Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecasting Time Series for Electricity Consumption Data Using Dynamic Weight Ensemble Model

Electricity load forecasting is a prevalent research topic in recent years. In this study, we predict the electricity consumption using only previous power data (i.e., without using weather information or other features). We survey existing univariate methods such as MLP-based, CNN-based, XGBoost-based, RF-based, and EN3-bestK. However, these existing methods do not perform well due to that the range of power values varies a lot. Therefore, we present an electricity consumption forecast system called Dynamic Weight Ensemble Model (DWEM). There are three stages in the proposed DWEM. First of all, we provide three types of data serialization in data preprocessing. Second, we train four types of models (i.e., MLP-based, CNN-based, XGBoost-based, and RF-based) for building the ensemble model later. Finally, we combine the four types of models into an ensemble model, using the proposed Two-Phase Ensemble. In the two-phase ensemble, the first phase is to ensemble the models trained using the same algorithm but different serializations, and the second phase is to ensemble the models from different algorithms. The two-phase ensemble method is designed to dynamically adjust weights based on the previous performance of the corresponding models. Moreover, we notice that properly handling missing values is an important factor in system performance. Therefore, we present a statistical method to estimate the missing values. We compare DWEM with various state-of-the-art methods. Comparison of DWEM and the state-of-the-art ensemble method, the results show that DWEM is on average about 46.95% and 44.47% better than EN3-bestK on the MAPE and MAE indicators, respectively.
BIO-MONITORING OF RADIONUCLIDES AIR POLLUTION USING PHYSIOLOGICAL

Contamination of environment by radionuclides in territories under urboecosystem conditions is an actual problem. The search for new express methods for radioactivity determination of environment is an important task of research. The present work evaluated environmental radionuclides air pollution. The accumulation features of radionuclides leaves of camphor trees ( production, Upper Egypt during the physical year 232Th and 40K in samples of soils, plant leaves and deposited dust have been compared. These activities were measured using high resolution gamma background level. On the basis of radionuclides analysis, leaves of coefficients according to the dis leaf deposited dust and soil samples was estimated by Pearson coefficients to resolve two main mechanisms of radionuclide accumulation, atmospheric deposition, and root upt sampling sites were not significant. determined. The results of investigation recommended the use pollution.
Genetic diversity of different geographical populations of Sesamia inferens as determined by AFLP

To study the genetic diversity of different Sesamia inferens Walker populations, specimens from ten geographical populations from ten Chinese provinces were collected. Using 37 pairs of AFLP selective primers,1 055 AFLP loci were recorded,of which 78. 75% were polymorphic. The coefficient of gene differentiation( Gst) between populations was 0. 7132,Nei's gene diversity index( h) ranged from 0. 2969 to 0. 4547 with an average of 0. 3752, Shannon's index ranged from 0. 2936 to 0. 5043 with an average of 0. 3888. Genetic identity between the 10 populations was greater than 0. 63. NJ cluster analysis showed that the ten populations are grouped into four major clades based on polymorphism of AFLP markers,of which the DY,LH and ZJ populations belong to three different clades,and the other seven populations belong to a single clade. Populations within each clade have similar geographic climates and proximity to each other. However,an isolated population that had longer genetic distances to all other tested populations probably clustered into a random clade. Investigations of genetic diversity between populations should use as many widely dispersed populations as possible.
The anatomy of a clinical trial

Cognition is the mental process by which an individual acquires knowledge and understanding of their physical, cultural, social and psychological world. The cognitive system not only endows awareness of the present situation, but also, via the processes of memory, a recollection of previous events and experiences. Current perceptions can be used either as a basis to modify and adapt existing mental organisations (schemata) or simply be assimilated into the pre‐existing neural circuits. Under these latter circumstances, the individual's cognisance of this world would not change, whereas, if a particular schema was accommodated and altered in some way to take account of a new experience then the individual world view would necessarily change.
Deep Replication of a Runoff Portfolio

