Miracle-dz/UniFAR-FTUs huggingface dataset:
DiPD: Disruptive event Prediction Dataset from Twitter

Riots and protests, if gone out of control, can cause havoc in a country. We have seen examples of this, such as the BLM movement, climate strikes, CAA Movement, and many more, which caused disruption to a large extent. Our motive behind creating this dataset was to use it to develop machine learning systems that can give its users insight into the trending events going on and alert them about the events that could lead to disruption in the nation. If any event starts going out of control, it can be handled and mitigated by monitoring it before the matter escalates. This dataset collects tweets of past or ongoing events known to have caused disruption and labels these tweets as 1. We also collect tweets that are considered non-eventful and label them as 0 so that they can also be used to train a classification system. The dataset contains 94855 records of unique events and 168706 records of unique non-events, thus giving the total dataset 263561 records. We extract multiple features from the tweets, such as the user's follower count and the user's location, to understand the impact and reach of the tweets. This dataset might be useful in various event related machine learning problems such as event classification, event recognition, and so on.
Experimental Study on a Gyromagnetic Piezo-Cantilever Generator

To meet the demands of the rotating structure for self-power, a novel gyromagnetic piezo-cantilever generator (GPCG) excited by the coupling between rotating magnets and those fixed on piezo-cantilever was presented. The influence of magnetic force (number and configuration of the magnets) and rotating speed on energy generation of the GPCG was investigated experimentally. The research results show that there are 9 optimal rotating speeds for the GPCG to achieve peak voltage at speed range of 0-1390r/min. With 1 magnet (ø12x2mm3) fixed on piezo-cantilever, the increasing number of rotating magnets (ø12x4mm3) in the same place (ns) of the rotator exerts no influence on the optimal rotating speeds, but leads to rising output voltage. At 1042.5r/min, the achieved peak voltages from the GPCG in the case of ns=1/2/4/6 are 13.2/16.6/23.8/27.8V respectively. The optimal speeds decrease and the peak voltage rises with the increasing number of magnets evenly distributed on the rotator (nd). In the case of 1 magnet fixed on piezo-cantilever and nd=1/2/4/8, the optimal rotating speeds and the peek voltages from the GPCG are 708.9/528.2/528.2/264.1r/min and 13.2/16.6/23.8/27.8V respectively.
Technical Report: Edge-centric Programming for IoT Applications with EdgeProg

IoT application development usually involves separate programming at the device side and server side. While separate programming style is sufficient for many simple applications, it is not suitable for many complex applications that involve complex interactions and intensive data processing. We propose EdgeProg, an edge-centric programming approach to simplify IoT application programming, motivated by the increasing popularity of edge computing. With EdgeProg, users could write application logic in a centralized manner with an augmented If-This-Then-That (IFTTT) syntax and virtual sensor mechanism. The program can be processed at the edge server, which can automatically generate the actual application code and intelligently partition the code into device code and server code, for achieving the optimal latency. EdgeProg employs dynamic linking and loading to deploy the device code on a variety of IoT devices, which do not run any application-specific codes at the start. Results show that EdgeProg achieves an average reduction of 20.96%, 27.8% and 79.41% in terms of execution latency, energy consumption, and lines of code compared with state-of-the-art approaches.
A unified framework for limit results in chemical reaction networks on multiple time-scales

If $(X^N)_{N=1,2,. }$ is a sequence of Markov processes which solve the martingale problems for some operators $(G^N)_{N=1,2,. }$, it is a classical task to derive a limit result as $N\to\infty$, in particular a weak process limit with limiting operator $G$. For slow-fast systems $X^N = (V^N, Z^N)$ where $V^N$ is slow and $Z^N$ is fast, $G^N$ consists of two (or more) terms, and we are interested in weak convergence of $V^N$ to some Markov process $V$. In this case, for some $f\in \mathcal D(G)$, the domain of $G$, depending only on $v$, the limit $Gf$ can sometimes be derived by using some $g_N\to 0$ (depending on $v$ and $z$), and study convergence of $G^N (f + g_N) \to Gf$. We develop this method further in order to obtain functional Laws of Large Numbers (LLNs) and Central Limit Theorems (CLTs). We then apply our general result to various examples from Chemical Reaction Network theory. We show that we can rederive most limits previously obtained, but also provide new results in the case when the fast-subsystem is first order. In particular, we allow that fast species to be consumed faster than they are produced, and we derive a CLT for Hill dynamics with coefficient~2.
Generic inertia emulation controller for multi-terminal voltage-source-converter high voltage direct current systems

