Miracle-dz/UniFAR-FTUs huggingface dataset:
Location-Fair Beamforming for High Speed Railway Communication Systems

In this paper, the transmission scheme for the high speed railway (HSR) communication systems in the presence of Doppler shift is investigated. A fair transmission scheme based on sectored beams, in which there is little variation in the received signal-to- noise ratio (SNR), is proposed. An optimization problem for the beam design is formulated to maximize the expected average power within the beam coverage range with limited variation in the instantaneous received power in different locations. Through numerical results, the proposed transmission scheme is shown to achieve fair performance along the time. Overall, a location-fair solution for improving the fairness of the HSR communication systems is provided.
Spatially resolved spectroscopy of Coma cluster early–type galaxies - I. The database

We present long slit spectra for a magnitude limited sample of 35 E and S0 galaxies of the Coma cluster. The high quality of the data allowed us to derive spatially resolved spectra for a substantial sample of Coma galaxies for the first time. From these spectra we obtained rotation curves, the velocity dispersion profiles and the H 3 and H 4 coefficients of the Hermite decomposition of the line of sight velocity distribution. Moreover, we derive the radial line index profiles of Mg, Fe and H line indices out to with high signal-to-noise ratio. We describe the galaxy sample, the observations and data reduction, and present the spectroscopic database. Ground-based photometry for a subsample of 8 galaxies is also presented. The Coma cluster is one of the richest known clusters of galaxies, spanning about 4 decades in density. Hence it is the ideal place to study the structure of galaxies as a function of environmental density in order to constrain the theories of galaxy formation and evolution. Based on the spectroscopic database presented, we will discuss these issues in a series of future papers.
Neurocognitive Profiles of Older Adults with Working-Memory Dysfunction

Abstract Individuals differ in how they perceive, remember, and think. There is evidence for the existence of distinct subgroups that differ in cognitive performance within the older population. However, it is less clear how individual differences in cognition in old age are linked to differences in brain-based measures. We used latent-profile analysis on n-back working-memory (WM) performance to identify subgroups in a large sample of older adults (n = 181; age = 64–68 years). Our analysis identified one larger normal subgroup with higher performance (n = 113; 63%), and a second smaller subgroup (n = 55; 31%) with lower performance. The low-performing subgroup showed weaker load-dependent BOLD modulation and lower connectivity within the fronto-parietal network (FPN) as well as between FPN and striatum during n-back, along with lower FPN connectivity at rest. This group also exhibited lower FPN structural integrity, lower frontal dopamine D2 binding potential, inferior performance on offline WM tests, and a trend-level genetic predisposition for lower dopamine-system efficiency. By contrast, this group exhibited relatively intact episodic memory and associated brain measures (i.e., hippocampal volume, structural, and functional connectivity within the default-mode network). Collectively, these data provide converging evidence for the existence of a group of older adults with impaired WM functioning characterized by reduced cortico-striatal coupling and aberrant cortico-cortical integrity within FPN.
Model construction of Niemann-Pick type C disease in zebrafish

Abstract Niemann-Pick type C disease (NPC) is a rare human disease, with limited effective treatment options. Most cases of NPC disease are associated with inactivating mutations of the NPC1 gene. However, cellular and molecular mechanisms responsible for the NPC1 pathogenesis remain poorly defined. This is partly due to the lack of a suitable animal model to monitor the disease progression. In this study, we used CRISPR to construct an NPC1−/− zebrafish model, which faithfully reproduced the cardinal pathological features of this disease. In contrast to the wild type (WT), the deletion of NPC1 alone caused significant hepatosplenomegaly, ataxia, Purkinje cell death, increased lipid storage, infertility and reduced body length and life span. Most of the NPC1−/− zebrafish died within the first month post fertilization, while the remaining specimens developed slower than the WT and died before reaching 8 months of age. Filipin-stained hepatocytes of the NPC1−/− zebrafish were clear, indicating abnormal accumulation of unesterified cholesterol. Lipid profiling showed a significant difference between NPC1−/− and WT zebrafish. An obvious accumulation of seven sphingolipids was detected in livers of NPC1−/− zebrafish. In summary, our results provide a valuable model system that could identify promising therapeutic targets and treatments for the NPC disease.
Super-Modular Game-Based User Scheduling and Power Allocation for Energy-Efficient NOMA Network

