Miracle-dz/UniFAR-FTUs huggingface dataset:
QoS-Aware Stochastic Power Management for Many-Cores

A many-core processor can execute hundreds of multi-threaded tasks in parallel on its 100s - 1000s of processing cores. When deployed in a Quality of Service (QoS)-based system, the many-core must execute a task at a target QoS. The amount of processing required by the task for the QoS varies over the task’s lifetime. Accordingly, Dynamic Voltage and Frequency Scaling (DVFS) allows the many-core to deliver precise amount of processing required to meet the task QoS guarantee while conserving power. Still, a global control is necessitated to ensure that the many-core overall does not exceed its power budget.Previously, only non-stochastic controls have been proposed for the problem of QoS-aware power budgeting in many-cores. We propose the first stochastic control for the problem, which has a computational complexity less than the non-stochastic control by a factor of O (ln n) but with equivalent performance. The proposed stochastic control can operate with 6.4× less overhead than the non-stochastic control for a 256-task workload.
RF Driven 5G System Design for Centimeter Waves

5G system design is a complex process due to a great variety of applications and their diverse requirements. This article describes our experiences in developing a centimeter waves mobile broadband concept satisfying future capacity requirements. The first step in the process was the radio channel measurement campaign and statistical modeling. Then the link level design was performed tightly together with the radio frequency (RF) implementation requirements to allow as large scalability of the air interface as possible. We started the concept development at 10 GHz frequency band and during the project World Radiocommunication Conference 2015 selected somewhat higher frequencies as new candidates for 5G. Thus, the main learning was to gain insight of interdependencies of different phenomena and find feasible combinations of techniques and parameter combinations that might actually work in practice, not only in theory.
Classification of Remote Sensing Images Using Attribute Profiles and Feature Profiles from Different Trees: A Comparative Study

The motivation of this paper is to conduct a comparative study on remote sensing image classification using the morphological attribute profiles (APs) and feature profiles (FPs) generated from different types of tree structures. Over the past few years, APs have been among the most effective methods to model the image's spatial and contextual information. Recently, a novel extension of APs called FPs has been proposed by replacing pixel gray-levels with some statistical and geometrical features when forming the output profiles. FPs have been proved to be more efficient than the standard APs when generated from component trees (max-tree and min-tree). In this work, we investigate their performance on the inclusion tree (tree of shapes) and partition trees (alpha tree and omega tree). Experimental results from both panchromatic and hyperspectral images again confirm the efficiency of FPs compared to APs.
Innovative Technologies Changing Cancer Treatment

Conventional therapies for cancer such as chemotherapy and radiotherapy remain a mainstay in treatment, but in many cases a targeted approach is lacking, and patients can be vulnerable to drug resistance. In recent years, novel concepts have been emerging to improve the traditional therapeutic options in cancers with poor survival outcomes. New therapeutic strategies involving areas like energy metabolism and extracellular vesicles along with advances in immunotherapy and nanotechnology are driving the next generation of cancer treatments. The development of fields such as theranostics in nanomedicine is also opening new doors for targeted drug delivery and nano-imaging. Here we discuss the use of innovative technologies presented at the Irish Association for Cancer Research (IACR) Annual Meeting, highlighting examples of where new approaches may lead to promising new treatment options for a range of cancer types.
Inhibition of somatosensory mechanotransduction by annexin A6

