Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic C code manipulation and transformation to rapid embedded systems design

Automatic Custom Architecture Generator (ACAgen) is a C-to-Hardware toolset that aims to facilitate and automate the design and development of complex and heterogeneous embedded systems. Framework and algorithms to analyze the initial C code to be exploited in the hardware generation process are needed. Indeed, in the process of Custom Instruction integration within reconfigurable SoC, the initial application C code needs to be automatically updated with the custom instruction opcode. This paper presents the design, and development of an ACAgen Java library able to read, manipulate, and write C code. Its implementation is based on C parsing approach making the SoC design fast and easy. In order to evaluate the usefulness of our ACAgen Java library, we conducted a 3D case study where we applied the new tool to source code of Scalar, Znormal, Projection and other applications. As a result, ACAgen has been able to effectively identify the characteristics of the initial application C code to update it with the hardware components.
ShapeSynth: Parameterizing model collections for coupled shape exploration and synthesis

Recent advances in modeling tools enable non‐expert users to synthesize novel shapes by assembling parts extracted from model databases. A major challenge for these tools is to provide users with relevant parts, which is especially difficult for large repositories with significant geometric variations. In this paper we analyze unorganized collections of 3D models to facilitate explorative shape synthesis by providing high‐level feedback of possible synthesizable shapes. By jointly analyzing arrangements and shapes of parts across models, we hierarchically embed the models into low‐dimensional spaces. The user can then use the parameterization to explore the existing models by clicking in different areas or by selecting groups to zoom on specific shape clusters. More importantly, any point in the embedded space can be lifted to an arrangement of parts to provide an abstracted view of possible shape variations. The abstraction can further be realized by appropriately deforming parts from neighboring models to produce synthesized geometry. Our experiments show that users can rapidly generate plausible and diverse shapes using our system, which also performs favorably with respect to previous modeling tools.
Who is a Better Tutor?: Gaze Hints with a Human or Humanoid Tutor in Game Play

In this paper, we present a study that analyses the effects of robot or human gaze hints on people's choices in a card game. We asked human participants to play a matching card game in the presence of a human or a robotic tutor. Our aim was to find out if gaze hints provided by the tutor can direct the attention and influence the choices of the human participants. The results show that participants performed significantly better when they received gaze hints from a tutor than when they did not. Furthermore, we found that people identified the tutor hints more often in robot condition than in human condition and, as a result, performed significantly better.
An Approach for Efficient Planning of Robotic Manipulation Tasks

Robot manipulation is a challenging task for planning as itinvolves a mixture of symbolic planning and geometric plan-ning. We would like to express goals and many action ef-fects symbolically, for example specifying a goal such as forall x, if x is a cup, then x should be on the tray, but to ac-complish this we may need to plan the geometry of fitting allthe cups on the tray and how to grasp, move and release thecups to achieve that geometry. In the ideal case, this couldbe accomplished by a fully hybrid planner that alternates be-tween geometric and symbolic reasoning to generate a solu-tion. However, in practice this is very complex, and the fullpower of this approach may only be required for a small sub-set of problems. Instead, we plan completely symbolically,and then attempt to generate a geometric plan by translatingthe symoblic predicates into geometric relationships. We thenexecute this plan in simulation, and if it fails, we backtrack,first in geometric space, and then if necessary in symbolic.We show that this approach, while not complete, solves anumber of challenging manipulation problems, and demon-strate it running on a robotic platform.
A unifying framework for partial volume segmentation of brain MR images

Accurate brain tissue segmentation by intensity-based voxel classification of magnetic resonance (MR) images is complicated by partial volume (PV) voxels that contain a mixture of two or more tissue types. In this paper, we present a statistical framework for PV segmentation that encompasses and extends existing techniques. We start from a commonly used parametric statistical image model in which each voxel belongs to one single tissue type, and introduce an additional downsampling step that causes partial voluming along the borders between tissues. An expectation-maximization approach is used to simultaneously estimate the parameters of the resulting model and perform a PV classification. We present results on well-chosen simulated images and on real MR images of the brain, and demonstrate that the use of appropriate spatial prior knowledge not only improves the classifications, but is often indispensable for robust parameter estimation as well. We conclude that general robust PV segmentation of MR brain images requires statistical models that describe the spatial distribution of brain tissues more accurately than currently available models.
Variable Gene Dispersal Conditions and Spatial Deforestation Patterns Can Interact to Affect Tropical Tree Conservation Outcomes

