We propose an expression model for conflict factors of complex assets. Additionally, to enhance parallel execution efficiency, we introduce a conflict resolution model based on dependency graphs that considers the conflict factors of complex assets. Experimental results demonstrate that the proposed approach significantly outperforms existing solutions. In high-conflict environments, the concurrency performance of this model improves by approximately 100%.
Dietary Sodium, Aldosterone, and Left Ventricular Mass Changes During Long-Term Inhibition of the Renin-Angiotensin System

In essential hypertension, the regression of left ventricular hypertrophy is an important goal of treatment. In addition to treatment-associated changes in blood pressure (BP), the roles of other determinants of left ventricular hypertrophy regression, including dietary sodium intake, deserve investigation. In the present study, the change in echographic left ventricular mass index (LVMI) was assessed in 182 patients with never-treated essential hypertension at baseline and after 3 years of treatment by angiotensin converting enzyme inhibitors or angiotensin II receptor antagonists given at recommended doses and associated with other antihypertensive agents. Treatment was associated with satisfactory control of BP (<140/90 mm Hg) in 64% of patients, and left ventricular hypertrophy prevalence decreased from 56% to 39%. Twenty-four–hour urinary sodium was positively related to LVMI at baseline and at the end of the study, independently of age, sex, and systolic BP. Supine plasma aldosterone concentration was correlated with LVMI only at baseline but not in multivariate analysis. In response to treatment, the percentage of change in LVMI was positively correlated with the absolute changes in systolic BP, urinary sodium, and plasma aldosterone concentration, independently of baseline LVMI. The population was divided into 3 tertiles according to final values of gender-specific urinary sodium. When, within each urinary sodium tertile, patients were divided into those with plasma aldosterone concentration below and above the median (11.6 ng/dL), LVMI progressively increased across sodium tertiles only in patients with high plasma aldosterone concentration. Systolic BP was similar across all of the groups. In conclusion, aldosterone requires the presence of high dietary salt for the expression of its unfavorable effect on the heart.
Cassiopeia: A model based on summarization and clusterization used for knowledge discovery in textual bases

This work proposes the creation of a model called Cassiopeia, whose aim is to allow for knowledge discovery in textual bases in distinct and/or antagonic domains by using a process of summarization and clusterization to obtain these pieces of knowledge. By proposing the Cassiopeia model, we hope to obtain a better cohesion of the clusters and to make feasible the issue of high dimensionality in knowledge discovery in textual bases.
Estimating Seasonally Frozen Ground Depth From Historical Climate Data and Site Measurements Using a Bayesian Model

We develop a Bayesian model to predict the maximum thickness of seasonally frozen ground (MTSFG) using historical air temperature and precipitation observations. We use the Stefan solution and meteorological data from 11 stations to estimate the MTSFG changes from 1961 to 2016 in the Yellow River source region of northwestern China. We employ an antecedent precipitation index model to estimate changes in the liquid soil water content. The marginal posterior probability distributions of the antecedent precipitation index parameters are estimated using Markov chain Monte Carlo sampling methods. We compare the results of our stochastic method with those obtained from the traditional deterministic method and find that they are consistent in general. The stochastic approach is effective for estimating the historical changes in the frozen ground depth (root‐mean‐square errors = 0.13–0.35 m), and it provides more information on model uncertainty regarding soil moisture variations. Additionally, simulation shows that the MTSFG has decreased by 0.31 cm per year over the last 56 years on the northeastern Qinghai‐Tibet Plateau. This decrease in frost depth accelerated in the 1990s and 2000s. Considering the lack of data on seasonally frozen soil monitoring, the Bayesian method provides a pragmatic approach to statistically model frozen ground changes using available meteorological data.
Analysis and Visualization of Maps Between Shapes

In this paper we propose a method for analysing and visualizing individual maps between shapes, or collections of such maps. Our method is based on isolating and highlighting areas where the maps induce significant distortion of a given measure in a multi‐scale way. Unlike the majority of prior work, which focuses on discovering maps in the context of shape matching, our main focus is on evaluating, analysing and visualizing a given map, and the distortion(s) it introduces, in an efficient and intuitive way. We are motivated primarily by the fact that most existing metrics for map evaluation are quadratic and expensive to compute in practice, and that current map visualization techniques are suitable primarily for global map understanding, and typically do not highlight areas where the map fails to meet certain quality criteria in a multi‐scale way. We propose to address these challenges in a unified way by considering the functional representation of a map, and performing spectral analysis on this representation. In particular, we propose a simple multi‐scale method for map evaluation and visualization, which provides detailed multi‐scale information about the distortion induced by a map, which can be used alongside existing global visualization techniques.
Resolving scale ambiguity for monocular visual odometry

