Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Multi-Gigabit Wireless: Design of High-Throughput MIMO Detectors With Hardware-Efficient Architecture

This paper presents a hardware-efficient architecture for 4×4 and 8×8 high-throughput MIMO detectors. The adopted non-constant K-best algorithm tends to keep more survival nodes in top search tree layers and reduce computational complexity in bottom layers as opposed to the conventional K-best algorithm. A pipelined architecture is used to generate one detection output per clock cycle, thus meeting multi-gigabit throughput requirements for advanced wireless communication systems. The proposed efficient folding scheme strikes a suitable balance between complexity and throughput. This paper also presents a discussion on the scalability of this architecture with respect to the setting of QAM size, K values, and antenna number. One 4×4 MIMO detector IC has been manufactured and one 8×8 MIMO detector layout has been realized, both in 90-nm CMOS technology. The 4×4 detector IC has 232 kilogates (KG). Its maximum measured throughput is 4.08 Gbps at 170-MHz operating frequency and 1.3-V core voltage. The 8×8 detector has 665 KG. Its post-layout simulation results show that it achieves 4.37-Gbps throughput at 182-MHz operating frequency and 0.9-V core voltage. Compared to earlier hard-output detectors, both implemented detectors demonstrate good normalized power and normalized hardware efficiencies.
Identification of Key Uric Acid Synthesis Pathway in a Unique Mutant Silkworm Bombyx mori Model of Parkinson’s Disease

Plasma uric acid (UA) levels decrease following clinical progression and stage development of Parkinson’s disease (PD). However, the molecular mechanisms underlying decreases in plasma UA levels remain unclear, and the potential to apply mutagenesis to a PD model has not previously been discovered. We identified a unique mutant of the silkworm Bombyx mori (B.mori) op. Initially, we investigated the causality of the phenotypic “op” by microarray analysis using our constructed KAIKO functional annotation pipeline. Consequently, we found a novel UA synthesis-modulating pathway, from DJ-1 to xanthine oxidase, and established methods for large-scale analysis of gene expression in B. mori. We found that the mRNA levels of genes in this pathway were significantly lower in B. mori op mutants, indicating that downstream events in the signal transduction cascade might be prevented. Additionally, levels of B.mori tyrosine hydroxylase (TH) and DJ-1 mRNA were significantly lower in the brain of B. mori op mutants. UA content was significantly lower in the B. mori op mutant tissues and hemolymph. The possibility that the B. mori op mutant might be due to loss of DJ-1 function was supported by the observed vulnerability to oxidative stress. These results suggest that UA synthesis, transport, elimination and accumulation are decreased by environmental oxidative stress in the B. mori op mutant. In the case of B. mori op mutants, the relatively low availability of UA appears to be due both to the oxidation of DJ-1 and to its expenditure to mitigate the effects of environmental oxidative stress. Our findings are expected to provide information needed to elucidate the molecular mechanism of decreased plasma UA levels in the clinical stage progression of PD.
Image indexing by spatial relationships between salient objects

In this paper, we presented our technique to extract and to use the spatial relationships between two or more salient objects. Using an object oriented hypergraph data structure, the spatial relationships are determined and stored in an object-oriented database. This work aims to unified the phases of processing, indexing and retrieval of images. The proposed model can be applicable to other types of data (video) and to semantic relations hidden in an image. The structure of the database used for image storage allow the construction of the indexes classes hierarchy in order to improve the results of image retrieval. Our method requires more experiments for datasets which come from different areas and for images which contain more salient objects.
“Win at Home and Draw Away”: Automatic Formation Analysis Highlighting the Differences in Home and Away Team Behaviors

In terms of analyzing soccer matches, two of the most important factors to consider are: 1) the formation the team played (e.g., 4-4-2, 4-2-3-1 etc.), and 2) the manner in which they executed it (e.g., conservative - sitting deep, or aggressive - pressing high). Despite the existence of ball and player tracking data, no current methods exist which can automatically detect and visualize formations. In this paper, we present an automatic method which can detect formations using an Expectation-Maximization (EM) approach. Using an entire season of Prozone data which consists of ball and player tracking information from a recent top-tier professional league, we showcase our method by investigating the “home advantage”. In a paper we published recently, using an entire season of ball tracking data we showed that home teams had significantly more possession in the forward third which correlated with more shots and goals while the shooting and passing proficiencies were the same. Using our automatic formation analysis, we extend this analysis, and show that teams tend to play the same formation at home as they do away, but the manner in which they execute it is significantly different. Specifically, we show that the formation of teams at home is significantly higher up the field compared to when they play away. This conservative approach at away games suggests that coaches aim to win their home games and draw their away games. Additionally, we also show that our method can visually summarize a game which gives an indication of dominance and tactics. While enabling new discoveries of team behavior which can enhance analysis, it is also worth mentioning that our automatic formation detection method is the first to be developed.
Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network

