Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Unnatural Questions Improves Reasoning over Text

Complex question answering (CQA) over raw text is a challenging task. A prominent approach to this task is based on the programmer-interpreter framework, where the programmer maps the question into a sequence of reasoning actions and the interpreter then executes these actions on the raw text. Learning an effective CQA model requires large amounts of human-annotated data, consisting of the ground-truth sequence of reasoning actions, which is time-consuming and expensive to collect at scale. In this paper, we address the challenge of learning a high-quality programmer (parser) by projecting natural human-generated questions into unnatural machine-generated questions which are more convenient to parse. We firstly generate synthetic (question, action sequence) pairs by a data generator, and train a semantic parser that associates synthetic questions with their corresponding action sequences. To capture the diversity when applied to natural questions, we learn a projection model to map natural questions into their most similar unnatural questions for which the parser can work well. Without any natural training data, our projection model provides high-quality action sequences for the CQA task. Experimental results show that the QA model trained exclusively with synthetic data outperforms its state-of-the-art counterpart trained on human-labeled data.
Geometric framework to predict structure from function in neural networks

Neural computation in biological and artificial networks relies on the nonlinear summation of many inputs. The structural connectivity matrix of synaptic weights between neurons is a critical determinant of overall network function, but quantitative links between neural network structure and function are complex and subtle. For example, many networks can give rise to similar functional responses, and the same network can function differently depending on context. Whether certain patterns of synaptic connectivity are required to generate specific network-level computations is largely unknown. Here we introduce a geometric framework for identifying synaptic connections required by steady-state responses in recurrent networks of threshold-linear neurons. Assuming that the number of specified response patterns does not exceed the number of input synapses, we analytically calculate the solution space of all feedforward and recurrent connectivity matrices that can generate the specified responses from the network inputs. A generalization accounting for noise further reveals that the solution space geometry can undergo topological transitions as the allowed error increases, which could provide insight into both neuroscience and machine learning. We ultimately use this geometric characterization to derive certainty conditions guaranteeing a nonzero synapse between neurons. Our theoretical framework could thus be applied to neural activity data to make rigorous anatomical predictions that follow generally from the model architecture.
A Game AI Competition to Foster Collaborative AI Research and Development

Game artificial intelligence (AI) competitions are important to foster research and development on Game AI and AI in general. These competitions supply different challenging problems that can be translated into other contexts, virtual or real. They provide frameworks and tools to facilitate the research on their core topics and provide means for comparing and sharing results. A competition is also a way to motivate new researchers to study these challenges. In this article, we present the Geometry Friends game AI competition. Geometry Friends is a two-player cooperative physics-based puzzle platformer computer game. The concept of the game is simple, though its solving has proven to be difficult. While the main and apparent focus of the game is cooperation, it also relies on other AI-related problems such as planning, plan execution, and motion control, all connected to situational awareness. All of these must be solved in real-time. In this article, we discuss the competition and the challenges it brings, and present an overview of the current solutions.
Secure Weighted Aggregation in Federated Learning

Federated learning (FL) schemes enable multiple clients to jointly solve a machine learning problem using their local data to train a local model, then aggregating these models under the coordination of a central server. To achieve such a practical FL system, we need to consider (i) how to deal with the disparity across clients' datasets, and (ii) how to further protect the privacy of clients' locally trained models, which may leak information. The first concern can be addressed using a weighted aggregation scheme where the weights of clients are determined based on their data size and quality. Approaches in previous works result in a good performance but do not provide any privacy guarantee. For the second concern, privacy-preserving aggregation schemes can provide privacy guarantees that can be mathematically analyzed. However, the security issue still exists that both the central server and clients may send fraudulent messages to each other for their own benefits, especially if there is an incentive mechanism where the reward provided by the server is distributed according to clients' weights. To address the issues mentioned above, we propose a secure weighted aggregation scheme. Precisely, relying on the homomorphic encryption (HE) crypto-system, each client's weight is calculated in a privacy-preserving manner. Furthermore, we adopt a zero-knowledge proof (ZKP) based verification scheme to prevent the central server and clients from receiving fraudulent messages from each other. To the best of our knowledge, this work proposes the first aggregation scheme to deal with data disparity and fraudulent messages in the FL system from both privacy and security perspective.
Distinct C-terminal Amino Acid Sequence Motifs Serve as the Targeting Signals for Outer Mitochondrial Membrane and Plastid Outer Envelope TA Proteins