Scale ambiguity is an inherent problem in monocular visual odometry and SLAM. Our approach is based on common assumptions such that the ground is locally planar and its distance to a camera is constant. The assumptions are usually valid in mobile robots and vehicles moving in indoor and on-road environments. Based on the assumptions, the scale factors are derived by finding the ground in locally reconstructed 3D points. Previously, kernel density estimation with a Gaussian kernel was applied to detect the ground plane, but it generated biased scale factors. This paper proposes an asymmetric Gaussian kernel to estimate unknown scale factors accurately. The asymmetric kernel is inspired from a probabilistic modeling of inliers and outliers, that is, 3D point can comes from the ground and also other objects such as buildings and trees. We experimentally verified that our asymmetric kernel had almost twice higher accuracy than the previous Gaussian kernel. Our experiments was based on an open-source visual odometry and two kinds of public datasets.
Research and Design of a Grid Based Electronic Commerce Recommendation System

Current electronic commerce recommendation system is designed for single electronic commerce website and current recommendation technologies have obvious deficiencies Centralized recommendation systems can not resolve the contradiction between high recommendation quality and timely response, as well as that between limited recommendation range and ever rich information on the web. Distributed recommendation systems are expected to improve the recommendation quality while maintaining high performance. This paper analyses the problems of traditional electronic commerce recommendation system and clarifies the advantage of applying grid technology into electronic commerce recommendation system. It discusses the prototype of an intelligent recommendation system, namely grid based electronic commerce recommendation system (GBECRS). The paper analyses rationale and mechanism of GBECRS and designs its logical structure. It focuses on the design of grid services which are needed to be deployed into electronic commerce recommendation grid. Finally it does deep analysis of key technologies that are applied in the system.
SOLSA: Neuromorphic Spatiotemporal Online Learning for Synaptic Adaptation

Spiking neural networks (SNNs) are bio-plausible computing models with high energy efficiency. The temporal dynamics of neurons and synapses enable them to detect temporal patterns and generate sequences. While Backpropagation Through Time (BPTT) is traditionally used to train SNNs, it is not suitable for online learning of embedded applications due to its high computation and memory cost as well as extended latency. In this work, we present Spatiotemporal Online Learning for Synaptic Adaptation (SOLSA), which is specifically designed for online learning of SNNs composed of Leaky Integrate and Fire (LIF) neurons with exponentially decayed synapses and soft reset. The algorithm not only learns the synaptic weight but also adapts the temporal filters associated to the synapses. Compared to the BPTT algorithm, SOLSA has much lower memory requirement and achieves a more balanced temporal workload distribution. Moreover, SOLSA incorporates enhancement techniques such as scheduled weight update, early stop training and adaptive synapse filter, which speed up the convergence and enhance the learning performance. When compared to other non-BPTT based SNN learning, SOLSA demonstrates an average learning accuracy improvement of 14.2%. Furthermore, compared to BPTT, SOLSA achieves a 5% higher average learning accuracy with a 72% reduction in memory cost.
Formulation of a mathematical model for the analysis of the emission of carbon dioxide from gaseous fuel using least square method

In this paper we formulate a model for the emission of Carbon Dioxide from gaseous fuel in Nigeria. We considered a third degree polynomial model using a least square method using the actual data set of twenty one years on State level Carbon Dioxide emissions in Nigeria from 1994-2014. The residual analysis was used to predict the short and medium term total Carbon Dioxide emissions trend. From the results the state of affairs of Carbon Dioxide Emission for subsequent years was forecast and this will help the Government to take control measures in curtailing the emission of Carbon Dioxide in the country. Keywords: Gaseous fuel, Automobile, Fossil fuels, Pollutants, Carbon Dioxide, Emissions
The Lower Anogenital Squamous Terminology Standardization Project for HPV-Associated Lesions: Background and Consensus Recommendations From the College of American Pathologists and the American Society for Colposcopy and Cervical Pathology

