Miracle-dz/UniFAR-FTUs huggingface dataset:
Cost Effective Optimization for Cost-related Hyperparameters

The increasing demand for democratizing machine learning algorithms for general software developers calls for hyperparameter optimization (HPO) solutions at low cost. Many machine learning algorithms have hyperparameters, which can cause a large variation in the training cost. But this effect is largely ignored in existing HPO methods, which are incapable to properly control cost during the optimization process. To address this problem, we develop a cost effective HPO solution. The core of our solution is a new randomized direct-search method. We prove a convergence rate of $O(\frac{\sqrt{d}}{\sqrt{K}})$ and provide an analysis on how it can be used to control evaluation cost under reasonable assumptions. Extensive evaluation using a latest AutoML benchmark shows a strong any time performance of the proposed HPO method when tuning cost-related hyperparameters.
Fast and Robust Unsupervised Contextual Biasing for Speech Recognition

Automatic speech recognition (ASR) system is becoming a ubiquitous technology. Although its accuracy is closing the gap with that of human level under certain settings, one area that can further improve is to incorporate user-specific information or context to bias its prediction. A common framework is to dynamically construct a small language model from the provided contextual mini corpus and interpolate its score with the main language model during the decoding process. Here we propose an alternative approach that does not entail explicit contextual language model. Instead, we derive the bias score for every word in the system vocabulary from the training corpus. The method is unique in that 1) it does not require meta-data or class-label annotation for the context or the training corpus. 2) The bias score is proportional to the word's log-probability, thus not only would it bias the provided context, but also robust against irrelevant context (e.g. user mis-specified or in case where it is hard to quantify a tight scope). 3) The bias score for the entire vocabulary is pre-determined during the training stage, thereby eliminating computationally expensive language model construction during inference. We show significant improvement in recognition accuracy when the relevant context is available. Additionally, we also demonstrate that the proposed method exhibits high tolerance to false-triggering errors in the presence of irrelevant context.
Supportive Actions for Manipulation in Human-Robot Coworker Teams

The increasing presence of robots alongside humans, such as in human-robot teams in manufacturing, gives rise to research questions about the kind of behaviors people prefer in their robot counterparts. We term actions that support interaction by reducing future interference with others as supportive robot actions and investigate their utility in a co-located manipulation scenario. We compare two robot modes in a shared table pick-and-place task: (1) Task-oriented: the robot only takes actions to further its task objective and (2) Supportive: the robot sometimes prefers supportive actions to task-oriented ones when they reduce future goal-conflicts. Our experiments in simulation, using a simplified human model, reveal that supportive actions reduce the interference between agents, especially in more difficult tasks, but also cause the robot to take longer to complete the task. We implemented these modes on a physical robot in a user study where a human and a robot perform object placement on a shared table. Our results show that a supportive robot was perceived more favorably as a coworker and also reduced interference with the human in one of two scenarios. However, it also took longer to complete the task highlighting an interesting trade-off between task-efficiency and human-preference that needs to be considered before designing robot behavior for close-proximity manipulation scenarios.
Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection

Graph-based models have been widely used to fraud detection tasks. Owing to the development of Graph Neural Networks~(GNNs), recent works have proposed many GNN-based fraud detectors based on either homogeneous or heterogeneous graphs. These works leverage existing GNNs and aggregate the neighborhood information to learn the node embeddings, which relies on the assumption that the neighbors share similar context, features, and relations. However, the inconsistency problem incurred by fraudsters is hardly investigated, i.e., the context inconsistency, feature inconsistency, and relation inconsistency. In this paper, we introduce these inconsistencies and design a new GNN framework, GraphConsis, to tackle the inconsistency problem: (1) for the context inconsistency, we propose to combine the context embeddings with node features; (2) for the feature inconsistency, we design a consistency score to filter the inconsistent neighbors and generate corresponding sampling probability; (3) for the relation inconsistency, we learn the relation attention weights associated with the sampled nodes. Empirical analysis on four datasets demonstrates that the inconsistency problem is critical in fraud detection tasks. Extensive experiments show the effectiveness of GraphConsis. We also released a GNN-based fraud detection toolbox with implementations of SOTA models. The code is available at \urlhttps://github.com/safe-graph/DGFraud
InfoCGAN classification of 2D square Ising configurations