A generic inertia emulation controller (INEC) scheme for multi-terminal voltage-source-converter (VSC)-based high voltage direct current (HVDC) systems is proposed in this study. The INEC can be incorporated in any grid-side VSC station, allowing the multi-terminal HVDC (MTDC) terminal to contribute an inertial response to connected AC systems during system disturbances, in a fashion similar to synchronous generators. The DC-link capacitors within the MTDC are utilised by the INEC scheme to exchange stored energy with the AC system by varying the overall DC voltage level of the MTDC network within a safe and pre-defined range. A theoretical treatment of the INEC algorithm and its implementation and integration within a conventional VSC control system are presented, and the impact on the total DC capacitance required within the MTDC network to ensure that DC voltages vary within an acceptable range is discussed. The proposed INEC scheme is validated using a MATLAB/SIMULINK model under various changes in demand and AC network faults. The model incorporates a multi-machine AC power system connected to a MTDC transmission system with multiple converter-interfaced nodes. The effectiveness of the INEC in damping post-fault oscillations and in enhancing AC grid frequency stability is also investigated.
Sparse deep computer-generated holography for optical microscopy

Computer-generated holography (CGH) has broad applications such as direct-view display, virtual and augmented reality, as well as optical microscopy. CGH usually utilizes a spatial light modulator that displays a computer-generated phase mask, modulating the phase of coherent light in order to generate customized patterns. The algorithm that computes the phase mask is the core of CGH and is usually tailored to meet different applications. CGH for optical microscopy usually requires 3D accessibility (i.e., generating overlapping patterns along the $z$-axis) and micron-scale spatial precision. Here, we propose a CGH algorithm using an unsupervised generative model designed for optical microscopy to synthesize 3D selected illumination. The algorithm, named sparse deep CGH, is able to generate sparsely distributed points in a large 3D volume with higher contrast than conventional CGH algorithms.
How Facial Features Convey Attention in Stationary Environments

Awareness detection technologies have been gaining traction in a variety of enterprises; most often used for driver fatigue detection, recent research has shifted towards using computer vision technologies to analyze user attention in environments such as online classrooms. This paper aims to extend previous research on distraction detection by analyzing which visual features contribute most to predicting awareness and fatigue. We utilized the open source facial analysis toolkit OpenFace in order to analyze visual data of subjects at varying levels of attentiveness. Then, using a Support-Vector Machine (SVM) we created several prediction models for user attention and identified Histogram of Oriented Gradients (HOG) and Action Units to be the greatest predictors of the features we tested. We also compared the performance of this SVM to deep learning approaches that utilize Convolutional and/or Recurrent neural networks (CNN's and CRNN's). Interestingly, CRNN's did not appear to perform significantly better than their CNN counterparts. While deep learning methods achieved greater prediction accuracy, SVMs utilized less resources and, using certain parameters, were able to approach the performance of deep learning methods.
Application of Equal Local Levels to Improve Q-Q Plot Testing Bands with R Package qqconf

