Miracle-dz/UniFAR-FTUs huggingface dataset:
On the ℓ 1 -ℓ q Regularized Regression

In this paper we consider the problem of grouped variable selection in high-dimensional regression using l1-lq regularization (1 ≤ q ≤ ∞), which can be viewed as a natural generalization of the l1-l2 regularization (the group Lasso). The key condition is that the dimensionality pn can increase much faster than the sample size n, i.e. pn ≫ n (in our case pn is the number of groups), but the number of relevant groups is small. The main conclusion is that many good properties from l1-regularization (Lasso) naturally carry on to the l1-lq cases (1 ≤ q ≤ ∞), even if the number of variables within each group also increases with the sample size. With fixed design, we show that the whole family of estimators are both estimation consistent and variable selection consistent under different conditions. We also show the persistency result with random design under a much weaker condition. These results provide a unified treatment for the whole family of estimators ranging from q = 1 (Lasso) to q = ∞ (iCAP), with q = 2 (group Lasso)as a special case. When there is no group structure available, all the analysis reduces to the current results of the Lasso estimator (q = 1).
MicroRNAs in breast cancer: oncogene and tumor suppressors with clinical potential

MicroRNAs (miRs) are small single-stranded RNA molecules, which function as key negative regulators of post-transcriptional modulation in almost all biological processes. Abnormal expression of microRNAs has been observed in various types of cancer including breast cancer. Great efforts have been made to identify an association between microRNA expression profiles and breast cancer, and to understand the functional role and molecular mechanism of aberrant-expressed microRNAs. As research progressed, ‘oncogenic microRNAs’ and ‘tumor suppressive microRNAs’ became a focus of interest. The potential of candidate microRNAs from both intercellular (tissue) and extracellular (serum) sources for clinical diagnosis and prognosis was revealed, and treatments involving microRNA achieved some amazing curative effects in cancer disease models. In this review, advances from the most recent studies of microRNAs in one of the most common cancers, breast cancer, are highlighted, especially the functions of specifically selected microRNAs. We also assess the potential value of these microRNAs as diagnostic and prognostic markers, and discuss the possible development of microRNA-based therapies. 概要搜集已报道的在乳腺癌发生发展过程中有重要作用的microRNA信息, 结合相关肿瘤模型的实验数据, 评估microRNA在乳腺癌诊断和治疗方面的应用前景。 以“癌基因”和“癌抑制基因”为分类依据, 全面地总结归纳乳腺癌相关microRNA的功能和作用机制, 进一步从临床诊断标记物和治疗靶点的层面分析microRNA的潜在临床应用价值。 表1和2总结了microRNA参与乳腺癌发展过程的具体事件及其调控的靶基因。 同时, 本文还深入探讨microRNA作为临床诊断标记物及治疗靶点的可行性, 揭示已有研究的不足之处, 为今后的相关工作方向提供一些建议。
Arterial stiffness evaluation in HIV-positive patients: a multicenter matched control study.

OBJECTIVE The purpose of this article is to assess the differences, if any, in the values of carotid artery stiffness parameters between HIV-infected subjects without known cardiovascular disease (CVD) or carotid artery plaques and HIV-uninfected control subjects matched for sex, age, body mass index, and other CVD risk factors (i.e., hypertension, hypercholesterolemia, diabetes, and cigarette smoking). Arterial stiffness is emerging as a predictor of CVD risk. By recording the blood pressure, an automated echo-tracking system implemented in ultrasound equipment allows evaluation of arterial stiffness. SUBJECTS AND METHODS Fifty-four HIV-infected patients without a history of CVD were closely matched for sex, age, body mass index, and CVD risk factors to 54 HIV-uninfected control subjects on an individual basis. Ultrasound studies of carotid artery stiffness parameters were performed using ultrasound equipment with a linear broadband high-frequency transducer. Carotid intima-media thickness was also measured. Repeatability between operators was assessed. Nonparametric Mann-Whitney U test, chi-square statistics, Fisher exact test, Pearson correlation coefficient, and intraclass correlation coefficient were used for statistical analysis. A p value less than 0.05 was considered statistically significant. RESULTS Except for arterial compliance in HIV-infected subjects, arterial stiffness parameters were correlated with age in both groups. Compared with matched control subjects, HIV-infected subjects showed lower arterial compliance parameter values (0.95 [interquartile range, 0.78-1.23] vs 0.76 [interquartile range, 0.62-1.00]; p = 0.0009), whereas other parameters were similar. Repeatability between operators was good. CONCLUSION HIV-infected subjects have an arterial compliance significantly lower than that of control subjects. The impairment of carotid artery distensibility may contribute to subclinical atherosclerosis.
fMRI robotic embodiment: A pilot study

