Miracle-dz/UniFAR-FTUs huggingface dataset:
WebPlan: Dynamic Planning for Domain-Speci(cid:12)c Search in the Internet

Searching in the Internet is a dynamic, iterative process which has to be planned. Current available search engines are not able to do this satisfactory. On the other side classic action planning systems are not applicable to this problem directly. In this paper we present the goal of the WebPlan project which is dynamic planning for domain-speci(cid:12)c search in the Internet. For this we extend the planning system CAPlan to build domain-speci(cid:12)c search agents.
Anionic lipopeptides from Bacillus mojavensis I4 as effective antihypertensive agents: Production, characterization, and identification

A new isolated Bacillus mojavensis strain I4 was found as producer of biosurfactants by different screening methods, such as parafilm M test, hemolytic activity, oil displacement test, emulsification index, surface tension, and lipase production assay. Enhanced biosurfactants production was obtained using glucose and glutamic acid as carbon and nitrogen sources, respectively. The optimal production of the biosurfactants was obtained by using a C/N ratio of 17, pH of 7.0, and temperature of 37°C. The surface tension was reduced to 29 mN/m and the emulsification index E24 of 62% was achieved after 72 h of culture. The purified biosurfactants showed stability with regard to surface tension reduction and emulsification in a wide range of temperatures (4–120°C), pH (4–10), and salinity (2–12% of NaCl). The thin‐layer chromatography showed that the produced biosurfactants were lipopeptides. The biosurfactants were characterized as a group of anionic lipopeptides with zeta potential measurement. Chromatographic characterization using HPLC revealed that I4 lipopeptides contained numerous isoforms and surfactin was the major component. Moreover, the I4 lipopeptides showed interesting angiotensin‐converting enzyme‐inhibitory activity.
Design and Evaluation of a Hair Combing System Using a General-Purpose Robotic Arm

This work introduces an approach for automatic hair combing by a lightweight robot. For people living with limited mobility, dexterity, or chronic fatigue, combing hair is often a difficult task that negatively impacts personal routines. We propose a modular system for enabling general robot manipulators to assist with a hair-combing task. The system consists of three main components. The first component is the segmentation module, which segments the location of hair in space. The second component is the path planning module that proposes automatically-generated paths through hair based on user input. The final component creates a trajectory for the robot to execute. We quantitatively evaluate the effectiveness of the paths planned by the system with 48 users and qualitatively evaluate the system with 30 users watching videos of the robot performing a hair-combing task in the physical world. The system is shown to effectively comb different hairstyles.
Antidepressant and Anxiolytic Potentials of Chebulinic Acid in Laboratory Rodent

Chebulinic acid, an ellagitannin, present in the fruits of Terminalia chebula has shown various positive neurological properties. However, there has not been a report of its antidepressant and anxiolytic potentials. This study was conducted to investigate the antidepressant and anxiolytic potentials of chebulinic acid in laboratory rodents. Chebulinic acid was evaluated for its antidepressant (Forced swimming test and Tail suspension test) and anxiolytic (Light-dark box test, Hole-board test and Elevated plus maze) potentials in mice. Our findings showed antidepressant-like activity which is dose consistent at p<0.01 (in forced swimming test) and p<0.05 (in tail suspension test). However, only doses 20mg/kg and 40mg/kg showed significant anxiolytic potentials in elevated plus maze, light-dark box test and hole-board paradigms when compared with the control (P<0.05). The results suggest antidepressant and anxiolytic potentials of chebulinic acid. Chebulinic acid may possess antidepressant and anxiolytic potentials which may correspond to its folkloric use in the treatment of neuro-physiological disorders. gout, e.t. c. Terminalia chebula is a deciduous tree growing up to 30-metre (98 ft) tall, with a trunk up to 1-metre (3 ft 3 in) in diameter. The fruits are drupe-like, long, broad and blackish, with five longitudinal ridges and are hard and yellowish-green in colour. Chebulinic acid is an ellagitannin found in the fruits of Terminalia chebula . It has the molecular formular of C₄₁H₃₂O₂₇ and molecular weight of 956.67658 It is the of Terminalia chebula by high-speed counter-current chromatography and of
ANTIHYPERGLYCEMIC, HYPOLIPIDEMIC AND ANTIOXIDANT PROPERTIES OF THE HERBAL MIXTURES IN DEXAMETHASONE-INDUCED INSULIN RESISTANT RATS