An InfoCGAN neural network is trained on 2D square Ising configurations conditioned on the external applied magnetic field and the temperature. The network is composed of two main sub-networks. The generator network learns to generate convincing Ising configurations and the discriminator network learns to discriminate between ‘real’ and ‘fake’ configurations with an additional categorical assignment prediction provided by an auxiliary network. Some of the predicted categorical assignments show agreement with the expected physical phases in the Ising model, the ferromagnetic spin-up and spin down phases as well as the high temperature weak external field phase. Additionally, configurations associated with the crossover phenomena are predicted by the model. The classification probabilities allow for a robust method of estimating the critical temperature in the vanishing field case, showing exceptional agreement with the known physics. This work indicates that a representation learning approach using an adversarial neural network can be used to identify categories that strongly resemble physical phases with no a priori information beyond raw physical configurations and the physical conditions they are subject to. Proper implementation of finite size scaling is essential for a complete machine learning approach in order to bring it in line with established statistical mechanics, which is worthwhile for future study.
Unsupervised Morphological Paradigm Completion

We propose the task of unsupervised morphological paradigm completion. Given only raw text and a lemma list, the task consists of generating the morphological paradigms, i.e., all inflected forms, of the lemmas. From a natural language processing (NLP) perspective, this is a challenging unsupervised task, and high-performing systems have the potential to improve tools for low-resource languages or to assist linguistic annotators. From a cognitive science perspective, this can shed light on how children acquire morphological knowledge. We further introduce a system for the task, which generates morphological paradigms via the following steps: (i) EDIT TREE retrieval, (ii) additional lemma retrieval, (iii) paradigm size discovery, and (iv) inflection generation. We perform an evaluation on 14 typologically diverse languages. Our system outperforms trivial baselines with ease and, for some languages, even obtains a higher accuracy than minimally supervised systems.
Customised fragment libraries for ab initio protein structure prediction using a structural alphabet

Motivation: Computational protein structure prediction has taken over the structural community in past few decades, mostly focusing on the development of Template-Free modelling (TFM) or ab initio modelling protocols. Fragment-based assembly (FBA), falls under this category and is by far the most popular approach to solve the spatial arrangements of proteins. FBA approaches usually rely on sequence based profile comparison to generate fragments from a representative structural database. Here we report the use of Protein Blocks (PBs), a structural alphabet (SA) to perform such sequence comparison and to build customised fragment libraries for TFM. Results: We demonstrate that predicted PB sequences for a query protein can be used to search for high quality fragments that overall cover above 90% of the query. The fragments generated are of minimum length of 11 residues, and fragments that cover more than 30% of the query length were often obtained. Our work shows that PBs can serve as a good way to extract structurally similar fragments from a database of representatives of non-homologous structures and of the proteins that contain less ordered regions.
Markovian Switching of Mutation Rates in Evolutionary Network Dynamics

The replicator–mutator dynamic was originally derived to model the evolution of language, and since the model was derived in such a general manner, it has been applied to the dynamics of social behavior and decision making in multi-agent networks. For the two type population, a bifurcation point of the mutation rate was derived, displaying different long-run behaviors above and below this point. The long-run behavior would naturally be subjected to noise from the environment, however, to date, there does not exist a model that dynamically accounts for the effects of the environment. To account for the environmental impacts on the evolution of the populace, mutation rates above and below this bifurcation point are switched according to a continuous-time Markov chain. The long-run behaviors of this model are derived, showing a counterintuitive result that the majority of initial conditions will favor the dominated type.
Dim Silicon and the Case for Improved DVFS Policies

Due to thermal and power supply limits, modern Intel CPUs reduce their frequency when AVX2 and AVX-512 instructions are executed. As the CPUs wait for 670{\mu}s before increasing the frequency again, the performance of some heterogeneous workloads is reduced. In this paper, we describe parallels between this situation and dynamic power management as well as between the policy implemented by these CPUs and fixed-timeout device shutdown policies. We show that the policy implemented by Intel CPUs is not optimal and describe potential better policies. In particular, we present a mechanism to classify applications based on their likeliness to cause frequency reduction. Our approach takes either the resulting classification information or information provided by the application and generates hints for the DVFS policy. We show that faster frequency changes based on these hints are able to improve performance for a web server using the OpenSSL library.
Feature-Metric Registration: A Fast Semi-Supervised Approach for Robust Point Cloud Registration Without Correspondences

We present a fast feature-metric point cloud registration framework, which enforces the optimisation of registration by minimising a feature-metric projection error without correspondences. The advantage of the feature-metric projection error is robust to noise, outliers and density difference in contrast to the geometric projection error. Besides, minimising the feature-metric projection error does not need to search the correspondences so that the optimisation speed is fast. The principle behind the proposed method is that the feature difference is smallest if point clouds are aligned very well. We train the proposed method in a semi-supervised or unsupervised approach, which requires limited or no registration label data. Experiments demonstrate our method obtains higher accuracy and robustness than the state-of-the-art methods. Besides, experimental results show that the proposed method can handle significant noise and density difference, and solve both same-source and cross-source point cloud registration.
Derivation of a Constant Velocity Motion Model for Visual Tracking