In this paper, we propose a novel learning based method for automated segmenta-tion of brain tumor in multimodal MRI images. The machine learned features from fully convolutional neural network (FCN) and hand-designed texton fea-tures are used to classify the MRI image voxels. The score map with pixel-wise predictions is used as a feature map which is learned from multimodal MRI train-ing dataset using the FCN. The learned features are then applied to random for-ests to classify each MRI image voxel into normal brain tissues and different parts of tumor. The method was evaluated on BRATS 2013 challenge dataset. The results show that the application of the random forest classifier to multimodal MRI images using machine-learned features based on FCN and hand-designed features based on textons provides promising segmentations. The Dice overlap measure for automatic brain tumor segmentation against ground truth is 0.88, 080 and 0.73 for complete tumor, core and enhancing tumor, respectively.
Oral Language and Reading Success: A Structural Equation Modeling Approach

Oral language skills and habits may serve as important resources for success or failure in school-related tasks such as learning to read. This article tests this hypothesis utilizing a unique data set, the original Woodcock-Johnson Psycho-Educational Battery-Revised norming sample. This article assesses the importance of oral language by focusing on auditory processing, a variable strongly affected by the oral language of the family and peer group within which the youth is raised. It estimates a structural equation model in which this variable, along with other measures of basic cognitive skills, serve as mediators between race and mother's schooling background and basic and advanced reading skill. The model fits very well, and the youth's basic skill at auditory processing is both a major determinant of basic reading success, and by far the most important of the mediating variables. In particular, for children ages 5 to 10, this measure accounts for much of the race effect, and for more than one half of the mother's education effect on reading. Research on the determinants of social inequality should pay greater attention to the central importance of family and peer group oral language in determining cognitive performance outcomes, particularly for elementary school aged children.
Expanded Genetic Map of Gibberella moniliformis (Fusarium verticillioides)

ABSTRACT Gibberella moniliformis (Fusarium verticillioides) is primarily a pathogen of maize, but it can also cause disease in other crop species. This pathogenicity, as well as the contamination of food- and feedstuffs with the fumonisin mycotoxins, results in economically significant losses to both farmers and food processors. The dissection of important biological characters in this fungus has been hampered by the lack of a uniformly dense genetic map. The existing restriction fragment length polymorphism-based map contains significant gaps, making it difficult to routinely locate biologically important genes, such as those involved in pathogenicity or mycotoxin production, with precision. We utilized amplified fragment length polymorphisms (AFLPs) to saturate the existing genetic map and added 486 AFLP markers to the ∼150 markers on the existing map. The resulting map has an average marker interval of 3.9 map units and averages ∼21 kb/map unit. The additional markers expanded the map from 1,452 to 2,188 map units distributed across 12 chromosomes. The maximum distance between adjacent markers is 29 map units. We identified AFLP markers less than 1 map unit from the mating type (MAT) locus and 2.5 map units from the spore killer (SK) locus; eight AFLP markers map within 8.5 units of the FUM1 (fumonisin biosynthetic) locus. The increased saturation of this map will facilitate further development of G. moniliformis as a model system for the genetic and population genetic studies of related, but less genetically tractable, plant pathogenic fungi.
The Greedy Independent Set in a Random Graph with Given Degrees

We analyse the size of an independent set in a random graph on n vertices with specified vertex degrees, constructed via a simple greedy algorithm: order the vertices arbitrarily, and, for each vertex in turn, place it in the independent set unless it is adjacent to some vertex already chosen. We find the limit of the expected proportion of vertices in the greedy independent set as n→∞ (the jamming constant), expressed as an integral whose upper limit is defined implicitly, valid whenever the second moment of a random vertex degree is uniformly bounded. We further show that the random proportion of vertices in the independent set converges in probability to the jamming constant as n→∞ . The results hold under weaker assumptions in a random multigraph with given degrees constructed via the configuration model. © 2017 Wiley Periodicals, Inc. Random Struct. Alg. , 51, 565–586, 2017
Second primary malignancy risk after radioactive iodine treatment for thyroid cancer: a systematic review and meta-analysis.

