Miracle-dz/UniFAR-FTUs huggingface dataset:
An Empirical Study on Energy Disaggregation via Deep Learning

Energy disaggregation is the task of estimating power consumption of each individual appliance from the whole-house electric signals. In this paper, we study this task based on deep learning methods which have achieved a lot of success in various domains recently. We introduce the feature extraction method that uses multiple parallel convolutional layers of varying filter sizes and present an LSTM (Long Short Term Memory) based recurrent network model as well as an auto-encoder model for energy disaggregation. Then we evaluate the proposed methods using the largest dataset available. And experimental results show the superiority of our feature extraction method and the LSTM based model. Keywords-energy disaggregation; neural networks; deep learning;NILM
Recognising Agreement and Disagreement between Stances with Reason Comparing Networks

We identify agreement and disagreement between utterances that express stances towards a topic of discussion. Existing methods focus mainly on conversational settings, where dialogic features are used for (dis)agreement inference. We extend this scope and seek to detect stance (dis)agreement in a broader setting, where independent stance-bearing utterances, which prevail in many stance corpora and real-world scenarios, are compared. To cope with such non-dialogic utterances, we find that the reasons uttered to back up a specific stance can help predict stance (dis)agreements. We propose a reason comparing network (RCN) to leverage reason information for stance comparison. Empirical results on a well-known stance corpus show that our method can discover useful reason information, enabling it to outperform several baselines in stance (dis)agreement detection.
Thesis: Experimental exploration of gold semi-continuous films in the near- and far-field

Metallic nanostructures can support so-called plasma oscillations (plasmons). Plasmons allow for the concentration of the energy from light, down to sizes well below the conventional diffraction limit known from optics. Plasmonics thus allows for a plethora of new optical applications at the nanoscale. In this thesis, we have investigated the optical and plasmonic properties of semi-continuous gold films (also called percolation films). These films consist of complex tortuous fractal patterns on the nanoscale. They are an easy, fast, and scalable method to fabricate metallic nanostructures. We show, that despite their very complex overall geometry, a large part of the films' properties can be understood alone based on their strongly localized plasmonic 'hotspots' - areas of just a few nanometres in the films, wherein optical fields are enhanced several thousand times. Additionally, we show that such films can be used for the enhancement of gold two-photon photoluminescence and white light continuum generation. We have also shown that it is possible, via femtosecond-laser pulses, to inscribe information into the films, via phototheral processes. This has potential applications for ultra-dense information storage, and plasmonic colour laser printing.
Time Series Featurization via Topological Data Analysis

We develop a novel algorithm for feature extraction in time series data by leveraging tools from topological data analysis. Our algorithm provides a simple, efficient way to successfully harness topological features of the attractor of the underlying dynamical system for an observed time series. The proposed methodology relies on the persistent landscapes and silhouette of the Rips complex obtained after a de-noising step based on principal components applied to a time-delayed embedding of a noisy, discrete time series sample. We analyze the stability properties of the proposed approach and show that the resulting TDA-based features are robust to sampling noise. Experiments on synthetic and real-world data demonstrate the effectiveness of our approach. We expect our method to provide new insights on feature extraction from granular, noisy time series data.
A Computational Linguistic Study of Personal Recovery in Bipolar Disorder

Mental health research can benefit increasingly fruitfully from computational linguistics methods, given the abundant availability of language data in the internet and advances of computational tools. This interdisciplinary project will collect and analyse social media data of individuals diagnosed with bipolar disorder with regard to their recovery experiences. Personal recovery - living a satisfying and contributing life along symptoms of severe mental health issues - so far has only been investigated qualitatively with structured interviews and quantitatively with standardised questionnaires with mainly English-speaking participants inWestern countries. Complementary to this evidence, computational linguistic methods allow us to analyse first-person accounts shared online in large quantities, representing unstructured settings and a more heterogeneous, multilingual population, to draw a more complete picture of the aspects and mechanisms of personal recovery in bipolar disorder.
A Review of Language and Speech Features for Cognitive-Linguistic Assessment

It is widely accepted that information derived from analyzing speech (the acoustic signal) and language production (words and sentences) serves as a useful window into the health of an individual's cognitive ability. In fact, most neuropsychological batteries used in cognitive assessment have a component related to speech and language where clinicians elicit speech from patients for subjective evaluation across a broad set of dimensions. With advances in speech signal processing and natural language processing, there has been recent interest in developing tools to detect more subtle changes in cognitive-linguistic function. This work relies on extracting a set of features from recorded and transcribed speech for objective assessments of cognition, early diagnosis of neurological disease, and objective tracking of disease after diagnosis. In this paper we provide a review of existing speech and language features used in this domain, discuss their clinical application, and highlight their advantages and disadvantages. Broadly speaking, the review is split into two categories: language features based on natural language processing and speech features based on speech signal processing. Within each category, we consider features that aim to measure complementary dimensions of cognitive-linguistics, including language diversity, syntactic complexity, semantic coherence, and timing. We conclude the review with a proposal of new research directions to further advance the field.
Gamification of Enterprise Systems: A Synthesis of Mechanics, Dynamics, and Risks

