Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Efficiency of Edge Computing Infrastructures through Orchestration Models †

Edge computing is an effective paradigm for proximity in computation, but must inexorably face mobility issues and traffic fluctuations. While software orchestration may provide effective service handover between different edge infrastructures, seamless operation with negligible service disruption necessarily requires pre-provisioning and the need to leave some network functions idle for most of the time, which eventually results in large energy waste and poor efficiency. Existing consolidation algorithms are largely ineffective in these conditions because they lack context, i.e., the knowledge of which resources are effectively used and which ones are just provisioned for other purposes (i.e., redundancy, resilience, scaling, migration). Though the concept is rather straightforward, its feasibility in real environments must be demonstrated. Motivated by the lack of energy-efficiency mechanisms in cloud management software, we have developed a set of extensions to OpenStack for power management and Quality of Service, explicitly targeting the introduction of more context for applications. In this paper, we briefly describe the overall architecture and evaluate its efficiency and effectiveness. We analyze performance metrics and their relationship with power consumption, hence extending the analysis to specific aspects that cannot be investigated by software simulations. We also show how the usage of context information can greatly improve the effectiveness of workload consolidation in terms of energy saving.
Reconstructing Four Centuries of Temperature-Induced Coral Bleaching on the Great Barrier Reef

Mass coral bleaching events during the last 20 years have caused major concern over the future of coral reefs worldwide. Despite damage to key ecosystem engineers, little is known about bleaching frequency prior to 1979 when regular modern systematic scientific observations began on the Great Barrier Reef (GBR). To understand the longer-term relevance of current bleaching trajectories, the likelihood of future coral acclimatization and adaptation, and thus persistence of corals, records and drivers of natural pre-industrial bleaching frequency and prevalence are needed. Here, we use linear extensions from 44 overlapping GBR coral cores to extend the observational bleaching record by reconstructing temperature-induced bleaching patterns over 381 years spanning 1620-2001. Porites spp. corals exhibited variable bleaching patterns with bleaching frequency (number of bleaching years per decade) increasing (1620-1753), decreasing (1754-1820) and increasing (1821-2001) again. Bleaching prevalence (the proportion of cores exhibiting bleaching) fell (1670-1774) before increasing by 10% since the late 1790s concurrent with positive temperature anomalies, placing recently observed increases in GBR coral bleaching into a wider context. Spatial inconsistency along with historically diverging patterns of bleaching frequency and prevalence provide queries over the capacity for holobiont (the coral host, the symbiotic microalgae and associated microorganisms) acclimatisation and adaptation via bleaching, but reconstructed increases in bleaching frequency and prevalence, may suggest coral populations are reaching an upper bleaching threshold, a ‘tipping point’ beyond which coral survival is uncertain.
The role of reduced expression of fragile X mental retardation protein in neurons and increased expression in astrocytes in idiopathic and syndromic autism (duplications 15q11.2‐q13)

Fragile X syndrome (FXS), caused by lack of fragile X mental retardation protein (FMRP), is associated with a high prevalence of autism. The deficit of FMRP reported in idiopathic autism suggests a mechanistic overlap between FXS and autism. The overall goal of this study is to detect neuropathological commonalities of FMRP deficits in the brains of people with idiopathic autism and with syndromic autism caused by dup15q11.2‐q13 (dup15). This study tests the hypothesis based on our preliminary data that both idiopathic and syndromic autism are associated with brain region‐specific deficits of neuronal FMRP and structural changes of the affected neurons. This immunocytochemical study revealed neuronal FMRP deficits and shrinkage of deficient neurons in the cerebral cortex, subcortical structures, and cerebellum in subjects with idiopathic and dup(15)/autism. Neuronal FMRP deficit coexists with surprising infiltration of the brains of autistic children and adults with FMRP‐positive astrocytes known to be typical only for the fetal and short postnatal periods. In the examined autistic subjects, these astrocytes selectively infiltrate the border between white and gray matter in the cerebral and cerebellar cortex, the molecular layer of the cortex, part of the amygdala and thalamus, central cerebellar white matter, and dentate nucleus. Astrocyte pathology results in an additional local loss of FMRP in neurons and their shrinkage. Neuronal deficit of FMRP and shrinkage of affected neurons in structures free of FMRP‐positive astrocytes and regions infiltrated with FMRP‐expressing astrocytes appear to reflect mechanistic, neuropathological, and functional commonalities of FMRP abnormalities in FXS and autism spectrum disorder. Autism Res 2018, 11: 1316–1331. © 2018 International Society for Autism Research, Wiley Periodicals, Inc.
A Review of Prebiotics Against Salmonella in Poultry: Current and Future Potential for Microbiome Research Applications