Quantile-Quantile (Q-Q) plots are often difficult to interpret because it is unclear how large the deviation from the theoretical distribution must be to indicate a lack of fit. Most Q-Q plots could benefit from the addition of meaningful global testing bands, but the use of such bands unfortunately remains rare because of the drawbacks of current approaches and packages. These drawbacks include incorrect global Type I error rate, lack of power to detect deviations in the tails of the distribution, relatively slow computation for large data sets, and limited applicability. To solve these problems, we apply the equal local levels global testing method, which we have implemented in the R Package qqconf, a versatile tool to create Q-Q plots and probability-probability (P-P) plots in a wide variety of settings, with simultaneous testing bands rapidly created using recently-developed algorithms. qqconf can easily be used to add global testing bands to Q-Q plots made by other packages. In addition to being quick to compute, these bands have a variety of desirable properties, including accurate global levels, equal sensitivity to deviations in all parts of the null distribution (including the tails), and applicability to a range of null distributions. We illustrate the use of qqconf in several applications: assessing normality of residuals from regression, assessing accuracy of p values, and use of Q-Q plots in genome-wide association studies.
Relativistic extended uncertainty principle from spacetime curvature

The investigations presented in this study are directed at relativistic modiﬁcations of the uncertainty relation derived from the curvature of the background spacetime. These ﬁndings generalize previous work which is recovered in the nonrelativistic limit. Applying the 3+1-splitting in accordance with the ADM-formalism, we ﬁnd the relativistic physical momentum operator and compute its standard deviation for wave functions conﬁned to a geodesic ball on a spacelike hypersurface. Its radius can then be understood as a measure of position uncertainty. Under the assumtion of small position uncertainties in comparison to background curvature length scales, we obtain the corresponding corrections to the uncertainty relation in ﬂat space. Those depend on the Ricci scalar of the eﬀective spatial metric, the particle is moving on, and, if there are nonvanishing time-space components of the spacetime metric, gradients of the shift vector and the lapse function. Interest-ingly, this result is applicable not only to massive but also to massless particles. Over all, this is not a covariant, yet a consistently general relativistic approach. We further speculate on a possible covariant extension.
Automatic Synthesis of Diverse Weak Supervision Sources for Behavior Analysis

Obtaining annotations for large training sets is expensive, especially in settings where domain knowledge is required, such as behavior analysis. Weak supervision has been studied to reduce annotation costs by using weak labels from task-specific labeling functions (LFs) to augment ground truth labels. However, domain experts still need to hand-craft different LFs for different tasks, limiting scalability. To reduce expert effort, we present AutoSWAP: a framework for automatically synthesizing data-efficient task-level LFs. The key to our approach is to efficiently represent expert knowledge in a reusable domain-specific language and more general domain-level LFs, with which we use state-of-the-art program synthesis techniques and a small labeled dataset to generate task-level LFs. Additionally, we propose a novel structural diversity cost that allows for efficient synthesis of diverse sets of LFs, further improving AutoSWAP's performance. We evaluate AutoSWAP in three behavior analysis domains and demonstrate that AutoSWAP outperforms existing approaches using only afraction of the data. Our results suggest that AutoSWAP is an effective way to automatically generate LFs that can significantly reduce expert effort for behavior analysis.
Neuron with Steady Response Leads to Better Generalization

Regularization can mitigate the generalization gap between training and inference by introducing inductive bias. Existing works have already proposed various inductive biases from diverse perspectives. However, none of them explores inductive bias from the perspective of class-dependent response distribution of individual neurons. In this paper, we conduct a substantial analysis of the characteristics of such distribution. Based on the analysis results, we articulate the Neuron Steadiness Hypothesis: the neuron with similar responses to instances of the same class leads to better generalization. Accordingly, we propose a new regularization method called Neuron Steadiness Regularization (NSR) to reduce neuron intra-class response variance. Based on the Complexity Measure, we theoretically guarantee the effectiveness of NSR for improving generalization. We conduct extensive experiments on Multilayer Perceptron, Convolutional Neural Networks, and Graph Neural Networks with popular benchmark datasets of diverse domains, which show that our Neuron Steadiness Regularization consistently outperforms the vanilla version of models with significant gain and low additional computational overhead.
Majorana Zero Modes in Ferromagnetic Wires without Spin-Orbit Coupling

