Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Data Driven Machine Learning Algorithm For Fuzzy Estimates of Optimal Portfolio Weights and Risk Tolerance Coefficient

Recently, there has been a growing interest in portfolio optimization using graphical LASSO (GL) machine learning method, by assuming normality for asset returns. However, a major drawback is that most of the asset returns follow non-normal distributions and sample percentiles are used to study the portfolio optimization with Value-at-Risk (VaR) as a risk measure. In this paper, a data-driven random weights algorithm (RWA) and a sign correlation based portfolio return distribution are used to study the fuzzy portfolio optimization. The superiority of RWA over the commonly used genetic algorithm (GA) in computing the optimal portfolio weights is demonstrated by comparing the computing time. When comparing the estimate of the risk tolerance coefficient and the theoretical value for tangency portfolios with volatility as a risk measure, RWA outperforms (smaller absolute error) the GA. The novelty of this paper is the use of RWA and GA to calculate the fuzzy estimates (interval estimates) of the risk tolerance coefficient/optimal weights and using the sign correlation to obtain the data-driven distribution of the portfolio returns. More specifically the novelty is to obtain the fuzzy estimates of the risk tolerance coefficient and portfolio weights by modelling the portfolio volatility as an asymmetric triangular fuzzy number from the data-driven observed portfolio volatilities. In particular, the proposed RWA as well as GA lead to machine learning solutions for the portfolio optimization problems without a closed form solution and provide fuzzy estimates of the risk tolerance coefficient and the optimal portfolio weights.
W-Band Bandpass Filter Using Rectangular Microcoaxial Structure

This letter presents a second-order and a third-order $W$ -band bandpass filter (BPF) based on rectangular microcoaxial structures. Rectangular microcoaxial line has the advantages of low dielectric and radiation loss, small size, and easy integration, which is conducive to the design of passive devices. The resonant unit based on rectangular microcoaxial structure consists of a metal cavity, a rectangular metal block suspended inside, and a grounded cylinder. It is used to design two Chebyshev filters. In order to facilitate the interconnection with other devices, ports of the filter are added with rectangular coaxial line to coplanar waveguide (CPW) transitions. The center frequencies of two filters are 93.3 and 94.3 GHz, respectively. Finally, the two filters are fabricated and measured to verify the design method. The minimum insertion loss in passband is 1.3 and 1.5 dB. Good agreements between the simulated and measured results are achieved.
Inter-Slice Radio Resource Management via Online Convex Optimization

Radio access network (RAN) slicing is one of the key technologies in 5G and beyond mobile networks, where multiple logical RANs, also referred to as RAN slices, are allowed to run on top of the same physical infrastructure so as to provide slice-specific services. Due to the dynamic environments of wireless cells and the diverse requirements of RAN slices, inter-slice radio resource management (IS-RRM) is a highly challenging task. In this paper, we propose a novel online convex optimization (OCO) framework for the IS-RRM, where the instant resource allocation is learned by using historical data revealed from previous allocations. Compared with the existing methods, OCO is an online optimization process that can avoid sophisticated modeling and tuning in highly complicated and dynamic environments. Specifically, a low-complexity online ISRRM algorithm is proposed, which employs multiple expert-algorithms running parallelly to keep track of environmental changes. Simulation results show that the proposed method can provide efficient IS-RRM with a comparable performance to the optimal strategies in hindsight.
Massive MIMO with Per-Antenna Digital Predistortion Size Optimization: Does it Help?

In this paper, we study the effect of optimizing the per-antenna digital predistortion (DPD) sizes on the performance of the downlink massive MIMO system with residual hardware impairment. In particular, we first quantify the capacity after applying some per-antenna DPD with different numbers of coefficients at each antenna and then maximize the capacity by finding optimum values of the per-antenna DPD sizes under a constraint on the total number of DPD coefficients in the system. Two closed-form sub-optimal solutions are derived and numerical examples illustrate that their performance is very close to the optimal solution. It is shown that when the channel large-scale gains have high variation over the transmitter array, which is the case in practical scenarios, using our proposed optimized DPD sizes can improve the system capacity significantly. This allows us to scale down the massive MIMO system but still maintain performance. We also study the asymptotic behavior of the derived capacity and show that by using the optimized DPD sizes, the system performance can approach the asymptotic bound with a significantly smaller number of antennas.
Analog Beamforming for Full-Duplex mmWave Communication with Low-Resolution Phase Shifters

We consider Full-Duplex (FD) communication in millimeter wave (mmWave) bands. The dense antenna arrays available in mmWave offer a large number of degrees of freedom which could be exploited towards self-interference (SI) mitigation in FD. However, analog beamforming architectures based on practical phase shifters make this task quite challenging due to the lack of amplitude control and finite phase resolution. In this context, we consider a single-stream bidirectional point-to-point mmWave FD link, and develop a new analog beamforming design requiring a single radio frequency chain in each direction at each node, explicitly taking into account such hardware-related constraints. Simulation results show that the proposed scheme significantly outperforms previous methods for practical phase shifter resolution values of a few bits, while providing SI cancellation in the analog domain and thus avoiding RF frontend saturation.
Psychology-informed Recommender Systems

