Miracle-dz/UniFAR-FTUs huggingface dataset:
Table Retrieval May Not Necessitate Table-specific Model Design

Tables are an important form of structured data for both human and machine readers alike, providing answers to questions that cannot, or cannot easily, be found in texts. Recent work has designed special models and training paradigms for table-related tasks such as table-based question answering and table retrieval. Though effective, they add complexity in both modeling and data acquisition compared to generic text solutions and obscure which elements are truly beneficial. In this work, we focus on the task of table retrieval, and ask: “is table-specific model design necessary for table retrieval, or can a simpler text-based model be effectively used to achieve a similar result? ’’ First, we perform an analysis on a table-based portion of the Natural Questions dataset (NQ-table), and find that structure plays a negligible role in more than 70% of the cases. Based on this, we experiment with a general Dense Passage Retriever (DPR) based on text and a specialized Dense Table Retriever (DTR) that uses table-specific model designs. We find that DPR performs well without any table-specific design and training, and even achieves superior results compared to DTR when fine-tuned on properly linearized tables. We then experiment with three modules to explicitly encode table structures, namely auxiliary row/column embeddings, hard attention masks, and soft relation-based attention biases. However, none of these yielded significant improvements, suggesting that table-specific model design may not be necessary for table retrieval.
An In-Pipe Inspection Robot With Sensorless Underactuated Magnets and Omnidirectional Tracks: Design and Implementation

This paper presents the plan of an in-pipe climbing robot that works utilizing an astute transmission part to investigate complex relationship of lines. Standard wheeled/proceeded in-pipe climbing robots are inclined to slip and take while investigating in pipe turns. The instrument helps in accomplishing the main inevitable result of getting out slip and drag in the robot tracks during advancement. The proposed transmission appreciates the practical furthest reaches of the standard two-yield transmission, which is developed the basic time for a transmission with three results. The instrument conclusively changes the track speeds of the robot considering the powers applied on each track inside the line relationship, by getting out the essential for any remarkable control. The amusement of the robot crossing in the line network in various orientation and in pipe-turns without slip shows the proposed game plan's adequacy.
SADAM: Stochastic Adam, A Stochastic Operator for First-Order Gradient-based Optimizer

In this work, to efficiently help escape the stationary and saddle points, we propose, analyze, and generalize a stochastic strategy performed as an operator for a first-order gradient descent algorithm in order to increase the target accuracy and reduce time consumption. Unlike existing algorithms, the proposed stochastic strategy does not require any batches and sampling techniques, enabling efficient implementation and maintaining the initial first-order optimizer's convergence rate, but provides an incomparable improvement of target accuracy when optimizing the target functions. In short, the proposed strategy is generalized, applied to Adam, and validated via the decomposition of biomedical signals using Deep Matrix Fitting and another four peer optimizers. The validation results show that the proposed random strategy can be easily generalized for first-order optimizers and efficiently improve the target accuracy.
Summarization as Indirect Supervision for Relation Extraction

Relation extraction (RE) models have been challenged by their reliance on training data with expensive annotations. Considering that summarization tasks aim at acquiring concise expressions of synoptical information from the longer context, these tasks naturally align with the objective of RE, i.e., extracting a kind of synoptical information that describes the relation of entity mentions. We present SuRE, which converts RE into a summarization formulation. SuRE leads to more precise and resource-efficient RE based on indirect supervision from summarization tasks. To achieve this goal, we develop sentence and relation conversion techniques that essentially bridge the formulation of summarization and RE tasks. We also incorporate constraint decoding techniques with Trie scoring to further enhance summarization-based RE with robust inference. Experiments on three RE datasets demonstrate the effectiveness of SuRE in both full-dataset and low-resource settings, showing that summarization is a promising source of indirect supervision to improve RE models.
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction

We propose Adaptive Deep Kernel Fitting with Implicit Function Theorem (ADKF-IFT), a novel framework for learning deep kernel Gaussian processes (GPs) by interpolating between meta-learning and conventional deep kernel learning. Our approach employs a bilevel optimization objective where we meta-learn generally useful feature representations across tasks, in the sense that task-specific GP models estimated on top of such features achieve the lowest possible predictive loss on average. We solve the resulting nested optimization problem using the implicit function theorem (IFT). We show that our ADKF-IFT framework contains previously proposed Deep Kernel Learning (DKL) and Deep Kernel Transfer (DKT) as special cases. Although ADKF-IFT is a completely general method, we argue that it is especially well-suited for drug discovery problems and demonstrate that it significantly outperforms previous state-of-the-art methods on a variety of real-world few-shot molecular property prediction tasks and out-of-domain molecular property prediction and optimization tasks.
Proposition-Level Clustering for Multi-Document Summarization

