Miracle-dz/UniFAR-FTUs huggingface dataset:
Double extraction of olive oil in large oil mills of Southern Italy: Effects on extraction efficiency, oil quality, and economy of the process

In recent years, in Italy, some large oil mills, equipped with centrifugal decanter at high loading capacity have adopted the complex diagram of the double extraction of oil and, in addition, have been supplied with suitable machine to recover part of stone fragments from olive pomace with the aim to reduce the costs. Our experimental texts were carried out in a large oil mill, located in Apulia region, which processes olives of Olea europaea L. “Coratina” cultivar by double centrifugation of olive pastes prepared by using the double crushing (millstones and metallic crusher) or the single crushing (metallic crusher). The results showed that the oil yield of the first extraction was high (between 85-86%), regardless to the method used for olive crushing. The quality of virgin olive oil of the first extraction was extra virgin with some differences due to the different batches of olives and, in part, also to the used crushing methods. The oil of the second extraction was recovered in a low quantity (0.3 kg/100 kg olives) and its composition was not normal only for the percentage of triterpene dialcohols. The recovery of stone fragments was satisfying (about 0.13 t/t olives) and helped to significantly increase the income of oil mill. Practical applications: The double extraction of oil, by the centrifugation system, and the recovery of olive stone fragments allow to reduce the costs of olive processing in medium-big sized oil mills. The second centrifugation, which permits to recover about 3 kg of oil/t of olives, and the recovery of olive stone fragments, suitable to sell as fuel, allow to increase the income of oil mill.
A high-resolution, spatially explicit estimate of fossil-fuel CO2 emissions from the Tokyo Metropolis, Japan

Background: The quantification of urban greenhouse gas (GHG) emissions is an important task in combating climate change. Emission inventories that include spatially explicit emission estimates facilitate the accurate tracking of emission changes, identification of emission sources, and formulation of policies for climate-change mitigation. Many currently available gridded emission estimates are based on the disaggregation of country- or state-wide emission estimates, which may be useful in describing city-wide emissions but are of limited value in tracking changes at subnational levels. Urban GHG emissions should therefore be quantified with a true bottom-up approach. Results: Multi-resolution, spatially explicit estimates of fossil-fuel carbon dioxide (FFCO2) emissions from the Tokyo Metropolis, Japan, were derived. Spatially explicit emission data were collected for point (e.g., power plants and waste incinerators), line (mostly traffic), and area (e.g., residential and commercial areas) sources. Emissions were mapped on the basis of emission rates calculated for source locations. Activity, emissions, and spatial data were integrated, and the results were visualized using a geographic information system approach.Conclusions: The annual total FFCO2 emissions from the Tokyo Metropolis in 2014 were 43,916 Gg CO2, with the road-transportation sector (16,323 Gg CO2) accounting for 37.2% of the total. Spatial emission patterns were verified via a comparison with the East Asian Air Pollutant Emission Grid Database for Japan (EAGrid-Japan) and the Open‐source Data Inventory for Anthropogenic CO2 (ODIAC), which demonstrated the applicability of this methodology to other prefectures and therefore the entire country.
Effects of Solvent on the Optical Nonlinearities of Multi-Walled Carbon Nanotubes under the Picosecond Laser Pulses

Based on Z-scan technique, the nonlinear absorption and refraction characteristics of multi-walled carbon nanotubes (MWNT) in chloroform , deionized water and isopropyl alcohol have been studied under the picosecond (ps) laser pulses, respectively. The experimental results show that the nonlinear absorption characteristics of the MWNT in chloroform, deionized water and isopropyl alcohol are all saturable absorption, and their nonlinear absorption coefficients are 1.83×10-11 m/W, 1.97×10-11 m/W, 2.31×10-11 m/W, respectively. The nonlinear refraction characteristics of the three solutions are all self-focusing, and the nonlinear refractive coefficients are 4.37×10-12 esu, 1.68×10-12 esu, 8.34×10-13 esu, respectively. The third-order nonlinear susceptibilities of the three solutions are 6.32×10-13 esu, 5.62×10-13 esu, 7.11×10-12 esu, respectively. The experimental results indicate that the nonlinear optical property of the MWNT isopropyl alcohol solution is the strongest, and the MWNT deionized water solutions is the weakest. The solvents have a big effects on the nonlinear optical properties of MWNT solutions.
Studies on the Reasons for Carbon Deposition over Ni/Al_2O_3 Catalyst in Partial Oxidation of CH_4 to Syngas with TPO Technique