The herbal mixtures due to the wide range of biologically active substances can influence on various links of the pathogenetic mechanism of development of diabetes mellitus and its complications. The present study investigated the antihyperglycemic, hypolipidemic and antioxidant properties of the herbal mixtures in dexamethasone-induced insulin resistant rats. For research it was used the male albino rats of the Wistar strain. The pathology was modeled by intramuscular introduction of dexamethasone (1 mg/kg/day) to rats during 15 days. The aqueous extracts of the herbal mixtures (12 mL/kg/day) and standard drug – metformin (60 mg/kg/day) were orally administered during 15 days. The results of the study showed that the using of aqueous extracts of herbal mixtures No. 3, No. 4, No. 7, No. 13 and No. 19 has the ability to reduce the hyperglycemia (p<0.05) and to normalize the impaired glucose tolerance in dexamethasone-induced insulin resistant rats, relative to the untreated group with pathology. In addition, these extracts are improved the lipid profile by reducing the concentration of total plasma cholesterol, serum triglyceride, low density lipoproteins cholesterol and increasing the concentration of high density lipoproteins cholesterol (p<0.05), relative to dexamethasone group. Also, the studied herbal mixtures showed pronounced antioxidant activity by reducing the malonic dialdehyde content, increasing the content of the reduced form of glutathione in the blood and liver, increasing the activity of superoxide dismutase in the blood and liver and increasing the activity of catalase in the blood and reducing its activity in the liver (p<0.05) with respect to dexamethasone group.
CSMCNet: Scalable Video Compressive Sensing Reconstruction with Interpretable Motion Estimation

Most deep network methods for compressive sensing reconstruction suffer from the black-box characteristic of DNN. In this paper, a deep neural network with interpretable motion estimation named CSMCNet is proposed. The network is able to realize high-quality reconstruction of video compressive sensing by unfolding the iterative steps of optimization based algorithms. A DNN based, multi-hypothesis motion estimation module is designed to improve the reconstruction quality, and a residual module is employed to further narrow down the gap between re-construction results and original signal in our proposed method. Besides, we propose an interpolation module with corresponding training strategy to realize scalable CS reconstruction, which is capable of using the same model to decode various compression ratios. Experiments show that a PSNR of 29.34dB can be achieved at 2% CS ratio (compressed by 98%), which is superior than other state-of-the-art methods. Moreover, the interpolation module is proved to be effective, with significant cost saving and acceptable performance losses.
HyperColor: A HyperNetwork Approach for Synthesizing Autocolored 3-D Models for Game Scenes Population

Designing a 3-D game scene is a tedious task that often requires a substantial amount of work. Typically, this task involves the synthesis and coloring of 3-D models within the scene. To lessen this workload, we can apply machine learning to automate some aspects of the game scene development. Earlier research has already tackled automated generation of the game scene background with machine learning. However, model autocoloring remains an underexplored problem. The automatic coloring of a 3-D model is a challenging task, especially when dealing with the digital representation of a colorful, multipart object. In such a case, we have to “understand” the object's composition and coloring scheme of each part. Moreover, existing single-stage methods have their caveats. We address these limitations by proposing a two-stage training approach to synthesize autocolored 3-D models. In the first stage, we obtain a 3-D point cloud representing a 3-D object, while in the second stage, we assign colors to points within such a cloud. Next, we generate a 3-D mesh in which the surfaces are colored based on the interpolation of colored points representing vertices of a given mesh triangle. This approach allows us to develop a smooth coloring scheme.
Spontaneous-emission-enabled dynamics at the laser threshold