Prebiotics are typically fermentable feed additives that can directly or indirectly support a healthy intestinal microbiota. Prebiotics have gained increasing attention in the poultry industry as wariness toward antibiotic use has grown in the face of foodborne pathogen drug resistance. Their potential as feed additives to improve growth, promote beneficial gastrointestinal microbiota, and reduce human-associated pathogens, has been well documented. However, their mechanisms remain relatively unknown. Prebiotics increasing short chain fatty acid (SCFA) production in the cecum have long since been considered a potential source for pathogen reduction. It has been previously concluded that prebiotics can improve the safety of poultry products by promoting the overall health and well-being of the bird as well as provide for an intestinal environment that is unfavorable for foodborne pathogens such as Salmonella. To better understand the precise benefit conferred by several prebiotics, “omic” technologies have been suggested and utilized. The data acquired from emerging technologies of microbiomics and metabolomics may be able to generate a more comprehensive detailed understanding of the microbiota and metabolome in the poultry gastrointestinal tract. This understanding, in turn, may allow for improved administration and optimization of prebiotics to prevent foodborne illness as well as elucidate unknown mechanisms of prebiotic actions. This review explores the use of prebiotics in poultry, their impact on gut Salmonella populations, and how utilization of next-generation technologies can elucidate the underlying mechanisms of prebiotics as feed additives.
Electromagnetic energy harvesting based on HfZrO tunneling junctions

HfZrO ferroelectrics with a thickness of 6 nm were grown directly on Si using atomic layer deposition, top and bottom metallic electrodes being subsequently deposited by electron-beam metallization techniques. Depending on the polarity of the ±10 V poling voltages, the current–voltage dependence of these tunneling diodes shows a rectifying behavior for different polarizations, the ON–OFF ratio being about 104. Because the currents are at mA level, the HfZrO tunneling diodes coupled to an antenna array can harvest electromagnetic energy at 26 GHz (a bandwidth designated for internet of things), with a responsivity of 63 V W−1 and a NEP of 4 nW/Hz0.5.
Traffic Offloading for Online Video Service in Vehicular Networks: A Cooperative Approach

Online video service becomes prevalent nowadays, as people are accustomed to getting information in the form of videos. While home users can easily retrieve all kinds of contents onto their laptops or smartphones, vehicular users are constrained by intermittent connectivity to roadside units (RSUs). Besides, as the load of cellular infrastructure increases dramatically, it is envisioned that the cellular network can be offloaded by utilizing RSUs and vehicular relays. In this paper, we propose a cooperative downloading mechanism in heterogeneous vehicular networks that comprises vehicular ad hoc network and cellular network. In this mechanism, RSUs act as traffic managers to fetch proper data from the Internet and then distribute to vehicles in an approximately optimal manner. Specifically, based on vehicular mobility prediction and internode throughput estimation, a storage time aggregated graph is constructed for planning transmission scheme; then, an iterative greedy-driven algorithm is designed for a suboptimal solution, which has polynomial computational time complexity. Compared with max-throughput and min-delay cooperative downloading (MMCD), simulation results show that our approach outperforms MMCD by $5\%\text{$-$}25\%$ with respect to offload fraction.
Mitochondrial DNA Mutations Induced by Carbon Ions Radiation: A Preliminary Study