Distinct C-terminal amino acid sequence motifs serve as the targeting signals for outer mitochondrial membrane and plastid outer envelope TA proteins Howard James Teresinski Advisor: University of Guelph, 2014 Dr. Robert T. Mullen Tail-anchored (TA) proteins are a unique class of functionally diverse membrane proteins that are defined by their single C-terminal transmembrane domain and their ability to insert post-translationally into specific organelles in an Ncytosol-CIMS orientation. While in recent years considerable progress has been made towards understanding the biogenesis of TA proteins in yeast and mammals, relatively little is known about how these proteins are properly partitioned within plant cells, mostly because so few plant TA proteins have been identified. Here we show the results of experiments aimed at cataloguing, in silico, all of the TA outer mitochondrial membrane and plastid outer envelope proteins in Arabidopsis and then identifying and characterising distinct, C-terminal targeting motifs responsible for the proper sorting of at least a subset of these proteins. Collectively, the results of this thesis provide important insight into the molecular mechanisms that underlie the biogenesis of TA proteins in plant cells.
Analysis of the impact of maker-taker fees on the stock market using agent-based simulation

Recently, most stock exchanges in the U.S. employ maker-taker fees, in which an exchange pays rebates to traders placing orders in the order book and charges fees to traders taking orders from the order book. Maker-taker fees encourage traders to place many orders that provide market liquidity to the exchange. However, it is not clear how maker-taker fees affect the total cost of a taking order, including all the charged fees and the market impact. In this study, we investigated the effect of maker-taker fees on the total cost of a taking order with our artificial market model, which is an agent-based model for financial markets. We found that maker-taker fees encourage market efficiency but increase the total costs of taking orders.
On the Generalisation Capabilities of Fingerprint Presentation Attack Detection Methods in the Short Wave Infrared Domain

Nowadays, fingerprint-based biometric recognition systems are becoming increasingly popular. However, in spite of their numerous advantages, biometric capture devices are usually exposed to the public and thus vulnerable to presentation attacks (PAs). Therefore, presentation attack detection (PAD) methods are of utmost importance in order to distinguish between bona fide and attack presentations. Due to the nearly unlimited possibilities to create new presentation attack instruments (PAIs), unknown attacks are a threat to existing PAD algorithms. This fact motivates research on generalisation capabilities in order to find PAD methods that are resilient to new attacks. In this context, we evaluate the generalisability of multiple PAD algorithms on a dataset of 19,711 bona fide and 4,339 PA samples, including 45 different PAI species. The PAD data is captured in the short wave infrared domain and the results discuss the advantages and drawbacks of this PAD technique regarding unknown attacks.
Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection

Automatic visual fire detection is used to complement traditional fire detection sensor systems (smoke/heat). In this work, we investigate different Convolutional Neural Network (CNN) architectures and their variants for the non-temporal real-time bounds detection of fire pixel regions in video (or still) imagery. Two reduced complexity compact CNN architectures (NasNet-A-OnFire and ShuffleNetV2-OnFire) are proposed through experimental analysis to optimise the computational efficiency for this task. The results improve upon the current state-of-the-art solution for fire detection, achieving an accuracy of 95% for full-frame binary classification and 97% for superpixel localisation. We notably achieve a classification speed up by a factor of 2.3× for binary classification and 1.3× for superpixel localisation, with runtime of 40 fps and 18 fps respectively, outperforming prior work in the field presenting an efficient, robust and real-time solution for fire region detection. Subsequent implementation on low-powered devices (Nvidia Xavier-NX, achieving 49 fps for full-frame classification via ShuffleNetV2-OnFire) demonstrates our architectures are suitable for various real-world deployment applications.
Back-Propagating System Dependency Impact for Attack Investigation

Causality analysis on system auditing data has emerged as an important solution for attack investigation. Given a POI (Point-Of-Interest) event (e.g., an alert ﬁred on a suspicious ﬁle creation), causality analysis constructs a dependency graph, in which nodes represent system entities (e.g., processes and ﬁles) and edges represent dependencies among entities, to reveal the attack sequence. However, causality analysis often produces a huge graph ( > 100 , 000 edges) that is hard for security analysts to inspect. From the dependency graphs of various attacks, we observe that (1) dependencies that are highly related to the POI event often exhibit a different set of properties (e.g., data ﬂow and time) from the less-relevant dependencies; (2) the POI event is often related to a few attack entries (e.g., downloading a ﬁle). Based on these insights, we propose D EP I MPACT , a framework that identiﬁes the critical component of a dependency graph (i.e., a sub-graph) by (1) assigning discriminative dependency weights to edges to distinguish critical edges that represent the attack sequence from less-important dependencies, (2) propagating dependency impacts backward from the POI event to entry points, and (3) performing forward causality analysis from the top-ranked entry nodes based on their dependency impacts to ﬁlter out edges that are not found in the forward causality analysis. Our evaluations on the 150 million real system auditing events of real attacks and the DARPA TC dataset show that D EP I MPACT can signiﬁcantly reduce the large dependency graphs ( ∼ 1 , 000 , 000 edges) to a small graph ( ∼ 234 edges), which is 4611 × smaller. The comparison with the other state-of-the-art causality analysis techniques shows that D EP I MPACT is 106 × more effective in reducing the dependency graphs while preserving the attack sequences
Summarizing graphs using the configuration model

