Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Network-Based Fixed-Complexity Precoder Selection for Multiple Antenna Systems

In this paper, we propose a neural network-based precoder selection method for multiple antenna systems that are equipped with maximum likelihood detectors. We train a fully connected neural network by supervised learning with novel soft labels that are derived from the error probability of maximum likelihood detection. The dimension of the input data is reduced by QR decomposition of the channel matrices, thereby reducing the number of nodes of the input layer. Furthermore, the dimension reduction improves the network accuracy. The number of connections between the layers are reduced by applying the network pruning technique, after which the surviving connections are retrained to recover the degraded accuracy due to the pruning. We also optimize the regularization method, considering not only network overfitting but also pruning and retraining. Our method achieves a near optimal bit error performance of the previous sphere decoding (SD)-based symbolic algorithm, of which complexity fluctuates depending on channel matrices. Unlike the conventional SD-based method, the complexity of the proposed method is fixed by the intrinsic characteristic of neural network, which is desirable from the perspective of hardware implementation. And the fixed complexity is lowered by pruning unimportant connections of the networks. With the aid of computer simulations, we show that the fixed complexity of the proposed method is close to the average complexity of the conventional SD-based symbolic algorithm, allowing only negligible degradation of the error performance.
Comparison of Machine Learning Prediction of Covid-19 Spread in Indonesia

COVID-19 is an infectious disease caused by the corona virus which is a respiratory pathogen. This work focuses on predicting the COVID-19 pandemic in Indonesia and seeing how each different model performs in making predictions. Prediction is done using the Support Vector Machine model with each kernel rbf, poly, and sigmoid, Linear Regression, and Logistic Regression. The category split into Indonesia Time Zone which are WIB, WITA, and WIT. The results of the number of predicted cases obtained from the Support Vector Machine kernel poly model on day 305 for the WIB time zone is 606344, WITA is 167757, and WIT is 38979. The Linear Regression model on day 305 for the WIB time zone is 321388, WITA is 86840, and WIT is 20406. Logistic Regression model on day 305 for the time zone of WIB is 361356, WITA is 84918, and WIT is 20826. From analyzing the number of predicted cases with the number of factual cases, the Support Vector Machine model with the poly kernel has the number of prediction cases that are closest to factual.
Optimal Ballistic Transport and Hopf-Lax Formulae on Wasserstein Space

We investigate the optimal mass transport problem associated to the following "ballistic" cost functional on phase space $M\times M^*$, $$ b_T(v, x):=\inf\{\langle v, \gamma (0)\rangle +\int_0^TL(\gamma (t), {\dot \gamma}(t))\, dt, \gamma \in C^1([0, T), M), \gamma(T)=x\}, $$ where $M=\mathbb{R}^d$, $T>0$, and $L:M\times M \to \mathbb{R}$ is a Lagrangian that is jointly convex in both variables. Under suitable conditions on the initial and final probability measures, we use convex duality a la Bolza and Monge-Kantorovich theory to lift classical Hopf-Lax formulae from state space to Wasserstein space. This allows us to relate optimal transport maps for the ballistic cost to those associated with the fixed-end cost defined on $M\times M$ by $$ c_T(x,y):=\inf\{\int_0^TL(\gamma(t), {\dot \gamma}(t))\, dt, \gamma\in C^1([0, T), M), \gamma(0)=x, \gamma(T)=y\}. $$ We also point to links with the theory of mean field games.
Privacy preserving and performance improvement in edgecomputing using Machine learning

