Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification with Large Sparse Datasets: Convergence Analysis and Scalable Algorithms

Large and sparse datasets, such as user ratings over a large collection of items, are common in the big data era. Many applications need to classify the users or items based on the highdimensional and sparse data vectors, e.g., to predict the profitability of a product or the age group of a user, etc. Linear classifiers are popular choices for classifying such datasets because of their efficiency. In order to classify the large sparse data more effectively, the following important questions need to be answered. 1. Sparse data and convergence behavior. How different properties of a dataset, such as the sparsity rate and the mechanism of missing data systematically affect convergence behavior of classification? 2. Handling sparse data with non-linear model. How to efficiently learn non-linear data structures when classifying large sparse data? This thesis attempts to address these questions with empirical and theoretical analysis on large and sparse datasets. We begin by studying the convergence behavior of popular classifiers on large and sparse data. It is known that a classifier gains better generalization ability after learning more and more training examples. Eventually, it will converge to the best generalization performance with respect to a given data distribution. In this thesis, we focus on how the sparsity rate and the missing data mechanism systematically affect such convergence behavior. Our study covers different types of classification models, including generative classifier and discriminative linear classifiers. To systematically explore the convergence behaviors, we use synthetic data sampled from statistical models of real-world large sparse datasets. We consider different types of missing data mechanisms that are common in practice. From the experiments, we have several useful observations about the convergence behavior of classifying large sparse data. Based on these observations, we further investigate the theoretical reasons and come to a series of useful conclusions. For better applicability, we provide practical guidelines for applying our results in practice. Our study helps to answer whether obtaining more data or missing values in the data is worthwhile in different situations, which is useful for efficient data collection and preparation. Despite being efficient, linear classifiers cannot learn the non-linear structures such as the low-rankness in a dataset. As a result, its accuracy may suffer. Meanwhile, most non-linear methods such as the kernel machines cannot scale to very large and high-dimensional datasets. The third part of this thesis studies how to efficiently learn non-linear structures in large sparse data. Towards this goal, we develop novel scalable feature mappings that can achieve better accuracy than linear classification. We demonstrate that the proposed methods not only out-
Interactive comment on “ Development of an atmospheric N 2 O isotopocule model and optimization procedure , and application to source estimation ” by K .

The study of K. Ishijima et al. entitled " Development of an atmospheric N2O isotopocule model and optimization procedure, and application to source estimation” embodies an attempt to better understand the tropospheric isotope-resolved budget of N2O throughout last decades with the use of an atmospheric chemistry transport model and an approximate inversion technique. Whilst the targeted scientific question is complex, interesting and obviously important, I find that that the study itself suffers from inconsistencies in model application and “optimisation” approach used, which unfortunately devalues the findings claimed.
Assessing molecular substitution patterns in the mitochondrial control region compared to protein coding genes in two marine mammals

This paper compares substitution patterns in three mitochondrial genes, control region, cytochrome b, and ND-1 from 245 bowhead whales representing three populations; the BCB stock, the eastern Canadian arctic stock and the Sea of Okhotsk stock. Substitution patterns for the hypervariable region I (HVRI) of the mitochondrial control region were compared to the more conservative protein coding genes in order to identify hypervariable sites which are sources of homoplasy and evolutionary “noise. ” Since HVR1 is one of the most frequently used genetic markers for population genetic and phylogeographic studies in mammals, a method to increase the resolution of this marker would increase our understanding of the population processes that drive genetic patterns. In particular, it will result in a better understanding of long-term effective population sizes. We present an evolutionary analysis of HVRI, cytochrome b and ND-1 sequences in order to quantify the occurrence of homoplasy in HVRI. The data from bowheads are compared to a larger dataset from Steller sea lions in which the estimated rate of substitution at HVRI is approximately 24 times the substitution rate at cytochrome b with an absolute rate of HVRI substitution estimated at 27.45% per million years. In contrast, bowheads exhibit a lower rate of mtDNA substitution but the depth of bowhead lineage leads to a considerable amount of recurrent substitution in protein coding genes; more than in the Steller sea lions. Analysis of historical population size in both species show considerable variation in female effective population size through evolutionary time.
FADD in Cancer: Mechanisms of Altered Expression and Function, and Clinical Implications