Organizations highly depend on enterprise systems (ES), which are unlikely to develop their full potential if end-users neglect system usage. Accordingly, organizations attempt to overcome barriers to end-user acceptance in the ES context, which can be attributed to several factors on ES, organizational, and end-user level. Trying to take advantage of the growing passion for games, Gamification is a phenomenon proposed to motivate people by applying elements common to games in other contexts that have the potential to increase end-user acceptance. While first applications of Gamification exist in areas such as finance, health, and education, utility of gamifying ES has not been explored in-depth. Aiming to understand how Gamification can be applied to ES to increase user motivation, we analyze literature concerning game elements (i.e., mechanics and dynamics) used in Gamification and related risks. Our study yields a synthesis of mechanics in clusters of system design, challenges, rewards, and user specifics as well as related dynamics. We discuss the extent to which the game elements can be used to address ES acceptance barriers. While our study reveals that Gamification has potential for motivating ES users, future research should analyze concrete implementations of Gamification in ES contexts to investigate long-term effects.
Exact Combinatorial Optimization with Graph Convolutional Neural Networks

Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose a new graph convolutional neural network model for learning branch-and-bound variable selection policies, which leverages the natural variable-constraint bipartite graph representation of mixed-integer linear programs. We train our model via imitation learning from the strong branching expert rule, and demonstrate on a series of hard problems that our approach produces policies that improve upon state-of-the-art machine-learning methods for branching and generalize to instances significantly larger than seen during training. Moreover, we improve for the first time over expert-designed branching rules implemented in a state-of-the-art solver on large problems. Code for reproducing all the experiments can be found at this https URL.
Improving Matching Models with Hierarchical Contextualized Representations for Multi-turn Response Selection

In this paper, we study context-response matching with pre-trained contextualized representations for multi-turn response selection in retrieval-based chatbots. Existing models, such as Cove and ELMo, are trained with limited context (often a single sentence or paragraph), and may not work well on multi-turn conversations, due to the hierarchical nature, informal language, and domain-specific words. To address the challenges, we propose pre-training hierarchical contextualized representations, including contextual word-level and sentence-level representations, by learning a dialogue generation model from large-scale conversations with a hierarchical encoder-decoder architecture. Then the two levels of representations are blended into the input and output layer of a matching model respectively. Experimental results on two benchmark conversation datasets indicate that the proposed hierarchical contextualized representations can bring significantly and consistently improvement to existing matching models for response selection.
Effect of the Temperature of Background Plasma and the Energy of Energetic Electrons on Z-mode Excitation

It has been suggested that the Z-mode instability driven by energetic electrons with a loss-cone type velocity distribution is one candidate process behind the continuum and zebra pattern of solar type-IV radio bursts. Both the temperature of background plasma (T0) and the energy of energetic electrons (ve) are considered to be important to the variation of the maximum growth rate (γmax). Here we present a detailed parameter study on the effect of T0 and ve, within a regime of the frequency ratio ( ). In addition to γmax, we also analyze the effect on the corresponding wave frequency ( ) and propagation angle (θmax). We find that (1) γmax generally decreases with increasing ve, while its variation with T0 is more complex depending on the exact value of ve. (2) With increasing T0 and ve, presents stepwise profiles with jumps separated by gradual or very weak variations, and due to the warm plasma effect on the wave dispersion relation can vary within the hybrid band (the harmonic band containing the upper hybrid frequency) and the higher band. (3) The propagation is either perpendicular or quasi-perpendicular, and θmax presents variations in line with those of , as constrained by the resonance condition. We also examine the profiles of γmax with for different combinations of T0 and ve to clarify some earlier calculations which show inconsistent results.
The crystal structure of full-length Sizzled from Xenopus laevis yields insights into Wnt-antagonistic function of secreted Frizzled-related proteins