With the increase in the usage of IoT and mobile devices in our day to day lives results in generating vast amount of data, as well as increase in advanced services and applications such as VR, augmented reality and a race for building smart cities made it a challenging situation for cloud computing in terms of latency, privacy and scalability to resolve all these challenges edge computing falls into play as a modern model of computing where computing and storage are located closer to the data center, allowing a new latency and bandwidth-sensitive application class where data is processed at the nearer edge without sending it to the cloud [9]. The goal of this study is to implement an effective and safe proactive strategy (instead of sending a response of a query to each node, node should have its own local computational resources that updates the parameter to the central server) on edge devices which will provide more privacy, as personal data are processed at the nearest edge that can be leaked or exploited by any intruder and because of latency problem in the centralized approach there is a need for effective decentralized system, which additionally should give better outcome as off-base outcome can at some point be calamitous, like in the case of auto-driving car. We will also be presenting how we can utilize numerous encryption technique to scramble data for model training which regardless of whether get hacked by an intruder would be of no use, as if confidential data learned by the model or leaked to an intruder might lead to a significant loss. The proposed method would be tested to all the machine learning problems like word suggestion, image classification and many more.
Lévy processes with finite variance conditioned to avoid an interval

Conditioning Markov processes to avoid a set is a classical problem that has been studied in many settings. In the present article we study the question if a Levy process can be conditioned to avoid an interval and, if so, the path behavior of the conditioned process. For Levy processes with finite second moments we show that conditioning is possible and identify the conditioned process as an h-transform of the original killed process. The h-transform is explicit in terms of successive overshoot distributions and is used to prove that the conditioned process diverges to plus infinity and minus infinity with positive probabilities.
Studying the Effect of Preprocessing on Simultaneous Localization and Mapping in Low-Light Conditions

Low light conditions make computer vision practices difficult. Although the current simultaneous localization and mapping methods are mature, it is difficult to have accurate maps with challenging low light conditions like the ones encountered when dealing with underwater images. This paper studies the effect of using preprocessing to improve the performance and accuracy of simultaneous localization and mapping that focuses on low-light scenes, especially those taken underwater. More specifically, the paper compares the effect of classical and deep learning preprocessing approaches. After hyperparameter tuning, the classical contrast limited adaptive histogram equalization approach is found to achieve the best results with a 20.74% increase in accuracy on the Aqualoc underwater dataset.
Clinical, Biochemical, and Radiological Presentation of RT-PCR-Positive vs RT-PCR-Negative SARS-CoV-2 Pneumonia Requiring ICU Care: An Observational, Cross-Sectional, Single-Center Study in Kalaburagi, Kalyana Karnataka

Introduction Studies have reported similar clinical, biochemical, and radiological features between real-time polymerase chain reaction (RT-PCR)-positive and RT-PCR-negative patients. Therefore, the present study aims to assess differences in RT-PCR-positive versus RT-PCR-negative patients' characteristics. Methods We prospectively included 70 consecutive patients with typical coronavirus disease 2019 (COVID-19)-like clinical features who were either RT-PCR-positive or negative, requiring admission to the intensive care unit. The patients were classified into positive and negative RT-PCR groups and evaluated for clinical features, comorbidities, laboratory findings, and radiologic features. Results Fifty-seven point one percent (57.1%; 40/70) were RT-PCR positive, and 42.9% (30/70) were RT-PCR negative patients. The respiratory rate was higher among negative patients (P = 0.02), whereas the mean duration of fever was longer (3.34 vs 2.5; P = 0.022) among positive patients. At presentation, RT-PCR-negative patients had lower saturation of peripheral oxygen (SpO2) (near significant P = 0.058). Evaluation of co-morbidities revealed no differences. The neutrophil/lymphocyte ratio (NLR) (4.57 vs 6.52; P = 0.048), C-reactive protein (CRP) (9.97 vs 22.7; P = 0.007), and serum ferritin (158 vs 248.52; P = 0.010) were higher in patients who tested negative for RT-PCR. Thrombocytopenia (2.42 vs 1.76; P = 0.009), D-dimer levels (408.91 vs 123.06; P = 0.03), and interleukin (IL-6) levels (219.3 vs 80.81; P = 0.04) were significantly elevated among RT-PCR positive patients. The percentage of lung involvement in negative cases was 42.29+/-22.78 vs 36.21+/-21.8 in positive cases (P=0.23). The CT severity score was similar in both cohorts. Conclusion Both RT-PCR-positive and negative patients have similar clinical, biochemical, and radiological features. Considering that we are amidst a pandemic, it is advisable to have a similar approach irrespective of the RT-PCR report and triage and isolate accordingly. We recommend an RT-PCR-negative intensive care unit (ICU) ward and that the treating physician take a call on the management with a holistic approach driven clinically by the laboratory findings and helped by radiological findings. Stressing only on the RT-PCR report for management can be counterproductive.
An Enzyme-Linked Immunosorbent Assay for the Detection of Mitochondrial DNA–Protein Cross-Links from Mammalian Cells

