Miracle-dz/UniFAR-FTUs huggingface dataset:
Multimodal Intelligence: Representation Learning, Information Fusion, and Applications

Deep learning methods haverevolutionized speech recognition, image recognition, and natural language processing since 2010. Each of these tasks involves a single modality in their input signals. However, many applications in the artificial intelligence field involve multiple modalities. Therefore, it is of broad interest to study the more difficult and complex problem of modeling and learning across multiple modalities. In this paper, we provide a technical review of available models and learning methods for multimodal intelligence. The main focus of this review is the combination of vision and natural language modalities, which has become an important topic in both the computer vision and natural language processing research communities. This review provides a comprehensive analysis of recent works on multimodal deep learning from three perspectives: learning multimodal representations, fusing multimodal signals at various levels, and multimodal applications. Regarding multimodal representation learning, we review the key concepts of embedding, which unify multimodal signals into a single vector space and thereby enable cross-modality signal processing. We also review the properties of many types of embeddings that are constructed and learned for general downstream tasks. Regarding multimodal fusion, this review focuses on special architectures for the integration of representations of unimodal signals for a particular task. Regarding applications, selected areas of a broad interest in the current literature are covered, including image-to-text caption generation, text-to-image generation, and visual question answering. We believe that this review will facilitate future studies in the emerging field of multimodal intelligence for related communities.
Word Sense Disambiguation using Knowledge-based Word Similarity

In natural language processing, word-sense disambiguation (WSD) is an open problem concerned with identifying the correct sense of words in a particular context. To address this problem, we introduce a novel knowledge-based WSD system. We suggest the adoption of two methods in our system. First, we suggest a novel method to encode the word vector representation by considering the graphical semantic relationships from the lexical knowledge-base. Second, we propose a method for extracting the contextual words from the text for analyzing an ambiguous word based on the similarity of word vector representations. To validate the effectiveness of our WSD system, we conducted experiments on the five benchmark English WSD corpora (Senseval-02, Senseval-03, SemEval-07, SemEval-13, and SemEval-15). The obtained results demonstrated that the suggested methods significantly enhanced the WSD performance. Furthermore, our system outperformed the existing knowledge-based WSD systems and showed a performance comparable to that of the state-of-the-art supervised WSD systems.
Inbreeding Calculated with Runs of Homozygosity Suggests Chromosome-Specific Inbreeding Depression Regions in Line 1 Hereford

Simple Summary Inbreeding depression is the decreased fitness of offspring of closely related individuals. It is a common problem in the livestock industry as a consequence of intense selection favoring few animals with higher genetic merit. The literature shows that inbred individuals may also exhibit decreased performance for economically relevant traits, such as growth in beef cattle. However, this relationship is not simple. The use of genetic markers can improve the detection of homozygous regions and improve the calculation of inbreeding coefficients. The working hypothesis of this study is that genomic homozygous regions can be used to predict animals’ performance. Moreover, homozygous regions across the genome differ in their effects. Our results indicate that the relationship between homozygosity and decreased performance varies by chromosome. Additionally, the inbreeding coefficient can be used to predict animals’ phenotype when calculated within a chromosome. This research warrants the development of a new approach in genomic selection to increase prediction accuracy by including information on homozygous regions. Additionally, management tools can be developed with the goal of breeding individuals with a purpose to decrease homozygosity only in the relevant regions of the genome. Abstract The goal of this study was to evaluate inbreeding in a closed beef cattle population and assess phenotype prediction accuracy using inbreeding information. Effects of inbreeding on average daily gain phenotype in the Line 1 Hereford cattle population were assessed in this study. Genomic data were used to calculate inbreeding based on runs of homozygosity (ROH), and pedigree information was used to calculate the probability of an allele being identical by descent. Prediction ability of phenotypes using inbreeding coefficients calculated based on pedigree information and runs of homozygosity over the whole genome was close to 0, even in the case of significant inbreeding coefficient effects. On the other hand, inbreeding calculated per individual chromosomes’ ROH yielded higher accuracies of prediction. Additionally, including only ROH from chromosomes with higher predicting ability further increased prediction accuracy. Phenotype prediction accuracy, inbreeding depression, and the effects of chromosome-specific ROHs varied widely across the genome. The results of this study suggest that inbreeding should be evaluated per individual regions of the genome. Moreover, mating schemes to avoid inbreeding depression should focus more on specific ROH with negative effects. Finally, using ROH as added information may increase prediction of the genetic merit of animals in a genomic selection program.
Venture Capital Booms and Startup Financing