Personalized recommender systems have become indispensable in today’s online world. Most of today’s recommendation algorithms are data-driven and based on behavioral data. While such systems can produce useful recommendations, they are often uninterpretable, black-box models, which do not incorporate the underlying cognitive reasons for user behavior in the algorithms’ design. The aim of this survey is to present a thorough review of the state of the art of recommender systems that leverage psychological constructs and theories to model and predict user behavior and improve the recommendation process. We call such systems psychology-informed recommender systems. The survey identifies three categories of psychology-informed recommender systems: cognition-inspired, personality-aware, and affectaware recommender systems. Moreover, for each category, Elisabeth Lex, Dominik Kowald, Paul Seitlinger, Thi Ngoc Trang Tran, Alexander Felfernig and Markus Schedl (2021), “Psychology-informed Recommender Systems”, Foundations and Trends® in Information Retrieval: Vol. 15, No. 2, pp 134–242. DOI: 10.1561/1500000090. Full text available at: http://dx.doi. org/10.1561/1500000090
Understanding the Function of a Locus Using the Knowledge Available at Single-Nucleotide Polymorphisms

Understanding the function of a locus is a challenge in molecular biology. Although numerous molecular data have been generated in the last decades, it remains difficult to grasp, how these data are related at a locus? In this study, we describe an analytical workflow that can solve this problem using the knowledge available at single-nucleotide polymorphisms (SNPs) level. The underlying algorithm uses SNPs as connectors to link omics data and identify correlation between them through a joint bioinformatical/statistical approach. We describe its application in finding the mechanism whereby a mutation causes a phenotype and in revealing the path whereby a gene is being regulated and impacts the phenotypes. We translated our workflow into freely available shell scripts that carry out the analyses. Our approach provides a basic framework to solve the information overload problem in biology.
Heterotrimeric G-Protein Signaling Is Required for Cellulose Degradation in <named-content content-type='genus-species'>Neurospora crassa</named-content>

The filamentous fungus Neurospora crassa decomposes lignocellulosic biomass to generate soluble sugars as carbon sources. In this study, we investigated a role for heterotrimeric G-protein signaling in cellulose degradation. Loss of the G subunit genes gna-1 and gna-3, the G subunit genes gnb-1 and cpc-2, the G gene gng-1, or the gene for downstream effector adenylyl cyclase (cr-1) resulted in loss of detectable cellulase activity. This defect was also observed in strains expressing a constitutively active version of gna-3 (gna-3Q208L). We found that GNA-1 levels are greatly reduced in Δgna-3, Δgnb-1, and Δgng-1 strains, likely contributing to cellulase defects in these genetic backgrounds. The observation that gna-3Q208L Δgnb-1 strains exhibit cellulase activity, despite greatly reduced levels of GNA-1 protein, is consistent with positive control of cellulase production by GNA-3 that is manifested in the absence of gnb-1. Expression patterns for five cellulase genes showed that Δgna-1, Δgnb-1, and Δgna-3 mutants produce less cellulase mRNA than the wild type, consistent with transcriptional regulation. Δcpc-2 mutants had wild-type levels of cellulase transcripts, suggesting posttranscriptional control. In contrast, results for Δcr-1 mutants support both transcriptional and posttranscriptional control of cellulase activity by cAMP signaling. Cellulase activity defects in Δgna-3 mutants were fully remediated by cAMP supplementation, consistent with GNA-3 operating upstream of cAMP signaling. In contrast, cAMP addition only partially corrected cellulase activity defects in Δgna-1 and Δgnb-1 mutants, suggesting participation of GNA-1 and GNB-1 in additional cAMP-independent pathways that control cellulase activity. IMPORTANCE Filamentous fungi are critical for the recycling of plant litter in the biosphere by degrading lignocellulosic biomass into simpler compounds for metabolism. Both saprophytic and pathogenic fungi utilize plant cell wall-degrading enzymes to liberate carbon for metabolism. Several studies have demonstrated a role for cellulase enzymes during infection of economically relevant crops by fungal pathogens. Especially in developing countries, severe plant disease means loss of entire crops, sometimes leading to starvation. In this study, we demonstrate that G-protein signaling is a key component of cellulase production. Therefore, understanding the role of G-protein signaling in the regulation of the unique metabolism of cellulose by these organisms can inform innovations in strain engineering of industrially relevant species for biofuel production and in combatting food shortages caused by plant pathogens.
Snf7p, a Component of the ESCRT-III Protein Complex, Is an Upstream Member of the <italic toggle='yes'>RIM101</italic> Pathway in <italic toggle='yes'>Candida albicans</italic>

