Miracle-dz/UniFAR-FTUs huggingface dataset:
Precode and Quantize Channel State Information Sharing for Cloud Radio Access Networks

Joint transmission by a cluster of smart remote radio heads is an important technique for enhancing network throughout in future cellular networks. This transmission requires global channel state information at the processing unit which optimizes the joint precoder. To this end, a large amount of channel state information must be shared between the smart remote radio heads and that unit. In this paper, we present a new channel state information sharing scheme, which drastically reduces the large overhead in acquiring global channel state information for joint transmission. The idea is that each smart remote radio head applies a local precoding matrix, whose objective is to create equivalent channels which can be quantized more accurately with fewer bits, thereby to reduce the overhead of sharing channel state information. The proposed scheme facilitate joint transmission in a cluster in which the smart remote radio heads are interconnected by limited-rate link.
Cosmetic and Therapeutic Applications of Fish Oil’s Fatty Acids on the Skin

Fish oil has been broadly reported as a potential supplement to ameliorate the severity of some skin disorders such as photoaging, skin cancer, allergy, dermatitis, cutaneous wounds, and melanogenesis. There has been increasing interest in the relationship of fish oil with skin protection and homeostasis, especially with respect to the omega-3 polyunsaturated fatty acids (PUFAs), docosahexaenoic acid (DHA), and eicosapentaenoic acid (EPA). The other PUFAs, such as α-linolenic acid (ALA) and linoleic acid (LA), also show a beneficial effect on the skin. The major mechanisms of PUFAs for attenuating cutaneous inflammation are the competition with the inflammatory arachidonic acid and the inhibition of proinflammatory eicosanoid production. On the other hand, PUFAs in fish oil can be the regulators that affect the synthesis and activity of cytokines for promoting wound healing. A systemic review was conducted to demonstrate the association between fish oil supplementation and the benefits to the skin. The following describes the different cosmetic and therapeutic approaches using fatty acids derived from fish oil, especially ALA, LA, DHA, and EPA. This review summarizes the cutaneous application of fish oil and the related fatty acids in the cell-based, animal-based, and clinical models. The research data relating to fish oil treatment of skin disorders suggest a way forward for generating advances in cosmetic and dermatological uses.
Urban Slums: A Supportive Ecosystem for Typhoidal Salmonellae

Abstract The typhoidal Salmonellae were controlled in cities in North America and Europe in the late 19th and early 20th century by development of centralized water treatment systems. In the early 21st century, large urban centers where drinking water routinely mixes with human feces have the highest burden of typhoid fever. Although improving municipal drinking water quality is the most robust approach to reduce enteric fever burden, the high costs and managerial capacity that such systems require and decreasing per capita water availability requires new approaches to reach the highest risk communities. The spread of antimicrobial resistance threatens to increase the burden of enteric fever much sooner than the extension of safe reliable water service delivery can be implemented. Thus, vaccination is an important interim measure.
Revenue Maximization of Multi-Class Charging Stations with Opportunistic Charger Sharing

Distribution of limited smart grid resources among electric vehicles (EVs) with diverse service demands in an unfavorable manner can potentially degrade the overall profit achievable by the operating charging station (CS). In fact, inefficient resource management can lead to customer dissatisfaction arising due to prolonged queueing and blockage of EVs arriving at the CS for service. In this paper, a dynamic electric power allocation scheme for a charging facility is proposed and modeled as a bi-variate continuous-time Markovian process, with exclusive charging outlets being allotted to EVs of different classes in real-time. The presented mechanism enables the CS to guarantee the quality-of-service expected by customers in terms of blocking probability, while also maximizing its own overall revenue. By adopting a practical congestion pricing model within the defined profit function, the revenue optimization framework for a single CS is further extended to a load-balanced network of CSs. Simulation results for the single CS and networked models reveal considerably higher satisfaction levels for congested fast charging EV customers and improved attainable system revenue as compared to a baseline scenario which assumes no classification based on EV service preferences.
A Novel Method of Articles Rating Based on Concerns Tracking and Matching for Public Opinion Recommendation

