Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectral and Energy Efficiency of ACO-OFDM in Visible Light Communication Systems

In this paper, we study the spectral efficiency (SE) and energy efficiency (EE) of asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) for visible light communication (VLC). Firstly, we derive the achievable rates for Gaussian distributions inputs and practical finite-alphabet inputs. Then, we investigate the SE maximization problems subject to both the total transmit power constraint and the average optical power constraint with the above two inputs, respectively. By exploiting the relationship between the mutual information and the minimum mean-squared error, an optimal power allocation scheme is proposed to maximize the SE with finite-alphabet inputs. To reduce the computational complexity of the power allocation scheme, we derive a closed-form lower bound of the SE. Also, considering the quality of service, we further tackle the non-convex EE maximization problems of ACO-OFDM with the two inputs, respectively. The problems are solved by the proposed Dinkelbach-type iterative algorithm. In each iteration, the interior point algorithm is applied to obtain the optimal power allocation.The performance of the proposed power allocation schemes for the SE and EE maximization are validated through numerical analysis.
Potential Role of Pleural Fluid Cytokine Profile in Myelomatous Pleural Effusions

Background Myelomatous pleural effusion (MPE), as a presentation of extramedullary infiltration of multiple myeloma (MM), is rare and currently associated with poor outcomes without effective therapy. The potential value of cytokine detection in pleural effusion to MPE has not been reported to date. Case Presentation We herein report a case of refractory and relapsed multiple myeloma that developed bilateral MPE due to disease progression caused by intolerance to various chemotherapy regimens. Cytomorphology and flow cytometry were adopted for diagnosis confirmation. Chemotherapy containing immunomodulators combined with thoracic catheterization drainage was applied to the patient, showing a certain therapeutic effect. During the course of disease, the change of cytokine profile in pleural effusion was monitored by cytometric bead array (CBA) technology, revealing that cytokines related to tumor load such as interleukin 6 (IL-6) and interleukin 10 (IL-10) in pleural effusion decreased with the improvement of disease, while other cytokines such as interleukin 2 (IL-2), interleukin 4 (IL-4), interleukin 17A (IL-17A), tumor necrosis factor α (TNF-α), interferon γ (IFN-γ), granzyme A, granzyme B, perforin and granulysin increased with the improvement of disease. Conclusion There is a prospect that cytokine level in pleural effusion may indicate treatment response of MPE, and in light of this case, immunomodulators may be utilized in treating patients suffering MPE. Due to limitations of our single case, we urge more groups to evaluate the potential role of cytokine profile in MPE.
Machine Learning Methods for UAV Flocks Management-A Survey

The development of unmanned aerial vehicles (UAVs) has been gaining momentum in recent years owing to technological advances and a significant reduction in their cost. UAV technology can be used in a wide range of domains, including communication, agriculture, security, and transportation. It may be useful to group the UAVs into clusters/flocks in certain domains, and various challenges associated with UAV usage can be alleviated by clustering. Several computational challenges arise in UAV flock management, which can be solved by using machine learning (ML) methods. In this survey, we describe the basic terms relating to UAVS and modern ML methods, and we provide an overview of related tutorials and surveys. We subsequently consider the different challenges that appear in UAV flocks. For each issue, we survey several machine learning-based methods that have been suggested in the literature to handle the associated challenges. Thereafter, we describe various open issues in which ML can be applied to solve the different challenges of flocks, and we suggest means of using ML methods for this purpose. This comprehensive review may be useful for both researchers and developers in providing a wide view of various aspects of state-of-the-art ML technologies that are applicable to flock management.
Towards Out-Of-Distribution Generalization: A Survey

Traditional machine learning paradigms are based on the assumption that both training and test data follow the same statistical pattern, which is mathematically referred to as Independent and Identically Distributed ($i.i.d.$). However, in real-world applications, this $i.i. d. $ assumption often fails to hold due to unforeseen distributional shifts, leading to considerable degradation in model performance upon deployment. This observed discrepancy indicates the significance of investigating the Out-of-Distribution (OOD) generalization problem. OOD generalization is an emerging topic of machine learning research that focuses on complex scenarios wherein the distributions of the test data differ from those of the training data. This paper represents the first comprehensive, systematic review of OOD generalization, encompassing a spectrum of aspects from problem definition, methodological development, and evaluation procedures, to the implications and future directions of the field. Our discussion begins with a precise, formal characterization of the OOD generalization problem. Following that, we categorize existing methodologies into three segments: unsupervised representation learning, supervised model learning, and optimization, according to their positions within the overarching learning process. We provide an in-depth discussion on representative methodologies for each category, further elucidating the theoretical links between them. Subsequently, we outline the prevailing benchmark datasets employed in OOD generalization studies. To conclude, we overview the existing body of work in this domain and suggest potential avenues for future research on OOD generalization. A summary of the OOD generalization methodologies surveyed in this paper can be accessed at http://out-of-distribution-generalization.com.
When Retriever-Reader Meets Scenario-Based Multiple-Choice Questions