DNA–Protein cross-links (DPCs) are cytotoxic DNA lesions with a protein covalently bound to the DNA. Although much has been learned about the formation, repair, and biological consequences of DPCs in the nucleus, little is known regarding mitochondrial DPCs. This is due in part to the lack of robust and specific methods to measure mitochondrial DPCs. Herein, we reported an enzyme-linked immunosorbent assay (ELISA)-based method for detecting mitochondrial DPCs formed between DNA and mitochondrial transcription factor A (TFAM) in cultured human cells. To optimize the purification and detection workflow, we prepared model TFAM-DPCs via Schiff base chemistry using recombinant human TFAM and a DNA substrate containing an abasic (AP) lesion. We optimized the isolation of TFAM-DPCs using commercial silica gel-based columns to achieve a high recovery yield for DPCs. We evaluated the microplate, DNA-coating solution, and HRP substrate for specific and sensitive detection of TFAM-DPCs. Additionally, we optimized the mtDNA isolation procedure to eliminate almost all nuclear DNA contaminants. For proof of concept, we detected the different levels of TFAM-DPCs in mtDNA from HEK293 cells under different biological conditions. The method is based on commercially available materials and can be amended to detect other types of DPCs in mitochondria.
The impact of recycled crumb rubber powder on the behavior of the asphalt mixture

The increase of automobile tires, which represent a significant environmental hazard, the widespread need to enhance the performance of road pavements, and the transition to a more sustainable transportation system have greatly pushed the use of waste materials in asphalt mixture. The use of tire shreds as a crumb rubber (CR) modifier in asphalt paving materials have shown the potential for vast benefits. Crumb rubber was mixed with asphalt bitumen with 15% of a measure of how much asphalt binder is used to make rubberized asphalt. This research was done to determine how rubberized asphalt affected surface hot mix asphalt's mechanical and volumetric characteristics (HMA). Laboratory tests included the following volumetrics: bulk specific gravity of compacted mixture (Gmb); theoretical mixture specific gravity of loose mixture (Gmm); air voids (A v); voids in mineral aggregate (VMA); and voids filled with asphalt. All asphalt mixes were created using the Marshall mix design method (VF A). Hot-mix asphalt is tested mechanically using the Marshall stability (MS), Marshall flow (MF), and indirect tensile strength (IDTS) tests (HMA). In comparison to the reference mixture, the asphalt mixture's volumetric and mechanical parameters showed a 15% CR improvement in behavior, according to the data. Gmb, Gmm, VFA, MS, and IDTS were raised by, respectively, 0.21 %,0.04%, 2.7%, 24%, and 15%. lowered MF by 16%, Av by 5%, and VMA by 1.3% as well. This information improved the qualities of the amended asphalt mix at high and low temperatures and its resistance to rutting and fatigue cracking. Overall, this study helps increase the usage of CR-modified asphalt in constructing high-quality pavement.
Spatiotemporal Modeling of Real World Backsheets Field Survey Data: Hierarchical (Multilevel) Generalized Additive Models