We present a novel controllable platform for engineering Majorana zero modes. The platform consists of a ferromagnetic metallic wire placed among conventional superconductors, which are in proximity to ferromagnetic insulators. We demonstrate that Majorana zero modes emerge localised at the edges of the ferromagnetic wire, due to the interplay of the applied supercurrents and the induced by proximity exchange fields with conventional superconductivity. Our mechanism does not rely on the pairing of helical fermions by combining conventional superconductivity with spin-orbit coupling, but rather exploits the misalignment between the magnetization of the ferromagnetic insulators and that of the ferromagnetic wire.
On the logarithmic bipartite fidelity of the open XXZ spin chain at $Δ=-1/2$

The open XXZ spin chain with the anisotropy \Delta=-\frac12Δ=−12 and a one-parameter family of diagonal boundary fields is studied at finite length. A determinant formula for an overlap involving the spin chain’s ground-state vectors for different lengths is found. The overlap allows one to obtain an exact finite-size formula for the ground state’s logarithmic bipartite fidelity. The leading terms of its asymptotic series for large chain lengths are evaluated. Their expressions confirm the predictions of conformal field theory for the fidelity.
Image denoising by Super Neurons: Why go deep?

Classical image denoising methods utilize the non-local self-similarity principle to effectively recover image content from noisy images. Current state-of-the-art methods use deep convolutional neural networks (CNNs) to effectively learn the mapping from noisy to clean images. Deep denoising CNNs manifest a high learning capacity and integrate non-local information owing to the large receptive field yielded by numerous cascade of hidden layers. However, deep networks are also computationally complex and require large data for training. To address these issues, this study draws the focus on the Self-organized Operational Neural Networks (Self-ONNs) empowered by a novel neuron model that can achieve a similar or better denoising performance with a compact and shallow model. Recently, the concept of super-neurons has been introduced which augment the non-linear transformations of generative neurons by utilizing non-localized kernel locations for an enhanced receptive field size. This is the key accomplishment which renders the need for a deep network configuration. As the integration of non-local information is known to benefit denoising, in this work we investigate the use of super neurons for both synthetic and real-world image denoising. We also discuss the practical issues in implementing the super neuron model on GPUs and propose a trade-off between the heterogeneity of non-localized operations and computational complexity. Our results demonstrate that with the same width and depth, Self-ONNs with super neurons provide a significant boost of denoising performance over the networks with generative and convolutional neurons for both denoising tasks. Moreover, results demonstrate that Self-ONNs with super neurons can achieve a competitive and superior synthetic denoising performances than well-known deep CNN denoisers for synthetic and real-world denoising, respectively.
A panchromatic view of star cluster formation in a simulated dwarf galaxy starburst

We present a photometric analysis of star and star cluster (SC) formation in a high-resolution simulation of a dwarf galaxy starburst that allows the formation of individual stars to be followed. Previous work demonstrated that the properties of the SCs formed in the simulation are in good agreement with observations. In this paper, we create mock spectral energy distributions and broad-band photometric images using the radiative transfer code skirt 9. We test several observational star formation rate (SFR) tracers and find that 24 μm, total infrared and Hα trace the underlying SFR during the (post)starburst phase, while UV tracers yield a more accurate picture of star formation during quiescent phases prior to and after the merger. We then place the simulated galaxy at distances of 10 and 50 Mpc and use aperture photometry at Hubble Space Telescope resolution to analyse the simulated SC population. During the starburst phase, a hierarchically forming set of SCs leads inaccurate source separation because of crowding. This results in estimated SC mass function slopes that are up to ∼0.3 shallower than the true slope of ∼−1.9 to −2 found for the bound clusters identified from the particle data in the simulation. The masses of the largest clusters are overestimated by a factor of up to 2.9 due to unresolved clusters within the apertures. The aperture-based analysis also produces a relation between cluster formation efficiency and SFR surface density that is slightly flatter than that recovered from bound clusters. The differences are strongest in quiescent SF environments.
RADU: Ray-Aligned Depth Update Convolutions for ToF Data Denoising