The membrane-binding protein annexin A6 suppresses mechanically induced pain in a mouse model of osteoarthritis. A blocker of osteoarthritic pain Patients with osteoarthritis experience pain when moving or, sometimes, touching the affected joints. Raouf et al. identified proteins that bound to a toxin that inhibits mechanically induced pain, including the membrane-binding protein annexin A6. Mice lacking annexin A6 were more sensitive to mechanically induced pain, whereas overexpression of annexin A6 in sensory neurons reduced pain in a mouse model of osteoarthritis. These results suggest that strategies that increase the abundance of annexin A6 could alleviate the chronic pain of osteoarthritis. Mechanically activated, slowly adapting currents in sensory neurons have been linked to noxious mechanosensation. The conotoxin NMB-1 (noxious mechanosensation blocker-1) blocks such currents and inhibits mechanical pain. Using a biotinylated form of NMB-1 in mass spectrometry analysis, we identified 67 binding proteins in sensory neurons and a sensory neuron–derived cell line, of which the top candidate was annexin A6, a membrane-associated calcium-binding protein. Annexin A6–deficient mice showed increased sensitivity to mechanical stimuli. Sensory neurons from these mice showed increased activity of the cation channel Piezo2, which mediates a rapidly adapting mechano-gated current linked to proprioception and touch, and a decrease in mechanically activated, slowly adapting currents. Conversely, overexpression of annexin A6 in sensory neurons inhibited rapidly adapting currents that were partially mediated by Piezo2. Furthermore, overexpression of annexin A6 in sensory neurons attenuated mechanical pain in a mouse model of osteoarthritis, a disease in which mechanically evoked pain is particularly problematic. These data suggest that annexin A6 can be exploited to inhibit chronic mechanical pain.
Positive selection on sociobiological traits in invasive fire ants

The fire ant Solenopsis invicta and its close relatives are highly invasive. Enhanced social cooperation may facilitate invasiveness in these and other invasive ant species. We investigated whether invasiveness in Solenopsis fire ants was accompanied by positive selection on sociobiological traits by applying a phylogenomics approach to infer ancient selection, and a population genomics approach to infer recent and ongoing selection in both native and introduced S. invicta populations. A combination of whole‐genome sequencing of 40 haploid males and reduced‐representation genomic sequencing of 112 diploid workers identified 1,758,116 and 169,682 polymorphic markers, respectively. The resulting high‐resolution maps of genomic polymorphism provide high inference power to test for positive selection. Our analyses provide evidence of positive selection on putative ion channel genes, which are implicated in neurological functions, and on vitellogenin, which is a key regulator of development and caste determination. Furthermore, molecular functions implicated in pheromonal signalling have experienced recent positive selection. Genes with signatures of positive selection were significantly more often those overexpressed in workers compared with queens and males, suggesting that worker traits are under stronger selection than queen and male traits. These results provide insights into selection pressures and ongoing adaptation in an invasive social insect and support the hypothesis that sociobiological traits are under more positive selection than nonsocial traits in such invasive species.
Using Fragment-Based Approaches to Discover New Antibiotics

Fragment-based lead discovery has emerged over the past two decades as a successful approach to generate novel lead candidates in drug discovery programs. The two main advantages over conventional high-throughput screening (HTS) are more efficient sampling of chemical space and tighter control over the physicochemical properties of the lead candidates. Antibiotics are a class of drugs with particularly strict property requirements for efficacy and safety. The development of novel antibiotics has slowed down so much that resistance has now evolved against every available antibiotic drug. Here we give an overview of fragment-based approaches in screening and lead discovery projects for new antibiotics. We discuss several successful hit-to-lead development examples. Finally, we highlight the current challenges and opportunities for fragment-based lead discovery toward new antibiotics.
Modeling and Mapping Coyote (Canis latrans) Abundance in Northwestern Vermont

