Miracle-dz/UniFAR-FTUs huggingface dataset:
Retrieval usingQuery-Question Similarity and BERT-BasedQuery-Answer Relevance Wataru Sakata

Frequently Asked Question (FAQ) retrieval is an important task where the objective is to retrieve an appropriate Question-Answer (QA) pair from a database based on a user’s query. We propose a FAQ retrieval system that considers the similarity between a user’s query and a question as well as the relevance between the query and an answer. Although a common approach to FAQ retrieval is to construct labeled data for training, it takes annotation costs. Therefore, we use a traditional unsupervised information retrieval system to calculate the similarity between the query and question. On the other hand, the relevance between the query and answer can be learned by using QA pairs in a FAQ database. The recentlyproposed BERT model is used for the relevance calculation. Since the number of QA pairs in FAQ page is not enough to train a model, we cope with this issue by leveraging FAQ sets that are similar to the one in question. We evaluate our approach on two datasets. The first one is localgovFAQ, a dataset we construct in a Japanese administrative municipality domain. The second is StackExchange dataset, which is the public dataset in English. We demonstrate that our proposed method outperforms baseline methods on these datasets. ACM Reference Format: Wataru Sakata, Tomohide Shibata, Ribeka Tanaka, and Sadao Kurohashi. 2019. FAQ Retrieval using Query-Question Similarity and BERT-Based Query-Answer Relevance. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’19), July 21–25, 2019, Paris, France. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3331184.3331326 ∗currently at Yahoo Japan Corporation Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. SIGIR ’19, July 21–25, 2019, Paris, France © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 978-1-4503-6172-9/19/07. $15.00 https://doi.org/10.1145/3331184.3331326
The Neuro-Symbolic Concept Learner: Interpreting Scenes Words and Sentences from Natural Supervision

We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns visual concepts, words, and semantic parsing of sentences without explicit supervision on any of them; instead, our model learns by simply looking at images and reading paired questions and answers. Our model builds an object-based scene representation and translates sentences into executable, symbolic programs. To bridge the learning of two modules, we use a neuro-symbolic reasoning module that executes these programs on the latent scene representation. Analogical to human concept learning, the perception module learns visual concepts based on the language description of the object being referred to. Meanwhile, the learned visual concepts facilitate learning new words and parsing new sentences. We use curriculum learning to guide the searching over the large compositional space of images and language. Extensive experiments demonstrate the accuracy and efficiency of our model on learning visual concepts, word representations, and semantic parsing of sentences. Further, our method allows easy generalization to new object attributes, compositions, language concepts, scenes and questions, and even new program domains. It also empowers applications including visual question answering and bidirectional image-text retrieval.
A vibration-based damage identification technique free of structural baseline information : experimental validation in multi-component plane structure

A vibration-based method for damage identification was established based on the “weak” formulation of Pseudo-excitation (PE) technique. The method can be effectively conducted without any prior knowledge of structural baseline parameters, e.g., Young’s Modulus, and is capable of detecting multi-damage in both one- and two-dimensional structural components. Moreover, relying on point-wise examination of structural dynamic equilibrium conditions, the baseline parameters of inspected structure can be inversely estimated in a statistical way, showing promising potential in engineering practices. As a proof-of-concept investigation, multi-damage in a plane structure, containing both beam and plate components, were identified experimentally using the proposed method.
SemEval 2019 Task 6 : An exploration of state-ofthe-art methods for offensive language detection

We provide a comprehensive investigation of different custom and off-the-shelf architectures as well as different approaches to generating feature vectors for offensive language detection. We also show that these approaches work well on small and noisy datasets such as on the Offensive Language Identification Dataset (OLID), so it should be possible to use them for other applications.
Assortative Mating on Education : A Genetic Assessment ∗