Time-of-Flight (ToF) cameras are subject to high levels of noise and distortions due to Multi-Path-Interference (MPI). While recent research showed that 2D neural networks are able to outperform previous traditional State-of-the-Art (SOTA) methods on correcting ToF-Data, little research on learning-based approaches has been done to make direct use of the 3D information present in depth images. In this paper, we propose an iterative correcting approach operating in 3D space, that is designed to learn on 2.5D data by enabling 3D point convolutions to correct the points’ positions along the view direction. As labeled real world data is scarce for this task, we further train our network with a self-training approach on unlabeled real world data to account for real world statistics. We demonstrate that our method is able to outperform SOTA methods on several datasets, including two real world datasets and a new large-scale synthetic data set introduced in this paper.
Breaking the Linear Iteration Cost Barrier for Some Well-known Conditional Gradient Methods Using MaxIP Data-structures

Conditional gradient methods (CGM) are widely used in modern machine learning. CGM's overall running time usually consists of two parts: the number of iterations and the cost of each iteration. Most efforts focus on reducing the number of iterations as a means to reduce the overall running time. In this work, we focus on improving the per iteration cost of CGM. The bottleneck step in most CGM is maximum inner product search (MaxIP), which requires a linear scan over the parameters. In practice, approximate MaxIP data-structures are found to be helpful heuristics. However, theoretically, nothing is known about the combination of approximate MaxIP data-structures and CGM. In this work, we answer this question positively by providing a formal framework to combine the locality sensitive hashing type approximate MaxIP data-structures with CGM algorithms. As a result, we show the first algorithm, where the cost per iteration is sublinear in the number of parameters, for many fundamental optimization algorithms, e.g., Frank-Wolfe, Herding algorithm, and policy gradient.
A Review on Graph Neural Network Methods in Financial Applications

With multiple components and relations, financial data are often presented as graph data, since it could represent both the individual features and the complicated relations. Due to the complexity and volatility of the financial market, the graph constructed on the financial data is often heterogeneous or time-varying, which imposes challenges on modeling technology. Among the graph modeling technologies, graph neural network (GNN) models are able to handle the complex graph structure and achieve great performance and thus could be used to solve financial tasks. In this work, we provide a comprehensive review of GNN models in recent financial context. We first categorize the commonly-used financial graphs and summarize the feature processing step for each node. Then we summarize the GNN methodology for each graph type, application in each area, and propose some potential research areas.
L'Eclipse De L'Objet Dans Les ThéOries Du Projet En Design

The contextual field of this research is the history of ideas in design, ‘ideas’ being understood as ideologies, dogmas, treatises, theories or philosophies (i.e. both descriptive and normative items), and ‘design’ including all practices related to our built environment with some aesthetic concern (graphic, product, architectural, urban, landscape design). Our corpus is constituted from written and published texts mainly but not exclusively from the second half of the last century. The objective of the research was to find out if some Gestalt or model (evolutionary, structural, thematic or else) emerged from this historical corpus and the purpose was essentially pedagogical and didactic. The research yielded the following interesting features: (1) the material object or product has long been the main focus of the theories, all the way up to the middle of the twentieth century; (2) the object tends to disappear from the concerns of the theoreticians after the Second World War, both upstream and downstream of the design process, i.e. on the side of the designers and on the side of the users - design theories mainly focus upstream on the design process and downstream on the functions of the product; (3) much more recently another shift took place away from the product in the preoccupations of the theoreticians, both upstream and downstream again. Theoretical models are now interested in either the actors of the design process or the experiences of the user as a ‘whole’ human being. These shifts correspond to a displacement of the centre of the interest respectively from the aesthetic to the logical, to the ethical fields of philosophy. This ‘eclipse of the object’ has important consequences for the theory and practice of design.