Assessing photovoltaic module backsheet durability is critical to increasing module lifetime. Laboratory-based accelerating testing has recently failed to predict large scale failures of widely adopted polymeric materials. Additionally, there is a growing concern on characterizing the non-uniformity of field exposure. Therefore, data from field surveys are critical to assess the performance of component lifetimes. Using a documented field survey protocol, 19 field surveys were conducted. The focus of this survey strategy is to investigate spatial continuity in degradation modes. By combining field survey data with real-time satellite weather data, stressor / response models have been trained. Generalized additive Models (GAM) model was created to predict the value of degradation based on measured predictors. Two different GAM constructions were testing using different implementations of basis splines. The model includes variables on the environmental stressors of the system and the location of each measurement in the PV mounting structure. The incorporation of hierarchical structure into the models allowed for material specific degradation rates, while maintaining the assumption of a global trend. The model performed well with an adjusted $R^{2}$ of 0.975 for yellowness index prediction.
Probabilistic Analysis of Network Availability

In recent years, quantitative verification concerning network availability has received increasing attention due to its practical importance in network management. Existing work extends upon qualitative verification and tries to answer whether a network would have any link overload under failures and dynamic traffic. The simple yes-or-no question falls short of accurately characterizing the robustness of a network under uncertainties, which the operators are in dire need of. Thus, we argue that it is necessary to design a probabilistic framework to analyze network availability comprehensively. We propose Pita, a novel network analysis framework that outputs the overall probability of the network being unavailable under a range of failure scenarios and traffic demands. We formalize the problem and show that it is #P-hard which does not admit deterministic approximation solutions. We further develop an improved randomized approximation that exploits the structural property of our problem to reduce the computational cost of the so-called boundary oracle procedure, a key bottleneck of the approximation, without any accuracy loss. Evaluation with real topologies shows that Pita provides up to 2.25x speedup over state-of-the-art solutions, and can be effectively used in many network management tasks such as identifying high-risk failure scenarios, and aiding robust traffic engineering design.
Forecasting Financial Time Series Using Deep Learning Networks: Evidence from Long-Short Term Memory and Gated Recurrent Unit

The ability to predict the stock market and analyze market trends is invaluable to researchers and anyone interested in investing. However, this task is a challenging problem due to a large number of parameters and unpredictable noise that may affect the stock price. To overcome this issue, researchers have employed numerous approaches such as Moving Average (MA), Support Vector Machine (SVM)
Constructive Proof of Global Lyapunov Function as Potential Function

We provide a constructive proof on the equivalence of two fundamental concepts: the global Lyapunov function in engineering and the potential function in physics, establishing a bridge between these distinct fields. This result suggests new approaches on the significant unsolved problem namely to construct Lyapunov functions for general nonlinear systems through the analogy with existing methods on potential functions. In addition, we show another connection that the Lyapunov equation is a reduced form of the generalized Einstein relation for linear systems.
Global existence for a free boundary problem of Fisher–KPP type

Motivated by the study of branching particle systems with selection, we establish global existence for the solution of the free boundary problem when the initial condition is non-increasing with as and as . We construct the solution as the limit of a sequence , where each un is the solution of a Fisher–KPP equation with the same initial condition, but with a different nonlinear term. Recent results of De Masi A et al (2017 (arXiv:1707.00799)) show that this global solution can be identified with the hydrodynamic limit of the so-called N-BBM, i.e. a branching Brownian motion in which the population size is kept constant equal to N by removing the leftmost particle at each branching event.
H∞-Based Control Design for Grid-Forming Inverters With Enhanced Damping and Virtual Inertia

Grid-forming inverters (GFMIs) are identified as an important asset for achieving renewable energy-rich power grids. GFMIs are attracting significant attention due to their superior characteristics over grid-following inverters in both grid-connected (GC) and standalone (SA) scenarios. In this article, a second-order discrete-time controller is proposed to achieve a well-damped step response for power reference commands and improved virtual inertia provision capability. In this article, a control design method based on $\mathcal {H}_{\infty} $ is proposed, which is based on the frequency response of the system, to tune the proposed controller. The proposed control design presents a methodical process to specify the desired performance indices through frequency-domain constraints. The performance of the controller is thoroughly validated analytically and through simulation results. The superior performance of the proposed controller over the virtual synchronous generator controller in terms of tracking performance and virtual inertia provision capability is verified through experimental results.
Subordination methods for free deconvolution