We present a robotic embodiment experiment based on real-time functional Magnetic Resonance Imaging (rtfMRI). To our knowledge, this is the first time fMRI is used as an input device to identify a subject's intentions and convert them into actions performed by a humanoid robot. The process, based on motor imagery, has allowed subjects located in Israel to control a HOAP3 humanoid robot in France, experiencing the whole experiment through the eyes of the robot.
Unsupervised Grammar Induction in a Framework of Information Compression by Multiple Alignment, Unification and Search

This paper describes a novel approach to grammar induction that has been developed within a framework designed to integrate learning with other aspects of computing, AI, mathematics and logic. This framework, called information compression by multiple alignment, unification and search (ICMAUS), is founded on principles of Minimum Length Encoding pioneered by Solomonoff and others. Most of the paper describes SP70, a computer model of the ICMAUS framework that incorporates processes for unsupervised learning of grammars. An example is presented to show how the model can infer a plausible grammar from appropriate input. Limitations of the current model and how they may be overcome are briefly discussed.
Molecular Detection of Fungal Communities in the Hawaiian Marine Sponges Suberites zeteki and Mycale armata

ABSTRACT Symbiotic microbes play a variety of fundamental roles in the health and habitat ranges of their hosts. While prokaryotes in marine sponges have been broadly characterized, the diversity of sponge-inhabiting fungi has barely been explored using molecular approaches. Fungi are an important component of many marine and terrestrial ecosystems, and they may be an ecologically significant group in sponge-microbe interactions. This study tested the feasibility of using existing fungal primers for molecular analysis of sponge-associated fungal communities. None of the eight selected primer pairs yielded satisfactory results in fungal rRNA gene or internal transcribed spacer (ITS) clone library constructions. However, 3 of 10 denaturing gradient gel electrophoresis (DGGE) primer sets, which were designed to preferentially amplify fungal rRNA gene or ITS regions from terrestrial environmental samples, were successfully amplified from fungal targets in marine sponges. DGGE analysis indicated that fungal communities differ among different sponge species (Suberites zeteki and Mycale armata) and also vary between sponges and seawater. Sequence analysis of DGGE bands identified 23 and 21 fungal species from each of the two sponge species S. zeteki and M. armata, respectively. These species were representatives of 11 taxonomic orders and belonged to the phyla of Ascomycota (seven orders) and Basidiomycota (four orders). Five of these taxonomic orders (Malasseziales, Corticiales, Polyporales, Agaricales, and Dothideomycetes et Chaetothyriomcetes incertae sedis) have now been identified for the first time in marine sponges. Seven and six fungal species from S. zeteki and M. armata, respectively, are potentially new species because of their low sequence identity (≤98%) with their references in GenBank. Phylogenetic analysis indicated sponge-derived sequences were clustered into “marine fungus clades” with those from other marine habitats. This is the first report of molecular analysis of fungal communities in marine sponges, adding depth and dimension to our understanding of sponge-associated microbial communities.
Wavelet-based OFDM analysis: BER performance and PAPR profile for various wavelets

This paper presents the analysis of BER and PAPR performance of Wavelet-based OFDM (WOFDM) using different wavelets. We do intensive study on several wavelets from different wavelet families to find out the BER performance and PAPR characteristic of WOFDM system using Haar, Daubechies, Symlet, Coiflet and Discrete Meyer wavelets. Simulation results for the selected wavelets show improvement of 3 dB gain Eb/N0 at the BER of 10-4, and significant PAPR reduction as compared to the conventional OFDM.
Thesis for the Degree of Licentiate of Engineering Frequency Reconfigurable and Linear Power Amplifiers Based on Doherty and Varactor Load Modulation Techniques

