Miracle-dz/UniFAR-FTUs huggingface dataset:
Multispectral RTI Analysis of Heterogeneous Artworks

We propose a novel multi-spectral reﬂectance transformation imaging (MS-RTI) framework for the acquisition and direct analysis of the reﬂectance behavior of heterogeneous artworks. Starting from free-form acquisitions, we compute per-pixel calibrated multi-spectral appearance proﬁles, which associate a reﬂectance value to each sampled light direction and frequency. Visualization, relighting, and feature extraction is performed directly on appearance proﬁle data, applying scattered data interpolation based on Radial Basis Functions to estimate per-pixel reﬂectance from novel lighting directions. We demonstrate how the proposed solution can convey more insights on the object materials and geometric details compared to classical multi-light methods that rely on low-frequency analytical model ﬁtting eventually mixed with a separate handling of high-frequency components, hence requiring constraining priors on material behavior. The ﬂexibility of our approach is illustrated on two heterogeneous case studies, a painting and a dark shiny metallic sculpture, that showcase feature extraction, visualization, and analysis of high-frequency properties of artworks using multi-light, multi-spectral (Visible, UV and IR) acquisitions. Categories and Subject Descriptors (according to ACM CCS) : I.4. 1 [Image processing and Computer Vision]: Digitization and Image Capture—Imaging Geometry I.3. 3 [Computer Graphics]: Digitizing and Scanning— I.3. 8 [Computer Graphics]: Applications—
Genomic and Biotechnological Characterization of the Heavy-Metal Resistant, Arsenic-Oxidizing Bacterium Ensifer sp. M14

Ensifer (Sinorhizobium) sp. M14 is an efficient arsenic-oxidizing bacterium (AOB) that displays high resistance to numerous metals and various stressors. Here, we report the draft genome sequence and genome-guided characterization of Ensifer sp. M14, and we describe a pilot-scale installation applying the M14 strain for remediation of arsenic-contaminated waters. The M14 genome contains 6874 protein coding sequences, including hundreds not found in related strains. Nearly all unique genes that are associated with metal resistance and arsenic oxidation are localized within the pSinA and pSinB megaplasmids. Comparative genomics revealed that multiple copies of high-affinity phosphate transport systems are common in AOBs, possibly as an As-resistance mechanism. Genome and antibiotic sensitivity analyses further suggested that the use of Ensifer sp. M14 in biotechnology does not pose serious biosafety risks. Therefore, a novel two-stage installation for remediation of arsenic-contaminated waters was developed. It consists of a microbiological module, where M14 oxidizes As(III) to As(V) ion, followed by an adsorption module for As(V) removal using granulated bog iron ores. During a 40-day pilot-scale test in an abandoned gold mine in Zloty Stok (Poland), water leaving the microbiological module generally contained trace amounts of As(III), and dramatic decreases in total arsenic concentrations were observed after passage through the adsorption module. These results demonstrate the usefulness of Ensifer sp. M14 in arsenic removal performed in environmental settings.
GMC: Greening MapReduce Clusters Considering Both Computational Energy and Cooling Energy

Increased processing power of MapReduce clusters generally enhances performance and availability at the cost of substantial energy consumption that often incurs higher operational costs (e.g., electricity bills) and negative environmental impacts (e.g., carbon dioxide emissions). There exist a few greening methods for computing clusters in the literature that focus mainly on computational energy consumption leaving cooling energy, which occupies a significant portion of the total energy consumed by the clusters. To this extent, in this paper, we propose a machine learning based approach named as Green MapReduce Cluster (GMC) that reduces the total energy consumption of a MapReduce cluster considering both computational energy and cooling energy. GMC predicts the number of machines that results in minimum total energy consumption. We perform the prediction through applying different machine learning techniques over year-long data collected from a real setup. We evaluate performance of GMC over a real testbed. Our evaluation reveals that GMC reduces total energy consumption by up to 47% compared to other alternatives while experiencing marginal throughput degradation in a few cases.
Association Between Superoxide Dismutase Isoenzyme Gene Expression and Total Antioxidant Status in Patients with an End-Stage Renal Disease