Text clustering methods were traditionally incorporated into multi-document summarization (MDS) as a means for coping with considerable information repetition. Particularly, clusters were leveraged to indicate information saliency as well as to avoid redundancy. Such prior methods focused on clustering sentences, even though closely related sentences usually contain also non-aligned parts. In this work, we revisit the clustering approach, grouping together sub-sentential propositions, aiming at more precise information alignment. Specifically, our method detects salient propositions, clusters them into paraphrastic clusters, and generates a representative sentence for each cluster via text fusion.Our summarization method improves over the previous state-of-the-art MDS method in the DUC 2004 and TAC 2011 datasets, both in automatic ROUGE scores and human preference.
I GBO S UM 1500 - I NTRODUCING THE I GBO T EXT S UMMARIZATION D ATASET

Igbo, like many low-resource African languages, faces the same challenge of inadequate or complete lack of resources dataset and methods to support the research and the development of even basic NLP tools for its over 30 millions users. One major gap in the IgboNLP research is the absence of a text summarization tool for Igbo. In this paper, we report our on-going effort in the creation of the IgboSum1500 dataset, the first standard Igbo text summarization dataset, which will serve as a fundamental precursor to development of the Igbo text summarization resources as well as the expansion of the Igbo and African NLP. 1 BACKGROUND Igbo1, along with Hausa and Yorùbá, is one of the three prominent indigenous Nigerian languages. It is spoken by the Igbos of southeastern Nigeria with over 30 million speakers resident in Nigeria and many more abroad. In NLP terms, Igbo is still considered to be acutely under-resourced and ‘scraping-by’ according to Joshi et al. (2020). Currently, efforts are on-going in developing IgboNLP e.g. part-of-speech tagging (Onyenwe et al., 2019), diacritic restoration (Ezeani et al., 2016), embedding based analogy and similarity (Ezeani et al., 2018), machine translation (Ezeani et al., 2020), (Nekoto et al., 2020), and named-entity recognition (Adelani et al., 2021). However, these efforts need to be sustained by creating more resources and expanding the scope of coverage of common downstream NLP tasks in Igbo, and one of such tasks is text summarization. 2 OVERVIEW OF TEXT SUMMARIZATION The growth the internet and web with the accompanying information overload has, among other things, necessitated the quest for tools that can summarise large volumes of texts (Gambhir & Gupta, 2017). The task of text summarization, therefore involves compressing a large body of text into its shorter version which contains only the relevant information in the text (Allahyari et al., 2017). In the text summarization study, a test dataset is needed to evaluate the performance of any proposed method. Some of the publicly available datasets for the English language summarization tasks include the CNN-Daily Mail dataset (See et al., 2017). Automatic text summarizers are computer programs that can identify relevant parts of a text document and put them together in coherent and readable way. Common techniques to building automatic text summarizers can be categorized into two key approaches: • extractive summarization (Nallapati et al., 2017; Liu, 2019; Mihalcea, 2005) where the summary contains the exact words and phrases in the original text, and Igbo: https://en.wikipedia. org/wiki/Igbo_language
Transforming the treatment of psoriasis to the 21st century: Detecting subclinical therapeutic response to secukinumab using optical coherence tomography as a prognostic indicator

Optical coherence tomography (OCT) is a noninvasive imaging device that scans the skin up to 2 mm in depth. OCT can capture real‐time epidermal thickness (ET) measurements and detect subclinical changes in inflammatory skin diseases like eczema and psoriasis. © 2022 Wiley Periodicals LLC.
The Influence of Processing Speed, Attention, and Inhibition on Texas Functional Living Scale Performance

Abstract Objective Attention, inhibition, and processing speed are related to functional decline among older adults. This study attempts to clarify the relationships between these cognitive factors and adaptive functioning. Method We examined relationships between attention, inhibition, and processing speed, with scores on the Texas Functional Living Scale (TFLS), a performance-based measure of daily functioning, in a mixed clinical sample of 530 older adults who were referred for an outpatient neuropsychological evaluation. Results The current study used a confirmatory factor analysis (CFA) to derive a three-factor cognitive model consisting of attention, inhibition, and processing speed. Results from a hierarchical regression, which included factor scores from the CFA, revealed that processing speed was the only significant predictor of TFLS performance when all three cognitive factors were included within a single model. Conclusion These results highlight the influence of processing speed as an important indicator of functional decline among a clinical population of older adults.
Tomato brown rugose fruit virus: An emerging and rapidly spreading plant RNA virus that threatens tomato production worldwide