In future mobile communication networks, there will be a shift towards higher carrier frequencies and highly integrated multiple antenna systems. The system performance will largely depend on the available RF hardware. As such, RF power amplifiers (PAs) with improved efficiency, linearity, and bandwidth are needed. Dynamic load modulation (DLM) is one of the most common PA efficiency enhancement techniques. By investigation of new DLM design techniques, the overall objective of this thesis is to improve the efficiencylinearity and efficiency-bandwidth trade-offs in PAs for future wireless systems. In the first part of the thesis, a method for improving the frequency agility of varactor-based DLM PAs is proposed. It is demonstrated that class-J operation provides the possibility of enhancing the efficiency for a large dynamic range of powers by means of purely reactive load modulation across a large bandwidth. This allows for very simple realization of a varactor-based tunable output matching network. This ideal analysis of the class-J operation establishes a profound theory behind wideband capabilities of varactor-based DLM PAs. The theory is experimentally verified with a prototype PA using a GaN HEMT and SiC varactors. In the second part, a method for improving the efficiency-linearity trade-off in Doherty PAs is proposed. The fundamental way the main and auxiliary transistors in the Doherty PA interact with each other is analyzed and generalized. The output combiner is treated as a black-box and its parameters are solved for arbitrary current profiles for the main and auxiliary branches. Solving for maximum efficiency and scaling the conventional current ratios results in new solutions with significantly higher gain. Solving for linear gain and high efficiency, and combining current scaling with reactive mismatch results in the possibility of controlling the phase response in the high power region. This control can be used to compensate the severe inherent phase distortion in Doherty PAs. The theory is experimentally verified with a highly efficient and highly linear prototype PA using GaN HEMTs. The thesis has presented two promising techniques for improving the efficiency-bandwidth and efficiency-linearity trade-offs in PAs. The results will therefore contribute to the development of more energy efficient and high capacity wireless services in the future.
Dietary Induction of Intestinal Fructose Absorption in Weaning Rats

ABSTRACT: The onset of developmentally induced changes in rat intestinal nutrient absorption is well known: brushborder glucose and fructose transporters appear during prenatal and postweaning periods, respectively. The onset of diet-induced regulation, however, is unknown. To test the hypothesis that intestinal glucose and fructose transport is regulated by diet during weaning and postweaning, we fed rats experimental diets containing high (65%) glucose, high fructose, high sucrose, or no carbohydrate. In 16-d-old rats, 6 d of dietary fructose but not glucose modestly increased fructose absorption in everted sleeves of small intestine (SI) over control (mother-fed with access to chow) rats (p = 0.02). In 21-d-old (age when sucrase is present) rats, dietary fructose and sucrose each dramatically enhanced (p = 0.004) fructose absorption over control rats and rats fed high glucose or carbohydrate-free diets. In 35− (postweaning) and 60-d-old rats, dietary fructose and sucrose, but not glucose, stimulated fructose absorption (p < 0.005) over rats fed a carbohydrate-free diet. In all age groups, intestinal glucose absorption was independent of diet (p ≥ 0.12), and experimental rats grew at the same rate as control rats. Absorption of fructose or glucose was 2–3 times greater in the proximal and middle than in the distal SI. Intestinal fructose, but not glucose, absorption can be induced by diet even during early weaning, and dietary fructose followed by sucrose is the most potent inducer. Thus, mechanisms of diet regulation can change ontogenetically, and early introduction of certain diets can induce appearance of certain nutrient transporters.
SHEAR CAPACITY ASSESSMENT OF CORROSION- DAMAGED REINFORCED CONCRETE BEAMS

This study investigated how the shear capacity of reinforced concrete bridge beams is affected by corrosion damage to the shear stirrups. It described the changes that occur in shear capacity and concrete cracking as shear stirrup corrosion progresses. Visual signs of corrosion distress were correlated with structural performance of large-size reinforced concrete beams that were corroded to four damage states. The corrosion products and damage were characterized for the beams and compared to field beams. Analysis methods incorporating quantified corrosion damage predicted reasonably well the shear capacity of the large-size beams. Recommendations were presented for improved inspection practice to allow for estimating shear capacity of corrosion-damaged sections in reinforced concrete bridges.
Maternal dietary patterns during pregnancy and intelligence quotients in the offspring at 8 years of age: Findings from the ALSPAC cohort