In this paper, we consider a single cell downlink non-orthogonal multiple access (NOMA) network and aim at maximizing the energy efficiency. The energy-efficient resource allocation problem is formulated as a non-convex and NP-hard problem. To decrease the computation complexity, we decouple the optimization problem as a subchannel matching scheme and power allocation subproblems. In the subchannel matching scheme, a non-cooperative game is applied to model this problem. To discuss the existence of Nash equilibrium (NE), we introduce a super-modular game and then design an algorithm to converge to the NE point. Moreover, a greed subchannel matching algorithm with low complexity is given through a two-way choice between users and subchannels. However, for given subchannel matching scheme, power allocation is still a non-convex problem, which is difficult to get the optimal solution. We then transform the non-convex problem to a convex problem by applying a successive convex approximation method. Afterward, we provide an algorithm to converge to suboptimal solution by solving a convex problem iteratively. Finally, simulation result demonstrates that the energy efficiency performance of the NOMA system is better than the orthogonal frequency division multiple access system.
Estimates of correction factors for lactation length and genetic parameters for milk yield in buffaloes

Correction factors for milk yield at 90, 240, 270 and 305 days of lactation of buffaloes and genetic and environmental parameters for milk yield of these lactation periods were estimated. The data used consisted of 3888 lactation records of 1630 buffaloes from 10 herds reared in the State of São Paulo. Genetic parameters were estimated by the derivative free restricted maximum likelihood method, fitting an animal repeatability model. Heritability estimates for milk yield corrected for 90, 240, 270 and 305 days of lactation were 0.17, 0.15, 0.14 and 0.14, respectively. In this same order, repeatability estimates were 0.40, 0.44, 0.41 and 0.41. The genetic correlation estimates between these corrected milk yields ranged from 0.96 to 1.00. The multiplicative correction factors for the different classes of lactation lengths were efficient for adjustment of milk yield at 90, 240, 270 and 305 days of lactation.
From outage probability to ALOHA MAC layer performance analysis in distributed WSNs

In cellular networks, the outage probability is the probability that the signal-to-interference-plus-noise-ratio (SINR) is less than a given threshold. In this article, firstly, we develop an explicit formula for outage probability for a wireless sensor network taking account of the problem parameters — i.e. the spatial density of nodes in the network, the noise power, the transmission power, the distance between the considered node and the base station, and finally the attenuation coefficient —. Secondly, we model a given node by a Markov chain to study the performance of the MAC layer in the case of a Slotted-ALOHA protocol and Slotted-ALOHA with preliminary channel reservation. Especially in this study, we theoretically estimate the transmission success likelihood after a certain number of attempts that we consider as a metric to evaluate the performance of the considered protocol. Finally we compare the theoretical results with experimental ones achieved on the large scale platform FIT IoT-Lab of Lille. To sum up, this paper responds to the following question: How many channels do we need to a achieve high performance distributed wireless sensor network? The answer is given by the necessary number of channels which maximizes the transmission success likelihood after a certain number of trials.
Generative Neural Machine Translation

We introduce Generative Neural Machine Translation (GNMT), a latent variable architecture which is designed to model the semantics of the source and target sentences. We modify an encoder-decoder translation model by adding a latent variable as a language agnostic representation which is encouraged to learn the meaning of the sentence. GNMT achieves competitive BLEU scores on pure translation tasks, and is superior when there are missing words in the source sentence. We augment the model to facilitate multilingual translation and semi-supervised learning without adding parameters. This framework significantly reduces overfitting when there is limited paired data available, and is effective for translating between pairs of languages not seen during training.
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection

Learning expressive low-dimensional representations of ultrahigh-dimensional data, e.g., data with thousands/millions of features, has been a major way to enable learning methods to address the curse of dimensionality. However, existing unsupervised representation learning methods mainly focus on preserving the data regularity information and learning the representations independently of subsequent outlier detection methods, which can result in suboptimal and unstable performance of detecting irregularities (i.e., outliers). This paper introduces a ranking model-based framework, called RAMODO, to address this issue. RAMODO unifies representation learning and outlier detection to learn low-dimensional representations that are tailored for a state-of-the-art outlier detection approach - the random distance-based approach. This customized learning yields more optimal and stable representations for the targeted outlier detectors. Additionally, RAMODO can leverage little labeled data as prior knowledge to learn more expressive and application-relevant representations. We instantiate RAMODO to an efficient method called REPEN to demonstrate the performance of RAMODO. Extensive empirical results on eight real-world ultrahigh dimensional data sets show that REPEN (i) enables a random distance-based detector to obtain significantly better AUC performance and two orders of magnitude speedup; (ii) performs substantially better and more stably than four state-of-the-art representation learning methods; and (iii) leverages less than 1% labeled data to achieve up to 32% AUC improvement.
Multiobjective Sequence Design via Gradient Descent Methods

Gradient descent provides a simple and flexible approach to sequence design. Simplicity is inherent in the gradient approach itself—The negative of the gradient of a given cost function gives an indication of the change to make to a sequence to find the local minimum of the cost function. Flexibility is provided by the lack of constraints on the cost function, allowing different aspects of a sequence to be optimized, e.g., autocorrelation or cross-correlation sidelobe energy, or spectral characteristics. Additional flexibility is provided by the linearity of gradients. The gradient of a linear combination of cost functions is simply an equivalent linear combination of their gradients. This allows multiple objectives to be optimized simultaneously. Gradient computational complexity has been one impediment to the use of gradient descent in sequence design in the past. In this paper, it is shown that gradients for a large variety of cost functions can be computed very efficiently. For a sequence of length <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula>, the computational complexity of computing the full gradient (the gradient with respect to all <inline-formula><tex-math notation="LaTeX">$N$</tex-math> </inline-formula> elements) is <inline-formula><tex-math notation="LaTeX">$O(N^3)$</tex-math></inline-formula> in a “naive” implementation. In this paper, equations are derived that allow the gradients to be computed with <inline-formula><tex-math notation="LaTeX">$O(N \log N)$</tex-math></inline-formula> operations, a substantial improvement over previous methods.
The IQ of Artificial Intelligence

All it takes to identify the computer programs which are Artificial Intelligence is to give them a test and award AI to those that pass the test. Let us say that the scores they earn at the test will be called IQ. We cannot pinpoint a minimum IQ threshold that a program has to cover in order to be AI; however, we will choose a certain value. Thus, our definition for AI will be any program whose IQ is above the chosen value. While thisidea has already been implemented in [3], here we will revisit this construct in order to introduce certain improvements.
Transforming Growth Factor-β and Interleukin-10 Synergistically Regulate Humoral Immunity via Modulating Metabolic Signals