In this paper, we give subordination functions for free additive and free multiplicative deconvolutions in some domain of the complex half-plane, under the condition that the distributions admit moments of respectively second order for the additive deconvolution and of fourth order for the multiplicative one. Our method of proof allows us to give an algorithm to calculate these subordinations functions, and thus the associated Cauchy transforms, for complex numbers with imaginary part bigger than a parameter depending on the measure to deconvolve. This reduces the problem of free deconvolutions to the one of the classical deconvolution with a Cauchy distribution. We present also an extension of these results to the case of operator valued distributions.
Activity in the Arbuscular Mycorrhizal Hyphosphere Warning Neighbouring Plants

Pathogen infections of the phyllosphere have been investigated in detail, however, the changes induced by these infections on the arbuscular mycorrhizal hyphosphere, and the consequent signalling to the neighbouring plants have been scarcely investigated. Here, our objectives were to document that B.fabae infection of connected Vicia faba plants resulted in changes in the metabolism and microbial community of the hyphosphere, confirming the induction of plant defence in connected plants through gene-expression evaluations. Infected plants were challenged with B. fabae for 72 h. Changes in gene-expression of pathogenesis-related proteins 1,2, and 5 (PR1, PR2, PR5) of both infected- and non-infected plants were analysed, to confirm signalling through the hyphosphere. The primary metabolic profiles and changes in the level of microbiota in the hyphosphere were assessed. Changes in expression of PR1, PR2, and PR5 genes occurred in the neighbouring plants 24 hours after infection. Mannitol levels decreased in presence of AMF. A decrease in the level of actinobacteria in the hyphosphere of infected plants was detected. We conclude that B.fabae infection induced a signalling event through the AM hyphosphere, confirmed by changes in defence gene-expression in non-infected neighbouring plants, influenced primary metabolic activity of-, and affected the microbial composition within-, the AM hyphosphere.
Assessment of the Impact of Zebra Mussels on the Ecosystem of the Tigris River

10 sites were selected for investigating zebra mussels in the Tigris River for the study that was conducted for the period from January to December 2020. The results of this investigation showed that zebra mussels were recorded in the Tigris at Taji on sites 7, 8, 9 and 10, while it was not found in the Tigris at Kut on sites 1, 2, 3, 4, 5 and 6. This is the first time that this species was ever recorded in the Tigris. It was found that the sites where zebra mussels appeared had a clear impact on the decrease in the total organic carbon %age and turbidity (NTU). High dissolved oxygen values were observed in the sites where zebra mussels were recorded, and the appearance of zebra mussels in these sites caused a clear decrease in the appearance and total density of benthic invertebrates, in addition to a clear increase in the total density of epiphytic algae. Zebra mussels caused the disappearance of the local species of the phylum Mollusca and crustaceans in the sites where it appeared.
Source Community and Assembly Processes Affect the Efficiency of Microbial Microcystin Degradation on Drinking Water Filtration Membranes

Microbial biofilms in gravity-driven membrane (GDM) filtration systems can efficiently degrade the cyanotoxin microcystin (MC), but it is unclear if this function depends on the presence of MC-producing cyanobacteria in the source water habitat. We assessed the removal of MC from added Microcystis aeruginosa biomass in GDMs fed with water from a lake with regular blooms of toxic cyanobacteria (ExpL) or from a stream without such background (ExpS). While initial MC removal was exclusively due to abiotic processes, significantly higher biological MC removal was observed in ExpL. By contrast, there was no difference in MC degradation capacity between lake and stream bacteria in separately conducted liquid enrichments on pure MC. Co-occurrence network analysis revealed a pronounced modularity of the biofilm communities, with a clear hierarchic distinction according to feed water origin and treatment type. Genotypes in the network modules associated with ExpS had significantly more links to each other, indicating that these biofilms had assembled from a more coherent source community. In turn, signals for stochastic community assembly were stronger in ExpL biofilms. We propose that the less “tightly knit” ExpL biofilm assemblages allowed for the better establishment of facultatively MC degrading bacteria, and thus for higher overall functional efficiency.