Abstract Dietary intake during pregnancy may influence child neurodevelopment and cognitive function. This study aims to investigate the associations between dietary patterns obtained in pregnancy and intelligence quotients (IQ) among offspring at 8 years of age. Pregnant women enrolled in the Avon Longitudinal Study of Parents and Children completed a food frequency questionnaire at 32 weeks' gestation (n = 12,195). Dietary patterns were obtained by cluster analysis. Three clusters best described women's diets during pregnancy: “fruit and vegetables,” “meat and potatoes,” and “white bread and coffee. ” The offspring's IQ at 8 years of age was assessed using the Wechsler Intelligence Scale for Children. Models, using variables correlated to IQ data, were performed to impute missing values. Linear regression models were employed to investigate associations between the maternal clusters and IQ in childhood. Children of women who were classified in the meat and potatoes cluster and white bread and coffee cluster during pregnancy had lower average verbal (β = −1.74; p < .001 and β = −3.05; p < .001), performance (β = −1.26; p = .011 and β = −1.75; p < .001), and full‐scale IQ (β = −1.74; p < .001 and β = −2.79; p < .001) at 8 years of age when compared to children of mothers in the fruit and vegetables cluster in imputed models of IQ and all confounders, after adjustment for a wide range of known confounders including maternal education. The pregnant women who were classified in the fruit and vegetables cluster had offspring with higher average IQ compared with offspring of mothers in the meat and potatoes cluster and white bread and coffee cluster.
Encoding of Gustatory Working Memory by Orbitofrontal Neurons

The content model regarding the functional organization of working memory in prefrontal cortex (PFC) states that different PFC areas encode different types of information in working memory depending on their afferent connections with other brain areas. Previous studies that tested this model focused on visual, auditory and somatosensory information. However, posterior areas processing this information project to widespread and overlapping regions of lateral PFC, making it difficult to establish the veracity of the model. In contrast, gustatory information enters PFC via orbitofrontal cortex (OFC), and so the content model would argue that OFC should be responsible for maintaining gustatory information in working memory. To test this, we recorded the activity of single neurons throughout PFC and gustatory cortex (GUS) from two subjects while they performed a gustatory delayed-match-to-sample task with intervening gustatory distraction. Neurons that encoded the identity of the gustatory stimulus across the delay, consistent with a role in gustatory working memory, were most prevalent in OFC and GUS compared with dorsolateral PFC and ventrolateral PFC. Gustatory information in OFC was more resilient to intervening distraction, paralleling previous findings regarding visual working memory processes in PFC and posterior sensory cortex. Our findings provide support for the content model of working memory organization. Maintaining gustatory information may be one aspect of a wider function for OFC in reward working memory that could contribute to its role in decision-making.
Asymptotics of eigenfunctions on plane domains

We consider a family of domains (ON)N>0 obtained by attaching an N × 1 rectangle to a fixed set O0 = {(x,y) : 0 < y < 1, - ? (y) < x < 0}, for a Lipschitz function ? = 0. We derive full asymptotic expansions, as N ? 8, for the m-th Dirichlet eigenvalue (for any fixed m in N) and for the associated eigenfunction on ON. The second term involves a scattering phase arising in the Dirichlet problem on the infinite domain O8. We determine the first variation of this scattering phase, with respect to ? , at ? = 0. This is then used to prove sharpness of results, obtained previously by the same authors, about the location of extrema and nodal line of eigenfunctions on convex domains.
Predictive Performance and Scalability Modeling of a Large-Scale Application