Predators such as, coyotes (Canis latrans), have profound effects on ecosystems. Coyotes are recent arrivals in the northeastern United States of America, and in Vermont their ecology remains poorly understood. Even basic population characteristics remain largely unknown. I used a Royle-Nichols Abundance-Induced Heterogeneity Model to estimate coyote site abundance in northwestern Vermont. The model was developed by averaging the outputs of supported candidate models of detection/non-detection data collected from 71 camera traps in 2008, 2011 and 2017. The averaged model included the null model and the following covariates: the proportion of water/wetland, agriculture, coniferous forest, deciduous forest, mixed forest, development, shrub/scrub and the mean bobcat habitat suitability within the radius of an average coyote home range of a site. The candidate model with the strongest empirical support was the null model, followed by the water/wetland model, but all the candidate models assessed had strong empirical support (Δ AIC < 2). The covariates water/wetland, agriculture, shrub/scrub and mixed forest had a positive effect on abundance, whereas the other covariates had a negative effect. Abundance ranged from 0.078 coyotes/km to 0.089 coyotes/km and was greatest in the western part of the study area. Using model outputs, I estimated abundance in the state Wildlife Management Units (WMUs) in the study area: B, G, I, F1 and F2. WMU B had the greatest abundance estimate (148 coyotes), while WMU I had the lowest (77 coyotes). Across all WMUs abundance was 457 coyotes. Abundance values predicted from the model were lower than expected based on the state’s abundance estimate. One advantage of the model approach is that it incorporated the influence of landscape variables on abundance and resulted in a measure of
A One-Sided Classification Toolkit with Applications in the Analysis of Spectroscopy Data

This dissertation investigates the use of one-sided classification algorithms in the application of separating hazardous chlorinated solvents from other materials, based on their Raman spectra. The experimentation is carried out using a new one-sided classification toolkit that was designed and developed from the ground up. In the one-sided classification paradigm, the objective is to separate elements of the target class from all outliers. These one-sided classifiers are generally chosen, in practice, when there is a deficiency of some sort in the training examples. Sometimes outlier examples can be rare, expensive to label, or even entirely absent. However, this author would like to note that they can be equally applicable when outlier examples are plentiful but nonetheless not statistically representative of the complete outlier concept. It is this scenario that is explicitly dealt with in this research work. In these circumstances, one-sided classifiers have been found to be more robust that conventional multi-class classifiers. The term "unexpected" outliers is introduced to represent outlier examples, encountered in the test set, that have been taken from a different distribution to the training set examples. These are examples that are a result of an inadequate representation of all possible outliers in the training set. It can often be impossible to fully characterise outlier examples given the fact that they can represent the immeasurable quantity of "everything else" that is not a target. The findings from this research have shown the potential drawbacks of using conventional multi-class classification algorithms when the test data come from a completely different distribution to that of the training samples.
Inferring Visual Semantic Similarity with Deep Learning and Wikidata: Introducing imagesim-353*

Aiming at multi-modal knowledge representation we construct a dataset with pairs of digital photos of objects. We manually score image pairs for semantic object similarity. A pre-trained ImageNet-based deep neural network predicts the objects and we use the output to estimate the similarity between two images. With a linkage between the neural network and Wikidata, we augment the model and incorporate knowledge graph information into the similarity measure. We compare the machine-based predicted similarity with the human-based semantic similarity.
Geographic and sociodemographic variation of cardiovascular disease risk in India: A cross-sectional study of 797,540 adults