Abstract Tomato brown rugose fruit virus (ToBRFV) is an emerging and rapidly spreading RNA virus that infects tomato and pepper, with tomato as the primary host. The virus causes severe crop losses and threatens tomato production worldwide. ToBRFV was discovered in greenhouse tomato plants grown in Jordan in spring 2015 and its first outbreak was traced back to 2014 in Israel. To date, the virus has been reported in at least 35 countries across four continents in the world. ToBRFV is transmitted mainly via contaminated seeds and mechanical contact (such as through standard horticultural practices). Given the global nature of the seed production and distribution chain, and ToBRFV's seed transmissibility, the extent of its spread is probably more severe than has been disclosed. ToBRFV can break down genetic resistance to tobamoviruses conferred by R genes Tm‐1, Tm‐2, and Tm‐2 2 in tomato and L 1 and L 2 alleles in pepper. Currently, no commercial ToBRFV‐resistant tomato cultivars are available. Integrated pest management‐based measures such as rotation, eradication of infected plants, disinfection of seeds, and chemical treatment of contaminated greenhouses have achieved very limited success. The generation and application of attenuated variants may be a fast and effective approach to protect greenhouse tomato against ToBRFV. Long‐term sustainable control will rely on the development of novel genetic resistance and resistant cultivars, which represents the most effective and environment‐friendly strategy for pathogen control. Taxonomy Tomato brown rugose fruit virus belongs to the genus Tobamovirus, in the family Virgaviridae. The genus also includes several economically important viruses such as Tobacco mosaic virus and Tomato mosaic virus. Genome and virion The ToBRFV genome is a single‐stranded, positive‐sense RNA of approximately 6.4 kb, encoding four open reading frames. The viral genomic RNA is encapsidated into virions that are rod‐shaped and about 300 nm long and 18 nm in diameter. Tobamovirus virions are considered extremely stable and can survive in plant debris or on seed surfaces for long periods of time. Disease symptoms Leaves, particularly young leaves, of tomato plants infected by ToBRFV exhibit mild to severe mosaic symptoms with dark green bulges, narrowness, and deformation. The peduncles and calyces often become necrotic and fail to produce fruit. Yellow blotches, brown or black spots, and rugose wrinkles appear on tomato fruits. In pepper plants, ToBRFV infection results in puckering and yellow mottling on leaves with stunted growth of young seedlings and small yellow to brown rugose dots and necrotic blotches on fruits.
Total ice content and lipid saturation determine adipose tissue cryolipolysis by injection of ice‐slurry

Cryolipolysis uses tissue cooling to solidify lipids, preferentially damaging lipid‐rich cells. Topical cooling is popular for the reduction of local subcutaneous fat. Injection of biocompatible ice‐slurry is a recently introduced alternative. We developed and verified a quantitative model that simulates the heat exchange and phase changes involved, offering insights into ice‐slurry injection for treating subcutaneous fat.
Communication pathways from the emergency department to community mental health services: A systematic review

Abstract This systematic review synthesizes existing peer reviewed evidence reporting on evaluated strategies used for enhancing communication pathways for continuity of care between the emergency department and mental health community supports. Following the PRISMA guidelines and the PICO framework, this review was conducted between January and July 2021. Included articles needed to evaluate communication pathway interventions for continuity of care between the emergency department and mental health community services which support service users with mental health and/or suicidal crisis. The seven included studies identified three support coordination interventions, two motivational interviewing interventions, an electronic record enhanced strategy and results from a phone follow‐up study. This review demonstrates that support coordination, motivational interviewing, education, or an enhanced electronic record strategy can improve continuity of care, and in some cases, reduce the need for people to re‐present to ED when they are experiencing mental health concerns or suicidal crisis. Results of this review reveal that a multipronged approach of communication pathways for continuity of care would enable more effective connections with mental health community supports and enable better outcomes for people requiring services.
Characteristics of Freight Transport Parking and Infrastructures Facilities of Sustainable Primary Arterial Road (A Case Study of Surakarta Ring Road - Central Java - Indonesia)

Freight transport parking on the sides of primary arterial roads directly declines road service function and indirectly increases cost which must be borne by road users due to reduced speed. Changes of land use, aesthetic value due to increased local activities are the continued impacts which must be faced. Provision of road infrastructure facilities such as rest area or freight transport terminals is a solution to solve parking on primary arterial roads. So that the implementation of the solution is optimum, the concept planning must consider the principle of sustainable transportation. The principle of sustainable transportation is finding short-term and long term solutions in various aspectsto improve efficient transportation system.Therefore, it’s important to determine the characteristics of truck parking to recommend appropriate infrastructure facilities.
On integrating apprentice learning and reinforcement learning TITLE2