Background: Chronic renal failure, particularly end-stage renal disease, is a serious health problem associated with a high mortality rate. Uremic syndrome leads to increased oxidative stress, inflammation, and dyslipidemia. Aims: To examine superoxide dismutase isoenzyme gene expression in peripheral blood mononuclear cells of patients on hemodialysis and to determine the associations between superoxide dismutase isoenzyme gene expression, oxidative stress, and non-enzymatic antioxidative protection. Study Design: Case control study. Methods: This study included 33 patients on hemodialysis (age, 55.33±15.31 years old) and 33 apparently healthy controls (age, 45.37±8.92 years old). Superoxide dismutase isoenzyme messenger ribonucleic acid levels were determined by real-time polymerase chain reaction. General biochemical parameters, high sensitivity C-reactive protein, total antioxidant status, thiobarbituric acid-reactive substances, and the superoxide anion radical were also determined. Results: Normalized Cu/Zn superoxide dismutase and Mn superoxide dismutase messenger ribonucleic acid levels were significantly higher in patients than controls (p<0.001 and p=0.011). A significant negative correlation was detected between normalized Cu/Zn superoxide dismutase messenger ribonucleic acid levels and total protein, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and total antioxidant status. Normalized Mn superoxide dismutase messenger ribonucleic acid levels were negatively correlated with total protein and total antioxidant status. A multiple regression analysis revealed independent associations between total antioxidant status and normalized Cu/Zn superoxide dismutase (p=0.038) and between total antioxidant status and normalized Mn superoxide dismutase messenger ribonucleic acid levels (p=0.038 and p=0.018, respectively). Conclusion: The superoxide dismutase isoenzyme gene is expressed at a higher rate in patients with end-stage renal failure, probably due to increased oxidative stress and attenuated antioxidative defense. The plasma total antioxidant status is an independent predictor of normalized superoxide dismutase isoenzyme messenger ribonucleic acid levels.
Histone methyltransferase Smyd1 regulates mitochondrial energetics in the heart

Significance Smyd1 is a muscle-specific histone methyltransferase, and its role in the regulation of growth and differentiation in skeletal and cardiac muscle is well established. However, despite the persistent expression of Smyd1 in postnatal cardiomyocytes, the role of Smyd1 in the adult heart is largely unknown. We show that Smyd1 regulates energy metabolism in the heart. Cardiac-specific ablation of Smyd1 in the mouse adult heart resulted in global downregulation of mitochondrial proteins involved in oxidative phosphorylation, concurrent with reduced mitochondrial respiration capacity. We further demonstrate that the regulation of Smyd1 in metabolism is through transcriptional control of PGC-1α, a key regulator of mitochondrial energetics. Thus, our data reveal a role for Smyd1 as a master regulator of cardiac energetics. Smyd1, a muscle-specific histone methyltransferase, has established roles in skeletal and cardiac muscle development, but its role in the adult heart remains poorly understood. Our prior work demonstrated that cardiac-specific deletion of Smyd1 in adult mice (Smyd1-KO) leads to hypertrophy and heart failure. Here we show that down-regulation of mitochondrial energetics is an early event in these Smyd1-KO mice preceding the onset of structural abnormalities. This early impairment of mitochondrial energetics in Smyd1-KO mice is associated with a significant reduction in gene and protein expression of PGC-1α, PPARα, and RXRα, the master regulators of cardiac energetics. The effect of Smyd1 on PGC-1α was recapitulated in primary cultured rat ventricular myocytes, in which acute siRNA-mediated silencing of Smyd1 resulted in a greater than twofold decrease in PGC-1α expression without affecting that of PPARα or RXRα. In addition, enrichment of histone H3 lysine 4 trimethylation (a mark of gene activation) at the PGC-1α locus was markedly reduced in Smyd1-KO mice, and Smyd1-induced transcriptional activation of PGC-1α was confirmed by luciferase reporter assays. Functional confirmation of Smyd1’s involvement showed an increase in mitochondrial respiration capacity induced by overexpression of Smyd1, which was abolished by siRNA-mediated PGC-1α knockdown. Conversely, overexpression of PGC-1α rescued transcript expression and mitochondrial respiration caused by silencing Smyd1 in cardiomyocytes. These findings provide functional evidence for a role of Smyd1, or any member of the Smyd family, in regulating cardiac energetics in the adult heart, which is mediated, at least in part, via modulating PGC-1α.
Opportunistic NOMA-Based Massive MIMO Precoding for 5G New Radio