To the best of our knowledge, the application of deep learning in the field of quantitative risk management is still a relatively recent phenomenon. This article presents the key notions of Deep Asset Liability Management (Deep~ALM) for a technological transformation in the management of assets and liabilities along a whole term structure. The approach has a profound impact on a wide range of applications such as optimal decision making for treasurers, optimal procurement of commodities or the optimisation of hydroelectric power plants. As a by-product, intriguing aspects of goal-based investing or Asset Liability Management (ALM) in abstract terms concerning urgent challenges of our society are expected alongside. We illustrate the potential of the approach in a stylised case.
Exploration of End-to-end Synthesisers forZero Resource Speech Challenge 2020

A Spoken dialogue system for an unseen language is referred to as Zero resource speech. It is especially beneficial for developing applications for languages that have low digital resources. Zero resource speech synthesis is the task of building text-to-speech (TTS) models in the absence of transcriptions. In this work, speech is modelled as a sequence of transient and steady-state acoustic units, and a unique set of acoustic units is discovered by iterative training. Using the acoustic unit sequence, TTS models are trained. The main goal of this work is to improve the synthesis quality of zero resource TTS system. Four different systems are proposed. All the systems consist of three stages: unit discovery, followed by unit sequence to spectrogram mapping, and finally spectrogram to speech inversion. Modifications are proposed to the spectrogram mapping stage. These modifications include training the mapping on voice data, using x-vectors to improve the mapping, two-stage learning, and gender-specific modelling. Evaluation of the proposed systems in the Zerospeech 2020 challenge shows that quite good quality synthesis can be achieved.
Neutron stars in f(ℛ,T) gravity using realistic equations of state in the light of massive pulsars and GW170817

In this work we investigate neutron stars (NS) in f(ℛ,T) gravity for the case R+2λ𝒯, ℛ is the Ricci scalar and 𝒯 the trace of the energy-momentum tensor. The hydrostatic equilibrium equations are solved considering realistic equations of state (EsoS). The NS masses and radii obtained are subject to a joint constrain from massive pulsars and the event GW170817. The parameter λ needs to be negative as in previous NS studies, however we found a minimum value for it. The value should be |λ|≲0.02 and the reason for so small value in comparison with previous ones obtained with simpler EsoS is due to the existence of the NS crust. The pressure in theory of gravity depends on the inverse of the sound velocity vs. Since, vs is low in the crust, |λ| need to be very small. We found that the increment in the star mass is less than 1%, much smaller than previous ones obtained not considering the realistic stellar structure, and the star radius cannot become larger, its changes compared to GR is less than 3.6% in all cases. The finding that using several relativistic and non-relativistic models the variation on the NS mass and radius are almost the same for all the EsoS, manifests that our results are insensitive to the high density part of the EsoS. It confirms that stellar mass and radii changes depend only on crust, where the EoS is essentially the same for all the models. The NS crust effect implying very small values of |λ| does not depend on the theory's function chosen, since for any other one the hydrostatic equilibrium equation would always have the dependence 1/vs. Finally, we highlight that our results indicate that conclusions obtained from NS studies done in modified theories of gravity without using realistic EsoS that describe correctly the NS interior can be unreliable.
Stringy invariants and toric Artin stacks

Abstract We propose a conjectural framework for computing Gorenstein measures and stringy Hodge numbers in terms of motivic integration over arcs of smooth Artin stacks, and we verify this framework in the case of fantastacks, which are certain toric Artin stacks that provide (nonseparated) resolutions of singularities for toric varieties. Specifically, let $\mathcal {X}$ be a smooth Artin stack admitting a good moduli space $\pi : \mathcal {X} \to X$ , and assume that X is a variety with log-terminal singularities, $\pi $ induces an isomorphism over a nonempty open subset of X and the exceptional locus of $\pi $ has codimension at least $2$ . We conjecture a change-of-variables formula relating the motivic measure for $\mathcal {X}$ to the Gorenstein measure for X and functions measuring the degree to which $\pi $ is nonseparated. We also conjecture that if the stabilisers of $\mathcal {X}$ are special groups in the sense of Serre, then almost all arcs of X lift to arcs of $\mathcal {X}$ , and we explain how in this case (assuming a finiteness hypothesis satisfied by fantastacks) our conjectures imply a formula for the stringy Hodge numbers of X in terms of a certain motivic integral over the arcs of $\mathcal {X}$ . We prove these conjectures in the case where $\mathcal {X}$ is a fantastack.
Spin polarization recovery and Hanle effect for charge carriers interacting with nuclear spins in semiconductors