Junlong Zou, Hanxu Zhou, Can Jiang, Gaofeng Wang, Gian Luca Lippi, and Tao Wang Engineering Research Center of Smart Microsensors and Microsystems of MOE, Hangzhou Dianzi University, Hangzhou, 310018, China School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018, China School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China and 4 Université Côte d’Azur, Institut de Physique de Nice (INPHYNI), UMR 7010 CNRS, 1361 Route des Lucioles, F-06560 Valbonne, France (Dated: August 4, 2021)
SPG-VTON: Semantic Prediction Guidance for Multi-Pose Virtual Try-on

Image-based virtual try-on is challenging in fitting a target in-shop clothes onto a reference person under diverse human poses. Previous works focus on preserving clothing details (e.g., texture, logos, patterns) when transferring desired clothes onto a target person under a fixed pose. However, the performances of existing methods significantly dropped when extending existing methods to multi-pose virtual try-on. In this paper, we propose an end-to-end Semantic Prediction Guidance multi-pose Virtual Try-On Network (SPG-VTON), which can fit the desired clothing into a reference person under arbitrary poses. Specifically, SPG-VTON is composed of three sub-modules. First, a Semantic Prediction Module (SPM) generates the desired semantic map. The predicted semantic map provides more abundant guidance to locate the desired clothing region and produce a coarse try-on image. Second, a Clothes Warping Module (CWM) warps in-shop clothes to the desired shape according to the predicted semantic map and the desired pose. Specifically, we introduce a conductible cycle consistency loss to alleviate the misalignment in the clothing warping process. Third, a Try-on Synthesis Module (TSM) combines the coarse result and the warped clothes to generate the final virtual try-on image, preserving details of the desired clothes and under the desired pose. In addition, we introduce a face identity loss to refine the facial appearance and maintain the identity of the final virtual try-on result at the same time. We evaluate the proposed method on the most massive multi-pose dataset (MPV) and the DeepFashion dataset. The qualitative and quantitative experiments show that SPG-VTON is superior to the state-of-the-art methods and is robust to data noise, including background and accessory changes, i.e., hats and handbags, showing good scalability to the real-world scenario.
PyEuroVoc: A Tool for Multilingual Legal Document Classification with EuroVoc Descriptors

EuroVoc is a multilingual thesaurus that was built for organizing the legislative documentary of the European Union institutions. It contains thousands of categories at different levels of specificity and its descriptors are targeted by legal texts in almost thirty languages. In this work we propose a unified framework for EuroVoc classification on 22 languages by fine-tuning modern Transformer-based pretrained language models. We study extensively the performance of our trained models and show that they significantly improve the results obtained by a similar tool - JEX - on the same dataset. The code and the fine-tuned models were open sourced, together with a programmatic interface that eases the process of loading the weights of a trained model and of classifying a new document.
Inscriptis - A Python-based HTML to text conversion library optimized for knowledge extraction from the Web

Inscriptis provides a library, command line client and Web service for converting HTML to plain text. Its development has been triggered by the need to obtain accurate text representations for knowledge extraction tasks that preserve the spatial alignment of text without drawing upon heavyweight, browser-based solutions such as Selenium. In contrast to related software packages, Inscriptis (i) provides a layout-aware conversion of HTML that more closely resembles the rendering obtained from standard Web browsers; and (ii) supports annotation rules, i.e., user-provided mappings that allow for annotating the extracted text based on structural and semantic information encoded in HTML tags and attributes. These unique features ensure that downstream knowledge extraction components can operate on accurate text representations, and may even use information on the semantics and structure of the original HTML document.
Characterizing the Fast Radio Burst Host Galaxy Population and its Connection to Transients in the Local and Extragalactic Universe