The reasons for carbon deposition over Ni/Al 2O 3 catalyst in partial oxidation of CH 4 to syngas were studied with temperature-programmed mass spectrometry on-line analysis. The results showed that, the Ni xC formed from CH 4 decomposition can transform firstly to a kind of carbon species (so-called transition carbon), which can react with O 2 at 700 ℃ but cannot react with CO 2 even at 800 ℃. At high temperature, the transition carbon species then transforms to graphite, and the higher the temperature is, the faster the transforming rate is. The main carbon species deposited on the catalyst in partial oxidation of CH 4 to syngas is graphite from Ni xC, not reacting with oxygen, which will affect the activity of the catalyst and even lead to deactivation. Adding appropriate assistants to improve the reactivity of Ni xC with oxygen will restrain the formation of graphite.
Efficacy of garlic extract and sodium hypochlorite on dental pulp dissolution: An in vitro study

Aim: The present study was aimed to assess and compare the human pulp dissolution capacity of sodium hypochlorite (NaOCl) (2.5%) and garlic extract. Materials and Methods: Several pulp tissues are obtained from both endodontically treated teeth and also by orthodontically extracted premolar teeth and were sectioned with a surgical blade into seventy-five pieces of same size and weight. Pulp tissues were then divided into Allium sativum extract (ASE) group, NaOCl group, and NaCl group and tissues were immersed in the test solutions for 30, 60, and 90 min, respectively. Further tissues were weighed using a precision balance at tested intervals, and the percentage weight loss was calculated and statistically analyzed. Results: In this study, pulp tissue treated with 2.5% NaOCl solution at all tested time intervals showed significant (P < 0.001) pulp tissue dissolution capacity and a maximum of dissolution was observed at 90 min. Pulp tissue treated with ASE showed little dissolution ability at all tested time periods. Whereas, 0.9% NaCl showed no ability to dissolve human pulp tissue. Conclusion: It can be concluded that 2.5% NaOCl had the maximum tissue dissolving capacity when compared to different concentrations of ASE. Saline had no effect on the human pulp dissolution.
Proofs of the weak version of the generalized Eshelby conjecture for anisotropic media

The Eshelby formalism for an inclusion in a solid is of significant theoretical and practical implications in mechanics and other fields of heterogeneous media. Eshelby’s finding that a uniform eigenstrain prescribed in a solitary ellipsoidal inclusion in an infinite isotropic medium results in a uniform elastic strain field in the inclusion leads to the conjecture that the ellipsoid is the only inclusion that possesses the so-called Eshelby property. Previously, the weak version of the conjecture has been proved for the isotropic medium, whereas the general validity of the conjecture for anisotropic media is yet to be explored. In this work, we present proofs of the weak version of the generalized Eshelby conjecture for anisotropic media possessing cubic, transversely isotropic, orthotropic, and monoclinic symmetries, which substantiates that only the ellipsoidal shape can transform all uniform eigenstrains into uniform elastic strain fields in a solitary inclusion in infinite media possessing these symmetries. Since the strain fields in embedded and inherently anisotropic quantum dot crystals are effective tuning knobs of the quality of the emitted photons by the quantum dots, the results may have implications in the technology of quantum information, as well as in mechanics and materials science.
A pragmatic approach for designing transparent WDM optical networks with multi-objectives