Motion models play a great role in visual tracking applications for predicting the possible locations of objects in the next frame. Unlike target tracking in radar or aerospace domain which considers only points, object tracking in computer vision involves sizes of objects. Constant velocity motion model is the most widely used motion model for visual tracking, however, there is no clear and understandable derivation involving sizes of objects specially for new researchers joining this research field. In this document, we derive the constant velocity motion model that incorporates sizes of objects that, we think, can help the new researchers to adapt to it very quickly.
The Effect of Constraining the Intensity of Solar Radiation on the Photosynthesis, Growth, Yield and Product Quality of Tomato

Recent studies propose the combination of electrical energy and plant production, in which often only radiation peaks were used for energy production. Then, the effect on plant growth is assumed to be negligible. However, photosynthesis is known to be a monotonically increasing function of radiation. We studied the response of tomato to constraining the intensity of solar radiation. Tomato crops in greenhouse compartments were shaded when the outside photosynthetic photon flux density (PPFD) exceeded 640 and 1280 μmol m−2 s−1, resulting in a 57 % and 34 % reduction in the PPFD integral over the growing period compared to the non-shaded control. Constraining the intensity of solar radiation significantly reduced photosynthesis, growth and yield of tomato plants. Model-derived estimates of reduction in crop dry matter increment were 50 % and 28 % for the strongly and moderately PPFD constrained crops. However, measured plant dry matter increment decreased only by 31 % and 19 %, respectively, that is, light use efficiency increased markedly. This indicates a strong adaptation of the plant's metabolism to cope with the limitation in light availability such as increasing the specific leaf area and reducing respiration. Surprisingly, this was only of little concern to the fruit quality, because no effect of constraining PPFD on the concentration of total dry matter, sugars and lycopene in the fruits could be observed. The concentration of titratable acids was significantly increased, however, when constraining PPFD, while s-carotene was slightly decreased. When combining plant and energy production, yield reductions in systems that reduce the supply of solar radiation to crops only at high irradiances will be less considerable than in systems that permanently shade the crop. However, yield losses in tomato production remain significant in both system types.
Differential gene expression during hypersensitive response in Phylloxera-resistant rootstock ‘Börner’ using custom oligonucleotide arrays

Abstract The aim of the present study was to identify genes involved in the hypersensitive response (HR) in ‘Börner’, a grapevine rootstock cultivar resistant to grape phylloxera. The HR was chemically induced in roots by an application of indol-3-acetic-acid (IAA). Comparisons between treated and untreated roots after different times of HR induction by IAA were realized by advanced custom DNA microarrays using ‘Riesling’, a phylloxera-sensitive scion, as a control. IAA induction resulted in higher numbers of differentially expressed genes in ‘Börner’ than in ‘Riesling’. In total, 27 putative HR-related genes were identified in ‘Börner’. These genes are presumably involved in the production of phytoalexins, ethylene-associated gene products, cell wall proteins and transcription factors. Thus, the present study is contributing to a better understanding of the signal transduction pathways involved in the hypersensitive reaction underlying the necrosis formation after phylloxera attack in the rootstock cultivar ‘Börner’.
The Paradigm Discovery Problem

This work treats the paradigm discovery problem (PDP), the task of learning an inflectional morphological system from unannotated sentences. We formalize the PDP and develop evaluation metrics for judging systems. Using currently available resources, we construct datasets for the task. We also devise a heuristic benchmark for the PDP and report empirical results on five diverse languages. Our benchmark system first makes use of word embeddings and string similarity to cluster forms by cell and by paradigm. Then, we bootstrap a neural transducer on top of the clustered data to predict words to realize the empty paradigm slots. An error analysis of our system suggests clustering by cell across different inflection classes is the most pressing challenge for future work.
System Metamodel Formalism

Differential equations have been widely and successfully used to describe the macroscopic or global behaviour of systems in general. With inhomogeneous, time-varying, specific, and often non-linear interactions, the dynamics of complex systems is in contrast more efficiently described by local rules and thus in an algorithmic and local or microscopic manner. In order to create computer models of systems, particularly complex systems, we recently presented a so-called allagmatic method including a system metamodel with two concrete implementations of cellular automata and artificial neural networks. Guidance from philosophy and its general concepts were helpful in these first studies focusing on computer modelling and simulation as well as feasibility. A rigorous mathematical formalism of the system metamodel, however, is still missing. This would not only more precisely describe and define the system metamodel, it would also further generalise it and with that extend its reach to formal treatment in applied mathematics and computational mathematics as well as extend its applicability to other mathematical models such as agent-based models. In this study, a mathematical definition of the system metamodel and its model parameters is provided and the creation of concrete mathematical models, i.e. cellular automata and artificial neural networks, from it is proved.
Rationalizing Medical Relation Prediction from Corpus-level Statistics