We present the localization and host galaxies of one repeating and two apparently nonrepeating fast radio bursts (FRBs). FRB 20180301A was detected and localized with the Karl G. Jansky Very Large Array to a star-forming galaxy at z = 0.3304. FRB20191228A and FRB20200906A were detected and localized by the Australian Square Kilometre Array Pathfinder to host galaxies at z = 0.2430 and z = 0.3688, respectively. We combine these with 13 other well-localized FRBs in the literature, and analyze the host galaxy properties. We find no significant differences in the host properties of repeating and apparently nonrepeating FRBs. FRB hosts are moderately star forming, with masses slightly offset from the star-forming main sequence. Star formation and low-ionization nuclear emission-line region emission are major sources of ionization in FRB host galaxies, with the former dominant in repeating FRB hosts. FRB hosts do not track stellar mass and star formation as seen in field galaxies (more than 95% confidence). FRBs are rare in massive red galaxies, suggesting that progenitor formation channels are not solely dominated by delayed channels which lag star formation by gigayears. The global properties of FRB hosts are indistinguishable from core-collapse supernovae and short gamma-ray bursts hosts, and the spatial offset (from galaxy centers) of FRBs is mostly inconsistent with that of the Galactic neutron star population (95% confidence). The spatial offsets of FRBs (normalized to the galaxy effective radius) also differ from those of globular clusters in late- and early-type galaxies with 95% confidence.
Comparison of Planetary Hα-emission Models: A New Correlation with Accretion Luminosity

Accreting planets have been detected through their hydrogen-line emission, specifically Hα. To interpret this, stellar-regime empirical correlations between the Hα luminosity L Hα and the accretion luminosity L acc or accretion rate Ṁ have been extrapolated to planetary masses, however without validation. We present a theoretical L acc–L Hα relationship applicable to a shock at the surface of a planet. We consider wide ranges of accretion rates and masses and use detailed spectrally resolved, nonequilibrium models of the postshock cooling. The new relationship gives a markedly higher L acc for a given L Hα than fits to young stellar objects, because Lyα, which is not observable, carries a large fraction of L acc. Specifically, an L Hα measurement needs 10 to 100 times higher L acc and Ṁ than previously predicted, which may explain the rarity of planetary Hα detections. We also compare the Ṁ –L Hα relationships coming from the planet-surface shock or implied by accretion-funnel emission. Both can contribute simultaneously to an observed Hα signal, but at low (high) Ṁ the planetary-surface shock (heated funnel) dominates. Only the shock produces Gaussian line wings. Finally, we discuss accretion contexts in which different emission scenarios may apply, putting recent literature models in perspective, and also present L acc–L line relationships for several other hydrogen lines.
Adaptively Optimize Content Recommendation Using Multi Armed Bandit Algorithms in E-commerce

E-commerce sites strive to provide users the most timely relevant information in order to reduce shopping frictions and increase customer satisfaction. Multi armed bandit models (MAB) as a type of adaptive optimization algorithms provide possible approaches for such purposes. In this paper, we analyze using three classic MAB algorithms, epsilon-greedy, Thompson sampling (TS), and upper confidence bound 1 (UCB1) for dynamic content recommendations, and walk through the process of developing these algorithms internally to solve a real world e-commerce use case. First, we analyze the three MAB algorithms using simulated purchasing datasets with non-stationary reward distributions to simulate the possible time-varying customer preferences, where the traffic allocation dynamics and the accumulative rewards of different algorithms are studied. Second, we compare the accumulative rewards of the three MAB algorithms with more than 1,000 trials using actual historical A/B test datasets. We find that the larger difference between the success rates of competing recommendations the more accumulative rewards the MAB algorithms can achieve. In addition, we find that TS shows the highest average accumulative rewards under different testing scenarios. Third, we develop a batch-updated MAB algorithm to overcome the delayed reward issue in e-commerce and enable an online content optimization on our App homepage. For a state-of-the-art comparison, a real A/B test among our batch-updated MAB algorithm, a third-party MAB solution, and the default business logic are conducted. The result shows that our batch-updated MAB algorithm outperforms the counterparts and achieves 6.13% relative click-through rate (CTR) increase and 16.1% relative conversion rate (CVR) increase compared to the default experience, and 2.9% relative CTR increase and 1.4% relative CVR increase compared to the external MAB service.
An Impossibility Result on Strong Linearizability in Message-Passing Systems