In facing with the explosive Internet traffic growth, optical transport networks based on WDM technologies forming the core part of Internet infrastructure carrying multi-Tb/s has to be re-considered from both designing, planning, operation and management perspectives to attain greater efficiency. Thanks to the convergence of significant advances in optical transmission technologies, and photonic switching, transparent (all-optical) architecture has come into practice, paving the way for eliminating the over-utilization of costly optical-electrical-optical (O-E-O) interfaces and hence, yielding remarkable savings of cost and energy consumption compared to opaque architecture. Traditional designs for transparent optical networks based on single-objective optimization model aiming at optimizing solely a single performance metric appears to be insufficient to capture the nuances of practical designs while conventional multi-objective approach tends to reach (non-) optimal solutions. Different from existing works, we present a new framework for multi-objective WDM network designs capturing several goals on one hand and on the other hand, achieving optimal solutions. Moreover, our proposal exploits the characteristics of each constituent objectives to lay the foundation for setting up weight coefficient so that the order of optimization is guaranteed. Equally important, our proposal is pragmatic in the sense that the complexity of the optimization model remains the same as the single-objective model while the quality of solution has been greatly improved. We have extensively tested realistic optical core networks topologies, that is, COST239 and NSFNET, with various network traffic conditions and it turns out that our design brings about a saving of wavelength link usage up to roughly $28\%$ in the most favorable cases while $14\%$ is expected for the least favorable cases.
Deep Metric Learning for Few-Shot Image Classification: A Selective Review

Few-shot image classiﬁcation is a challenging problem which aims to achieve the human level of recognition based only on a small number of images. Deep learning algorithms such as meta-learning, transfer learning, and metric learning have been employed recently and achieved the state-of-the-art performance. In this survey, we review representative deep metric learning methods for few-shot classiﬁcation, and categorize them into three groups according to the major problems and novelties they focus on. We conclude this review with a discussion on current challenges and future trends in few-shot image classiﬁcation
Multi-Modal Image Captioning for the Visually Impaired

One of the ways blind people understand their surroundings is by clicking images and relying on descriptions generated by image-captioning systems. Current work on captioning images for the visually impaired do not use the textual data present in the image when generating captions. This problem is critical as many visual scenes contain text, and 21% of the questions asked by blind people about the images they click pertain to the text present in them. In this work, we propose altering AoANet, a state-of-the-art image-captioning system, to leverage text detected in the image as an input feature. In addition, we use a pointer-generator network to copy detected text to the caption when tokens need to be reproduced accurately. Our model outperforms AoANet on the benchmark dataset VizWiz, giving a 35% and 16.2% performance improvement on CIDEr and SPICE scores, respectively.
GPR: Grasp Pose Refinement Network for Cluttered Scenes

Object grasping in cluttered scenes is a widely investigated field of robot manipulation. Most of the current works focus on estimating grasp pose from point clouds based on an efficient single-shot grasp detection network. However, due to the lack of geometry awareness of the local grasping area, it may cause severe collisions and unstable grasp configurations. In this paper, we propose a two-stage grasp pose refinement network which detects grasps globally while fine-tuning low-quality grasps and filtering noisy grasps locally. Furthermore, we extend the 6-DoF grasp with an extra dimension as grasp width which is critical for collisionless grasping in cluttered scenes. It takes a single-view point cloud as input and predicts dense and precise grasp configurations. To enhance the generalization ability, we build a synthetic single-object grasp dataset including 150 commodities of various shapes, and a complex multi-object cluttered scene dataset including 100k point clouds with robust, dense grasp poses and mask annotations. Experiments conducted on Yumi IRB-1400 Robot demonstrate that the model trained on our dataset performs well in real environments and outperforms previous methods by a large margin.
CoTexT: Multi-task Learning with Code-Text Transformer