We review the growing literature on the relationship between venture capital (VC) booms and start-up financing, focusing on three broad areas. First, we discuss the drivers of large inflows into the VC asset class, particularly in recent years, which are related to but also distinct from macroeconomic business cycles and stock market fluctuations. Second, we review the emerging literature on the real effects of VC financing booms. A particular focus of this work is to highlight the potential impact that booms (and busts) can have on the types of firms that VC investors choose to fund and the terms at which they are funded, independent of investment opportunities—thereby shaping the trajectory of innovation being conducted by start-ups. Third, an important insight from recent research is that booms in VC financing are not just a temporal phenomenon but can also be seen in terms of the concentration of VC investment in certain industries and geographies. We also review the role of government policy, exploring the degree to which it can explain the concentration of VC funding in the United States over the past 40 years in just two broad areas—information and communication technologies and biotechnology. We conclude by highlighting promising areas of further research.
Análisis de contenidos: el cáncer de piel en los principales medios de prensa españoles = Content analysis: skin cancer in Spanish media

Resumen: Introducción: la prensa digital tiene una gran responsabilidad en la información que ofrece sobre el cáncer de piel, dado que el 60% de los españoles asegura consultar sobre salud en internet. Obje­tivos: conocer el espacio que la prensa le dedica al cáncer de piel, comparar diferentes medios y determinar si la publicidad de protectores solares afecta a la información. Métodos: análisis cuanti­tativo y cualitativo de las publicaciones sobre el cáncer de piel en las versiones digitales de El País, ABC y El Mundo durante seis meses. Resultados: ABC es el periódico con más publicaciones. Los artículos más extensos pertenecen a El País. El Mundo usa con más frecuencia a investigadores como fuente principal y ABC es el medio con más tendencia hacia la promoción de la salud. Discusión: los medios, además de informar sobre salud, deben promocionarla. La publicidad asociada a los productos de protección solar puede ser perjudicial, pues la información que ofrece genera desconfianza. Conclusiones: El País, ABC y El Mundo le dedican un espacio aceptable al cáncer de piel, ofreciendo cada uno de ellos información variada, aumentando el conocimiento del público sobre el tema desde diversas fuentes.Palabras clave: Cáncer de piel; melanoma; protector solar; salud; internet; prensa; España. Abstract: Introduction: the digital edition press has a great responsability about information that offers of skin cancer because 60% of Spanish people declare consulting on the internet questions about health. Objectives: to know the space that the press dedicates to skin cancer, compare different media and determine if the advertising of sunscreens affects the information. Methods: quantitative and qualitative analysis of publications about skin cancer in the digital editions of El País, ABC and El Mundo for six months. Results: ABC is the newspaper with the most publications. The longest articles are from El País. Researchers are the main source inside El Mundo and ABC is the most worried about health promotion. Discussion: The media, in addition to reporting on health, should promote it. The publicity associated with sunscreen products can be harmful, because the information it offers generates distrust. Conclusions: El País, ABC and El Mundo dedicate an admissible space to skin cancer and offer mixed information. It increases knowledge about skin cancer from diversed sources.Keywords: Skin cancer; melanoma; sunscreen; health; internet; press; Spain.
Adaptivity in Adaptive Submodularity

Adaptive sequential decision making is one of the central challenges in machine learning and artificial intelligence. In such problems, the goal is to design an interactive policy that plans for an action to take, from a finite set of $n$ actions, given some partial observations. It has been shown that in many applications such as active learning, robotics, sequential experimental design, and active detection, the utility function satisfies adaptive submodularity, a notion that generalizes the notion of diminishing returns to policies. In this paper, we revisit the power of adaptivity in maximizing an adaptive monotone submodular function. We propose an efficient batch policy that with $O(\log n \times\log k)$ adaptive rounds of observations can achieve an almost tight $(1-1/e-\epsilon)$ approximation guarantee with respect to an optimal policy that carries out $k$ actions in a fully sequential setting. To complement our results, we also show that it is impossible to achieve a constant factor approximation with $o(\log n)$ adaptive rounds. We also extend our result to the case of adaptive stochastic minimum cost coverage where the goal is to reach a desired utility $Q$ with the cheapest policy. We first prove the conjecture by Golovin and Krause that the greedy policy achieves the asymptotically tight logarithmic approximation guarantee without resorting to stronger notions of adaptivity. We then propose a batch policy that provides the same guarantee in polylogarithmic adaptive rounds through a similar information-parallelism scheme. Our results shrink the adaptivity gap in adaptive submodular maximization by an exponential factor.
Incentive-aware Contextual Pricing with Non-parametric Market Noise