Given a large graph, how can we summarize it with fewer nodes and edges while maintaining its key properties, such as spectral property? Although graphs play more and more important roles in many real-world applications, the growth of their size presents great challenges to graph analysis. As a solution, graph summarization, which aims to find a compact representation that preserves the important properties of a given graph, has received much attention, and numerous algorithms have been developed for it. However, most of the algorithms adopt the uniform reconstruction scheme, which is based on an unrealistic assumption that edges are uniformly distributed. In this work, we propose a novel and realistic reconstruction scheme, which preserves the degree of nodes, and we develop an efficient graph summarization algorithm called DPGS based on the Minimum Description Length principle. We theoretically analyze the difference between the original and summary graphs from a spectral perspective, and we perform extensive experiments on multiple real-world datasets. The results show that DPGS yields compact representation that preserves the essential properties of the original graph.
Binary matrix factorization on special purpose hardware

Many fundamental problems in data mining can be reduced to one or more NP-hard combinatorial optimization problems. Recent advances in novel technologies such as quantum and quantum-inspired hardware promise a substantial speedup for solving these problems compared to when using general purpose computers but often require the problem to be modeled in a special form, such as an Ising or quadratic unconstrained binary optimization (QUBO) model, in order to take advantage of these devices. In this work, we focus on the important binary matrix factorization (BMF) problem which has many applications in data mining. We propose two QUBO formulations for BMF. We show how clustering constraints can easily be incorporated into these formulations. The special purpose hardware we consider is limited in the number of variables it can handle which presents a challenge when factorizing large matrices. We propose a sampling based approach to overcome this challenge, allowing us to factorize large rectangular matrices. In addition to these methods, we also propose a simple baseline algorithm which outperforms our more sophisticated methods in a few situations. We run experiments on the Fujitsu Digital Annealer, a quantum-inspired complementary metal-oxide-semiconductor (CMOS) annealer, on both synthetic and real data, including gene expression data. These experiments show that our approach is able to produce more accurate BMFs than competing methods.
Adaptive Attentional Network for Few-Shot Knowledge Graph Completion

Few-shot Knowledge Graph (KG) completion is a focus of current research, where each task aims at querying unseen facts of a relation given its few-shot reference entity pairs. Recent attempts solve this problem by learning static representations of entities and references, ignoring their dynamic properties, i.e., entities may exhibit diverse roles within task relations, and references may make different contributions to queries. This work proposes an adaptive attentional network for few-shot KG completion by learning adaptive entity and reference representations. Specifically, entities are modeled by an adaptive neighbor encoder to discern their task-oriented roles, while references are modeled by an adaptive query-aware aggregator to differentiate their contributions. Through the attention mechanism, both entities and references can capture their fine-grained semantic meanings, and thus render more expressive representations. This will be more predictive for knowledge acquisition in the few-shot scenario. Evaluation in link prediction on two public datasets shows that our approach achieves new state-of-the-art results with different few-shot sizes. The source code is available at https://github.com/JiaweiSheng/FAAN.
A Distance Determination to the Small Magellanic Cloud with an Accuracy of Better than Two Percent Based on Late-type Eclipsing Binary Stars

We present a new study of late-type eclipsing binary stars in the Small Magellanic Cloud (SMC) undertaken with the aim of improving the distance determination to this important galaxy. A sample of 10 new detached, double-lined eclipsing binaries identified from the OGLE variable star catalogs and consisting of F- and G-type giant components has been analyzed. The absolute physical parameters of the individual components have been measured with a typical accuracy of better than 3%. All but one of the systems consist of young and intermediate population stars with masses in the range of 1.4 to 3.8 M☉. This new sample has been combined with five SMC eclipsing binaries previously published by our team. Distances to the binary systems were calculated using a surface brightness—color calibration. The targets form an elongated structure, highly inclined to the plane of the sky. The distance difference between the nearest and most-distant system amounts to 10 kpc with the line-of-sight depth reaching 7 kpc. We find tentative evidence of the existence of a spherical stellar substructure (core) in the SMC coinciding with its stellar center, containing about 40% of the young and intermediate age stars in the galaxy. The radial extension of this substructure is ∼1.5 kpc. We derive a distance to the SMC center of DSMC = 62.44 ± 0.47 (stat.) ± 0.81 (syst.) kpc corresponding to a distance modulus (m − M)SMC = 18.977 ± 0.016 ± 0.028 mag, representing an accuracy of better than 2%.
Characterization of 14-3-3 [chi] phosphorylation in phosphorus stress in Arabidopsis thaliana and investigation of acetyl-coA carboxylase regulation using quantitative mass spectrometry