The Wnt-signaling pathway is crucial to cell proliferation, differentiation, and migration. The secreted Frizzled-related proteins (sFRPs) represent the largest family of secreted Wnt inhibitors. However, their function in antagonizing Wnt signaling has remained somewhat controversial. Here, we report the crystal structure of Sizzled from Xenopus laevis, the first full-length structure of an sFRP. Tethered by an inter-domain disulfide bond and a linker, the N-terminal cysteine-rich domain (CRD) and the C-terminal netrin-like domain (NTR) of Sizzled are arranged in a tandem fashion, with the NTR domain occluding the groove of CRD for Wnt accessibility. A Dual-Luciferase assay demonstrated that removing the NTR domain and replacing the CRD groove residues His-116 and His-118 with aromatic residues may significantly enhance antagonistic function of Sizzled in inhibiting Wnt3A signaling. Sizzled is a monomer in solution, and Sizzled CRD exhibited different packing in the crystal, suggesting that sFRPs do not have a conserved CRD dimerization mode. Distinct from the canonical NTR domain, the Sizzled NTR adopts a novel α/β folding with two perpendicular helices facing the central mixed β-sheet. The subgroup of human sFRP1/2/5 and Sizzled should have a similar NTR domain that features a highly positively charged region, opposite the NTR–CRD interface, suggesting that the NTR domain in human sFRPs, at least sFRP1/2/5, is unlikely to bind to Wnt but is likely involved in biphasic Wnt signaling modulation. In summary, the Sizzled structure provides the first insights into how the CRD and the NTR domains relate to each other for modulating Wnt-antagonistic function of sFRPs.
Finding Strong Gravitational Lenses in the DESI DECam Legacy Survey

We perform a semi-automated search for strong gravitational lensing systems in the 9000 deg2 Dark Energy Camera Legacy Survey (DECaLS), part of the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys. The combination of the depth and breadth of these surveys are unparalleled at this time, making them particularly suitable for discovering new strong gravitational lensing systems. We adopt the deep residual neural network architecture developed by Lanusse et al. for the purpose of finding strong lenses in photometric surveys. We compile a training sample that consists of known lensing systems in the Legacy Surveys and the Dark Energy Survey as well as non-lenses in the footprint of DECaLS. In this paper we show the results of applying our trained neural network to the cutout images centered on galaxies typed as ellipticals in DECaLS. The images that receive the highest scores (probabilities) are visually inspected and ranked. Here we present 335 candidate strong lensing systems, identified for the first time.
Motivo: Fast Motif Counting via Succinct Color Coding and Adaptive Sampling

The randomized technique of color coding is behind state-of-the-art algorithms for estimating graph motif counts. Those algorithms, however, are not yet capable of scaling well to very large graphs with billions of edges. In this paper we develop novel tools for the "motif counting via color coding" framework. As a result, our new algorithm, MOTIYO, scales to much larger graphs while at the same time providing more accurate motif counts than ever before. This is achieved thanks to two types of improvements. First, we design new succinct data structures for fast color coding operations, and a biased coloring trick that trades accuracy versus resource usage. These optimizations drastically reduce the resource requirements of color coding. Second, we develop an adaptive motif sampling strategy, based on a fractional set cover problem, that breaks the additive approximation barrier of standard sampling. This gives multiplicative approximations for all motifs at once, allowing us to count not only the most frequent motifs but also extremely rare ones. To give an idea of the improvements, in 40 minutes MOTIVO counts 7-nodes motifs on a graph with 65M nodes and 1.8B edges; this is 30 and 500 times larger than the state of the art, respectively in terms of nodes and edges. On the accuracy side, in one hour MOTIVO produces accurate counts of ≈ 10.000 distinct 8-node motifs on graphs where state-of-the-art algorithms fail even to find the second most frequent motif. Our method requires just a high-end desktop machine. These results show how color coding can bring motif mining to the realm of truly massive graphs using only ordinary hardware.
Dynamics of geodesics, and Maass cusp forms

The correspondence principle in physics between quantum mechanics and classical mechanics suggests deep relations between spectral and geometric entities of Riemannian manifolds. We survey---in a way intended to be accessible to a wide audience of mathematicians---a mathematically rigorous instance of such a relation that emerged in recent years, showing a dynamical interpretation of certain Laplace eigenfunctions of hyperbolic surfaces.
Bialgebraic Semantics for String Diagrams

Turi and Plotkin's bialgebraic semantics is an abstract approach to specifying the operational semantics of a system, by means of a distributive law between its syntax (encoded as a monad) and its dynamics (an endofunctor). This setup is instrumental in showing that a semantic specification (a coalgebra) satisfies desirable properties: in particular, that it is compositional. In this work, we use the bialgebraic approach to derive well-behaved structural operational semantics of string diagrams, a graphical syntax that is increasingly used in the study of interacting systems across different disciplines. Our analysis relies on representing the two-dimensional operations underlying string diagrams in various categories as a monad, and their bialgebraic semantics in terms of a distributive law over that monad. As a proof of concept, we provide bialgebraic compositional semantics for a versatile string diagrammatic language which has been used to model both signal flow graphs (control theory) and Petri nets (concurrency theory). Moreover, our approach reveals a correspondence between two different interpretations of the Frobenius equations on string diagrams and two synchronisation mechanisms for processes, a la Hoare and a la Milner.
Closed-Loop Control of a Delta-Wing Unmanned Aerial-Aquatic Vehicle