This work investigates multicast services, which play a critical role in 5G new radio (NR). In particular, we propose a hybrid unicast/multicast MIMO precoding based on NOMA. Our scheme first categorizes unicast users into NOMA and non-NOMA types and then utilizes null space and successive interference cancellation to eliminate the signal leakage. To justify the effectiveness and efficiency of our design, we also present simulation results in typical massive MIMO scenarios.
An Online Mechanism for Crowdsensing with Uncertain Task Arriving

In this paper, an online incentive mechanism in crowdsensing systems is studied. Different from most of the existing works which considered the smartphone users arriving at the crowdsourcer in an online fashion, we concentrate on the uncertain task arrivals, and consider the smartphone user allocation problem by jointly taking the cost capacity of each smartphone user and the sensing data quality requirement into consideration. In our model, since the tasks arrive at the crowdsourcer in an online manner, the crowdsourcer must make decisions timely to choose a suitable subset of smartphone users to achieve a sound competitive ratio (compared to the offline solution) once the tasks arrive. For the purpose of minimizing social cost in the whole system and achieving truthfulness, an online strategy proof incentive mechanism is designed by applying randomized auction framework. Theoretical and simulation results verify the effectiveness of the proposed online incentive mechanism.
Machine Learning for Automatic Annotation of References in DH scholarly papers

Introduction In this paper, we present Bilbo, new software for automatic annotation of references that employs a machine learning approach, and the three CLEO’s OpenEdition corpora in the DH fields we annotated to train it. Our aim is to allow a reliable and language independent detection and annotation of common references in papers and to provide users with a dynamic reference database and crosslinking facilities including for scientific blogs and research notebooks.
Intrusion Detection and Prevention in CoAP Wireless Sensor Networks Using Anomaly Detection

It is well recognized that security will play a major role in enabling most of the applications envisioned for the Internet of Things (IoT). We must also note that most of such applications will employ sensing and actuating devices integrated with the Internet communications infrastructure and, from the minute such devices start to support end-to-end communications with external (Internet) hosts, they will be exposed to all kinds of threats and attacks. With this in mind, we propose an IDS framework for the detection and prevention of attacks in the context of Internet-integrated CoAP communication environments and, in the context of this framework, we implement and experimentally evaluate the effectiveness of anomaly-based intrusion detection, with the goal of detecting Denial of Service (DoS) attacks and attacks against the 6LoWPAN and CoAP communication protocols. From the results obtained in our experimental evaluation we observe that the proposed approach may viably protect devices against the considered attacks. We are able to achieve an accuracy of 93% considering the multi-class problem, thus when the pattern of specific intrusions is known. Considering the binary class problem, which allows us to recognize compromised devices, and though a lower accuracy of 92% is observed, a recall and an F_Measure of 98% were achieved. As far as our knowledge goes, ours is the first proposal targeting the usage of anomaly detection and prevention approaches to deal with application-layer and DoS attacks in 6LoWPAN and CoAP communication environments.
Streamlining Simulation Experiments with Agent-Based Models in Demography