We combine an instrumental variables approach –exploiting genetic variation in polygenic scores and controlling for population stratification– with a stochastic linear bi-dimensional matching model –which allows us to test the validity of the exclusion restriction. Individuals match on an additive index of overall marital attractiveness which has two components: an observable component (education) and a non-observable component (anything else) to the econometrician. The model allows us to investigate whether exogenous shocks to the observable component satisfy the exclusion restriction, and if so, use them to identify the degree of assortative mating on the observable component. Using data from the Health Retirement Study, we find that polygenic scores satisfy the exclusion restriction and are relevant instruments, and cannot reject that the (estimated) degree of assortative mating on education is the same using OLS and IV. Our study illustrates how to combine quasi-experimental variation in spouses’ characteristics with parsimonious matching models to investigate assortative mating. JEL Classification Codes : D1, J1, J12.
Dual Self-Awareness Value Decomposition Framework without Individual Global Max for Cooperative Multi-Agent Reinforcement Learning

Value decomposition methods have gradually become popular in the cooperative multi-agent re-inforcement learning ﬁeld. However, almost all value decomposition methods follow the Individual Global Max (IGM) principle or its variants, which restricts the range of issues that value decomposition methods can resolve. Inspired by the notion of dual self-awareness in psychology, we propose a dual self-awareness value decomposition framework that entirely rejects the IGM premise. Each agent consists of an ego policy that carries out actions and an alter ego value function that takes part in credit assignment. The value function factorization can ignore the IGM assumption by using an explicit search procedure. We also suggest a novel anti-ego exploration mechanism to avoid the algorithm becoming stuck in a local optimum. As the ﬁrst fully IGM-free value decomposition method, our proposed framework achieves desirable performance in various cooperative tasks.
Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset

We introduce a large-scale dataset of human actions and eye movements while playing Atari videos games. The dataset currently has 44 hours of gameplay data from 16 games and a total of 2.97 million demonstrated actions. Human subjects played games in a frame-by-frame manner to allow enough decision time in order to obtain near-optimal decisions. This dataset could be potentially used for research in imitation learning, reinforcement learning, and visual saliency.
Explorer Exploring the potential of metallic nanoparticles within synthetic biology

General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact openaccess@ed.ac. uk providing details, and we will remove access to the work immediately and investigate your claim. The fields of metallic nanoparticle study and synthetic biology have a great deal to offer one another. Metallic nanoparticles as a class of material have many useful properties. Their small size allows for more points of contact than would be the case with a similar bulk compound, making nanoparticles excellent candidates for catalysts or for when increased levels of binding are required. Some nanoparticles have unique optical qualities, making them well suited as sensors, while others display para-magnetism, useful in medical imaging, especially by magnetic resonance imaging (MRI). Many of these metallic nanoparticles could be used in creating tools for synthetic biology, and conversely the use of synthetic biology could itself be utilised to create nanoparticle tools. Examples given here include the potential use of quantum dots (QDs) and gold nanoparticles as sensing mechanisms in synthetic biology, and the use of synthetic biology to create nanoparticle-sensing devices based on current methods of detecting metals and metalloids such as arsenate. There are a number of organisms which are able to produce a range of metallic nanoparticles naturally, such as species of the fungus Phoma which produces anti-microbial silver nanoparticles. The biological synthesis of nanoparticles may have many advantages over their more traditional industrial synthesis. If the proteins involved in biological nanoparticle synthesis can be put into a suitable bacterial chassis then they might be manipulated and the pathways engineered in order to produce more valuable nanoparticles.
PaletteNeRF: Palette-based Appearance Editing of Neural Radiance Fields

Recent advances in neural radiance fields have enabled the high-fidelity 3D reconstruction of complex scenes for novel view synthesis. However, it remains underexplored how the appearance of such representations can be efficiently edited while maintaining photorealism. In this work, we present PaletteNeRF, a novel method for photorealistic appearance editing of neural radiance fields (NeRF) based on 3D color decomposition. Our method decom-poses the appearance of each 3D point into a linear combination of palette-based bases (i.e., 3D segmentations defined by a group of NeRF-type functions) that are shared across the scene. While our palette-based bases are view-independent, we also predict a view-dependent function to capture the color residual (e.g., specular shading). During training, we jointly optimize the basis functions and the color palettes, and we also introduce novel regulariz-ers to encourage the spatial coherence of the decomposition. Our method allows users to efficiently edit the appearance of the 3D scene by modifying the color palettes. We also extend our framework with compressed semantic features for semantic-aware appearance editing. We demonstrate that our technique is superior to baseline methods both quantitatively and qualitatively for appearance editing of complex real-world scenes. Our project page is https://palettenerf.github. io.
Kernel selection for sensorless speed measurement of AC machines (Wigner vs Page representation)