Abstract The terminology for human papillomavirus (HPV)–associated squamous lesions of the lower anogenital tract has a long history marked by disparate diagnostic terms derived from multiple specialties. It often does not reflect current knowledge of HPV biology and pathogenesis. A consensus process was convened to recommend terminology unified across lower anogenital sites. The goal was to create a histopathologic nomenclature system that reflects current knowledge of HPV biology, optimally uses available biomarkers, and facilitates clear communication across different medical specialties. The Lower Anogenital Squamous Terminology (LAST) Project was cosponsored by the College of American Pathologists and the American Society for Colposcopy and Cervical Pathology and included 5 working groups; 3 work groups performed comprehensive literature reviews and developed draft recommendations. Another work group provided the historical background and the fifth will continue to foster implementation of the LAST recommendations. After an open comment period, the draft recommendations were presented at a consensus conference attended by LAST work group members, advisors, and representatives from 35 stakeholder organizations including professional societies and government agencies. Recommendations were finalized and voted on at the consensus meeting. The final, approved recommendations standardize biologically relevant histopathologic terminology for HPV-associated squamous intraepithelial lesions and superficially invasive squamous carcinomas across all lower anogenital tract sites and detail the appropriate use of specific biomarkers to clarify histologic interpretations and enhance diagnostic accuracy. A plan for disseminating and monitoring recommendation implementation in the practicing community was also developed. The implemented recommendations will facilitate communication between pathologists and their clinical colleagues and improve accuracy of histologic diagnosis with the ultimate goal of providing optimal patient care.
CiDRA: A cache-inspired DRAM resilience architecture

Although aggressive technology scaling has allowed manufacturers to integrate Giga bits of cells into a cost-sensitive main memory DRAM device, these cells have become more defect-prone. With increased cell failure rates, conventional solutions such as populating spare DRAM rows and relying on error-correcting codes (ECCs) have shown limited success due to high area overhead, the latency penalties of data coding, and interference between ECC within a device (in-DRAM ECC) and other ECC across devices (rank-level ECC). In this paper, we propose CiDRA, a cache-inspired DRAM resilience architecture, which substantially reduces the area and latency overheads of correcting bit errors on random locations due to these faulty cells. We put a small SRAM cache within a DRAM device to replace accesses to the addresses including the faulty cells with ones that correspond to the cache data array. This CiDRA cache is paired with a Bloom filter to minimize the energy overhead of accessing the cache tags for every DRAM access and is also partitioned into small pieces, each being associated with the I/O pads for better area efficiency. Both the cache and DRAM banks are accessed in parallel while the banks are much slower. Consequently, the cache and filter are not in the critical path for normal DRAM accesses and incur no latency overhead. We also enhance the traditional in-DRAM ECC with error position bits and the appropriate error detecting capability while preventing interference with the traditional rank-level ECC scheme. By combining this enhanced in-DRAM ECC with the cache and Bloom filter, CiDRA becomes more area efficient because the in-DRAM ECC corrects most bit errors that are sporadic while the cache deals with the remaining relatively few pathological cases.
Disaster Report of Koslanda Landslide in Sri Lanka on October 29, 2014

On 29 th October, 2014 during a heavy rain event, a large landslide occurred in Koslanda, Badulla District, Sri Lanka. The landslide and associated debris flow destroyed several houses and killed more than 30 people. Following the landslide, an aerial survey by helicopter on 5 th November, 2014 and field survey on 19 th and 20 th November, 2014 were undertaken by JICA (Japan International Cooperation Agency) Project Team to understand the occurrence mechanism of the landslide and identify further hazard of the landslide. Further, we conducted numerical simulation to understand processes of these sediment disasters. This report summarizes the observation results conducted by the JICA Project Team, presents the geological explanation of the mechanism and causes of the landslide, and gives suggestions on emergency mitigation measure for further movements of the landslides.
CS167: Reading in Algorithms Counting Triangles

In these notes we discuss the earlier sections of a paper of Suri and Vassilvitskii, with the great title \Counting Triangles and the Curse of the Last Reducer" [2]. It is a good example of an applied paper with interesting algorithmic content. Consider an unweighted, undirected graph G = (V;E). G could be anything, but the authors are motivated by graphs derived from social networks. For example, G could be the Facebook graph, where vertices represent people, and the edges represent (bilateral) friendship links. Or G could be derived from a naturally directed social network, such at the Twitter graph (with an arc (u;v) if u follows v), by ignoring the directions of the links. Having an application domain in mind, like social network analysis, can suggest extra properties of the data which guide the decision of what algorithm to use for a computational problem. This point often dierentiates \pure" algorithms papers and \applied" algorithms papers. In the former, there is usually a premium on \general purpose" algorithms that work pretty well on all inputs (your undergrad algorithms class likely had this avor). In the latter, one often wants to tailor the algorithm somewhat to the application domain of interest, to obtain results superior to that of the general-purpose algorithm. We’ll see many more examples of this later in the course. The relevant properties of social networks for today’s discussion are: 1. They are big. Big enough that one needs an algorithm that run in close to linear time. Possibly so big that it doesn’t even
Operating systems support for process dynamic integrity measurement