The success of Candida albicans as an opportunistic pathogen is based in part on its ability to adapt to diverse environments. The RIM101 pathway governs adaptation to neutral-alkaline environments and is required for virulence. Analysis of a genomic two-hybrid study conducted with Saccharomyces cerevisiae revealed that components involved in multivesicular bodies (MVB) transport may interact with RIM101 pathway members. Thus, we hypothesized that these proteins may function in the RIM101 pathway in C. albicans. We identified C. albicans homologs to S. cerevisiae Snf7p, Vps4p, and Bro1p and generated mutants in the cognate gene. We found that snf7 / mutants, but not vps4 / nor bro1 / mutants, had phenotypes similar to, but more severe than, those of RIM101 pathway mutants. We found that the constitutively active RIM101-405 allele partially rescued snf7 / mutant phenotypes. The vps4 / mutant had subtle phenotypes, but these were not rescued by the RIM101-405 allele. Further, we found that the snf7 / , vps4 / , and bro1 / mutants did not efficiently localize the vital dye FM4-64 to the vacuole and that it was often accumulated in an MVB-like compartment. This phenotype was not rescued by RIM101-405 or observed in RIM101 pathway mutants. These results suggest that Snf7p may serve two functions in the cell: one as a RIM101 pathway member and one for MVB transport to the vacuole.
Synthetic mRNAs; Their Analogue Caps and Contribution to Disease

The structure of synthetic mRNAs as used in vaccination against cancer and infectious diseases contain specifically designed caps followed by sequences of the 5′ untranslated repeats of β-globin gene. The strategy for successful design of synthetic mRNAs by chemically modifying their caps aims to increase resistance to the enzymatic deccapping complex, offer a higher affinity for binding to the eukaryotic translation initiation factor 4E (elF4E) protein and enforce increased translation of their encoded proteins. However, the cellular homeostasis is finely balanced and obeys to specific laws of thermodynamics conferring balance between complexity and growth rate in evolution. An overwhelming and forced translation even under alarming conditions of the cell during a concurrent viral infection, or when molecular pathways are trying to circumvent precursor events that lead to autoimmunity and cancer, may cause the recipient cells to ignore their differential sensitivities which are essential for keeping normal conditions. The elF4E which is a powerful RNA regulon and a potent oncogene governing cell cycle progression and proliferation at a post-transcriptional level, may then be a great contributor to disease development. The mechanistic target of rapamycin (mTOR) axis manly inhibits the elF4E to proceed with mRNA translation but disturbance in fine balances between mTOR and elF4E action may provide a premature step towards oncogenesis, ignite pre-causal mechanisms of immune deregulation and cause maturation (aging) defects.
Machine Learning for Spaced Repetition in Human Learning

—In this report, we analyse how an exponential human learning model can be used to predict the learning rate of students using the Ari9000 application. In particular, we apply gradient descent and online gradient descent to ﬁt the exponential model. We compare a least squares loss, a regularized loss, and a maximum likelihood estimation. Overall, simple baseline models performed slightly better in terms of prediction accuracy than the exponential model. This later model is limited by how well the features can describe a student’s surrounding given available data. Even if our engineered features failed to produce high predictive accuracy, the resulting half-life values could still be useful for creating improved learning schemes.
Implementation on Surveillance Camera Optimum Angle Extraction using Polarizing Filter

The surveillance camera market has developed and plays an important role in the field of video surveillance. However, in recent years, the identification of areas requiring surveillance has been limited by reflective light in the surveillance camera market. Cameras using polarization filters are being developed to reduce reflective light and facilitate identification. Programs are required to automatically adjust polarization filters. In this paper, we proposed an optimal angle extraction method from surveillance cameras using polarization filters through histogram analysis. First of all, transformed to grayscale to analyze the specifications of frames in multiple polarized angle images, reducing computational throughput. Then we generated and analyzed a histogram of the corresponding frame to extract the angle when the highlights are the fewest. Experiments with 0˚ and 90˚ showed high performance in extracting optimal angles. At this point, it is hoped this technology would be used for surveillance cameras in place like beach with a lot of reflective light.
A Comprehensive Note On Techniques Used For Face Detection And Application (cid:3)

Image processing is a technique to apply mathematical operation on a digital image in order to get a better image or to understand other useful information from image data. A methodological study on the signi(cid:133)cance of image processing and its application in daily life is carried out here. This study paper describes various image processes with techniques and applications such as gender identi(cid:133)cation, biometric attendance, face detection, feature detection like nose, mouth, eyes, etc. with the help of MATLAB. This paper also deals with some of the face detection techniques, models and feature analysis
MigrOS: Transparent Live-Migration Support for Containerised RDMA Applications