Inhibitory cytokines, such as transforming growth factor-β (TGF-β) and interleukin-10 (IL-10), are humoral factors involved in the suppressive function of regulatory T cells and play critical roles in maintaining immune homeostasis. However, TGF-β and IL-10 also have pleiotropic effects and induce humoral immune responses depending on conditions, and thus their therapeutic application to autoimmune diseases remains limited. Here, we show that a combination of TGF-β and IL-10, but not single cytokine, is required to suppress B cell activation induced by toll-like receptor (TLR) stimulation. In in vivo analyses, the simultaneous presence of TGF-β and IL-10 effectively suppressed TLR-mediated antigen-specific immune responses and ameliorated pathologies in imiquimod (TLR7 agonist)-induced lupus model and lupus-prone MRL/lpr mice. Intriguingly, TGF-β and IL-10 synergistically modulated transcriptional programs and suppressed cellular energetics of both glycolysis and oxidative phosphorylation via inhibition of the mammalian target of rapamycin complex 1 (mTORC1)/S6 kinase 1 (S6K1) pathway in TLR-stimulated B cells. On the other hand, enhancement of mTOR signaling and mitochondrial biosynthesis in TLR-stimulated B cells counteracted the synergistic inhibitory effects. The inhibitory cytokine synergy of TGF-β and IL-10 via suppression of energy metabolism was also observed in human TLR-stimulated B cells. There is increasing evidence supporting the importance of adequate metabolic signals in various immune cells to exert their immune function. In this study, we have shown that a previously unrecognized synergy of inhibitory cytokines regulates systemic humoral immune responses via modulating immunometabolism in B cells. Our findings indicate that inhibition of B cell metabolism mediated by two synergistic cytokines contributes to the induction of immune tolerance and could be a new therapeutic strategy for autoimmune diseases such as systemic lupus erythematosus.
OFDM-based automotive joint radar-communication system

We propose a novel automotive joint radar-communication (JRC) system, where the system first transmits OFDM sub-carriers for radar processing followed by sub-carriers enabling radar and communication functionalities. The receiver processing includes iterative estimation of parameters to alleviate the shortage of samples to estimate range. The receiver first estimates the target parameters from the sub-carriers dedicated to radar; these parameters then determine the channel for the communication link. The communication data is then extracted, thereby enabling the use of all the carriers for improving the range estimation. It is shown that the range estimation improves significantly after efficient use of all the sub-carriers. Furthermore, for radar parameter estimation, we propose an effective iterative method based on alternating least square (ALS) to recover the angle of arrival (AoA), Doppler and Range. Numerical results demonstrate the feasibility of our proposed system.
Frequent Itemsets Mining With Differential Privacy Over Large-Scale Data

Frequent itemsets mining with differential privacy refers to the problem of mining all frequent itemsets whose supports are above a given threshold in a given transactional dataset, with the constraint that the mined results should not break the privacy of any single transaction. Current solutions for this problem cannot well balance efficiency, privacy, and data utility over large-scale data. Toward this end, we propose an efficient, differential private frequent itemsets mining algorithm over large-scale data. Based on the ideas of sampling and transaction truncation using length constraints, our algorithm reduces the computation intensity, reduces mining sensitivity, and thus improves data utility given a fixed privacy budget. Experimental results show that our algorithm achieves better performance than prior approaches on multiple datasets.
Upcycling of e-waste plastics containing brominated flame retardants into valuable carbon material

Electric and electronic plastic wastes (e-wastes) are the fastest growing waste stream. The e-waste plastics are heterogeneous mixture of various plastics and additives including brominated flame retardants (BFRs). This study is to show ability of generation of valuable carbonic material from gases formed during pyrolysis of e-waste plastics by chemical vapor deposition on the selected catalyst.
Detection of unhealthy plant leaves using image processing and genetic algorithm with Arduino

India is an agricultural country and about seventy percent of our population depends on agriculture. One-third of our national income comes from agriculture. So the disease detection of plants plays an important role in the agricultural field. Majority of the plant diseases are caused by the attack of bacteria, fungi, virus etc. If proper care is not taken in this area, it may lead to serious effects on plants and adversely affects the productivity and quality. To detect, the plant diseases we need a fast automatic way. The main approach adopted in practice for detection and identification of plant diseases is naked eye observation through experts. The decision making capability of an expert also depends on his/her physical condition, such as fatigue and eye sight, work pressure, climate etc. So this method is time consuming and less efficient. Here, a project is proposed with an idea of detecting plant diseases using image processing. Image processing toolbox of Matlab is used for measuring affected area of disease and to determine the difference in the color of the disease affected area. This concept can be extended to detect the symptoms of any type of plant diseases that is affected on different horticulture crops. The algorithm can be used to classify the leaves and the classified outcomes are separated using Arduino based conveyor belt system. This reduces an important task of monitoring of farms crops at very early stage itself to detect the symptom of diseases appear on plant leaves.
SPATA7 maintains a novel photoreceptor-specific zone in the distal connecting cilium