Scenario-based question answering (SQA) requires retrieving and reading paragraphs from a large corpus to answer a question which is contextualized by a long scenario description. Since a scenario contains both keyphrases for retrieval and much noise, retrieval for SQA is extremely difficult. Moreover, it can hardly be supervised due to the lack of relevance labels of paragraphs for SQA. To meet the challenge, in this paper we propose a joint retriever-reader model called JEEVES where the retriever is implicitly supervised only using QA labels via a novel word weighting mechanism. JEEVES significantly outperforms a variety of strong baselines on multiple-choice questions in three SQA datasets.
Protein Nanoparticles Modified with PDGF-B as a Novel Therapy After Acute Cerebral Infarction

Visual Abstract Treatment options for cerebral infarction beyond the time window of reperfusion therapy are limited, and novel approaches are needed. PDGF-B is considered neuroprotective; however, it is difficult to administer at effective concentrations to infarct areas. Nanoparticles (NPs) are small and stable; therefore, we modified PDGF-B to the surface of naturally occurring heat shock protein NPs (HSPNPs) to examine its therapeutic effect in cerebral infarction. PDGF-B modified HSPNPs (PDGF-B HSPNPs) were injected 1 d after transient middle cerebral artery occlusion (t-MCAO) in CB-17 model mice. We analyzed the infarct volume and motor functional recovery at 3 and 7 d. PDGF-B HSPNPs were specifically distributed in the infarct area, and compared with HSPNPs alone, they significantly reduced infarct volumes and improved neurologic function 3 and 7 d after administration. PDGF-B HSPNP administration was associated with strong phosphorylation of Akt in infarct areas and significantly increased neurotrophin (NT)-3 production as well as reduced cell apoptosis compared with HSPNPs alone. Moreover, astrogliosis in peri-infarct area was significantly upregulated with PDGF-B HSPNPs compared with HSPNPs alone. Treatment with PDGF-B HSPNPs might be a novel approach for treating cerebral infarction.
Generalizing weighted trees: a bridge from Bitcoin to GHOST

Despite the tremendous interest in cryptocurrencies like Bitcoin and Ethereum today, many aspects of the underlying consensus protocols are poorly understood. Therefore, the search for protocols that improve either throughput or security (or both) continues. Bitcoin always selects the longest chain (i.e., the one with most work). Forks may occur when two miners extend the same block simultaneously, and the frequency of forks depends on how fast blocks are propagated in the network. In the GHOST protocol, used by Ethereum, all blocks involved in the fork contribute to the security. However, the greedy chain selection rule of GHOST does not consider the full information available in the block tree, which has led to some concerns about its security. This paper introduces a new family of protocols, called Medium, which takes the structure of the whole block tree into account, by weighting blocks differently according to their depths. Bitcoin and GHOST result as special cases. This protocol leads to new insights about the security of Bitcoin and GHOST and paves the way for developing network- and application-specific protocols, in which the influence of forks on the chain-selection process can be controlled. It is shown that almost all protocols in this family achieve strictly greater throughput than Bitcoin (at the same security level) and resist attacks that can be mounted against GHOST.
Disentanglement Analysis with Partial Information Decomposition

We propose a framework to analyze how multivariate representations disentangle ground-truth generative factors. A quantitative analysis of disentanglement has been based on metrics designed to compare how one variable explains each generative factor. Current metrics, however, may fail to detect entanglement that involves more than two variables, e.g., representations that duplicate and rotate generative factors in high dimensional spaces. In this work, we establish a framework to analyze information sharing in a multivariate representation with Partial Information Decomposition and propose a new disentanglement metric. This framework enables us to understand disentanglement in terms of uniqueness, redundancy, and synergy. We develop an experimental protocol to assess how increasingly entangled representations are evaluated with each metric and confirm that the proposed metric correctly responds to entanglement. Through experiments on variational autoencoders, we find that models with similar disentanglement scores have a variety of characteristics in entanglement, for each of which a distinct strategy may be required to obtain a disentangled representation.
Scene Synthesis via Uncertainty-Driven Attribute Synchronization