This paper presents the analysis of the kernel effect on time-frequency representations used in measuring AC machine rotor speed, without the use of shaft mounted sensors. This speed measuring method is based on the analysis of speed related harmonics that arise from rotor slotting. In the present work, the use of the Wigner-Ville distribution and the Page distribution are analyzed.
Feature extraction Feature vector Prediction Adversarial feature vector Prediction

Binary code authorship identification determines authors of a binary program. Existing techniques have used supervised machine learning for this task. In this paper, we look this problem from an attacker’s perspective. We aim to modify a test binary, such that it not only causes misprediction but also maintains the functionality of the original input binary. Attacks against binary code are intrinsically more difficult than attacks against domains such as computer vision, where attackers can change each pixel of the input image independently and still maintain a valid image. For binary code, even flipping one bit of a binary may cause the binary to be invalid, to crash at the run-time, or to lose the original functionality. We investigate two types of attacks: untargeted attacks, causing misprediction to any of the incorrect authors, and targeted attacks, causing misprediction to a specific one among the incorrect authors. We develop two key attack capabilities: feature vector modification, generating an adversarial feature vector that both corresponds to a real binary and causes the required misprediction, and input binary modification, modifying the input binary to match the adversarial feature vector while maintaining the functionality of the input binary. We evaluated our attack against classifiers trained with a state-of-the-art method for authorship attribution. The classifiers for authorship identification have 91% accuracy on average. Our untargeted attack has a 96% success rate on average, showing that we can effectively suppress authorship signal. Our targeted attack has a 46% success rate on average, showing that it is possible, but significantly more difficult to impersonate a specific programmer’s style. Our attack reveals that existing binary code authorship identification techniques rely on code features that are easy to modify, and thus are vulnerable to attacks.
Event detection: BJTU-SED at Trecvid 2012

: In trecvid 2012, our team takes part in 2 event detection competition including embrace and pointing. We build two systems to recognize these events separately. For embracing, we use a probability accumulated method. For pointing, we use texture and silhouette. Different from the former works, the two systems are interactive systems and feedback strategy is used in the detection of events. In the experiment, both two actions of our work obtain good performance.
Data augmentation using learned transforms for one-shot medical image segmentation

Biomedical image segmentation is an important task in many medical applications. Segmentation methods based on convolutional neural networks attain state-of-the-art accuracy; however, they typically rely on supervised training with large labeled datasets. Labeling datasets of medical images requires significant expertise and time, and is infeasible at large scales. To tackle the lack of labeled data, researchers use techniques such as hand-engineered preprocessing steps, hand-tuned architectures, and data augmentation. However, these techniques involve costly engineering efforts, and are typically dataset-specific. We present an automated data augmentation method for medical images. We demonstrate our method on the task of segmenting magnetic resonance imaging (MRI) brain scans, focusing on the one-shot segmentation scenario – a practical challenge in many medical applications. Our method requires only a single segmented scan, and leverages other unlabeled scans in a semi-supervised approach. We learn a model of transforms from the images, and use the model along with the labeled example to synthesize additional labeled training examples for supervised segmentation. Each transform is comprised of a spatial deformation field and an intensity change, enabling the synthesis of complex effects such as variations in anatomy and image acquisition procedures. Augmenting the training of a supervised segmenter with these new examples provides significant improvements over state-of-the-art methods for one-shot biomedical image segmentation. Our code is available at https://github.com/xamyzhao/brainstorm.
Chalk Talk: Investigating Reading Coaches' Role in Implementation