Photoreceptor-specific ciliopathies often affect a structure that is considered functionally homologous to the ciliary transition zone (TZ) called the connecting cilium (CC). However, it is unclear how mutations in certain ciliary genes disrupt the photoreceptor CC without impacting the primary cilia systemically. By applying stochastic optical reconstruction microscopy technology in different genetic models, we show that the CC can be partitioned into two regions: the proximal CC (PCC), which is homologous to the TZ of primary cilia, and the distal CC (DCC), a photoreceptor-specific extension of the ciliary TZ. This specialized distal zone of the CC in photoreceptors is maintained by SPATA7, which interacts with other photoreceptor-specific ciliary proteins such as RPGR and RPGRIP1. The absence of Spata7 results in the mislocalization of DCC proteins without affecting the PCC protein complexes. This collapse results in destabilization of the axonemal microtubules, which consequently results in photoreceptor degeneration. These data provide a novel mechanism to explain how genetic disruption of ubiquitously present ciliary proteins exerts tissue-specific ciliopathy phenotypes.
Impact of hardware impairments on mmWave MIMO systems with hybrid precoding

A notable commonality in the existing studies on millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems is the assumption of perfect hardware during communication. However, this is a strong hypothesis since residual tranceiver hardware impairments (RTHIs) are inevitable in reality. Hence, this paper proposes a mmWave MIMO system that generalizes the state of the art by considering the inevitable RTHIs. We shed light on the impact of three major hardware impairments, namely, the multiplicative phase noise (PN), the amplified thermal noise (ATN), and the residual additive transceiver hardware impairments (RATHIs). In particular, we derive the spectral efficiency of the system under the presence of RTHIs. Additionally, we provide a detailed analysis to quantify the degradation caused to the spectral efficiency of the system by each separate impairment. Among the noteworthy outcomes is that the multiplicative PN is the most critical of the three impairments. Furthermore, we verify that the merits of the mmWave MIMO system are improved in line with large MIMO, which needs to be deployed in a cost-efficient manner.
Assessing the nicotine content of smoked and smokeless forms of Tobacco Available in Bhopal

Context: Abuse of tobacco, such as drug and alcohol abuse, is a worldwide public health problem. Once a person is addicted to nicotine, quitting smoking is difficult. A measure of the addictive potential of tobacco products is the amount of nicotine available from them. The present study is an attempt to assess the nicotine content of tobacco products available in Bhopal. Aims: This study aims to assess the nicotine content of some popular brands of smoked (cigarettes and bidis) and chewed forms (pan masalas containing tobacco) of tobacco available in Bhopal. Settings and Design: This was an in vitro cross-sectional study. Subjects and Methods: Six brands of cigarettes (filtered), six brands of bidis, and six brands of chewed tobacco (pan masalas) were used for the study. The methodology published by Association of Official Analytical Chemists was followed, and reagents conforming to American Chemical Society specifications were used. Statistical Analysis Used: One-way ANOVA, Bonferroni post hoc test. Results: The mean nicotine levels for cigarettes, bidis, and chewed tobacco were 7.84 ± 5.10, 16.86 ± 5.66, and 16.30 ± 3.33, respectively. The differences in the mean scores were compared using one-way ANOVA and were found to be statistically significant with F = 6.636 and P = 0.009. Bonferroni post hoc test assessed the difference in mean nicotine content among the groups indicating that the difference between cigarettes versus bidis and cigarette versus chewed tobacco was significant with P = 0.016 and 0.024, respectively. Conclusions: Bidis had the highest content of nicotine, followed by chewed tobacco (pan masalas) and cigarettes.