Tropical lowland rain forest (TLRF) biodiversity is under threat from anthropogenic factors including deforestation which creates forest fragments of different sizes that can further undergo various internal patterns of logging. Such interventions can modify previous equilibrium abundance and spatial distribution patterns of offspring recruitment and/or pollen dispersal. Little is known about how these aspects of deforestation and fragmentation might synergistically affect TLRF tree recovery demographics and population genetics in newly formed forest fragments. To investigate these TLRF anthropogenic disturbance processes we used the computer program NEWGARDEN (NG), which models spatially-explicit, individual-based plant populations, to simulate 10% deforestation in six different spatial logging patterns for the plant functional type of a long-lived TLRF canopy tree species. Further, each logging pattern was analyzed under nine varying patterns of offspring versus pollen dispersal distances that could have arisen post-fragmentation. Results indicated that gene dispersal condition (especially via offspring) had a greater effect on population growth and genetic diversity retention (explaining 98.5% and 88.8% of the variance respectively) than spatial logging pattern (0.2% and 4.7% respectively), with ‘Near’ distance dispersal maximizing population growth and genetic diversity relative to distant dispersal. Within logged regions of the fragment, deforestation patterns closer to fragment borders more often exhibited lower population recovery rates and founding genetic diversity retention relative to more centrally located logging. These results suggest newly isolated fragments have populations that are more sensitive to the way in which their offspring and pollen dispersers are affected than the spatial pattern in which subsequent logging occurs, and that large variation in the recovery rates of different TLRF tree species attributable to altered gene dispersal regimens will be a likely outcome of fragmentation. Conservation implications include possible manual interventions (manual manipulations of offspring dispersers and/or pollinators) in forest fragments to increase population recovery and genetic diversity retention.
Robust semi-definite relaxation MIMO detection in a non-gaussian channel

Semi-definite relaxation (SDR) is a popular technique for Multi-Input Multi-Output (MIMO) detection. For Binary Phase-Shift Keying (BPSK) and Quadratic Phase-Shift Keying (QPSK), it has been found that SDR can provide a near-optimal Bit Error rate (BER) performance in a Gaussian channel. However if the noise in the channel deviates from the Gaussian model, as it does in many real wireless channels, BER performance drops considerably. In this paper we show that SDR can be applied for detection in a non-Gaussian channel using Huber's M-estimation method for robust regression.
Fine Mapping of Five Loci Associated with Low-Density Lipoprotein Cholesterol Detects Variants That Double the Explained Heritability

Complex trait genome-wide association studies (GWAS) provide an efficient strategy for evaluating large numbers of common variants in large numbers of individuals and for identifying trait-associated variants. Nevertheless, GWAS often leave much of the trait heritability unexplained. We hypothesized that some of this unexplained heritability might be due to common and rare variants that reside in GWAS identified loci but lack appropriate proxies in modern genotyping arrays. To assess this hypothesis, we re-examined 7 genes (APOE, APOC1, APOC2, SORT1, LDLR, APOB, and PCSK9) in 5 loci associated with low-density lipoprotein cholesterol (LDL-C) in multiple GWAS. For each gene, we first catalogued genetic variation by re-sequencing 256 Sardinian individuals with extreme LDL-C values. Next, we genotyped variants identified by us and by the 1000 Genomes Project (totaling 3,277 SNPs) in 5,524 volunteers. We found that in one locus (PCSK9) the GWAS signal could be explained by a previously described low-frequency variant and that in three loci (PCSK9, APOE, and LDLR) there were additional variants independently associated with LDL-C, including a novel and rare LDLR variant that seems specific to Sardinians. Overall, this more detailed assessment of SNP variation in these loci increased estimates of the heritability of LDL-C accounted for by these genes from 3.1% to 6.5%. All association signals and the heritability estimates were successfully confirmed in a sample of ∼10,000 Finnish and Norwegian individuals. Our results thus suggest that focusing on variants accessible via GWAS can lead to clear underestimates of the trait heritability explained by a set of loci. Further, our results suggest that, as prelude to large-scale sequencing efforts, targeted re-sequencing efforts paired with large-scale genotyping will increase estimates of complex trait heritability explained by known loci.
Finding Outlier from Large Dataset Using Online OSPCA