Developing deep neural networks to generate 3D scenes is a fundamental problem in neural synthesis with immediate applications in architectural CAD, computer graphics, as well as in generating virtual robot training environments. This task is challenging because 3D scenes exhibit diverse patterns, ranging from continuous ones, such as object sizes and the relative poses between pairs of shapes, to discrete patterns, such as occurrence and co-occurrence of objects with symmetrical relationships. This paper introduces a novel neural scene synthesis approach that can capture diverse feature patterns of 3D scenes. Our method combines the strength of both neural network-based and conventional scene synthesis approaches. We use the parametric prior distributions learned from training data, which provide uncertainties of object attributes and relative attributes, to regularize the outputs of feed-forward neural models. Moreover, instead of merely predicting a scene layout, our approach predicts an over-complete set of attributes. This methodology allows us to utilize the underlying consistency constraints among the predicted attributes to prune infeasible predictions. Experimental results show that our approach outperforms existing methods considerably. The generated 3D scenes interpolate the training data faithfully while preserving both continuous and discrete feature patterns.
Analysis of Innovative Technologies and Identification of New Information Tools for Effective Activities of Enterprises on the Example of the Financial Market of Ukraine

The object of research is modern innovative information technologies as a factor in market transformation. The subject of research is the theoretical and methodological foundations of the use of financial technologies in the financial sector of the economy. One of the important problems for the world financial market today is to increase the efficiency of its functioning through the introduction of modern management methods based on innovative information technologies. The most problematic areas is the use of proprietary software. The importance of the latter factor is increasing every year due to the strengthening of legislative requirements and pressure from rightholders. That is why it is advisable to offer the Blueshift platform, which will help expand the capabilities and functions of financial and economic activities. The study used methods of collecting, processing and interpreting information about innovative financial products, and theoretical generalization, analysis of definitions, comparative analysis, concretization and systematization of modern financial technologies, observation and visual presentation of the analysis results. The main hypothesis of the study is the assumption that the use of modern innovative information platforms will allow active users to increase the efficiency of making ready-made decisions when solving problems arising in the financial sector of the economy. It is impossible to confirm this hypothesis without a detailed analysis of financial technologies, as well as innovations associated with modern open source systems, which allow market participants to reduce costs and maximize the likelihood of data protection. In paper, it was proposed to use the Blueshift platform for research and implementation of systematic investment strategies based on its built-in capabilities. The advantages and disadvantages of using the Blueshift platform as a «platform as a service» are analyzed. Thanks to this innovation, such advantages in functionality as information security, ease of adaptation, and economic benefits can be obtained.
Quantum Communication Using Semiconductor Quantum Dots

Worldwide, enormous efforts are directed toward the development of the so‐called quantum internet. Turning this long‐sought‐after dream into reality is a great challenge that will require breakthroughs in quantum communication and computing. To establish a global, quantum‐secured communication infrastructure, photonic quantum technologies will doubtlessly play a major role, by providing and interfacing essential quantum resources, for example, flying‐ and stationary qubits or quantum memories. Over the last decade, significant progress has been made in the engineering of on‐demand quantum light sources based on semiconductor quantum dots, which enable the generation of close‐to‐ideal single‐ and entangled‐photon states, useful for applications in quantum information processing. This review focuses on implementations of, and building blocks for, quantum communication using quantum‐light sources based on epitaxial semiconductor quantum dots. After reviewing the main notions of quantum communication and introducing the devices used for single‐photon and entangled‐photon generation, an overview of experimental implementations of quantum key distribution protocols using quantum dot based quantum light sources is provided. Furthermore, recent progress toward quantum‐secured communication networks as well as building blocks thereof is summarized. The article closes with an outlook, discussing future perspectives in the field and identifying the main challenges to be solved.
Interaction between insulin and androgen signalling in decidualization, cell migration and trophoblast invasion in vitro

Finely tuned decidualization of endometrial stromal fibroblasts into decidual cells is crucial for successful implantation and a healthy pregnancy. Both insulin and androgens are known to modulate decidualization, however, their complex effect on this process has not been fully elucidated. As hyperinsulinemia and hyperandrogenism are associated in clinical conditions, we aimed to investigate the interaction between insulin and androgens on decidualization. Primary human endometrial stromal cells were decidualized in vitro in the presence of insulin and/or androgens (dihydrotestosterone (DHT), testosterone). Gene or protein expressions of decidualization markers were measured, and cells size characteristics were determined. Migration of decidualizing endometrial stromal cells and invasion of HTR‐8/SVneo trophoblast spheroids were assessed. We found that insulin and androgens in combination enhanced the upregulation of several decidualization markers including prolactin, tissue factor, tissue inhibitor of matrix metalloproteinase 3 and connexin‐43, and also interacted in modulating cell size characteristics resulting in enlarged decidualizing cells. However, insulin and DHT together restricted the migration of decidualizing cells and invasion of trophoblast spheroids. Our findings suggest that insulin and androgens interact to potentiate the process of decidualization. On the other hand, inhibited cell migration and trophoblast invasion might negatively impact the function of decidualizing endometrial stromal cells.
ProtRe-CN: Protein Remote Homology Detection by Combining Classification Methods and Network Methods via Learning to Rank