Nowadays, the interpretability of machine learning models is becoming increasingly important, especially in the medical domain. Aiming to shed some light on how to rationalize medical relation prediction, we present a new interpretable framework inspired by existing theories on how human memory works, e.g., theories of recall and recognition. Given the corpus-level statistics, i.e., a global co-occurrence graph of a clinical text corpus, to predict the relations between two entities, we first recall rich contexts associated with the target entities, and then recognize relational interactions between these contexts to form model rationales, which will contribute to the final prediction. We conduct experiments on a real-world public clinical dataset and show that our framework can not only achieve competitive predictive performance against a comprehensive list of neural baseline models, but also present rationales to justify its prediction. We further collaborate with medical experts deeply to verify the usefulness of our model rationales for clinical decision making.
DoQA - Accessing Domain-Specific FAQs via Conversational QA

The goal of this work is to build conversational Question Answering (QA) interfaces for the large body of domain-specific information available in FAQ sites. We present DoQA, a dataset with 2,437 dialogues and 10,917 QA pairs. The dialogues are collected from three Stack Exchange sites using the Wizard of Oz method with crowdsourcing. Compared to previous work, DoQA comprises well-defined information needs, leading to more coherent and natural conversations with less factoid questions and is multi-domain. In addition, we introduce a more realistic information retrieval (IR) scenario where the system needs to find the answer in any of the FAQ documents. The results of an existing, strong, system show that, thanks to transfer learning from a Wikipedia QA dataset and fine tuning on a single FAQ domain, it is possible to build high quality conversational QA systems for FAQs without in-domain training data. The good results carry over into the more challenging IR scenario. In both cases, there is still ample room for improvement, as indicated by the higher human upperbound.
A Parallel Optimal Task Allocation Mechanism for Large-Scale Mobile Edge Computing

We consider the problem of intelligent and efficient task allocation mechanism in large-scale mobile edge computing (MEC), which can reduce delay and energy consumption in a parallel and distributed optimization. In this paper, we study the joint optimization model to consider cooperative task management mechanism among mobile terminals (MT), macro cell base station (MBS), and multiple small cell base station (SBS) for large-scale MEC applications. We propose a parallel multi-block Alternating Direction Method of Multipliers (ADMM) based method to model both requirements of low delay and low energy consumption in the MEC system which formulates the task allocation under those requirements as a nonlinear 0-1 integer programming problem. To solve the optimization problem, we develop an efficient combination of conjugate gradient, Newton and linear search techniques based algorithm with Logarithmic Smoothing (for global variables updating) and the Cyclic Block coordinate Gradient Projection (CBGP, for local variables updating) methods, which can guarantee convergence and reduce computational complexity with a good scalability. Numerical results demonstrate the effectiveness of the proposed mechanism and it can effectively reduce delay and energy consumption for a large-scale MEC system.
Dynamical Modeling of Galaxies and Supermassive Black Holes: Axisymmetry in Triaxial Schwarzschild Orbit Superposition Models

We present a detailed analysis of the behavior of the triaxial Schwarzschild orbit superposition method near the axisymmetric limit. Orbit superposition modeling is the primary method used to determine dynamical masses of supermassive black holes (M BH) in nearby galaxies; however, prior studies have reported conflicting results when comparing the outcome from axisymmetric orbit codes with that from a triaxial orbit code in the axisymmetric limit. We show that in order to achieve (oblate) axisymmetry in a triaxial code, care needs to be taken to axisymmetrize the short-axis tube orbits and to exclude both the long-axis tube and box orbits from the orbit library. Using up to 12 Gauss–Hermite moments of the line-of-sight velocity distributions as constraints, we demonstrate the effects of orbit types on the best-fit M BH in orbit modeling of the massive elliptical galaxy NGC 1453 reported in Liepold et al. In addition, we verify the efficacy of our updated code on a mock galaxy data set. We identify a subset of slowly precessing quasi-planar orbits for which the typical integration times can be insufficient to fully capture the equilibrium orbital behavior in both axisymmetric and triaxial systems with central black holes. Further investigation is needed for a more reliable treatment of these orbits.