Anomaly Detection is the term which is widely used in Data Mining. Anomaly Detection means Fraud Detection. Anomalous Intrusion became a key issue in security because of the heavy data in network. So it becomes hard to prevent such attacks. Previous techniques works only on batch mode means those techniques are not applied for large dataset. For this purpose it is important to find technique which provides support for large dataset. The HMM and OSPCA are the techniques which are applied for large dataset by using online updating technique. These Techniques are used in the applications such as Fraud Detections Systems like Intrusion Detection Technique.
Topic segmentation using weighted lexical links (WLL)

This paper presents two new approaches of lexical chains for topic segmentation using weighted lexical chains (WLC) or weighted lexical links (WLL) between repeated occurrences of lemmas along the text. The main advantage of using these new approaches is the suppression of the empirical parameter called hiatus in lexical chain processing. An evaluation according to the WindowDiff measure on a large automatically built corpus shows slight improvements in WLL compared to state-of-the-art methods based on lexical chains.
5-Aminoimidazole-4-carboxamide-1- (cid:1) - D -ribofuranoside renders glucose output by the liver of the dog insensitive to a pharmacological increment in insulin

5-Aminoimidazole-4-carboxamide-1- (cid:1) - D -ribofuranoside renders glucose output by the liver of the dog insensitive to a pharmacological increment in insulin. Am J 2005. study aimed to test whether stimulation of net hepatic glucose output (NHGO) by increased concentrations of the AMP analog, 5-amino-imidazole-4-carboxamide-1- (cid:1) - D -ribosyl-5-monophosphate, can be suppressed by pharmacological insulin levels. Dogs had sampling (artery, portal vein, hepatic vein) and infusion (vena cava, portal vein) catheters and ﬂow probes (hepatic artery, portal vein) implanted (cid:2) 16 days before study. Protocols consisted of equilibration ( (cid:3) 130 to (cid:3) 30 min), basal ( (cid:3) 30 to 0 min), and hyperinsulinemic-euglycemic (0–150 min) periods. At time ( t ) (cid:4) 0 min, somatostatin was infused, and basal glucagon was replaced via the portal vein. Insulin was infused in the portal vein at either 2 (INS2) or 5 (INS5) mU (cid:1) kg (cid:3) 1 (cid:1) min (cid:3) 1 . At t (cid:4) 60 min, 1 mg (cid:1) kg (cid:3) 1 (cid:1) min (cid:3) 1 portal venous 5-aminoimidazole-4-carbox-amide-1- (cid:1) - D -ribofuranoside (AICAR) infusion was initiated. Arterial insulin rose (cid:5) 9- and (cid:5) 27-fold in INS2 and INS5, respectively. Glucagon, catecholamines, and cortisol did not change throughout the study. NHGO was completely suppressed before t (cid:4) 60 min. Intraportal AICAR stimulated NHGO by 1.9 (cid:6) 0.5 and 2.0 (cid:6) 0.5 mg (cid:1) kg (cid:3) 1 (cid:1) min (cid:3) 1 in INS2 and INS5, respectively. AICAR stimulated tracer-determined endogenous glucose production similarly in both groups. Intraportal AICAR infusion signiﬁcantly increased hepatic acetyl-CoA carboxylase (ACC, Ser 79 ) phosphorylation in INS2. Hepatic ACC (Ser 79 ) phosphorylation, however, was not increased in INS5. Thus intraportal AICAR infusion renders hepatic glucose output insensitive to pharmacological insulin. The effectiveness of AICAR in countering the suppressive effect of pharmacological insulin on NHGO occurs even though AICAR-stimulated ACC phosphorylation is completely blocked.
Acyl substrate preferences of an IAA-amido synthetase account for variations in grape (Vitis vinifera L.) berry ripening caused by different auxinic compounds indicating the importance of auxin conjugation in plant development