Facing limitations of existing systems for process integrity measurement, we put forward a method with its prototype system PDIMS to measure process runtime integrity. Based on structure of process and format of executable file, PDIMS anatomizes the codepage layout of runtime process. Combining OS mechanisms and modern CPU's support for code execution, PDIMS catches and measures codepage in the kernel when it executes. PDIMS depends on CPU's non-executable bit to detect code execution and on the binary format of executables of the process as criterion to verify code modifications in kernel. PDIMS provides trustworthy information about whether a running process is modified. PDIMS introduces less than 4% overhead to OS.
The Magic Number: Impact of Sample Size for Dementia Screening Using Transfer Learning and Data Augmentation of Clock Drawing Test Images

Dementia is a disease characterized by memory impairment and a gradual disability in performing daily activities. Automated screening for early detection of dementia can lead to more adequate and timely treatment. Our work focuses on predicting various stages of dementia severity using pre-trained Deep Learning (DL) models and a public Clock Drawing Test (CDT) dataset. However, the relationship between sample size and model performance is not yet well understood. This may lead to an overreliance on a large number of samples for model training, which may eventually deter reliable outcomes. We found that the classification performance of DL models tends to plateau once a certain number of samples is reached, therefore, it is possible to work on a small data regime with DL models in this task. This research not only advances the field of medical image analysis for dementia screening but also offers broader implications for DL applications in healthcare. Ultimately, the understanding of how sample size affects model performance can guide future research and support more intelligent and efficient utilization of DL models in addressing complex health-related challenges.
Post Flood Analysis of Heavy Metal Level in River Benue along Makurdi Metropolis, Nigeria

River water was sampled across various segments of the River within Makurdi town to ascertain the anthropogenic contribution of heavy metal contents, a day after the torrential flood of 2012, and analysed using Atomic Absorption Spectrophotometric techniques. The mean concentration (mg/L) of the metals in sample sites were determined to be; Fe (0.46Â±0.1), Pb (0.03Â±0.01), Mn (0.002Â±0.002), Cr (0.02Â±0.01), Cd (0.001Â±0.001) and Zn (0.004Â±0.001). The results were compared with standards of WHO and EU recommended values for drinking water quality. It was found that Mn, Cr, Cd and Zn were below the WHO/EU limits while Fe and Pb were above recommended value. Statistical analysis shows that there is no significant difference observed for Fe, Mn, Cd and Zn between sites. However, Pb and Cr showed significant differences between sites. This shows that the metals are distributed at different points as a result of human activities, coupled with the impact of flood in their transport and solubility.
HINT1 inhibits β‐catenin/TCF4, USF2 and NFκB activity in human hepatoma cells

In this study we explored the relevance of Hint, a novel tumor suppressor gene, to human hepatoma. The human hepatoma cell lines Hep3B and HepG2 express very low levels of the HINT1 protein but the Huh7 cells express a relatively high level. In Hep3B and HepG2 cells, but not in Huh7 cells, the promoter region of Hint1 is partially methylated and treatment with 5‐azadcdeoxycytidine increased expression of the HINT1 protein and Hint1 mRNA in Hep3B and HepG2 cells. Increased expression of HINT1 in HepG2 cells markedly inhibited their growth. It also inhibited the transcriptional activities of β‐catenin/TCF4, and USF2, and inhibited the expression of endogenous cyclin D1 and TGFβ2. Furthermore, HINT1 co‐immunoprecipitated with USF2 in extracts of Hep2 cells. HINT1 also inhibited NFκB transcription factor reporter activity and inhibited translocation of the endogenous p65 protein to the nucleus of HepG2 cells. Therefore, decreased expression of the Hint1 gene through epigenetic silencing may play a role in enhancing the growth of a subset of human hepatoma by increasing the expression of genes controlled by the transcription factors β‐catenin, USF2, and NFκB. © 2008 Wiley‐Liss, Inc.