We report on theoretical and experimental study of the spin polarization recovery and Hanle effect for the charge carriers interacting with the fluctuating nuclear spins in the semiconductor structures. We start the theoretical description from the simplest model of static and isotropic nuclear spin fluctuations. Then we describe the modification of the polarization recovery and Hanle curves due to the anisotropy of the hyperfine interaction, finite nuclear spin correlation time, and the strong pulsed spin excitation. For the latter case, we describe the resonance spin amplification effect in the Faraday geometry and discuss the manifestations of the quantum Zeno effect. The set of the experimental results for various structures and experimental conditions is chosen to highlight the specific effects predicted theoretically. We show that the spin polarization recovery is a very valuable tool for addressing carrier spin dynamics in semiconductors and their nanostructures.
Revisiting Tardos's Framework for Linear Programming: Faster Exact Solutions using Approximate Solvers

In breakthrough work, Tardos (Oper. Res. '86) gave a proximity based framework for solving linear programming (LP) in time depending only on the constraint matrix in the bit complexity model. In Tardos's framework, one reduces solving the LP $\min\langle c, x \rangle, Ax=b, x\geq 0, A\in \mathbb{Z}^{m\times n}$, to solving $O(nm)$ LPs in $A$ having small integer coefficient objectives and right-hand sides using any exact LP algorithm. This gives rise to an LP algorithm in time $\text{poly} (n, m\log\Delta_{A})$, where $\Delta_{A}$ is the largest subdeterminant of $A$. A significant extension to the real model of computation was given by Vavasis and Ye (Math. Prog. '96), giving a specialized interior point method that runs in time $\text{poly} (n, m,\log\bar{\chi}_{A})$, depending on Stewart's $\bar{\chi}_{A}$, a well-studied condition number. In this work, we extend Tardos's original framework to obtain such a running time dependence. In particular, we replace the exact LP solves with approximate ones, enabling us to directly leverage the tremendous recent algorithmic progress for approximate linear programming. More precisely, we show that the fundamental “accuracy” needed to exactly solve any LP in $A$ is inverse polynomial in $n$ and $\log\bar{\chi}_{A}$. Plugging in the recent algorithm of van den Brand (SODA '20), our method computes an optimal primal and dual solution using $O(mn^{\omega+1+0(1)}\log(\bar{\chi}_{A}+n))$ arithmetic operations, outperforming the specialized interior point method of Vavasis and Ye and its recent improvement by Dadush et al (STOC '20). By applying the preprocessing algorithm of the latter paper, the dependence can also be reduced from $\bar{\chi}_{A}$ to $\bar{\chi}_{A}^{\ast}$, the minimum value of $\bar{\chi}_{AD}$ attainable via column rescalings. Our framework is applicable to achieve the $\text{poly} (n, m,\log\bar{\chi}_{A}^{\ast})$ bound using essentially any weakly polynomial LP algorithm, such as the ellipsoid method. At a technical level, our framework combines together approximate LP solutions to compute exact ones, making use of constructive proximity theorems—which bound the distance between solutions of “nearby” LPs—to keep the required accuracy low.
Dynamic generation of GHZ states of three coupled charge qubits