In the last decade, the uptake of agent-based modeling in demography and other population sciences has been slowly increasing. Still, in such areas, where traditional data-driven, statistical approaches prevail, the hypothesis-driven design of agent-based models leads to questioning the validity of these models. Consequently, suitable means to increase the confidence into models and simulation results are required. To that end, explicit, replicable simulation experiments play a central role in model design and validation. However, the analysis of more complex models implies executing various experiments, each of which combines various methods. To streamline these experimentation processes a flexible computational simulation environment is necessary. With a new binding between SESSL -- an internal domain-specific language for simulation experiments -- and ML3 -- a simulator for linked lives designed specifically for agent-based demographic models -- we cater for these objectives and provide a powerful simulation tool. The proposed approach can serve as a foundation for current efforts of employing advanced and statistical model analysis of agent-based demographic models, as part of a wider process of iterative model building. We demonstrate its potential in specifying and executing different experiments with a simple model of return migration and a more complex model of social care.
Incentivizing Exploration by Heterogeneous Users

We consider the problem of incentivizing exploration with heterogeneous agents. In this problem, N bandit arms provide vector-valued outcomes equal to an unknown arm-specific attribute vector, perturbed by independent noise. Agents arrive sequentially and choose arms to pull based on their own private and heterogeneous linear utility functions over attributes and the estimates of the arms’ attribute vectors derived from observations of other agents’ past pulls. Agents are myopic and selfish and thus would choose the arm with maximum estimated utility. A principal, knowing only the distribution from which agents’ preferences are drawn, but not the specific draws, can offer arm-specific incentive payments to encourage agents to explore underplayed arms. The principal seeks to minimize the total expected cumulative regret incurred by agents relative to their best arms, while also making a small expected cumulative payment. We propose an algorithm that incentivizes arms played infrequently in the past whose probability of being played in the next round would be small without incentives. Under the assumption that each arm is preferred by at least a fraction p > 0 of agents, we show that this algorithm achieves expected cumulative regret of O(Ne + N log(T )), using expected cumulative payments of O(Ne). If p is known or the distribution over agent preferences is discrete, the exponential term e can be replaced with suitable polynomials in N and 1/p. For discrete preferences, the regret’s dependence on T can be eliminated entirely, giving constant (depending only polynomially on N and 1/p) expected regret and payments. This constant regret stands in contrast to the Θ(log(T )) dependence of regret in standard multi-armed bandit problems. It arises because even unobserved heterogeneity in agent preferences causes exploitation of arms to also explore arms fully; succinctly, heterogeneity provides free exploration.
Exploiting Common Characters in Chinese and Japanese to Learn Cross-Lingual Word Embeddings via Matrix Factorization

Learning vector space representation of words (i.e., word embeddings) has recently attracted wide research interests, and has been extended to cross-lingual scenario. Currently most cross-lingual word embedding learning models are based on sentence alignment, which inevitably introduces much noise. In this paper, we show in Chinese and Japanese, the acquisition of semantic relation among words can benefit from the large number of common characters shared by both languages; inspired by this unique feature, we design a method named CJC targeting to generate cross-lingual context of words. We combine CJC with GloVe based on matrix factorization, and then propose an integrated model named CJ-Glo. Taking two sentence-aligned models and CJ-BOC (also exploits common characters but is based on CBOW) as baseline algorithms, we compare them with CJ-Glo on a series of NLP tasks including cross-lingual synonym, word analogy and sentence alignment. The result indicates CJ-Glo achieves the best performance among these methods, and is more stable in cross-lingual tasks; moreover, compared with CJ-BOC, CJ-Glo is less sensitive to the alteration of parameters.
Hybrid Multi-User Precoding with Amplitude and Phase Control

There has been a strong interest in understanding hybrid precoding tradeoffs for millimeter wave (mmW) multi-input multi-output (MIMO) systems. A common assumption in most of these works is that the analog part of the hybrid precoder can only be designed with phase shifters. The consequent search for analog and digital precoding matrices is solved with different black box-type optimization algorithms. In contrast, this work motivates an analog precoding structure at the base-station end that can be realized with both phase shifters and gain controls. Such a structure is necessary for interference management in multi- user transmissions and is easily realized with low complexity and cost. We then propose a feedback framework of the top-P beams over a beam alignment phase from each user. This framework allows the base-station to reconstruct the channel matrix between it and each user, and to manage interference with a simple zeroforcing solution. Such a structured solution is implemented with the amplitude and phase control of the analog part of the hybrid precoder. We finally illustrate the performance improvement with the proposed solution over simpler constructions such as beam steering that can be implemented with phase shifters alone.
Composite Capabilities for Cloud Manufacturing