We consider a dynamic pricing problem for repeated contextual second-price auctions with strategic buyers whose goals are to maximize their long-term time discounted utility. The seller has very limited information about buyers' overall demand curves, which depends on $d$-dimensional context vectors characterizing auctioned items, and a non-parametric market noise distribution that captures buyers' idiosyncratic tastes. The noise distribution and the relationship between the context vectors and buyers' demand curves are both unknown to the seller. We focus on designing the seller's learning policy to set contextual reserve prices where the seller's goal is to minimize his regret for revenue. We first propose a pricing policy when buyers are truthful and show that it achieves a $T$-period regret bound of $\tilde{\mathcal{O}}(\sqrt{dT})$ against a clairvoyant policy that has full information of the buyers' demand. Next, under the setting where buyers bid strategically to maximize their long-term discounted utility, we develop a variant of our first policy that is robust to strategic (corrupted) bids. This policy incorporates randomized "isolation" periods, during which a buyer is randomly chosen to solely participate in the auction. We show that this design allows the seller to control the number of periods in which buyers significantly corrupt their bids. Because of this nice property, our robust policy enjoys a $T$-period regret of $\tilde{\mathcal{O}}(\sqrt{dT})$, matching that under the truthful setting up to a constant factor that depends on the utility discount factor.
Study and Design of a Horizontal Axis Wind Turbine (0.5 kW) Using Software Solid Works

In the present work, the study and design of a horizontal axis wind turbine suitable for the Cotonou site were investigated on the coast of Benin. A statistical study using the Weibull distribution was carried out on the hourly wind data measured at 10 m from the ground by the Agency for Air Navigation Safety in Africa and Madagascar (ASECNA) over the period from January 1981 to December 2014. Then, the models, techniques, tools and approaches used to design horizontal axis wind turbines were presented and the wind turbine components characteristics were determined. The numerical design and assembly of these components were carried out using SolidWorks software. The results revealed that the designed wind turbine has a power of 571W. It is equipped with a permanent magnet synchronous generator and has three aluminum blades with NACA 4412 biconvex asymmetrical profile. The values obtained for the optimum coefficient of lift and drag are estimated at 1.196 and 0.0189 respectively. The blades are characterised by an attack optimum angle estimated at 6° and the wedge angle at 5°. Their length is 2.50 m and the maximum thickness is estimated at 0.032 m for a rope length of 0.27 m. The wind turbine efficiency is 44%. The computer program designed on SolidWorks gives three-dimensional views of the geometrical shape of the wind turbine components and their assembly has allowed to visualize the compact shape of the wind turbine after export via its graphical interface. The energy quantity that can be obtained from the wind turbine was estimated at 2712,718 kWh/year. This wind turbine design study is the first of its kind for the study area. In order to reduce the technological dependence and the import of wind energy systems, the results of this study could be used to produce lower cost wind energy available on our study site.
Safe Deep Reinforcement Learning for Adaptive Cruise Control by Imposing State-Specific Safe Sets

Deep reinforcement learning has been increasingly discussed for solving continuous control tasks in the field of autonomous driving and driver assistance systems. However, trial-and-error learning and the black-box character of neural networks make it prone to accidental damage in safety-critical environments. We propose to learn a safe vehicle following controller with deep reinforcement learning by imposing state-specific safe sets as output constraints on the policy and call the approach ACC 4S. The main safety goal is the avoidance of rear-end collisions with the front vehicle. To achieve this, we build on the Responsibility-Sensitive Safety model and derive an upper bound for the demanded acceleration. Further limitations emerge from regulatory standards and system limits. We end up with state-specific intervals of safe actions, the safe sets. To impose these safe sets as hard output constraints on the policy, we leverage the recently proposed neural network architecture ConstraintNet. We compare ConstraintNet with an unconstrained neural network, additional clipping as postprocessing, and clipping as part of the neural network. The results show, that the proposed safe sets ensure collision avoidance and ConstraintNet shows superior performance compared to the other approaches.
Un systeme de lemmatisation pour les applications de TALN

This paper presents a method of stemming for the Arabian texts based on the linguistic techniques of the natural language processing. This method leans on the notion of scheme (one of the strong points of the morphology of the Arabian language). The advantage of this approach is that it doesn't use a dictionary of inflexions but a smart dynamic recognition of the different words of the language.
Comparative Transcriptomic Analysis Identifies Key Cellulose Synthase Genes (CESA) and Cellulose Synthase-like Genes (CSL) in Fast Growth Period of Flax Stem (Linum Usitatissimum L.)

ABSTRACT The cellulose synthase gene superfamily is vital for cell wall biogenesis during plant growth, particularly for flax fiber development. This study performed asequencing search of key CESA and CSL genes from several flax stem parts at different fiber development stages by comparing RNA-Seq. Quantitative RT-PCR was used to validate the expression of these genes. This study revealed that CESA4 genes (Lus10008225.g and Lus10008226.g), CESA6 genes (Lus10006161.g and Lus10041063.g), CESA8 genes (Lus10007296.g and Lus10029245.g), CSLD4 gene (Lus10026568.g), CSLE1 (Lus10016625.g) and CSLG genes (Lus10023056.g and Lus10023057.g) were specifically expressed in stem below the snap point where fibers is increased amounts of secondary cell wall deposition. LusCESA4 genes, LusCESA8, genes and LusCSLD4 gene were specifically expressed in fiber development stage during the fast growth period of flax plants. Based on GO and KEGG analyses, it was found that genes involved in pathways of cellulose microfibril organization, galactosyl transferase activity and galactose metabolism were specifically enriched in the stem tissue of the fiber development stage. Other genes involved in cellulose biosynthesis and cell wall development were also analyzed and discussed. The results will provide an important foundation for understanding fiber cell wall biogenesis, particularly the roles of LusCESAs and LusCSLs in the process of fiber development.
Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy

We analyze the trade-off between model complexity and accuracy for random forests by breaking the trees up into individual classification rules and selecting a subset of them. We show experimentally that already a few rules are sufficient to achieve an acceptable accuracy close to that of the original model. Moreover, our results indicate that in many cases, this can lead to simpler models that clearly outperform the original ones.
Deep learning-based surrogate model for fast multi-material topology optimization of IPM motor

Purpose This paper aims to present a deep learning–based surrogate model for fast multi-material topology optimization of an interior permanent magnet (IPM) motor. The multi-material topology optimization based on genetic algorithm needs large computational burden because of execution of finite element (FE) analysis for many times. To overcome this difficulty, a convolutional neural network (CNN) is adopted to predict the motor performance from the cross-sectional motor image and reduce the number of FE analysis. Design/methodology/approach To predict the average torque of an IPM motor, CNN is used as a surrogate model. From the input cross-sectional motor image, CNN infers dq-inductance and magnet flux to compute the average torque. It is shown that the average torque for any current phase angle can be predicted by this approach, which allows the maximization of the average torque by changing the current phase angle. The individuals in the multi-material topology optimization are evaluated by the trained CNN, and the limited individuals with higher potentials are evaluated by finite element method. Findings It is shown that the proposed method doubles the computing speed of the multi-material topology optimization without loss of search ability. In addition, the optimized motor obtained by the proposed method followed by simplification for manufacturing is shown to have higher average torque than a reference model. Originality/value This paper proposes a novel method based on deep learning for fast multi-material topology optimization considering the current phase angle.
LATS1 exerts tumor suppressor functions via targeting Gli1 in colorectal cancer

Background: The Hippo pathway's primary kinase component, large tumor suppressor 1 (LATS1), has been hypothesized as a tumor suppressor in a variety of cancers. LATS1's biological effects on colorectal cancer (CRC) are yet to be determined. Methods: The analysis of LATS1 mRNA expression in CRC was conducted using public databases from the Gene Expressing Profiling Interactive Analysis database (GEPIA). Investigation for the expression of LATS1 protein in 102 CRC tumor tissues and 57 normal tissues was performed using immunohistochemistry (IHC) analysis. In vitro genetic manipulation was used to explore the potential role and mechanism of LATS1 in the regulation of proliferation and migration of CRC cells. Results: LATS1 was found to be considerably downregulated in CRC tissues, with much lower levels in individuals with bigger tumors of size (≥5 cm), deeper invasion (T3-4), positive lymph node metastasis (LNM), and advanced tumor-node-metastasis (TNM) stage (III-IV). As exhibited by clinical data analysis, LATS1 loss was significantly associated with TNM and LNM staging in CRC patients. Furthermore, our in vitro investigations revealed that LATS1 depletion increased CRC cell proliferation and migration in HCT116 cells, whereas overexpressing LATS1 had the opposite effect in SW620 cells. LATS1 suppressed the expression of glioma-associated oncogene-1 (Gli1), and LATS1's tumor-suppressive actions in CRC are dependent on Gli1. Moreover, LATS1 could modulate Yes-associated protein 1 (YAP1) expression and mTOR activation in CRC cells. Conclusion: Our findings identify the LATS1 as a unique Gli1 regulator in CRC cell migration and proliferation, and suggest that LATS1 may serve as a potential therapeutic target for CRC.
Study on Graphene-modified FRP Bars Used in the Process of Construction Health Management

The distributed long-gauge optical sensor on fiber reinforced polymer (FRP) bar cannot be manufactured through integrated production in current situation, which is hard for construction management. On the other hand, the point-sensing technology of the self-sensing bar will cause deviations in structural health monitoring (SHM). To solve these issues, applying the graphene/epoxy on FRP members is a feasible method for the piezoresistive characteristics of graphene. In this paper, basalt FRP (BFRP) bars with graphene/epoxy film were tested under repeated and cyclic loads and the resistance was measured at the same time until they were broken down. The results suggested that the test pieces can maintain stable sensing performance under repeated and cyclic loads. These facts indicated that the graphene-modified BFRP bars can well reflect the stress condition of the structural member within a safe range and can maintain stable self-sensing performance in the construction health management.