In this paper, we present a proof-of-principle of the formation of pure maximally entangled states from the Greenberger-Horne-Zeilinger class for three-qubits, in the experimental context of charged quantum dots. Each qubit must be identified as a pair of quantum dots coupled through a tunneling parameter, that allows electron tunneling between the dots. The Coulomb interaction is accounted for and is responsible for the coupling between the qubits. The interplay between coherent tunneling events and many-body interaction gives rise to the formation of highly entangled states. An effective model sheds light on the role of a third-order tunneling process behind the dynamics, and the action of charge dephasing is quantified in the process.
A performance study of some approximation algorithms for minimum dominating set in a graph

We implement and test the performances of several approximation algorithms for computing the minimum dominating set of a graph. These algorithms are the standard greedy algorithm, the recent LP rounding algorithms and a hybrid algorithm that we design by combining the greedy and LP rounding algorithms. All algorithms perform better than anticipated in their theoretical analysis, and have small performance ratios, measured as the size of output divided by the LP objective lower-bound. However, each may have advantages over the others. For instance, LP rounding algorithm normally outperforms the other algorithms on sparse real-world graphs. On a graph with 400,000+ vertices, LP rounding took less than 15 seconds of CPU time to generate a solution with performance ratio 1.011, while the greedy and hybrid algorithms generated solutions of performance ratio 1.12 in similar time. For synthetic graphs, the hybrid algorithm normally outperforms the others, whereas for hypercubes and k-Queens graphs, greedy outperforms the rest. Another advantage of the hybrid algorithm is to solve very large problems where LP solvers crash, as demonstrated on a real-world graph with 7.7 million+ vertices.
Development of a Predictive Process Design kit for15-nm FinFETs: FreePDK15

FinFETs are predicted to advance semiconductorscaling for sub-20nm devices. In order to support their intro-duction into research and universities it is crucial to develop anopen source predictive process design kit. This paper discussesin detail the design process for such a kit for 15nm FinFETdevices, called the FreePDK15. The kit consists of a layerstack with thirteen-metal layers based on hierarchical-scalingused in ASIC architecture, Middle-of-Line local interconnectlayers and a set of Front-End-of-Line layers. The physical andgeometrical properties of these layers are defined and theseproperties determine the density and parasitics of the design. Thedesign rules are laid down considering additional guidelines forprocess variability, challenges involved in FinFET fabrication anda unique set of design rules are developed for critical dimensions.Layout extraction including modified rules for determining thegeometrical characteristics of FinFET layouts are implementedand discussed to obtain successful Layout Versus Schematicchecks for a set of layouts. Moreover, additional parasiticcomponents of a standard FinFET device are analyzed andthe parasitic extraction of sample layouts is performed. Theseextraction results are then compared and assessed against thevalidation models.
Comparing Relaxation Mechanisms in Quantum and Classical Transverse-Field Annealing

Annealing schedule control provides new opportunities to better understand the manner and mechanisms by which putative quantum annealers operate. By appropriately modifying the annealing schedule to include a pause (keeping the Hamiltonian fixed) for a period of time, we show it is possible to more directly probe the dissipative dynamics of the system at intermediate points along the anneal and examine thermal relaxation rates, for example, by observing the re-population of the ground state after the minimum spectral gap. We provide a detailed comparison of experiments from a D-Wave device, simulations of the quantum adiabatic master equation and a classical analogue of quantum annealing, spin-vector Monte Carlo, and we observe qualitative agreement, showing that the characteristic increase in success probability when pausing is not a uniquely quantum phenomena. We find that the relaxation in our system is dominated by a single time-scale, which allows us to give a simple condition for when we can expect pausing to improve the time-to-solution, the relevant metric for classical optimization. In the system studied here, only for short pauses is there expected to be an improvement. Finally, we also explore in simulation the role of temperature whilst pausing as a means to better distinguish quantum and classical models of quantum annealers.
Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation

We propose a new active learning (AL) framework, Active Learning++, which can utilize an annotator's labels as well as its rationale. Annotators can provide their rationale for choosing a label by ranking input features based on their importance for a given query. To incorporate this additional input, we modified the disagreement measure for a bagging-based Query by Committee (QBC) sampling strategy. Instead of weighing all committee models equally to select the next instance, we assign higher weight to the committee model with higher agreement with the annotator's ranking. Specifically, we generated a feature importance-based local explanation for each committee model. The similarity score between feature rankings provided by the annotator and the local model explanation is used to assign a weight to each corresponding committee model. This approach is applicable to any kind of ML model using model-agnostic techniques to generate local explanation such as LIME. With a simulation study, we show that our framework significantly outperforms a QBC based vanilla AL framework.
Multimodal Noisy Segmentation based fragmented burn scars identification in Amazon Rainforest

Detection of burn marks due to wildfires in inaccessible rain forests is important for various disaster management and ecological studies. Diverse cropping patterns and the fragmented nature of arable landscapes amidst similar looking land patterns often thwart the precise mapping of burn scars. Recent advances in remote-sensing and availability of multimodal data offer a viable time-sensitive solution to classical methods, which often requires human expert intervention. However, computer vision based segmentation methods have not been used, largely due to lack of labelled datasets.In this work we present AmazonNET – a convolutional based network that allows extracting of burn patters from multimodal remote sensing images. The network consists of UNet- a well-known encoder decoder type of architecture with skip connections commonly used in biomedical segmentation. The proposed framework utilises stacked RGB-NIR channels to segment burn scars from the pastures by training on a new weakly labelled noisy dataset from Amazonia.Our model illustrates superior performance by correctly identifying partially labelled burn scars and rejecting incorrectly labelled samples, demonstrating our approach as one of the first to effectively utilise deep learning based segmentation models in multimodal burn scar identification.
Distributed Variable-Baseline Stereo SLAM from two UAVs

Visual-Inertial Odometry (VIO) has been widely used and researched to control and aid the automation of navigation of robots especially in the absence of absolute position measurements, such as GPS. However, when the observable landmarks in the scene lie far away, as in high-altitude flights for example, the fidelity of the metric scale estimate in VIO greatly degrades. Aiming to tackle this issue, in this work, we utilize the virtual stereo setup formed by two Unmanned Aerial Vehicles (UAVs), equipped with one camera and one Inertial Measurement Unit (IMU) each, exploiting their view overlap and relative distance measurements between them using onboard Ultra-Wideband (UWB) modules to enable collaborative VIO. In particular, we propose a decentralized collaborative estimation scheme, where each agent holds its own local map, achieving a low pose estimation latency, while ensuring consistency of each agents’ estimates via consensus-based optimization. Following a thorough evaluation in photorealistic simulations, we demonstrate the effectiveness of the approach at high-altitude flights of up to 160m, going significantly beyond the capabilities of state-of-the-art VIO methods. Finally, we show the advantage of actively adjusting the baseline on-the-fly over a fixed, target baseline, resulting in a significant reduction of the estimation error.
Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection

In this paper, we study the task of selecting the optimal response given a user and system utterance history in retrieval-based multi-turn dialog systems. Recently, pre-trained language models (e.g., BERT, RoBERTa, and ELECTRA) showed significant improvements in various natural language processing tasks. This and similar response selection tasks can also be solved using such language models by formulating the tasks as dialog--response binary classification tasks. Although existing works using this approach successfully obtained state-of-the-art results, we observe that language models trained in this manner tend to make predictions based on the relatedness of history and candidates, ignoring the sequential nature of multi-turn dialog systems. This suggests that the response selection task alone is insufficient for learning temporal dependencies between utterances. To this end, we propose utterance manipulation strategies (UMS) to address this problem. Specifically, UMS consist of several strategies (i.e., insertion, deletion, and search), which aid the response selection model towards maintaining dialog coherence. Further, UMS are self-supervised methods that do not require additional annotation and thus can be easily incorporated into existing approaches. Extensive evaluation across multiple languages and models shows that UMS are highly effective in teaching dialog consistency, which leads to models pushing the state-of-the-art with significant margins on multiple public benchmark datasets.