Heavy-ion irradiation-induced nuclear DNA damage and mutations have been studied comprehensively. However, there is no information about the deleterious effect of heavy-ion irradiation on mitochondrial DNA (mtDNA). In this study, 2 typical mtDNA mutations were examined, including 4977 deletions and D310 point mutations. The 4977 deletions were quantified by real-time polymerase chain reaction, and D310 point mutations were analyzed by direct sequencing and a specific enzyme digestion genotyping method. Results showed that carbon ions radiation can induce temporal fluctuation of mtDNA 4977 deletions in 72 hours after irradiation, while survived clones were free from this deletion. Carbon ions induced more D310 mutations than X-rays, and the single-cell heteroplasmy was eliminated. This is the first study investigating mtDNA mutations induced by carbon ions irradiation in vitro. These findings would provide fundamental information for further investigation of radiation-induced mitochondrial biogenesis.
Recognition of Continuous Sign Language Alphabet Using Leap Motion Controller

This paper describes the development of an SLR system for Israeli Sign Language designed to enable people with hearing impairments to communicate easily and independently using sign language. The research focuses on fingerspelling and the problem of acquiring and extracting data from signers and translating it into written form. SLR entails two main challenges. The first involves acquiring isolated instances of letters and translating each letter individually. This was accomplished by training the Support Vector Machines classifiers using vectors consisting of the Euclidean distances between the fingertips and the center of the palm. The second and more complex challenge involves translating a signed word and converting it into a stream of letters. This was solved in two sub-stages: extracting a single frame from the Leap controller and identifying the sign formed in that frame; and analyzing a continuous stream of frames and determining when the signs for a letter are formed. The recognition process in the system developed, eventually combined the letters to form words, moreover, provides a low-cost solution and is, therefore, suitable for everyone.
Towards Dynamic Interaction-Based Reputation Models

In this paper, we investigate how dynamic properties of reputation can influence the quality of users' ranking. Reputation systems should be based on rules that can guarantee high level of trust and help identify unreliable units. To understand the effectiveness of dynamic properties in the evaluation of reputation, we propose our own model (DIB-RM) that utilizes three factors: forgetting, cumulative, and activity period. In order to evaluate the model, we use data from StackOverflow which also has its own reputation model. We estimate similarity of ratings between DIB-RM and the StackOverflow reputation model to test our hypothesis. We use two values to calculate our metrics: DIB-RM reputation and historical reputation. We found out that historical reputation gives better metric values. Our preliminary results are presented for different sets of values of the aforementioned factors in order to analyze how effectively the model can be used for modeling reputation systems.
Adaptive Fuzzy Pseudo Inverse Output-Feedback Control for a Class of Asymmetric Hysteresis Nonlinear System

In this paper, an adaptive fuzzy output-feedback Pesudo inverse dynamic surface control scheme is proposed for a class of hysteretic nonlinear systems. By using fuzzy logic systems, the system model is extended to a general one. The high-gain observer is designed to estimate the states of the system and deal with the uncertainties and environmental disturbances, therefore there is no need to known the system parameters before designing the controller. The pseudo inverse compensator of the asymmetric hysteresis model is constructed instead of precise inverse model. It is proved that all the signals in the closed-loop systems are semi-globally ultimately uniformly bounded (SUUB). Simulation results show the validity of the proposed scheme.
Constructing Hard Functions from Learning Algorithms