We prove that there is no 1-resilient strongly linearizable implementation of a weak object in asynchronous message-passing systems. This object, that we call Test-or -Set (ToS), allows a single distinguished process to apply the set operation once, and a diﬀerent distinguished process to apply the test operation also once. Since this weak object can be directly implemented by a single-writer single-reader (SWSR) register (and other common objects such as max-register, snapshot, counter, test-and-set, queue, stack, etc.), this result implies that there is no 1-resilient strongly linearizable implementation of a SWSR register (and of these other objects) in message-passing systems.
A dynamic real time scheduling algorithm for optimum garbage collection in a city using IoT smart bins.

INTRODUCTION According to the 2018 Revision of World Urbanization Prospects produced by the Population Division of the UN Department of Economic and Social Affairs (UN DESA) almost 55% of the world’s population lives in urban areas and the prediction is expected to be 68% by 2050. One of the crucial matters that the modern cities have to face is the urban garbage. It is common to see overfull garbage bins on the streets and especially on summer months the problem is aggravated because of the heat and the bad smell of the decomposition. This situation of overflowed garbage bins in public places creates an unhygienic atmosphere and it may induce numerous diseases both in humans and animals. The generation levels of urban solid waste in Municipals, as stated by the World Bank’s review report [1], in 2012, were around 1.3 billion tons per year and it is expected to reach 2.2 billion tons per year by 2025. Keeping a rural city clean is a great challenge and involves several factors such as optimized management of different stakeholders, efficient financial factors, collection & transportation, etc [2]. Furthermore, according to [3] the 85% of solid waste management funds are spent on garbage collection and transportation. The operational cost can be reduced by optimizing the quantity and deployment of collection bins and their collection rate [4]. Using the Internet of Things (IoT) with a dynamic scheduling and routing can reduce the collection and disposal cost up to 20% and the transport distance to 26% [5]. For a truly dynamic and automatic system, it is important to know the current and actual garbage fill level, as well as the individual history trend, to derive a prediction on the expected overflow timestamp. Current waste management trends are not sophisticated enough to achieve a robust and efficient waste management mechanism. For avoiding these negative effects, the garbage bins and the garbage management system must be improved by utilizing Internet of Things (IoT). In this research, we propose an “e-smart bin management system” based on an innovative algorithm developed by our team. Our system consists of three parts a) the hardware, b) the software and c) the communication. We are using a microcontroller Arduino Uno and ultrasonic sensors, to measure the volume of garbage. The collected information is stored temporarily in microcontroller’s RAM and sends it via the LoRa (Long Range) protocol to the Information System (IS) where the users can monitor each bin and be notified for emergency incidents.
C HEMISTRY QA: A C OMPLEX Q UESTION A NSWERING D ATASET FROM C HEMISTRY

Many Question Answering (QA) tasks have been studied in NLP and employed to evaluate the progress of machine intelligence. One kind of QA tasks, such as Machine Reading Comprehension QA, is well solved by end-to-end neural networks; another kind of QA tasks, such as Knowledge Base QA, needs to be translated to a formatted representations and then solved by a well-designed solver. We notice that some real-world QA tasks are more complex, which cannot be solved by end-to-end neural networks or translated to any kind of formal representations. To further stimulate the research of QA and development of QA techniques, in this work, we create a new and complex QA dataset, ChemistryQA, based on real-world chemical calculation questions. To answer chemical questions, machines need to understand questions, apply chemistry and math knowledge, and do calculation and reasoning. To help researchers ramp up, we build two baselines: the first one is BERT-based sequence to sequence model, and the second one is an extraction system plus a graph search based solver. These two methods achieved 0.164 and 0.169 accuracy on the development set, respectively, which clearly demonstrates that new techniques are needed for complex QA tasks. ChemistryQA dataset will be available for public download once the paper is published.