We present CoTexT, a pre-trained, transformer-based encoder-decoder model that learns the representative context between natural language (NL) and programming language (PL). Using self-supervision, CoTexT is pre-trained on large programming language corpora to learn a general understanding of language and code. CoTexT supports downstream NL-PL tasks such as code summarizing/documentation, code generation, defect detection, and code debugging. We train CoTexT on different combinations of available PL corpus including both “bimodal” and “unimodal” data. Here, bimodal data is the combination of text and corresponding code snippets, whereas unimodal data is merely code snippets. We first evaluate CoTexT with multi-task learning: we perform Code Summarization on 6 different programming languages and Code Refinement on both small and medium size featured in the CodeXGLUE dataset. We further conduct extensive experiments to investigate CoTexT on other tasks within the CodeXGlue dataset, including Code Generation and Defect Detection. We consistently achieve SOTA results in these tasks, demonstrating the versatility of our models.
Towards Performance Clarity of Edge Video Analytics

Edge video analytics is becoming the solution to many safety and management tasks. Its wide deployment, however, must first address the tension between inference accuracy and resource (compute/network) cost. This has led to the development of video analytics pipelines (VAPs), which reduce resource cost by combining deep neural network compression and speedup techniques with video processing heuristics. Our measurement study, however, shows that today's methods for evaluating VAPs are incomplete, often producing premature conclusions or ambiguous results. This is because each VAP's performance varies largely across videos and time, and is sensitive to different subsets of video content characteristics. We argue that accurate VAP evaluation must first characterize the complex interaction between VAPs and video characteristics, which we refer to as VAP performance clarity. Following this concept, we design and implement Yoda, the first VAP benchmark to achieve performance clarity. Using primitive-based profiling and a carefully curated bench-mark video set, Yoda builds a performance clarity profile for each VAP to precisely define its accuracy vs. cost trade-off and its relationship with video characteristics. We show that Yoda substantially improves VAP evaluations by (1) providing a comprehensive, transparent assessment of VAP performance and its dependencies on video characteristics; (2) explicitly identifying fine-grained VAP behaviors that were previously hidden by large performance variance; and (3) revealing strengths/weaknesses among different VAPs and new design opportunities.
Decorating Your Own Bedroom: Locally Controlling Image Generation with Generative Adversarial Networks

Generative Adversarial Networks (GANs) have made great success in synthesizing high-quality images. However, how to steer the generation process of a well-trained GAN model and customize the output image is much less explored. It has been recently found that modulating the input latent code used in GANs can reasonably alter some variation factors in the output image, but such manipulation usually presents to change the entire image as a whole. In this work, we propose an effective approach, termed as LoGAN, to support local editing of the output image. Concretely, we introduce two operators, i.e., content modulation and style modulation, together with a priority mask to facilitate the precise control of the intermediate generative features. Taking bedroom synthesis as an instance, we are able to seamlessly remove, insert, shift, and rotate the individual objects inside a room. Furthermore, our method can completely clear out a room and then refurnish it with customized furniture and styles. Experimental results show the great potentials of steering the image generation of pre-trained GANs for versatile image editing.
Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising

In most real-world large-scale online applications (e.g., e-commerce or finance), customer acquisition is usually a multi-step conversion process of audiences. For example, an impression->click->purchase process is usually performed of audiences for e-commerce platforms. However, it is more difficult to acquire customers in financial advertising (e.g., credit card advertising) than in traditional advertising. On the one hand, the audience multi-step conversion path is longer, an impression->click->application->approval->activation process usually occurs during the audience conversion for credit card business in financial advertising. On the other hand, the positive feedback is sparser (class imbalance) step by step, and it is difficult to obtain the final positive feedback due to the delayed feedback of activation. Therefore, it is necessary to use the positive feedback information of the former step to alleviate the class imbalance of the latter step. Multi-task learning is a typical solution in this direction. While considerable multi-task efforts have been made in this direction, a long-standing challenge is how to explicitly model the long-path sequential dependence among audience multi-step conversions for improving the end-to-end conversion. In this paper, we propose an Adaptive Information Transfer Multi-task (AITM) framework, which models the sequential dependence among audience multi-step conversions via the Adaptive Information Transfer (AIT) module. The AIT module can adaptively learn what and how much information to transfer for different conversion stages. Besides, by combining the Behavioral Expectation Calibrator in the loss function, the AITM framework can yield more accurate end-to-end conversion identification. The proposed framework is deployed in Meituan app, which utilizes it to real-timely show a banner to the audience with a high end-to-end conversion rate for Meituan Co-Branded Credit Cards. Offline experimental results on both industrial and public real-world datasets clearly demonstrate that the proposed framework achieves significantly better performance compared with state-of-the-art baselines. Besides, online experiments also demonstrate significant improvement compared with existing online models. Furthermore,we have released the source code of the proposed framework at https://github.com/xidongbo/AITM.
Image-to-Character-to-Word Transformers for Accurate Scene Text Recognition