Fortnow and Klivans proved the following relationship between efficient learning algorithms and circuit lower bounds: if a class C ⊆ P/poly of Boolean circuits is exactly learnable with membership and equivalence queries in polynomial-time, then EXP * C (the class EXP was subsequently improved to EXP by Hitchcock and Harkins). In this paper, we improve on these results and show • If C is exactly learnable with membership and equivalence queries in polynomial-time, then DTIME(n) 6⊆ C. We obtain even stronger consequences if the class C is learnable in the mistake-bounded model, in which case we prove an average-case hardness result against C. • If C is learnable in polynomial time in the PAC model then PSPACE * C, unless PSPACE ⊆ BPP. Removing this extra assumption from the statement of the theorem would provide an unconditional proof that PSPACE * BPP. • If C is efficiently learnable in the Correlational Statistical Query (CSQ) model, we show that there exists an explicit function f that is average-case hard for circuits in C. This result provides stronger average-case hardness guarantees than those obtained by SQ-dimension arguments (Blum et al. 1993). We also obtain a non-constructive extension of this result to the stronger Statistical Query (SQ) model. Similar results hold in the case where the learning algorithm runs in subexponential time. Our proofs regarding exact and mistake-bounded learning are simple and self-contained, yield explicit hard functions, and show how to use mistake-bounded learners to “diagonalize” over families of polynomial-size circuits. Our consequences for PAC learning lead to new proofs of Karp-Lipton-style collapse results, and the lower bounds from SQ learning make use of recent work relating combinatorial discrepancy to the existence of hard-on-average functions. ∗Supported in part by NSF grants CCF-0915929 and CCF-1115703.
Context-Based Multimodal Output for Human-Robot Collaboration

Research on multimodal systems for human-robot interaction mostly focuses on the processing of inputs. Yet, the output is equally important: A robot that is able to use different modalities in an interaction appears more natural and can be understood more easily. In this paper, we present our multimodal fission framework, called MMF framework, which is a framework for incorporating planning criteria to select the most suitable set of modalities based on information about the interaction context. We describe our input and output layer, present an algorithm for an automated selection of suitable attributes for referencing objects verbally as well as a simple assessment of the suitability of pointing gestures in the given context. Furthermore, we describe a new approach for the modality and device selection as formulation of constraint optimization problems. In the end, we will report the results of a user study, which has been conducted to evaluate the generated multimodal output.
Mobile Privacy Protection Enhanced with Multi-access Edge Computing

Nowadays cellular data network carries the majority of Internet mobile access traffic and becomes de facto the solution of accessing the Internet in a mobile fashion. However comparing to ordinary Internet access, connecting through cellular network yet provides equivalent connection stability or longevity, and more critically raises particular privacy concerns. Meanwhile, Mobile/Multi-access Edge Computing(MEC), as one of the most practical Edge Computing architectures, has great potentials of implementing(or enhancing) emerging network paradigms, such as 5G network, Fog computing, and Edge Cloud Computing. It roots in cellular networks, and could quickly become prevailing without long ramp-up years. We observed the opportunities of leveraging MEC to solve above issues. In this paper, we present our improved Mobility Support System(MSS) using MEC as a key pivot to protect a mobile user's network privacy with little impact on communication performance. While the system operation cost is reduced further, MSS could also close the gap of connection interruption for roaming mobile users.
Switching From Tobacco Smoking to Electronic Cigarettes and the Impact on Clozapine Levels.