BACKGROUND The risk of second primary malignancies (SPMs) associated with cancer therapies is an important concern of thyroid cancer survivors and physicians. Our objective was to determine if the risk of SPMs is increased in individuals with thyroid cancer treated with radioactive iodine (RAI), compared to those not treated with RAI. METHODS We performed a systematic review of the literature and meta-analysis. Two independent reviewers screened citations and reviewed full-text papers. If not reported by the primary authors, the relative risk (RR) of SPMs was calculated by dividing the standardized incidence ratio of SPM in individuals with thyroid cancer treated with RAI compared to those not treated with RAI (with associated 95% confidence intervals [CI]). The natural logarithms of RRs of respective SPMs, weighted by the inverse of the variance, were pooled using fixed effects models and the exponential of the results was reported. RESULTS Two multi-center studies (one from Europe and the other from North America) were included in this review. The RR of SPMs in thyroid cancer survivors treated with RAI was significantly increased at 1.19 (95% confidence interval [CI] 1.04, 1.36, p = 0.010), relative to thyroid cancer survivors not treated with RAI (data from 16,502 individuals), using a minimum latency period of 2 to 3 years after thyroid cancer diagnosis. The RR of leukemia was also significantly increased in thyroid cancer survivors treated with RAI, with an RR of 2.5 (95% CI 1.13, 5.53, p = 0.024). We did not observe a significantly increased risk of the following cancers related to prior RAI treatment: bladder, breast, central nervous system, colon and rectum, digestive tract, stomach, pancreas, kidney (and renal pelvis), lung, or melanoma of skin. CONCLUSIONS The risk of SPMs in thyroid cancer survivors treated with RAI is slightly increased compared to thyroid cancer survivors not treated with RAI.
Anthropometric indices of central obesity how discriminators of metabolic syndrome in Brazilian women with polycystic ovary syndrome

Background: Central obesity is highly prevalent in women with polycystic ovary syndrome (PCOS) and is strongly associated with metabolic syndrome (MetS). Objectives: To define cut-off points of waist circumference (WC), waist-to-height ratio (WHtR), waist-to-hip ratio (WHR), and conicity index (C-Index) to discriminate metabolic syndrome (MetS) in Brazilian women with PCOS. Methods: In a cross-sectional study, anthropometric, biochemical and clinical parameters were measured in 113 Brazilian PCOS women (27.2 ± 4.5 years). Receiver operating characteristic (ROC) analysis was used to find out the cut-off points of anthropometric indices to predict MetS according with National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP-III) criteria. Results: Considering the ROC curve analysis the WC and WHtR had a similar performance in predicting MetS and these parameters were better than WHR and C-Index. The optimal cut-off values of the anthropometric indices for discriminate MetS were: WC = 95 cm; WHtR = 0.59; WHR = 0.88; and C-Index = 1.25. By using these cut-off points the sensitivity and specificity rates of WC and WHtR were higher than those observed for WHR and C-Index.Conclusion: Our results indicated that WC and WHtR are more accurate than WHR and C-Index to predict MetS in Brazilian PCOS women.
Recovery From Substance Abuse: A Narrative Approach to Understanding the Motivation and Ambivalence About Change

Various studies have employed quantitative and qualitative methods to explore the process of behavioral, emotional, and cognitive change that emerges during drug addiction treatment, including the factors both encouraging and discouraging such change. This article focuses on the narrative approach to the motivation and ambivalence inherent in recovery over the course of a residential treatment program. The study sample consisted of 46 clients undergoing substance abuse treatment in a residential facility in Greece. All conversations were taped and later elaborated on with the help of narrative analysis. Seven main types of narrative emerged from the overall analysis: optimistic, overly optimistic, pessimistic, overly pessimistic, “tough life,” troubled/confused, and balanced. Results based on these categorizations reveal common thoughts, skepticism, and difficulties patients face during recovery, with the linkage between each type of narrative.
Spatiotemporal analysis of single-trial EEG of emotional pictures based on independent component analysis and source location

The present study combined the Independent Component Analysis (ICA) and low-resolution brain electromagnetic tomography (LORETA) algorithms to identify the spatial distribution and time course of single-trial EEG record differences between neural responses to emotional stimuli vs. the neutral. Single-trial multichannel (129-sensor) EEG records were collected from 21 healthy, right-handed subjects viewing the emotion emotional (pleasant/unpleasant) and neutral pictures selected from International Affective Picture System (IAPS). For each subject, the single-trial EEG records of each emotional pictures were concatenated with the neutral, and a three-step analysis was applied to each of them in the same way. First, the ICA was performed to decompose each concatenated single-trial EEG records into temporally independent and spatially fixed components, namely independent components (ICs). The IC associated with artifacts were isolated. Second, the clustering analysis classified, across subjects, the temporally and spatially similar ICs into the same clusters, in which nonparametric permutation test for Global Field Power (GFP) of IC projection scalp maps identified significantly different temporal segments of each emotional condition vs. neutral. Third, the brain regions accounted for those significant segments were localized spatially with LORETA analysis. In each cluster, a voxel-by-voxel randomization test identified significantly different brain regions between each emotional condition vs. the neutral. Compared to the neutral, both emotional pictures elicited activation in the visual, temporal, ventromedial and dorsomedial prefrontal cortex and anterior cingulated gyrus. In addition, the pleasant pictures activated the left middle prefrontal cortex and the posterior precuneus, while the unpleasant pictures activated the right orbitofrontal cortex, posterior cingulated gyrus and somatosensory region. Our results were well consistent with other functional imaging studies, while revealed temporal dynamics of emotional processing of specific brain structure with high temporal resolution.
Autosomal recessive polycystic kidney disease: The prototype of the hepato-renal fibrocystic diseases