Background Cardiovascular disease (CVD) is the leading cause of mortality in India. Yet, evidence on the CVD risk of India’s population is limited. To inform health system planning and effective targeting of interventions, this study aimed to determine how CVD risk—and the factors that determine risk—varies among states in India, by rural–urban location, and by individual-level sociodemographic characteristics. Methods and findings We used 2 large household surveys carried out between 2012 and 2014, which included a sample of 797,540 adults aged 30 to 74 years across India. The main outcome variable was the predicted 10-year risk of a CVD event as calculated with the Framingham risk score. The Harvard–NHANES, Globorisk, and WHO–ISH scores were used in secondary analyses. CVD risk and the prevalence of CVD risk factors were examined by state, rural–urban residence, age, sex, household wealth, and education. Mean CVD risk varied from 13.2% (95% CI: 12.7%–13.6%) in Jharkhand to 19.5% (95% CI: 19.1%–19.9%) in Kerala. CVD risk tended to be highest in North, Northeast, and South India. District-level wealth quintile (based on median household wealth in a district) and urbanization were both positively associated with CVD risk. Similarly, household wealth quintile and living in an urban area were positively associated with CVD risk among both sexes, but the associations were stronger among women than men. Smoking was more prevalent in poorer household wealth quintiles and in rural areas, whereas body mass index, high blood glucose, and systolic blood pressure were positively associated with household wealth and urban location. Men had a substantially higher (age-standardized) smoking prevalence (26.2% [95% CI: 25.7%–26.7%] versus 1.8% [95% CI: 1.7%–1.9%]) and mean systolic blood pressure (126.9 mm Hg [95% CI: 126.7–127.1] versus 124.3 mm Hg [95% CI: 124.1–124.5]) than women. Important limitations of this analysis are the high proportion of missing values (27.1%) in the main outcome variable, assessment of diabetes through a 1-time capillary blood glucose measurement, and the inability to exclude participants with a current or previous CVD event. Conclusions This study identified substantial variation in CVD risk among states and sociodemographic groups in India—findings that can facilitate effective targeting of CVD programs to those most at risk and most in need. While the CVD risk scores used have not been validated in South Asian populations, the patterns of variation in CVD risk among the Indian population were similar across all 4 risk scoring systems.
Schizotypal personality traits and social cognition are associated with childhood trauma exposure

OBJECTIVES Childhood trauma is a common risk factor for adult psychiatric disorders, such as schizophrenia (SZ) and bipolar-I disorder (BD). However, its association with schizotypal personality traits, as well as cognitive and social cognitive abilities, is less well studied in these populations. METHODS In a cohort of 79 SZ cases, 84 BD cases, and 75 healthy controls (HCs), clinically significant levels of childhood trauma exposure (according to scores on the Childhood Trauma Questionnaire; CTQ) were evident in 54 SZ, 55 BD, and 26 HC individuals. Trauma-exposed and non-exposed groups were compared on schizotypal personality features (schizotypy) measured with the Schizotypal Personality Questionnaire (SPQ). Cognitive assessments included executive function, working memory, attention, and immediate and delayed memory. Social cognitive measures assessed facial emotion processing and theory-of-mind abilities. RESULTS Trauma-exposed participants showed higher levels of schizotypy, especially suspiciousness, relative to non-exposed individuals, regardless of clinical or HC status. Furthermore, trauma-exposed individuals showed deficits specifically in social cognitive, but not general cognitive abilities, regardless of clinical or HC status. These trauma-related results were found in the context of higher schizotypy levels in both SZ and BD relative to HC, and lower cognitive and social cognitive performance in SZ, relative to BD and HC groups. CONCLUSIONS These findings suggest that childhood trauma exposure impacts long-term schizotypy outcomes, especially paranoid ideation (suspiciousness), as well as complex social cognitive abilities in both healthy and psychotic populations. However, cognitive deficits associated with psychotic illness may not be distinguishable from those related to trauma exposure in previous studies. PRACTITIONER POINTS Findings Childhood trauma exposure is associated with increased schizotypal features (in particular paranoid ideation) and complex social cognitive abilities, independently of the diagnosis of psychotic disorder. Cognitive and social cognitive deficits were larger in schizophrenia compared to bipolar-I cases and healthy controls, but increased schizotypal features were observed in both schizophrenia and bipolar-I disorder relative to healthy controls. Limitations We were unable to distinguish the specific effects of particular childhood trauma exposures due to the high rate of exposure to more than one type of maltreatment. Retrospective assessment of childhood trauma in adulthood cannot be externally validated, and associations with behavioural traits in later life may be confounded by other factors not studied here.
Neuropathic pain drives anxiety behavior in mice, results consistent with anxiety levels in diabetic neuropathy patients