FIGURE 1. Clozapine dosage (inmg/d) and blood serum levels (in ng/mL) over the course of inpatient treatment, before and after switching from smoking combustible (CC) to e-cigarettes (EC). The gray bar indicates the time window in which the patient reported a switch from CC to EC. Clozapine blood levels are depicted as sums of clozapine and D-methyl-clozapine. E lectronic cigarettes (e-cigarette) were invented in Beijing, China, in 2003 and first sold in 2004. Since then, the prevalence of e-cigarette use rapidly increased over the globe, and a variety of e-cigarette models have emerged over the last decade. The key principle of an e-cigarette is based on the production of an aerosol by heating a liquid that contains a solvent, 1 or more flavorings, and—in most cases—nicotine. The liquid evaporates at the heating device and is then rapidly cooled. This leads to the formation of an aerosol that can be inhaled. Consumers consider e-cigarettes as less harmful than combustible cigarettes. However, the evidence for this assumption is ambiguous, and data on long-term safety are missing. A US survey indicated that adults with mental health conditions were more likely to try or use e-cigarettes than those without. Notably, the more frequent use of e-cigarettes is accompanied by new challenges regarding pharmacological interactions, specifically those mediated by the cytochrome p450 system (CYP450). In this letter, we illustrate this issue on the case of a patient diagnosed with schizophrenia. The patient switched from conventional tobacco smoking to the use of e-cigarettes during the psychopharmacological treatment with clozapine. Subsequently, clozapine blood serum levels increased and were accompanied by clinically relevant adverse effects. For this letter, written informed consent was obtained from the patient. The 23-year-old white female patient with previously diagnosed paranoid schizophrenia was admitted in our clinic with bizarre delusions and auditory hallucinations. The antipsychotic medication consisted of clozapine 75 mg/d. Because several other antipsychotics were not effective in the past, we continued the medication with clozapine and gradually increased the dosage to 300 mg/d. Thereafter, the dosage was held stable for 7 weeks, and clozapine blood serum levels were within the therapeutic range (Fig. 1). Under this dosage, the hallucinations subsided. The only adverse drug effects reported by the patient were weight gain and hypersalivation, the latter counteracted by pirenzepine. However, the
New Automatic Algorithms for Computing Characteristics of Three Dimensional Pelvic and Lower Limb Motions in Race Walking

Optical motion systems are precise and thus they may be applied in analysis of athletes' performance. It is particularly important in the analysis of motion in walker sports where pelvic rotation translates into movement speed. The aim of this paper is to present and test algorithms that can be used in analysis of the workload on the pelvic hyperextension at the knee, and the resulting movement speed during race walking. The test data were obtained by organizing a motion capture session whose participant was a woman professional race walker and a member of the National Senior Team. The algorithms presented compute parameters that strictly characterize race walking. They include: race walking speed, pelvic rotation angles and knee deflection angles. The test recordings were made using a Vicon motion capture system. The frame rate was set to 100 frames per second. The session was conducted in a shaded room so that no additional reflections appeared. The equipment was supplied with Vicon's Nexus 2.0 software, which was used for system calibration, data recording and data processing. The participant was measured for the purpose of creating and scaling a new subject, which was in turn required for computing the motion parameters. The results include computed pelvis rotation, knee flexion angles, walking speed as well as execution times of the proposed algorithms. Execution times are very short. The longest execution time of 0.15 seconds was recorded for a C3D file consisting of 192 frames and containing a recording of race-walking on the distance of 5.3 meters.
Dissecting KMT2D missense mutations in Kabuki syndrome patients

Abstract Kabuki syndrome is a rare autosomal dominant condition characterized by facial features, various organs malformations, postnatal growth deficiency and intellectual disability. The discovery of frequent germline mutations in the histone methyltransferase KMT2D and the demethylase KDM6A revealed a causative role for histone modifiers in this disease. However, the role of missense mutations has remained unexplored. Here, we expanded the mutation spectrum of KMT2D and KDM6A in KS by identifying 37 new KMT2D sequence variants. Moreover, we functionally dissected 14 KMT2D missense variants, by investigating their impact on the protein enzymatic activity and the binding to members of the WRAD complex. We demonstrate impaired H3K4 methyltransferase activity in 9 of the 14 mutant alleles and show that this reduced activity is due in part to disruption of protein complex formation. These findings have relevant implications for diagnostic and counseling purposes in this disease.
Predictive Controller Design for a Matrix-Converter Based IPMSM Position Control System

This paper proposes a matrix-converter based IPMSM position control system. The model-based predictive position-loop controller and the model-free predictive current-loop controller for an IPMSM are investigated to improve the dynamic responses, which include transient responses, load disturbance responses, and tracking responses. In addition, these two predictive controllers are easy to implement. Experimental results show the proposed drive system has better performance than with a PI controller. A 32-bit digital signal processor, TMS-320LF-2407A, is used to execute the predictive controllers for this matrix-converter IPMSM drive system.