We present a closed-loop control strategy for a delta-wing unmanned aerial aquatic-vehicle (UAAV) that enables autonomous swim, fly, and water-to-air transition. Our control system consists of a hybrid state estimator and a closed-loop feedback policy which is capable of trajectory following through the water, air and transition domains. To test our estimator and control approach in hardware, we instrument the vehicle with a minimalistic set of commercial off-the-shelf sensors. Finally, we demonstrate a successful autonomous water-to-air transition with our prototype UAAV system and discuss the implications of these results with regards to robustness.
On the model dependence of fiducial cross-section measurements

Fiducial production cross-section measurements of Standard Model processes, in principle, provide constraints on new physics scenarios via a comparison of the predicted Standard Model cross-section and the observed cross-section. This approach received significant attention in recent years, both from direct constraints on specific models and the interpretation of measurements in the view of effective field theories. A generic problem in the reinterpretation of Standard Model measurements is the corrections application of to data to account for detector effects. These corrections inherently assume the Standard Model to be valid, thus implying a model bias of the final result. In this work, we study the size of this bias by studying several new physics models and fiducial phase–space regions. The studies are based on fast detector simulations of a generic multi-purpose detector at the Large Hadron Collider. We conclude that the model bias in the associated reinterpretations is negligible only in specific cases, however, typically on the same level as systematic uncertainties of the available measurements.
Assessing Performance Implications of Deep Copy Operations via Microbenchmarking

As scientific frameworks become sophisticated, so do their data structures. Current data structures are no longer simple in design and they have been progressively complicated. The typical trend in designing data structures in scientific applications are basically nested data structures: pointing to a data structure within another one. Managing nested data structures on a modern heterogeneous system requires tremendous effort due to the separate memory space design. In this paper, we will discuss the implications of deep copy on data transfers on current heterogeneous. Then, we will discuss the two options that are currently available to perform the memory copy operations on complex structures and will introduce pointerchain directive that we proposed. Afterwards, we will introduce a set of extensive benchmarks to compare the available approaches. Our goal is to make our proposed benchmarks a basis to examine the efficiency of upcoming approaches that address the challenge of deep copy operations.
Two families of novel second-order fractional numerical formulas and their applications to fractional differential equations

In this article, we introduce two families of novel fractional $\theta$-methods by constructing some new generating functions to discretize the Riemann-Liouville fractional calculus operator $\mathit{I}^{\alpha}$ with a second order convergence rate. A new fractional BT-$\theta$ method connects the fractional BDF2 (when $\theta=0$) with fractional trapezoidal rule (when $\theta=1/2$), and another novel fractional BN-$\theta$ method joins the fractional BDF2 (when $\theta=0$) with the second order fractional Newton-Gregory formula (when $\theta=1/2$). To deal with the initial singularity, correction terms are added to achieve an optimal convergence order. In addition, stability regions of different $\theta$-methods when applied to the Abel equations of the second kind are depicted, which demonstrate the fact that the fractional $\theta$-methods are A($\vartheta$)-stable. Finally, numerical experiments are implemented to verify our theoretical result on the convergence analysis.
Triangulation Learning Network: From Monocular to Stereo 3D Object Detection

In this paper, we study the problem of 3D object detection from stereo images, in which the key challenge is how to effectively utilize stereo information. Different from previous methods using pixel-level depth maps, we propose to employ 3D anchors to explicitly construct object-level correspondences between the regions of interest in stereo images, from which the deep neural network learns to detect and triangulate the targeted object in 3D space. We also introduce a cost-efficient channel reweighting strategy that enhances representational features and weakens noisy signals to facilitate the learning process. All of these are flexibly integrated into a solid baseline detector that inputs monocular images. We demonstrate that both the monocular baseline and the stereo triangulation learning network outperform the prior state-of-the-arts in 3D object detection and localization on the challenging KITTI dataset.
Constructing a learner centric semantic open syllabus for automated text book generation

This paper proposes a methodology for semantic open syllabus construction to be eventually used by an automated and collaborative book authoring tool. It proposes the use of RDF\OWL over the existing use of XML to define an Open Syllabus. Using RDF\OWL will eliminate fixed schema requirement of XML allowing seamless semantic integration with other semantic e-learning objects. The paper also delineates the importance of using semantic technologies in an active learning community for specifying the syllabus and general course development. It emphasizes on creating a learner centric syllabi which fosters collaborative learning and knowledge generation from the collaborative learning community. The student indirectly participates in the formation of course objectives and hence course development leading to a truly learner centric teaching environment. This paper proposes detailed semantic open syllabus ontology to specify course contents and a corresponding methodology that would aid in automated textbook generation in a collaborative learning environment.