Abstract Autosomal recessive polycystic kidney disease (ARPKD) is a severe, typically early onset form of renal cystic disease. The care of ARPKD patients has traditionally been the purview of pediatric nephrologists for management of systemic hypertension and progressive renal insufficiency. However, the disease has multisystem manifestations and a comprehensive care strategy frequently requires a multidisciplinary team. In severely affected infants, the diagnosis often is first suspected by obstetricians when enlarged, echogenic kidneys and oligohydramnios are detected on prenatal ultrasounds. Neonatologists are central to the care of these infants, who may have respiratory compromise due to pulmonary hypoplasia and massively enlarged kidneys. Among neonatal survivors, a subset of ARPKD patients has clinically significant congenital hepatic fibrosis, which can lead to portal hypertension, requiring close monitoring by pediatric hepatologists. Surgical consultation may be sought to access pre-emptive nephrectomy to relieve mass effect, placement of dialysis access, surgical shunting procedures, and kidney and/or liver transplantation. Recent data suggest that children with ARPKD may be at risk of neurocognitive dysfunction, and may require neuropsychological referral. In addition to these morbidities, families of patients with ARPKD face decisions regarding genetic testing of affected children, testing of asymptomatic siblings, or consideration of preimplantation genetic diagnosis for future pregnancies. These issues require the input of genetic counselors, geneticists, and reproductive endocrinologists. As a result, the management of ARPKD requires the involvement of multiple subspecialists, as well as the general pediatrician, in a complex care network. In this review, we discuss the genetics of this disorder and provide an overview of the associated pathobiology; outline the spectrum of clinical manifestations of ARPKD and the management of organ-specific complications; discuss other disorders that involve genes encoding cilia-associated proteins that can clinically mimic ARPKD; review the animal models available for preclinical studies; and finally, consider future directions for potential targeted therapies.
The relation between Finnish university students’ perceived level of study-related burnout, perceptions of the teaching–learning environment and perceived achievement motivation

This study examined the relation between university students’ perceived level of study-related burnout (SRB) and their perceptions of the teaching–learning environment (TLE), as well as their perceived achievement motivation (AM). The data are based on a survey of nine Finnish universities in the spring of 2009. Altogether, 3035 university students completed the online survey. Results show that the more negative the students’ perceptions concerning TLE are, the higher their level of burnout is. However, the role of AM, which includes studying abilities, study success and appreciation of studies, is even more significant for the students’ well-being. University teachers should understand the phenomenon of SRB and how teaching and overall pedagogical decisions are related to stress. If the teachers’ and students’ ways of understanding these components vary significantly, this may lead to dissatisfaction and perhaps even burnout. The students have a responsibility to improve their studying skills, which would help them cope with stress, and to find self-generated or active solutions instead of reacting passively to stressful situations and tasks.
Strong direct product theorems for quantum communication and query complexity

A strong direct product theorem (SDPT) states that solving n instances of a problem requires Omega(n) times the resources for a single instance, even to achieve success probability 2-Ω(n). We prove that quantum communication complexity obeys an SDPT whenever the communication lower bound for a single instance is proved by the generalized discrepancy method, the strongest technique in that model. We prove that quantum query complexity obeys an SDPT whenever the query lower bound for a single instance is proved by the polynomial method, one of the two main techniques in that model. In both models, we prove the corresponding XOR lemmas and threshold direct product theorems.
A framework for preprocessor-aware C source code analyses

Analyses of C source code usually ignore the C preprocessor because of its complexity. Instead, these analyses either dene their own approximate parser or scanner or else they require that their input already be preprocessed. Neither approach is entirely satisfactory: the rst gives up accuracy (or incurs large implementation costs), while the second loses the preprocessor-based abstractions. We describe a framework that permits analyses to be expressed in terms of both preprocessing and parsing actions, allowing the implementer to focus on the analysis. We discuss an implementation of such a framework that embeds a C preprocessor, a parser, and a Perl interpreter for the action \hooks. " Many common software engineering analyses can be written surprisingly easily using our implementation, replacing numerous ad-hoc tools. The framework’s integration of the preprocessor and the parser further enables some analyses that otherwise would be especially dicult to perform.