Public opinion events on the Internet are gaining more and more attention from the supervisory institutions for the possibility of malicious guide. Since the number of the events on the Internet is quite enormous, the process of supervision often costs a lot of manpower, which is contrary to the purposes and objectives of Sustainable Computing. However, most traditional methods for news recommendation are designed for netizens who do not have specific responsibilities like supervisory institutions. It is also difficult for supervisory institutions themselves to rate the public opinion articles, which is indispensable for recommendation. In this paper, a novel articles rating method based on tracking and matching (ARTM), is proposed for public opinion recommendation. The ARTM method can mine institution concerns from the browsing history and keep them updating automatically with the changing of institution attention. The processing flow of ARTM is as follows. Firstly, a set of institution concerns are established in terms of three aspects: fixed concerns, potential concerns and reading preferences. Then ratings of public opinion articles are computed by measuring the similarities between the vector of article keywords and the vector of institution concerns. Finally, articles are sorted by ratings and high- ranking articles are added to the recommendation list. In addition, the proposed rating algorithm and tracking algorithm can also be used as standalone modules for other services. In the end, comprehensive evaluation of the proposed method based on real data (78 supervisory institutions browsing history in one month) is made. Experimental results show that the proposed ARTM method can significantly improve recommendation efficiency.
How to Teach an Old File System Dog New Object Store Tricks

Many data service platforms use local file systems as their backend storage. Although this approach offers advantages in portability, extensibility, and ease of development, it may suffer from severe performance degradation if the mapping between the services required by the data service platform and the functions provided by the local file system is not carefully managed. This paper presents in-depth analysis of performance problems in current data service platforms that use file systems as their backend storage and proposes three novel strategies that are essential to solving the current performance problems. We demonstrate the efficacy of our strategies by implementing a prototype object store in Ceph, called SwimStore (Shadowing with Immutable Metadata Store). We experimentally show that SwimStore provides high performance with little variation, as well as a large reduction in write traffic.
A type and scope safe universe of syntaxes with binding: their semantics and proofs

Almost every programming language’s syntax includes a notion of binder and corresponding bound occurrences, along with the accompanying notions of α-equivalence, capture avoiding substitution, typing contexts, runtime environments, and so on. In the past, implementing and reasoning about programming languages required careful handling to maintain the correct behaviour of bound variables. Modern programming languages include features that enable constraints like scope safety to be expressed in types. Nevertheless, the programmer is still forced to write the same boilerplate over again for each new implementation of a scope safe operation (e.g., renaming, substitution, desugaring, printing, etc.), and then again for correctness proofs. We present an expressive universe of syntaxes with binding and demonstrate how to (1) implement scope safe traversals once and for all by generic programming; and (2) how to derive properties of these traversals by generic proving. Our universe description, generic traversals and proofs, and our examples have all been formalised in Agda and are available in the accompanying material. NB. we recommend printing the paper in colour to benefit from syntax highlighting in code fragments.
Germ layer-specific regulation of cell polarity and adhesion gives insight into the evolution of mesoderm

In triploblastic animals, Par-proteins regulate cell-polarity and adherens junctions of both ectodermal and endodermal epithelia. But, in embryos of the diploblastic cnidarian Nematostella vectensis, Par-proteins are degraded in all cells in the bifunctional gastrodermal epithelium. Using immunohistochemistry, CRISPR/Cas9 mutagenesis, and overexpression of specific mRNAs, we describe the functional association between Par-proteins, ß-catenin, and snail transcription factor genes in N. vectensis embryos. We demonstrate that the aPKC/Par complex regulates the localization of ß-catenin in the ectoderm by stabilizing its role in cell-adhesion, and that endomesodermal epithelial cells are organized by a different cell-adhesion system than that of overlying ectoderm. We also show that ectopic expression of snail genes, which are expressed in mesodermal derivatives in bilaterians, are sufficient to downregulate Par-proteins and translocate ß-catenin from the junctions to the cytoplasm in ectodermal cells. These data provide molecular insight into the evolution of epithelial structure and distinct mesodermal tissue in metazoan embryos.
A Simple Abstract Interpretation for Petri Net Queries