We present a tool for extracting and abstracting the composite or "collective" capabilities of a multi-agent system (MAS) given the individual capabilities of the agents in the MAS. We consider a setting where agents represent manufacturing or assembly resources such as CNC machines and robots, and the goal is to determine the composite capabilities of the manufacturing system as a whole, i.e., the products it can make. This differs from previous work that studied whether and how a particular product can be manufactured by a given set of manufacturing resources. Our question is "which products---or more generally, which manufacturing activities---can the agents jointly perform? " This is key to realising the Industry 4.0 vision of flexible, adaptive and networked manufacturing systems, in which decentralised production resources form "smart factories" that communicate and collaborate and where manufacturing capabilities are advertised as manufacturing services in a manufacturing cloud.
Optimal Portfolio under Fast Mean-Reverting Fractional Stochastic Environment

Empirical studies indicate the existence of long-range dependence in the volatility of the underlying asset. This feature can be captured by modeling its return and volatility using functions of a stationary fractional Ornstein--Uhlenbeck (fOU) process with Hurst index $H \in (\frac{1}{2}, 1)$. In this paper, we analyze the nonlinear optimal portfolio allocation problem under this model and in the regime where the fOU process is fast mean-reverting. We first consider the case of power utility, and rigorously give first order approximations of the value and the optimal strategy by a martingale distortion transformation. We also establish the asymptotic optimality in all admissible controls of a zeroth order trading strategy. Then, we consider the case with general utility functions using the epsilon-martingale decomposition technique, and we obtain similar asymptotic optimality results within a specific family of admissible strategies.
Long non‐coding RNA SNHG14 induces trastuzumab resistance of breast cancer via regulating PABPC1 expression through H3K27 acetylation

Currently, resistance to trastuzumab, a human epidermal growth factor receptor 2 (HER2) inhibitor, has become one major obstacle for improving the clinical outcome of patients with advanced HER2+ breast cancer. While cell behaviour can be modulated by long non‐coding RNAs (lncRNAs), the contributions of lncRNAs in progression and trastuzumab resistance of breast cancer are largely unknown. To this end, the involvement and regulatory functions of lncRNA SNHG14 in human breast cancer were investigated. RT‐qPCR assay showed that SNHG14 was up‐regulated in breast cancer tissues and associated with trastuzumab response. Gain‐ and loss‐of‐function experiments revealed that overexpression of SNHG14 promotes cell proliferation, invasion and trastuzumab resistance, whereas knockdown of SNHG14 showed an opposite effect. PABPC1 gene was identified as a downstream target of SNHG14, and PABPC1 mediates the SNHG14‐induced oncogenic effects. More importantly, ChIP assays demonstrated that lncRNA SNHG14 may induce PABPC1 expression through modulating H3K27 acetylation in the promoter of PABPC1 gene, thus resulting in the activation of Nrf2 signalling pathway. These data suggest that lncRNA SNHG14 promotes breast cancer tumorigenesis and trastuzumab resistance through regulating PABPC1 expression through H3K27 acetylation. Therefore, SNHG14 may serve as a novel diagnostic and therapeutic target for breast cancer patients.
A Driver Guidance System for Taxis in Singapore

Traditional taxi fleet operators world-over have been facing intense competitions from various ride-hailing services such as Uber and Grab. Based on our studies on the taxi industry in Singapore, we see that the emergence of Uber and Grab in the ride-hailing market has greatly impacted the taxi industry: the average daily taxi ridership for the past two years has been falling continuously, by close to 20% in total. In this work, we discuss how efficient real-time data analytics and large-scale multiagent optimization technology could help taxi drivers compete against more technologically advanced service platforms. Our system has been in field trial with close to 400 drivers, and our initial results show that by following our recommendations, drivers on average save 21.5% on roaming time.