FADD was initially described as an adaptor molecule for death receptor-mediated apoptosis, but subsequently it has been implicated in nonapoptotic cellular processes such as proliferation and cell cycle control. During the last decade, FADD has been shown to play a pivotal role in most of the signalosome complexes, such as the necroptosome and the inflammasome. Interestingly, various mechanisms involved in regulating FADD functions have been identified, essentially posttranslational modifications and secretion. All these aspects have been thoroughly addressed in previous reviews. However, FADD implication in cancer is complex, due to pleiotropic effects. It has been reported either as anti- or protumorigenic, depending on the cell type. Regulation of FADD expression in cancer is a complex issue since both overexpression and downregulation have been reported, but the mechanisms underlying such alterations have not been fully unveiled. Posttranslational modifications also constitute a relevant mechanism controlling FADD levels and functions in tumor cells. In this review, we aim to provide detailed, updated information on alterations leading to changes in FADD expression and function in cancer. The participation of FADD in various biological processes is recapitulated, with a mention of interesting novel functions recently proposed for FADD, such as regulation of gene expression and control of metabolic pathways. Finally, we gather all the available evidence regarding the clinical implications of FADD alterations in cancer, especially as it has been proposed as a potential biomarker with prognostic value.
Self-Heating Graphene Nanocomposite Bricks: A Case Study in China

In cold climate regions, the energy associated with indoor heating constitutes a large portion of energy consumption. Increasing energy utilization efficiency is critically important for both economic and environmental reasons. Directly converting electrical energy to thermal energy using joule heating construction elements can save energy and investment to the water pipelines which have been extensively used for indoor heating in China. The fired brick has been extensively used to make pavements, walls and other masonry. Taking advantage of the high dispersion quality of graphene oxide (GO) in water, as well as the firing process used to make fired bricks, graphene nanocomposite bricks with excellent electrical properties and improved mechanical performance were prepared in China. The compressive strength of the bricks showed a substantial increase from 3.15 MPa to 7.21 MPa when GO concentration was 0.1 wt. %. Through applying 5 volts of electrical field within 5 minutes, the nanocomposites can be heated from room temperature to 60 °C, 110 °C and 160 °C for the nanocomposite bricks with graphene concentration of 3 wt. %, 4 wt. % and 5 wt. %, respectively, due to the extremely low percolation threshold (~0.5 wt.%) and high conductivity (10 Ω·cm at 1 wt.%). The sheets were connected more tightly when the GO content was increased. The thermal efficiency can reach up to 88% based on the applied voltage, measured resistance and temperature rise curves.
Green closed-loop supply chain network design considering cost control and CO2 emission

Environmental issues have become an important concern in modern supply chain management. The structure of closed-loop supply chain (CLSC) networks, which considers both forward and reverse logistics, can greatly improve the utilization of materials and enhance the performance of the supply chain in coping with environmental impacts and cost control. ,A biobjective mixed-integer programming model is developed to achieve the balance between environmental impact control and operational cost reduction. Various factors regarding the capacity level and the environmental level of facilities are incorporated in this study. The scenario-based method and the Epsilon method are employed to solve the stochastic programming model under uncertain demand. ,The proposed stochastic mixed-integer programming (MIP) model is an effective way of formulating and solving the CLSC network design problem. The reliability and precision of the Epsilon method are verified based on the numerical experiments. Conversion efficiency calculation can achieve the trade-off between cost control and CO2 emissions. Managers should pay more attention to activities about facility operation. These nodes might be the main factors of costs and environmental impacts in the CLSC network. Both costs and CO2 emissions are influenced by return rate especially costs. Managers should be discreet in coping with cost control for CO2 emissions barely affected by return rate. It is advisable to convert the double target into a single target by the idea of “Efficiency of CO2 Emissions Control Reduction. ” It can provide managers with a way to double-target conversion. ,We proposed a biobjective optimization problem in the CLSC network considering environmental impact control and operational cost reduction. The scenario-based method and the Epsilon method are employed to solve the mixed-integer programming model under uncertain demand.
Streaming Algorithms for Estimating High Set Similarities in LogLog Space

Estimating set similarity and detecting highly similar sets are fundamental problems in areas such as databases and machine learning. MinHash is a well-known technique for approximating Jaccard similarity of sets and has been successfully used for many applications. Its two compressed versions, <inline-formula><tex-math notation="LaTeX">$b$</tex-math><alternatives><mml:math><mml:mi>b</mml:mi></mml:math><inline-graphic xlink:href="qi-ieq1-2969423.gif"/></alternatives></inline-formula>-bit MinHash and Odd Sketch, can significantly reduce the memory usage of the MinHash, especially for estimating high similarities (i.e., similarities around 1). Although MinHash can be applied to static sets as well as streaming sets, of which elements are given in a streaming fashion, unfortunately, <inline-formula><tex-math notation="LaTeX">$b$</tex-math><alternatives><mml:math><mml:mi>b</mml:mi></mml:math><inline-graphic xlink:href="qi-ieq2-2969423.gif"/></alternatives></inline-formula>-bit MinHash and Odd Sketch fail to deal with streaming data. To solve this problem, we previously designed a memory-efficient sketch method, <italic>MaxLogHash</italic>, to accurately estimate Jaccard similarities in streaming sets. Compared with MinHash, our method uses smaller sized registers (each register consists of less than 7 bits) to build a compact sketch for each set. In this paper, we further develop a faster method, <italic>MaxLogOPH++</italic>. Compared with MaxLogHash, MaxLogOPH++ reduces the time complexity for updating each coming element from <inline-formula><tex-math notation="LaTeX">$O(k)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mi>k</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="qi-ieq3-2969423.gif"/></alternatives></inline-formula> to <inline-formula><tex-math notation="LaTeX">$O(1)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="qi-ieq4-2969423.gif"/></alternatives></inline-formula> with a small additional memory. We conduct experiments on a variety of datasets, and experimental results demonstrate the efficiency and effectiveness of our methods.
Lipids, Nutrition and Development