RDMA networks ofﬂoad packet processing onto specialised circuitry of the network interface controllers (NICs) and by-pass the OS to improve network latency and bandwidth. As a consequence, the OS forfeits control over active RDMA connections and loses the possibility to migrate RDMA applications transparently. This paper presents MigrOS, an OS-level architecture for transparent live migration of containerised RDMA applications. MigrOS shows that a set of minimal changes to the RDMA communication protocol reenables live migration without interposing the critical path operations. Our approach requires no changes to the user applications and maintains backwards compatibility at all levels of the network stack. Overall, MigrOS can achieve up to 33% lower network latency in comparison to software-only techniques.
Recent Advances in Friction Stir Welding/Processing of Aluminum Alloys: Microstructural Evolution and Mechanical Properties

ABSTRACT Friction stir welding (FSW), a highly efficient solid-state joining technique, has been termed as “green” technology due to its energy efficiency and environment friendliness. It is an enabling technology for joining metallic materials, in particular lightweight high-strength aluminum and magnesium alloys which were classified as unweldable by traditional fusion welding. It is thus considered to be the most significant development in the area of material joining over the past two decades. Friction stir processing (FSP) was later developed based on the basic principles of FSW. FSP has been proven to be an effective and versatile metal-working technique for modifying and fabricating metallic materials. FSW/FSP of aluminum alloys has prompted considerable scientific and technological interest since it has a potential for revolutionizing the manufacturing process in the aerospace, defense, marine, automotive, and railway industries. To promote widespread applications of FSW/FSP technology and ensure the structural integrity, safety and durability of the FSW/FSP components, it is essential to optimize the process parameters, and to evaluate thoroughly the microstructural changes and mechanical properties of the welded/processed samples. This review article is thus aimed at summarizing recent advances in the microstructural evolution and mechanical properties of FSW/FSP aluminum alloys. Particular attention is paid to recrystallization mechanism, grain boundary characteristics, phase transformation, texture evolution, characteristic microstructures, and the effect of these factors on the hardness, tensile and fatigue properties as well as superplastic behavior of FSW/FSP aluminum alloys.
CROSS-STATES HETEROGENEITIES IN THE TRANSMISSION OF THE U.S. NARRATIVE TAX CHANGES

This paper investigates the assumption of homogeneous effects of federal tax changes across the U.S. states and identifies where and why that assumption may not be valid. More specifically, what determines the transmission mechanism of tax shocks at the state level? How vital are states' fiscal structures, financial conditions, labor market rigidities, and industry mix? Do these economic and structural characteristics drive the transmission mechanism of the tax changes at the state level at different horizons? This study employs a panel factor-augmented vector autoregression (FAVAR) technique to answer these issues. The model identifies tax shocks using sign restrictions with Uhlig's penalty function. The findings show state economies respond homogeneously in terms of employment and price levels; however, do react heterogeneously in terms of real GDP and personal income, and, in most states, these reactions are statistically significant. The heterogeneity in the effects of tax cuts is significantly related to the state's fiscal structure, manufacturing and financial composition, and the labor market's rigidity. The cross-state regression analysis shows that states with higher tax elasticity, higher personal income tax, strict labor market rigidity, and policy uncertainties are relatively less responsive to federal taxes. In contrast, the magnitude of the response in real GDP, personal income, and employment to tax cuts is relatively higher in states with a larger share of finance, manufacturing, lower tax burdens, and flexible credit markets.
Extended abstract for the EU-SPRI 2021 conference – Track 17 An empirical investigation of intellectual property for sustainability and its relevance to IPR policy

Intellectual property (IP) is a well-established global policy instrument for innovation and diffusion of novel technologies based on established legal systems, but also a strategic instrument for IP owners to manage relationships and collaborative innovation processes. The role of IP for sustainability however is not well understood. To drive sustainability transitions on a global scale for a carbon-neutral future, we need sustainable and green innovations. The development and diffusion of green innovations however involve complex and intertwined intellectual property (IP) related issues. The IP models relevant to facilitate green innovations are not adequately recognized and addressed. Further, lack of evidence-based insights hinders structured policy discussions.
Conceptual representations in bicultural bilinguals: An ERP approach

Abstract We investigated conceptual representations for translation word pairs in bilinguals who learned their languages in different cultural contexts. Mandarin–English bilinguals were presented with a word, and then a picture, and decided if they matched. Both behavioural and ERP data were collected. In one session, words were in English and in another they were the Mandarin translations. Critical pictures matched the prior word and were either biased to Chinese or Canadian culture. There was an interaction of test language and picture type in RT and errors in the behavioural data, and in five components in the ERP data, indicating that the task was easier when the culture depicted in the picture was congruent with the language of the preceding word. These findings provide evidence that the specific perceptual experiences that bilinguals encounter when learning words in each language have an impact on the semantic features that are activated by those words.