We propose a simple but effective method for the detection of duplicates in state predicates for Petri nets. We map sub-formulas to integer numbers using a mapping abstr. The assignment guarantees that abstr(φ1) = abstr(φ2) implies equivalence of φ1 and φ2 while abstr(φ1) = − abstr(φ2) implies equivalence of φ1 and ¬φ2. The obtained knowledge can be used for reducing the size of a formula that is obtained by expensive constructions such as the transformation into disjunctive normal form. The method is implemented in our tool LoLA 2.0.
Investigation on Tradeoff between Hardware Noise and Outage Performance in Cooperative NOMA

Thanks to improvement in successive interference cancellation (SIC), non-orthogonal multiple access (NOMA) can be implemented in 5G communications. In this paper, the cooperative NOMA (C-NOMA) systems is investigated in two proposed schemes, namely perfect hardware cancellation (PC) and imperfect hardware cancellation (IC) considering on efficiency of hardware noise cancellation operations. The first scenario is that perfect processing between the relay and the far user. In the second scenario, the hardware noise resulted by imperfect circuit at relay for NOMA operation. To illustrate the performance of C-NOMA in two considered scenarios, the closed-form expressions for both exact and asymptotic outage probability are derived for each NOMA user. Simulation results validate that the outage performance of C-NOMA with IC scheme is superior to C-NOMA with PC scheme at low SNR region rather than at high SNR region.
Multimodal Radiomic Features for the Predicting Gleason Score of Prostate Cancer

Background: Novel radiomic features are enabling the extraction of biological data from routine sequences of MRI images. This study’s purpose was to establish a new model, based on the joint intensity matrix (JIM), to predict the Gleason score (GS) of prostate cancer (PCa) patients. Methods: A retrospective dataset comprised of the diagnostic imaging data of 99 PCa patients was used, extracted from The Cancer Imaging Archive’s (TCIA) T2-Weighted (T2-WI) and apparent diffusion coefficient (ADC) images. Radiomic features derived from JIM and the grey level co-occurrence matrix (GLCM) were extracted from the reported tumor locations. The Kruskal-Wallis test and Spearman’s rank correlation identified features related to the GS. The Random Forest classifier model was implemented to identify the best performing signature of JIM and GLCM radiomic features to predict for GS. Results: Five JIM-derived features: contrast, homogeneity, difference variance, dissimilarity, and inverse difference were independent predictors of GS (p < 0.05). Combined JIM and GLCM analysis provided the best performing area-under-the-curve, with values of 78.40% for GS ≤ 6, 82.35% for GS = 3 + 4, and 64.76% for GS ≥ 4 + 3. Conclusion: This retrospective study produced a novel predictive model for GS by the incorporation of JIM data from standard diagnostic MRI images.
Asynchronous circuits: innovations in components, cell libraries and design templates