thaliana and investigation of acetyl-coA carboxylase regulation using quantitative mass spectrometry Rashaun S. Wilson, Advisor: Jay J. Thelen ABSTRACT Oilseed development involves coordination and regulation of a complex metabolic network for efficient production of storage compounds, including fatty acids, proteins, and carbohydrates. Recent advances in mass spectrometry have enabled identification of thousands of proteins and post-translational modifications in a variety of oilseed species, however, much remains to be known about their regulatory roles in seed development. A current focus in oilseed research is regulatory elucidation of these proteins and modifications, which can ultimately be applied to genetic engineering applications for higher production of storage compounds.
Metabolic Engineering for the Biosynthesis of Styrene and its Derivatives

This chapter describes background information on the field of metabolic engineering and the motivation for my project on the construction of pathways from Lphenylalanine. The metabolic pathway for styrene and its derivatives (S)-styrene oxide and (R)-1,2-phenylethanediol will be provided. This chapter concludes with the dissertation organization. 1.1 Background and motivation A demand for commodity chemicals by renewable means rather than fossil fuels has been increasing in recent years. Microorganisms are capable of naturally producing various useful products including pharmaceuticals, fuels, and polymers; however, often at too small of quantities for industrial economic viability. Metabolic engineering enables efficient construction of both high yield pathways that already exist in nature and novel pathways that may be constructed from the enzymatic parts of various microorganisms. Utilizing a diversity of enzymes through the tools of metabolic engineering, researchers have successfully catalyzed a wide array of biochemical reactions producing important biofuels, pharmaceuticals, and other fine chemicals. The most prominent application of metabolic engineering is currently in the transportation fuel sector with a heavy focus on bio-ethanol and bio-butanol production. Though these biofuels represent the highest volume of biologically produced chemicals, they also have the lowest profit margin due to the discrepancy between the high cost of 2 processing suitable carbon substrates (sugars such as glucose) and the value of the final product (Keasling 2010). While the biofuels sector has been of primary interest for the last several decades, recent interest in utilizing metabolic engineering for the production of pharmaceuticals and fine chemicals has developed. Not only does a biosynthetic route offer a renewable means of producing commodity chemicals, it also gives us the ability to circumvent the high energetic requirements of chemocatalytic production (as is the case of styrene from ethylbenzene), control the stereospecificity of chiral compounds (a necessity for pharmaceutical precursors), as well as manufacture compounds which can only be produced naturally (such as amino acids, vitamins, flavors, and fragrances). Several examples of fine chemicals synthesized from microbial platforms have recently been reported, most notably monomers of polymers and co-polymers such as 1,5diaminopentane and 1,4-diaminopentane (Qian, Xia, and Lee 2011) (used to make polyamids like Nylon) as well as isoprene (Lindberg, Park, and Melis 2010) (a monomer for rubber and copolymer synthesis). Since these products are indistinguishable from those of petrochemical origin, no change in industry infrastructure is necessary for their incorporation into existing polymer and co-polymer synthesis methods. Thus, metabolic engineering strategies provide new opportunities to develop products of commercial interest by biosynthetic means thereby reducing our dependence on nonrenewable resources and offering a ‘green chemistry’ approach to producing essential commodity
Introduction to the Foundations of Quantum Optimal Control

Optimal Control Theory is a powerful mathematical tool, which has known a rapid development since the 1950s, mainly for engineering applications. More recently, it has become a widely used method to improve process performance in quantum technologies by means of highly efficient control of quantum dynamics. This review aims at providing an introduction to key concepts of optimal control theory which is accessible to physicists and engineers working in quantum control or in related fields. The different mathematical results are introduced intuitively, before being rigorously stated. This review describes modern aspects of optimal control theory, with a particular focus on the Pontryagin Maximum Principle, which is the main tool for determining open-loop control laws without experimental feedback. The different steps to solve an optimal control problem are discussed, before moving on to more advanced topics such as the existence of optimal solutions or the definition of the different types of extremals, namely normal, abnormal, and singular. The review covers various quantum control issues and describes their mathematical formulation suitable for optimal control. The optimal solution of different low-dimensional quantum systems is presented in detail, illustrating how the mathematical tools are applied in a practical way.