Apprentice learning and reinforcement learning have each been developed in order to endow computerized agents with the capacity to learn to perform multiple-step tasks. In apprentice learning, the autonomous agent learns based on examples of a training agent''s problem-solving performance; and in reinforcement learning, the agent changes its performance based on scalar feedback about the consequences of its own actions. A thorough comparison of these disparate methods reveals that each possesses strengths as well as weaknesses and that neither is clearly better than the other. This dissertation demonstrates that a careful integration of the two learning methods can produce a more powerful method than either one alone. An argument based on the characteristics of the two methods maintains that a hybrid will be an improvement because of the complimentary strengths of its constituents. In designing a new integrated approach, we consider the following: how the learner and trainer interact during training, how the learner will assimilate the trainer''s expertise, how the proficiency of the trainer affects the learner''s ability to perform the task, and when during training the learner should acquire information from the trainer. The study demonstrates that even a straightforward technique of asking the trainer for aid randomly throughout training is clearly better than reinforcement learning alone, and sometimes better than apprentice learning alone. Moreover, the results indicate that the trainer''s expertise in performing the task must be considered thoroughly, because it has a direct bearing on how well the learner will benefit from the trainer''s aid, and may even preclude the learner from acquiring an appropriate policy. Thus, one must be careful when designing an integrated system to examine the trainer''s ability to perform the task. Finally, a more sophisticated strategy of acquiring the trainer''s help is shown to be an improvement over asking for aid uniformly. This suggests that sophisticated strategies for obtaining information from the trainer are worth studying further.
Lamb wave thickness measurement potential with angle beam and normal beam excitation

It is shown that guided waves can be effectively used for thickness measurement. The measurement gives an estimate of thickness value over some distance, therefore it is valuable for large area thickness measurement or monitoring. In this paper, lamb wave thickness measurement was studied utilizing guided waves generated from normal beam and angle beam transducers. A stainless steel plate with four sections of different thickness was used for the experiments. Experiments were carried out for studying the thickness measurement potential by using peak frequency shifts and group velocity changes. A broadband shock excitation system and a narrowband tone burst excitation system were used for guided wave generation. It was found that normal beam transducers and angle beam transducers with small incident angles were good for the peak frequency shift method while the group velocity method was suitable for angle beam transducers with large incident angles. A difference study of angle beam and normal beam transducer utilization was carried out by using source influence theory.
UvA-DARE (Digital Academic Repository) Reminisce hot and cold

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Platform Revolution: How Networked Markets Are Transforming the Economy--and How to Make Them Work for You

A practical guide to the new economy that is transforming the way we live, work, and play. Uber. Airbnb. Amazon. Apple. PayPal. All of these companies disrupted their markets when they launched. Today they are industry leaders. Whats the secret to their success? These cutting-edge businesses are built on platforms: two-sided markets that are revolutionizing the way we do business. Written by three of the most sought-after experts on platform businesses, Platform Revolution is the first authoritative, fact-based book on platform models. Whether platforms are connecting sellers and buyers, hosts and visitors, or drivers with people who need a ride, Geoffrey G. Parker, Marshall W. Van Alstyne, and Sangeet Paul Choudary reveal the what, how, and why of this revolution and provide the first owners manual for creating a successful platform business. Platform Revolution teaches newcomers how to start and run a successful platform business, explaining ways to identify prime markets and monetize networks. Addressing current business leaders, the authors reveal strategies behind some of todays up-and-coming platforms, such as Tinder and SkillShare, and explain how traditional companies can adapt in a changing marketplace. The authors also cover essential issues concerning security, regulation, and consumer trust, while examining markets that may be ripe for a platform revolution, including healthcare, education, and energy. As digital networks increase in ubiquity, businesses that do a better job of harnessing the power of the platform will win. An indispensable guide, Platform Revolution charts out the brilliant future of platforms and reveals how they will irrevocably alter the lives and careers of millions.
M ULTI - TASK R EINFORCEMENT L EARNING WITH T ASK R EPRESENTATION M ETHOD

Multi-task reinforcement learning (RL) algorithms can train agents to acquire generalized skills across various tasks. However, jointly learning with multiple tasks can induce negative transfer between different tasks, resulting in unstable training. In this paper, we newly propose a task representation method that prevents negative transfer in policy learning. The proposed method for multi-task RL adopts a task embedding network in addition to a policy network, where the policy network takes the output of the task embedding network and states as inputs. Furthermore, we propose a measure of negative transfer and design an overall update method that can minimize the suggested measure. In addition, we raise an issue of the negative effect on soft Q-function learning resulting in unstable Q learning and introduce the clipping method to reduce this issue. The proposed multi-task algorithm is evaluated on various robotics manipulation tasks. Numerical results show that the proposed multi-task RL algorithm effectively minimizes negative transfer and achieves better performance than previous state-of-the-art multi-task RL algorithms