O paradigma sincrono foi, por decadas, a principal escolha da industria para o projeto de circuitos integrados. Infelizmente, com o desenvolvimento da industria de semicondutores, restricoes de projeto relativas a potencia de um circuito e incertezas de atrasos aumentaram, dificultando o projeto sincrono. Alguns dos motivos para isso sao o aumento na variabilidade dos processos de fabricacao de dispositivo, as perdas de desempenho relativas em fios e as incertezas temporais causadas por variabilidades nas condicoes operacionais de dispositivos. Dessa forma, o paradigma assincrono surge como uma alternativa, devido a sua robustez contra variacoes temporais e suporte ao projeto de circuitos de alto desepenho e baixo consumo. Entretanto, grande parte da industria de ferramentas de automacao de projeto eletronico foi desenvolvida visando o projeto de circuitos sincronos e atualmente o suporte a circuitos assincronos e consideravelmente limitado. Esta Tese propoe novas tecnicas de projeto para otimizar circuitos assincronos, desde o nivel de celulas ao nivel de sistema. Comecamos analisando e otimizando componentes basicos para o projeto desses circuitos e depois apresentamos novas solucoes para implementa-los no nivel de transistores. As otimizacoes propostas permitem uma melhor exploracao dos parâmetros desses circuitos, incluindo potencia, atraso e area. Em um segundo momento, exploramos o uso desses componentes como celulas para a geracao de uma biblioteca de suporte ao projeto semi-dedicado de circuitos assincronos. Nesse contexto, propomos um fluxo completamente automatizado para projetar tais bibliotecas. O fluxo compreende ferramentas de dimensionamento de transistores e caracterizacao eletrica, desenvolvidas nesta Tese, e uma ferramenta de projeto de leiaute, desenvolvida por um grupo de pesquisa parceiro. Esse trabalho tambem apresenta uma biblioteca aberta, com centenas de componentes validados extensivamente atraves de simulacoes pos-leiaute. Alem disso, usando essa biblioteca desenvolvemos novos templates para o projeto de circuitos assincronos no nivel de sistema, propondo um fluxo automatico para sintese e mapeamento tecnologico. Comparado a uma solucao assincrona no estado da arte, nosso mais novo template apresenta uma eficiencia energetica quase duas vezes maior. As contribuicoes desta Tese permitiram a construcao de uma infraestrutura para o projeto de circuitos assincronos, abrindo caminho para a exploracao do uso de templates assincronos para solucionar problemas modernos e futuros no projeto de circuitos integrados.
360 ◦ versus 3D Environments in VR Headsets for an Exploration Task

For entertainment, pedagogical or cultural purposes, there is a need for fast and easy setup of virtual environments that represent real ones. The use of 360 ◦ video in Virtual Reality Headsets seems like a powerful tool for producing fun and engaging content in a fast manner. This applies even more when we need to set up realistic views of actual environments. However, in terms of user experience in virtual reality headsets, can 360 ◦ shots of a real environment be an interesting alternative to a full 3D model? In this work, we have conducted a user study during a ﬁlm festival comparing the reaction of a wide public to two versions of a Virtual Reality cultural heritage visit of a Paleolithic cave, the "Grotte de Commarque" located in the south of France. The ﬁrst version is a full 3D textured model of the cave, the second is a series of 360 ◦ pictures, presented in a VR Headset. We have set up a scenario of observation and exploration. The users were able to navigate with the same teleportation metaphor in both conditions. We focused on evaluating the sense of presence during the visit. We have also sought for trends in perceived fun, sickness and easiness of navigation. Our results suggest that the full 3D environment is where the participants feel more present. However, the difference in rating the measures between the two conditions were not strongly marked. Moreover, a relevant result that we retain is that this rating is correlated to the degree of familiarity of the user with virtual reality.
Smooth Interpolation of Curve Networks with Surface Normals

Recent surface acquisition technologies based on microsensors produce three-space tangential curve data which can be transformed into a network of space curves with surface normals. This paper addresses the problem of surfacing an arbitrary closed 3D curve network with given surface normals. Thanks to the normal vector input, the patch finding problem can be solved unambiguously and an initial piecewise smooth triangle mesh is computed. The input normals are propagated throughout the mesh and used to compute mean curvature vectors. We then introduce a new variational optimization method in which the standard bi-Laplacian is penalized by a term based on the mean curvature vectors. The intuition behind this original approach is to guide the standard Laplacian-based variational methods by the curvature information extracted from the input normals. The normal input increases shape fidelity and allows to achieve globally smooth and visually pleasing shapes.
On Perfect Matchings and Tilings in Uniform Hypergraphs