Nine Gretchen Hagen (GH3) genes were identified in grapevine (Vitis vinifera L.) and six of these were predicted on the basis of protein sequence similarity to act as indole-3-acetic acid (IAA)-amido synthetases. The activity of these enzymes is thought to be important in controlling free IAA levels and one auxin-inducible grapevine GH3 protein, GH3-1, has previously been implicated in the berry ripening process. Ex planta assays showed that the expression of only one other GH3 gene, GH3-2, increased following the treatment of grape berries with auxinic compounds. One of these was the naturally occurring IAA and the other two were synthetic, α-naphthalene acetic acid (NAA) and benzothiazole-2-oxyacetic acid (BTOA). The determination of steady-state kinetic parameters for the recombinant GH3-1 and GH3-2 proteins revealed that both enzymes efficiently conjugated aspartic acid (Asp) to IAA and less well to NAA, while BTOA was a poor substrate. GH3-2 gene expression was induced by IAA treatment of pre-ripening berries with an associated increase in levels of IAA-Asp and a decrease in free IAA levels. This indicates that GH3-2 responded to excess auxin to maintain low levels of free IAA. Grape berry ripening was not affected by IAA application prior to veraison (ripening onset) but was considerably delayed by NAA and even more so by BTOA. The differential effects of the three auxinic compounds on berry ripening can therefore be explained by the induction and acyl substrate specificity of GH3-2. These results further indicate an important role for GH3 proteins in controlling auxin-related plant developmental processes.
Pacific Asia Conference on Information Systems ( PACIS ) July 2009 CHICKEN AND EGG ? INTERPLAY BETWEEN MUSIC BLOG BUZZ AND ALBUM SALES

This paper examines the interrelationship between music blog buzz and album sales, examining both time precedence and the contemporaneous relationship between the two. Using the Granger Causality methodology to examine time precedence, we find evidence of bi-directional causality for the full sample of music albums we are examining as well as for both independently released and major-label released music albums. Next, we employ two-stage least squares analysis to quantify the contemporaneous relationships between music blog buzz and album sales. Preliminary results indicate that blog buzz has a positive and significant relationship with album sales and that this relationship is stronger for independently released music. Results from this study have implications for the music industry, particularly music labels and artists in making decisions about album promotion.
Control of Cortical Axon Elongation by a GABA-Driven Ca2+/Calmodulin-Dependent Protein Kinase Cascade

Ca2+ signaling plays important roles during both axonal and dendritic growth. Yet whether and how Ca2+ rises may trigger and contribute to the development of long-range cortical connections remains mostly unknown. Here, we demonstrate that two separate limbs of the Ca2+/calmodulin-dependent protein kinase kinase (CaMKK)–CaMKI cascades, CaMKK–CaMKIα and CaMKK–CaMKIγ, critically coordinate axonal and dendritic morphogenesis of cortical neurons, respectively. The axon-specific morphological phenotype required a diffuse cytoplasmic localization and a strikingly α-isoform-specific kinase activity of CaMKI. Unexpectedly, treatment with muscimol, a GABAA receptor agonist, selectively stimulated elongation of axons but not of dendrites, and the CaMKK–CaMKIα cascade critically mediated this axonogenic effect. Consistent with these findings, during early brain development, in vivo knockdown of CaMKIα significantly impaired the terminal axonal extension and thereby perturbed the refinement of the interhemispheric callosal projections into the contralateral cortices. Our findings thus indicate a novel role for the GABA-driven CaMKK–CaMKIα cascade as a mechanism critical for accurate cortical axon pathfinding, an essential process that may contribute to fine-tuning the formation of interhemispheric connectivity during the perinatal development of the CNS.
Characterization of a Truncated Lipoarabinomannan from the Actinomycete Turicella otitidis