Leveraging the advances of natural language processing, most recent scene text recognizers adopt an encoder-decoder architecture where text images are first converted to representative features and then a sequence of characters via ‘sequential decoding’. However, scene text images suffer from rich noises of different sources such as complex background and geometric distortions which often confuse the decoder and lead to incorrect alignment of visual features at noisy decoding time steps. This paper presents I2C2W, a novel scene text recognition technique that is tolerant to geometric and photometric degradation by decomposing scene text recognition into two inter-connected tasks. The first task focuses on image-to-character (I2C) mapping which detects a set of character candidates from images based on different alignments of visual features in an non-sequential way. The second task tackles character-to-word (C2W) mapping which recognizes scene text by decoding words from the detected character candidates. The direct learning from character semantics (instead of noisy image features) corrects falsely detected character candidates effectively which improves the final text recognition accuracy greatly. Extensive experiments over nine public datasets show that the proposed I2C2W outperforms the state-of-the-art by large margins for challenging scene text datasets with various curvature and perspective distortions. It also achieves very competitive recognition performance over multiple normal scene text datasets.
Revisiting CMB constraints on dark matter annihilation

The precision measurements of the cosmic microwave background power spectrum put a strong constraint on the dark matter annihilation cross section since the electromagnetic energy injection by the dark matter annihilation affects the ionization history of the universe. In this paper, we update our previous simulation code for calculating the ionization history with the effect of dark matter annihilation by including Helium interactions and improving the precision of calculations. We give an updated constraint on the annihilation cross section and mass of dark matter using the modified RECFAST code with the Planck 2018 datasets.
The Commodities News Corpus: A Resource forUnderstanding Commodity News Better

Commodity News contains a wealth of information, one of the key information is the analysis of recent commodity price movement and notable events that led to the movement. Through event extraction, useful information extracted from commodity news is extremely useful in mining for causal relation between events and commodity price movement, which can be used for commodity price prediction. To facilitate the future research in this area, we introduce a new dataset with the following information identified and annotated: (i) entities (both nominal and named), (ii) events (trigger words and argument roles), (iii) event metadata: modality, polarity and intensity, and (iv) event-event relations.
To be a fast adaptive learner: using game history to defeat opponents

In many real-world games, such as traders repeatedly bargaining with customers, it is very hard for a single AI trader to make good deals with various customers in a few turns, since customers may adopt different strategies even the strategies they choose are quite simple. In this paper, we model this problem as fast adaptive learning in the finitely repeated games. We believe that past game history plays a vital role in such a learning procedure, and therefore we propose a novel framework (named, F3) to fuse the past and current game history with an Opponent Action Estimator (OAE) module that uses past game history to estimate the opponent's future behaviors. The experiments show that the agent trained by F3 can quickly defeat opponents who adopt unknown new strategies. The F3 trained agent obtains more rewards in a fixed number of turns than the agents that are trained by deep reinforcement learning. Further studies show that the OAE module in F3 contains meta-knowledge that can even be transferred across different games.