Bridging the macro and micro levels, this dissertation uses the case of coaching to investigate how people and ideas affect implementation. I drew on institutional theory and framing theory to reveal that coaches, situated between the district and school levels, serve as intermediaries in the education system. I conducted a qualitative cross-case study of coaching in one urban school district. I collected data on the field of reading instruction, the policy environment, coaches' work, and teachers' classroom practice. To advance our understanding of instructional reform, I determined how reading coaches engaged with logics from the broader environment. I paid particular attention to coaches' political role in persuading teachers to change their practice. I found that two logics of reading instruction, Accountability First and Just Read, co-existed in an urban school district in California. These logics held conflicting principles about appropriate and effective ways to teach reading. Coaches actively transformed and combined these logics, building them into structures and practices at the district and school-levels while generating rules, instructional materials, and trainings. Thus, coach-developed reading policies were infused with the principles and practices of Accountability First and Just Read. Coaches then actively drew upon logics from the environment to frame reading policy for teachers during implementation. Coaches differentiated their framing to meet the perceived needs of their school and teachers and used socially skilled tactics to motivate change. Ultimately, coaches' framing of the two logics of reading instruction helped influence teachers' adoption of the new reading program by persuading teachers to respond positively to particular specific pedagogical elements of the program. These findings have implications for institutional theory, research on coaching, and the preparation of educational leaders.
On nodal and generalized singular structures of Laplacian eigenfunctions and applications in $\mathbb{R}^3$

This paper is a continuation and an extension of our recent work [3] on the geometric structures of Laplacian eigenfunctions and their applications to inverse scattering problems. In [3], the analytic behaviour of the Laplacian eigenfunctions is investigated at a point where two nodal or generalised singular lines intersect. It reveals a certain intriguing property that the vanishing order of the eigenfunction at the intersecting point is related to the rationality of the intersecting angle. In this paper, we consider the 3D counterpart of such a study by studying the analytic behaviours of the Laplacian eigenfunctions at places where nodal or generalised singular planes are intersect. Compared to the 2D case, the geometric situation is more complicated: the intersection of two planes generates an edge corner, whereas the intersection of more than three planes generates a vertex corner. We provide a comprehensive characterisation for all of those cases. Moreover, we apply the spectral results to establish some novel unique identifiability results for the geometric inverse problems of recovering the shape as well as the (possible) surface impedance parameter by the associated scattering far-field measurements.
Computational complexity of exterior products and multiparticle amplitudes of noninteracting fermions in entangled states

Noninteracting bosons were proposed to be used for a demonstration of quantum-computing supremacy in a boson-sampling setup. A similar demonstration with fermions would require that the fermions are initially prepared in an entangled state. I suggest that pairwise entanglement of fermions would be sufficient for this purpose. Namely, it is shown that computing multi-particle scattering amplitudes for fermions entangled pairwise in groups of four single-particle states is #P hard. In linear algebra, such amplitudes are expressed as exterior products of two-forms of rank two. In particular, a permanent of a NxN matrix may be expressed as an exterior product of N^2 two-forms of rank two in dimension 2N^2, which establishes the #P-hardness of the latter.
A Survey on Session-based Recommender Systems

Session-based recommender systems (SBRS) are an emerging topic in the recommendation domain and have attracted much attention from both academia and industry in recent years. Most of existing works only work on modelling the general item-level dependency for recommendation tasks. However, there are many more other challenges at different levels, e.g., item feature level and session level, and from various perspectives, e.g., item heterogeneity and intra- and inter-item feature coupling relations, associated with SBRS. In this paper, we provide a systematic and comprehensive review on SBRS and create a hierarchical and in-depth understanding of a variety of challenges in SBRS. To be specific, we first illustrate the value and significance of SBRS, followed by a hierarchical framework to categorize the related research issues and methods of SBRS and to reveal its intrinsic challenges and complexities. Further, a summary together with a detailed introduction of the research progress is provided. Lastly, we share some prospects in this research area.
PettingZoo: Gym for Multi-Agent Reinforcement Learning

This paper introduces the PettingZoo library and the accompanying Agent Environment Cycle (“AEC”) games model. PettingZoo is a library of diverse sets of multi-agent environments with a universal, elegant Python API. PettingZoo was developed with the goal of accelerating research in Multi-Agent Reinforcement Learning (“MARL”), by making work more interchangeable, accessible and re-producible akin to what OpenAI’s Gym library did for single-agent reinforcement learning. PettingZoo’s API, while inheriting many features of Gym, is unique amongst MARL APIs in that it’s based around the novel AEC games model. We argue, in part through case studies on major problems in popular MARL environments, that the popular game models are poor conceptual models of the games commonly used with MARL, that they promote severe bugs that are hard to detect, and that the AEC games model addresses these problems.