ABSTRACT Lipoarabinomannan (LAM) lipoglycans have been characterized from a range of mycolic acid-containing actinomycetes and from the amycolate actinomycete Amycolatopsis sulphurea. To further understand the structural diversity of this family, we have characterized the lipoglycan of the otic commensal Turicella otitidis. T. otitidis LAM (TotLAM) has been determined to consist of a mannosyl phosphatidylinositol anchor unit carrying an (α 1→6)-linked mannan core and substituted with terminal-arabinosyl branches. Thus, TotLAM has a novel truncated LAM structure. Using the human monocytic THP-1 cell line, it was found that TotLAM exhibited only minimal ability to induce tumor necrosis factor alpha. These findings contribute further to our understanding of actinomycete LAM diversity and allow further speculation as to the correlation between LAM structure and the immunomodulatory activities of these lipoglycans.
Kernel-based autoregressive modeling with a pre-image technique

Autoregressive (AR) modeling is a very popular method for time series analysis. Being linear by nature, it obviously fails to adequately describe nonlinear systems. In this paper, we propose a kernel-based AR modeling, by combining two main concepts in kernel machines. One the one hand, we map samples to some nonlinear feature space, where an AR model is considered. We show that the model parameters can be determined without the need to exhibit the nonlinear map, by computing inner products thanks to the kernel trick. On the other hand, we propose a prediction scheme, where the prediction in the feature space is mapped back into the input space, the original samples space. For this purpose, a pre-image technique is derived to predict the future back in the input space. The efficiency of the proposed method is illustrated on real-life time-series, by comparing it to other linear and nonlinear time series prediction techniques.
Functional Impairment and the Economic Consequences of Female Breast Cancer

ABSTRACT Recent trends in breast cancer diagnosis and mortality suggest that long-term survivors are now more likely to be functionally impaired and, hence, more likely to experience adverse economic outcomes. This study tests whether women who have survived breast cancer for at least five years exhibit more, or more severe, functional impairments than otherwise similar women without breast cancer. It also tests whether women with more severe impairments experience poorer economic outcomes attributable to their functional status. A group of 105 breast cancer survivors was interviewed to obtain data on health and economic changes in the five-year period since diagnosis and initial treatment. An age- and work-matched group of 105 women without cancer was also interviewed to obtain the same data over the same time period. Key changes in the functional status of the subjects as well as economic outcomes such as changes in market earnings, household income, and insurance coverage were measured. Whether impairment is more severe in the breast cancer group than the comparison group was then tested statistically; whether economic outcomes are more adverse in more impaired than less impaired women regardless of their breast cancer status was also tested. The analysis turned up statistically significant evidence in regard to each of these relationships. Breast cancer survivors were more likely than controls to be functionally impaired at the five-year benchmark. Impaired women, in turn, were more likely to reduce work effort and experience downturns in market earnings, among other things. Policy and research implications are discussed.
SwiftCloud: Fault-Tolerant Geo-Replication Integrated all the Way to the Client Machine

Client-side logic and storage are increasingly used in web and mobile applications to improve response time and availability. Current approaches tend to be ad-hoc and poorly integrated with the server-side logic. We present a principled approach to integrate client-and server-side storage. We support both mergeable and strongly consistent transactions that target either client or server replicas and provide access to causally-consistent snapshots efficiently. In the presence of infrastructure faults, a client-assisted failover solution allows client execution to resume immediately and seamlessly access consistent snapshots without waiting. We implement this approach in SwiftCloud, the first transactional system to bring geo-replication all the way to the client machine. Example applications show that our programming model is useful across a range of application areas. Our experimental evaluation shows that SwiftCloud provides better fault tolerance and at the same time can improve both latency and throughput by up to an order of magnitude, compared to classical geo-replication techniques.