Lipids are classified as simple, compound and derived based on the hydrolysis, which result in breaking the fatty acids off, leaving free fatty acids and a glycerol, using up three water molecules. Simple lipids are esters of fatty acids with various types of alcohol. They are distinguished into fats and oils. Compound lipids contain an inorganic or organic group in addition to fatty acids and glycerol. They include phospholipids, glicolipids and lipoproteins. Finally, derived lipids are obtained by hydrolysis of simple and complex lipids. These lipids contain glycerol and other alcohols. They correspond to steroid hormones, ketone bodies, hydrocarbons, fatty acids, fatty alcohols, mono and diglycerides, terpenes and carotenoids. Sometimes they are present as waste products of metabolism. Lipids also can be classified, depending on its solubility or function, as polar or apolar compounds and as structural or reserve substances, respectively (Basso, 2007). The main source of body energy comes from the triglycerides. These compounds are esters formed by one molecule of glycerol and three molecules of fatty acids. Fatty acids are carboxylic acids that usually have in its structure an unbranched carbon chain and one carboxyl. According to the saturation of the carbon chain, they can be classified as saturated, monounsaturated and polyunsaturated fatty acids (Basso, 2007). In general, all mammals are able to synthesize saturated and monounsaturated fatty acids, but this ability is limited to polyunsaturated fatty acids (PUFAs), without them the organisms could not function properly. For this reason, these compounds are considered "essential" fatty acids. Thus, these fatty acids must be supplied by the diet. Linoleic acid 18:2 (n-6), a member of the n-6 family of fatty acids, was identified as the first ‘‘essential’’ fatty acid, whereas linolenic acid, 18:3 (n-3) represents the other essential fatty acid. These two essential fatty acids are the only sources for important longer chain fatty acids and physiological synthesis of complex lipids (Yehuda et al., 1999). Linoleic acid [18:2 (n-6)] is usually found in large quantities in soybean, corn, canola and safflower oil while   linolenic [18:3 (n-3)] is easily found in green leafy vegetables, linseed and marine fish oil (Takahashi, 2005). The main source of dietary lipids is obtained through the intake of triglycerides which can be found as fats or oils. The concept of fat or oil is based on the consistency and on the fatty acid present in the triglyceride molecule. At room temperature, oils are liquid because are constituted of tiacylglycerols containing a high proportion of mono and/or polyunsaturated fatty acids. These come from the vegetable sources such as soybean, corn, sunflower, olive or canola oil or from animal source such as fish oil. On the other hand, fats are solid or pasty
A rapid method for screening macadamia seedlings for resistance to Kretzschmaria clavus.

Nuts of macadamia (Macadamia integrifolia Maiden & Betche) are one of the famous delicacies of Hawaii. Macadamia trees have been planted mostly on the island of Ha waii, and the industry has expanded rapidly in recent years. Root rot caused by Kretz schmaria clavus (Fr.) Sace. of the Xylariaceas is the most serious disease problem of macadamia in Hawaii". The disease causes an unthrifty appearance, followed by defo liation and death of trees that are older than 6 years of age. Kretzschmaria clavus forms black lines in infected roots and trunks, and produces kretzschmarioid and hypo xyloid stromata on the surface of infected tissues2). Attempts to control the disease by injecting infected trees with fungicides have not been successful because extensive root damage usually precedes the appearance of external symptoms on the trees. Use of resistant root stocks has been considered a promising approach to solve this disease problem. However, advancement of the disease is extremely slow. Six months after in oculation, K. clavus colonizes only a few mm of the inoculated root tissues. It is, there fore, very difficult to compare susceptibility among macadamia trees. We report here a rapid method for screening macadamia seedlings for resistance to K. clavus. Kretzschmaria clavus (isolate Ma-P12) was grown in a wheat-oat medium (125ml whole wheat, 125ml whole oat and 100ml distilled water) for 1 month at 24C. Stems of macadamia seedlings (12 to 18 months old) were scraped gently to remove the epider mis from bark tissues at 10-20mm above the ground. About 30ml of wheat-oat culture on a thin bed of moist sphagunm moss was placed on the scraped portion of the stem, wrapped with a sheet of clear plastic and secured on both ends. Macadamia seedlings similarly inoculated with wheat-oat medium were used as controls. Three replicates were used for each treatment. Young leaves of inoculated seedlings showed water soa king and turned brown within three weeks and died within three months. Infected stems showed black lines and K. clavus was reisolated from the diseased tissues. All three control seedlings remained healthy. Similar results were obtained in two subse quent experiments. The method was used to screen macadamia seedlings for resistance to K. clavus. For
Affinity LCFCN: Learning to Segment Fish with Weak Supervision