Protein remote homology detection is one of fundamental research tasks for downstream analysis (i.e., protein structure and function prediction). Many advanced methods are proposed from different views with complementary detection ability, such as the classification method, the network method, and the ranking method. A framework integrating these heterogeneous methods is urgently desired to reduce the false positive rate and predictive bias. We propose a novel ranking method called ProtRe-CN by fusing the classification methods and network methods via Learning to Rank. Experimental results on the benchmark dataset and the independent dataset show that ProtRe-CN outperforms other existing state-of-the-art predictors. ProtRe-CN improves the detective performance via correcting the false positives in the ranking list by combining the heterogeneous methods. The web server of ProtRe-CN can be accessed at http://bliulab.net/ProtRe-CN.
Successful Recovery Performance Guarantees of Noisy SOMP

The simultaneous orthogonal matching pursuit (SOMP) is a popular, greedy approach for common support recovery of a row-sparse matrix. The support recovery guarantee of SOMP has been extensively studied under the noiseless scenario. Compared to the noiseless scenario, the performance analysis of noisy SOMP is still nascent, in which only the restricted isometry property (RIP)-based analysis has been studied. In this paper, we present the mutual incoherence property (MIP)-based study for performance analysis of noisy SOMP. Speciﬁcally, when noise is bounded, we provide the condition on which the exact support recovery is guaranteed in terms of the MIP. When noise is unbounded, we instead derive a bound on the successful recovery probability (SRP) that depends on the speciﬁc distribution of noise. Then we focus on the common case when noise is random Gaussian and show that the lower bound of SRP follows Tracy-Widom law distribution. The analysis reveals the number of measurements, noise level, the number of sparse vectors, and the value of MIP constant that are required to guarantee a predeﬁned recovery performance. Theoretically, we show that the MIP constant of the measurement matrix must increase proportional to the noise standard deviation, and the number of sparse vectors needs to grow proportional to the noise variance. Finally, we extensively validate the derived analysis through numerical simulations.
Declining fungal diversity in Arctic freshwaters along a permafrost thaw gradient

Climate change–driven permafrost thaw has a strong influence on pan‐Arctic regions, via, for example, the formation of thermokarst ponds. These ponds are hotspots of microbial carbon cycling and greenhouse gas production, and efforts have been put on disentangling the role of bacteria and archaea in recycling the increasing amounts of carbon arriving to the ponds from degrading watersheds. However, despite the well‐established role of fungi in carbon cycling in the terrestrial environments, the interactions between permafrost thaw and fungal communities in Arctic freshwaters have remained unknown. We integrated data from 60 ponds in Arctic hydro‐ecosystems, representing a gradient of permafrost integrity and spanning over five regions, namely Alaska, Greenland, Canada, Sweden, and Western Siberia. The results revealed that differences in pH and organic matter quality and availability were linked to distinct fungal community compositions and that a large fraction of the community represented unknown fungal phyla. Results display a 16%–19% decrease in fungal diversity, assessed by beta diversity, across ponds in landscapes with more degraded permafrost. At the same time, sites with similar carbon quality shared more species, aligning a shift in species composition with the quality and availability of terrestrial dissolved organic matter. We demonstrate that the degradation of permafrost has a strong negative impact on aquatic fungal diversity, likely via interactions with the carbon pool released from ancient deposits. This is expected to have implications for carbon cycling and climate feedback loops in the rapidly warming Arctic.
Fine Cryo-SEM Observation of the Microstructure of Emulsions Frozen Via High-Pressure Freezing.

An emulsion, a type of soft matter, is complexed with at least two materials in the liquid state (e.g., water and oil). Emulsions are classified into two types: water-in-oil (W/O) and oil-in-water (O/W), depending on the strength of the emulsifier. The properties and behavior of emulsions are directly correlated with the size, number, localization, and structure of the dispersed phases in the continuous phase. Therefore, an understanding of the microstructure comprising liquid-state emulsions is essential for producing and evaluating these emulsions. Generally, it is impossible for conventional electron microscopy to examine liquid specimens, such as emulsion. Recent advances in cryo-scanning electron microscopy (cryo-SEM) could allow us to visualize the microstructure of the emulsions in a frozen state. Immersion freezing in slush nitrogen has often been used for preparing the frozen samples of soft matters. This preparation could generate ice crystals, and they would deform the microstructure of specimens. High-pressure freezing contributes to the inhibition of ice-crystal formation and is commonly used for preparing frozen biological samples with high moisture content. In this study, we compared the microstructures of immersion-frozen and high-pressure frozen emulsions (O/W and W/O types, respectively). The cryo-SEM observations suggested that high-pressure freezing is more suitable for preserving the microstructure of emulsions than immersion freezing.