In this work we present a predictive analytical model that encompasses the performance and scaling characteristics of an important ASCI application. SAGE (SAIC’s Adaptive Grid Eulerian hydrocode) is a multidimensional hydrodynamics code with adaptive mesh refinement. The model is validated against measurements on several systems including ASCI Blue Mountain, ASCI White, and a Compaq Alphaserver ES45 system showing high accuracy. It is parametric - basic machine performance numbers (latency, MFLOPS rate, bandwidth) and application characteristics (problem size, decomposition method, etc.) serve as input. The model is applied to add insight into the performance of current systems, to reveal bottlenecks, and to illustrate where tuning efforts can be effective. We also use the model to predict performance on future systems.
Variational Memory Addressing in Generative Models

Aiming to augment generative models with external memory, we interpret the output of a memory module with stochastic addressing as a conditional mixture distribution, where a read operation corresponds to sampling a discrete memory address and retrieving the corresponding content from memory. This perspective allows us to apply variational inference to memory addressing, which enables effective training of the memory module by using the target information to guide memory lookups. Stochastic addressing is particularly well-suited for generative models as it naturally encourages multimodality which is a prominent aspect of most high-dimensional datasets. Treating the chosen address as a latent variable also allows us to quantify the amount of information gained with a memory lookup and measure the contribution of the memory module to the generative process. To illustrate the advantages of this approach we incorporate it into a variational autoencoder and apply the resulting model to the task of generative few-shot learning. The intuition behind this architecture is that the memory module can pick a relevant template from memory and the continuous part of the model can concentrate on modeling remaining variations. We demonstrate empirically that our model is able to identify and access the relevant memory contents even with hundreds of unseen Omniglot characters in memory
Emotional expression recognition with a cross-channel convolutional neural network for human-robot interaction

The study of emotions has attracted considerable attention in several areas, from artificial intelligence and psychology to neuroscience. The use of emotions in decision-making processes is an example of how multi-disciplinary they are. To be able to communicate better with humans, robots should use appropriate communicational gestures, considering the emotions of their human conversation partners. In this paper we propose a deep neural network model which is able to recognize spontaneous emotional expressions and to classify them as positive or negative. We evaluate our model in two experiments, one using benchmark datasets, and the other using an HRI scenario with a humanoid robotic head, which itself gives emotional feedback.
Recycling of cotton textiles: Characterization, pretreatment, and purification

In many parts of the world, there are well-established systems for material recycling of metal, glass, and paper. However, no large-scale chemical recycling of textiles currently exists. Since the collection of second hand textiles is established on a large-scale, one of the main barriers to obtaining chemical textile recycling is the development of recycling technology. Recently, it has been proposed that recycling cotton may be accomplished using post-consumer cotton to produce regenerated cellulose fibers, such as viscose and lyocell. These fibers are, today, made either from wood-derived dissolving pulp or cotton linters, both of which contain almost pure cellulose. The incorporation of used cotton fibers from textiles is an interesting possibility since cotton also contains almost pure cellulose. The most common textile material on the market contains a mix of polyester, i.e. poly(ethylene terephthalate) (PET), and cotton, referred to as polycotton, and the separation of the two components is necessary before recycling. This thesis investigates some important aspects of the chemical recycling of textiles. The focus is on four areas; property changes during laundering and use of cotton, acid pretreatment of cotton, swelling properties of cotton, and separation of cotton from polycotton textiles. The first part is based on that during the service life of cotton textiles, laundering is performed many times, and this may change the properties of cotton. When new cotton fibers are compared to cotton fibers from sheets that have been used in hospitals for a long period of time, findings show that laundering and use do not have a large impact on the supramolecular structure of cotton cellulose. However, the cellulose the degree of polymerization decrease greatly after long-term use. The second and third parts of the thesis investigate the pretreatment of cotton. Pretreatments of cellulose fibers are used to enhance susceptibility to dissolution before the production of regenerated fibers. Three different pretreatments were investigated, acid hydrolysis in water, acid hydrolysis in ethanol and hydrothermal treatment. Findings show that the degradation pattern is similar in cotton and dissolving pulp for all pretreatments. The last part of the thesis investigates the separation of the components in polycotton. In the process, polyester is hydrolyzed by the action of alkali while cotton is maintained. The process yields three product streams; two containing the two different monomers obtained when hydrolyzing the PET, and one with residual cotton. The yield of the process is high, and the fractions showed high purity.