In this paper we study some variants of Dirac-type problems in hypergraphs. First, we show that for $k\ge 3$, if $H$ is a $k$-graph on $n\in k\mathbb N$ vertices with independence number at most $n/p$ and minimum codegree at least $(1/p+o(1))n$, where $p$ is the smallest prime factor of $k$, then $H$ contains a perfect matching. Second, we show that if $H$ is a $3$-graph on $n\in 3\mathbb N$ vertices which does not contain any induced copy of $K_4^-$ (the unique $3$-graph with $4$ vertices and $3$ edges) and has minimum codegree at least $(1/3+o(1)))n$, then $H$ contains a perfect matching. Moreover, if we allow the matching to miss at most $3$ vertices, then the minimum degree condition can be reduced to $(1/6+o(1)))n$. Third, we show that if $H$ is a $3$-graph on $n\in 4\mathbb N$ vertices which does not contain any induced copy of $K_4^-$ and has minimum codegree at least $(1/8+o(1)))n$, then $H$ contains a perfect $Y$-tiling, where $Y$ represents the unique $3$-graph with $4$ vertices and $2$ edges. We also provide the examples showing that our minimum codegree conditions are asymptotically best possible. Our main tool for finding the perfect matching is a characterization theorem that characterizes the $k$-graphs with minimum codegree at least $n/k$ which contain a perfect matching.
Exploration of Neural Activity under Cognitive Reappraisal Using Simultaneous EEG-fMRI Data and Kernel Canonical Correlation Analysis

Background Neural activity under cognitive reappraisal can be more accurately investigated using simultaneous EEG- (electroencephalography) fMRI (functional magnetic resonance imaging) than using EEG or fMRI only. Complementary spatiotemporal information can be found from simultaneous EEG-fMRI data to study brain function. Method An effective EEG-fMRI fusion framework is proposed in this work. EEG-fMRI data is simultaneously sampled on fifteen visually stimulated healthy adult participants. Net-station toolbox and empirical mode decomposition are employed for EEG denoising. Sparse spectral clustering is used to construct fMRI masks that are used to constrain fMRI activated regions. A kernel-based canonical correlation analysis is utilized to fuse nonlinear EEG-fMRI data. Results The experimental results show a distinct late positive potential (LPP, latency 200-700ms) from the correlated EEG components that are reconstructed from nonlinear EEG-fMRI data. Peak value of LPP under reappraisal state is smaller than that under negative state, however, larger than that under neutral state. For correlated fMRI components, obvious activation can be observed in cerebral regions, e.g., the amygdala, temporal lobe, cingulate gyrus, hippocampus, and frontal lobe. Meanwhile, in these regions, activated intensity under reappraisal state is obviously smaller than that under negative state and larger than that under neutral state. Conclusions The proposed EEG-fMRI fusion approach provides an effective way to study the neural activities of cognitive reappraisal with high spatiotemporal resolution. It is also suitable for other neuroimaging technologies using simultaneous EEG-fMRI data.
Discovering Implicit Knowledge with Unary Relations

State-of-the-art relation extraction approaches are only able to recognize relationships between mentions of entity arguments stated explicitly in the text and typically localized to the same sentence. However, the vast majority of relations are either implicit or not sententially localized. This is a major problem for Knowledge Base Population, severely limiting recall. In this paper we propose a new methodology to identify relations between two entities, consisting of detecting a very large number of unary relations, and using them to infer missing entities. We describe a deep learning architecture able to learn thousands of such relations very efficiently by using a common deep learning based representation. Our approach largely outperforms state of the art relation extraction technology on a newly introduced web scale knowledge base population benchmark, that we release to the research community.
Introspective Reinforcement Learning and Learning from Demonstration

Reinforcement learning is a paradigm to model how an autonomous agent learns to maximise its cumulative reward by interacting with the environment. One challenge faced by reinforcement learning is that in many environments the reward signal is sparse, leading to slow improvement of the agent's performance in early learning episodes. Potential-based reward shaping is a technique to resolve the aforementioned issue of sparse reward by incorporating an expert's domain knowledge in the learning via a potential function. Past work on reinforcement learning from demonstration directly mapped (sub-optimal) human expert demonstration to a potential function, which can speed up reinforcement learning. In this paper we propose an introspective reinforcement learning agent that significantly speeds up the learning further. An introspective Reinforcement learning agent records its state-action decisions and experience during learning in a priority queue. Good quality decisions will be kept in the queue, while poorer decisions will be rejected. The queue is then used as demonstration to speed up reinforcement learning via reward shaping. A human expert's demonstration can be used to initialise the priority queue before the learning process starts. Experimental validations in the 4-dimensional CartPole domain and the 27-dimensional Super Mario AI domain show that our approach significantly outperforms state-of-the-art approaches to reinforcement learning from demonstration in both domains.