Background: Epidemiological studies in patients with neuropathic pain demonstrate a strong association with psychiatric conditions such as anxiety; however, the precipitating pathology between these symptoms remains unclear. To investigate this, we studied the effects of lifelong stress on levels of neuropathic pain–like behavior and conversely, the effects of chronic neuropathic injury on anxiety-like status in male and female mice. In addition, we assayed this link in painful and painless diabetic peripheral neuropathy patients. Methods: Male and female mice were subject to ongoing life-stress or control living conditions. Baseline sensitivity and anxiety tests were measured followed by spared nerve injury (SNI) to the sciatic nerve. Subsequent sensory testing occurred until 3 weeks after SNI followed by anxiety tests between 4 and 6 weeks after SNI. Results: Levels of tactile or cold allodynia did not differ between adult mice subject to lifelong chronic stress, relative to nonstressed controls, for at least 3 weeks after SNI. By contrast, longer-term neuropathic mice of both sexes displayed pronounced anxiety-like behavior, regardless of exposure to stress. If sex differences were present, females usually exhibited more pronounced anxiety-like behavior. These ongoing anxiety behaviors were corroborated with plasma corticosterone levels in distinct animal groups. In addition, data from patients with painful and nonpainful diabetic neuropathy showed a clear relationship between ongoing pain and anxiety, with females generally more affected than males. Discussion: Taken together, these data demonstrate a strong link between chronic neuropathic pain and chronic anxiety, with the driver of this comorbidity being neuropathic pain as opposed to on-going stress.
A Novel Micropayment Scheme with Variable Denomination

Recently, the research of micropayment systems has at-tracted a lot of attention in the literature. Because of its special feature that large amount of transactions may occur while each transaction only deals with small value, not only security but also eﬃciency should be carefully considered in these systems, especially when considering that these systems may be implemented in resource constrained mobile devices. In this paper, a novel lightweight micropayment scheme with variable denomination is proposed. Compared with those existing schemes with ﬁxed denomination, our proposed scheme not only guarantees security goals, but also dramatically reduces the computational cost as well as the storage burden.
Terminal Hydride Species in [FeFe]-Hydrogenases Are Vibrationally Coupled to the Active Site Environment.

A combination of nuclear resonance vibrational spectroscopy (NRVS), FTIR spectroscopy, and DFT calculations was used to observe and characterize Fe-H/D bending modes in CrHydA1 [FeFe]-hydrogenase Cys-to-Ser variant C169S. Mutagenesis of cysteine to serine at position 169 changes the functional group adjacent to the H-cluster from a -SH to -OH, thus altering the proton transfer pathway. The catalytic activity of C169S is significantly reduced compared to that of native CrHydA1, presumably owing to less efficient proton transfer to the H-cluster. This mutation enabled effective capture of a hydride/deuteride intermediate and facilitated direct detection of the Fe-H/D normal modes. We observed a significant shift to higher frequency in an Fe-H bending mode of the C169S variant, as compared to previous findings with reconstituted native and oxadithiolate (ODT)-substituted CrHydA1. On the basis of DFT calculations, we propose that this shift is caused by the stronger interaction of the -OH group of C169S with the bridgehead -NH- moiety of the active site, as compared to that of the -SH group of C169 in the native enzyme.
An Empirical Comparison of Knowledge Graph Embeddings for Item Recommendation

In the past years, knowledge graphs have proven to be ben-eﬁcial for recommender systems, eﬃciently addressing paramount issues such as new items and data sparsity. At the same time, several works have recently tackled the problem of knowledge graph completion through machine learning algorithms able to learn knowledge graph embeddings. In this paper, we show that the item recommendation problem can be seen as a speciﬁc case of knowledge graph completion problem, where the “feedback” property, which connects users to items that they like, has to be predicted. We empirically compare a set of state-of-the-art knowledge graph embeddings algorithms on the task of item recommendation on the Movielens 1M dataset. The results show that knowledge graph embeddings models outperform traditional collaborative ﬁltering baselines and that TransH obtains the best performance.