Aquaculture industries rely on the availability of accurate fish body measurements, e.g., length, width and mass. Manual methods that rely on physical tools like rulers are time and labour intensive. Leading automatic approaches rely on fully-supervised segmentation models to acquire these measurements but these require collecting per-pixel labels -- also time consuming and laborious: i.e., it can take up to two minutes per fish to generate accurate segmentation labels, almost always requiring at least some manual intervention. We propose an automatic segmentation model efficiently trained on images labeled with only point-level supervision, where each fish is annotated with a single click. This labeling process requires significantly less manual intervention, averaging roughly one second per fish. Our approach uses a fully convolutional neural network with one branch that outputs per-pixel scores and another that outputs an affinity matrix. We aggregate these two outputs using a random walk to obtain the final, refined per-pixel segmentation output. We train the entire model end-to-end with an LCFCN loss, resulting in our A-LCFCN method. We validate our model on the DeepFish dataset, which contains many fish habitats from the north-eastern Australian region. Our experimental results confirm that A-LCFCN outperforms a fully-supervised segmentation model at fixed annotation budget. Moreover, we show that A-LCFCN achieves better segmentation results than LCFCN and a standard baseline. We have released the code at \url{this https URL}.
Capturing a Faster Evader by Some Energy-Limited Speed-Controllable Pursuers

Multiagent pursuit-evasion problems have been widely investigated in a number of related areas. In many situations, the pursuers have energy limitation when pursuing the evader, and the pursuers moving at higher speeds consume energy more quickly. The energy limitation creates two problems for the pursuers. First, constantly moving at the maximum speed is not optimal because the energy will be exhausted quickly. Therefore, it is necessary to control the speed to achieve the optimal energy consumption, but previous studies have not presented a suitable speed-control algorithm. Second, a fraction of pursuers with insufficient energy may fail to complete the sub-tasks, and the evader can vary the escape strategy by considering the energy limitation of the pursuers. Therefore, the pursuers with sufficient energy need to help the pursuers with insufficient energy and address the variety of the evader's escape strategy. The existing pursuing strategies are not self-adaptable to satisfy this requirement. In this paper, we present a pursuing strategy consisting of both a speed-control algorithm and a path planning algorithm as sub-algorithms. Additionally, we integrate the subalgorithms into a self-adaptable cooperation strategy based on simulated annealing, which makes the pursuers adaptably help each other and is adaptable to the variety of escape strategies. The experimental results show that our strategy leads to higher capture success ratios than previous strategies when the energy of pursuers is limited. The experimental data also show that our strategy is more adaptable to the variety of the pursuers' maximum speed and the variety of the evader's escape strategies.
Spacetime Quantum Reference Frames and superpositions of proper times

In general relativity, the description of spacetime relies on idealised rods and clocks, which identify a reference frame. In any concrete scenario, reference frames are associated to physical systems, which are ultimately quantum in nature. A relativistic description of the laws of physics hence needs to take into account such quantum reference frames (QRFs), through which spacetime can be given an operational meaning. Here, we introduce the notion of a spacetime quantum reference frame, associated to a quantum particle in spacetime. Such formulation has the advantage of treating space and time on equal footing, and of allowing us to describe the dynamical evolution of a set of quantum systems from the perspective of another quantum system, where the parameter in which the rest of the physical systems evolves coincides with the proper time of the particle taken as the QRF. Crucially, the proper times in two different QRFs are not related by a standard transformation, but they might be in a quantum superposition one with respect to the other.Concretely, we consider a system of N relativistic quantum particles in a weak gravitational field, and introduce a timeless formulation in which the global state of the N particles appears "frozen", but the dynamical evolution is recovered in terms of relational quantities. The position and momentum Hilbert space of the particles is used to fix the QRF via a transformation to the local frame of the particle such that the metric is locally inertial at the origin of the QRF. The internal Hilbert space corresponds to the clock space, which keeps the proper time in the local frame of the particle. Thanks to this fully relational construction we show how the remaining particles evolve dynamically in the relational variables from the perspective of the QRF. The construction proposed here includes the Page-Wootters mechanism for non interacting clocks when the external degrees of freedom are neglected. Finally, we find that a quantum superposition of gravitational redshifts and a quantum superposition of special-relativistic time dilations can be observed in the QRF.