Miracle-dz/UniFAR-FTUs huggingface dataset:
Daily Energy Demand Forecasting Using A Neural Data Mining Approach with Emphasis on Holidays Treatment

Monthly energy demand forecasting, peak load demand fore- casting and daily energy demand forecasting for the next month are im- portant issues in energy markets. The flrst and second issue has received much attention from researchers and practitioners in the last decade, the third one not much. The purpose of this paper is to present a neural data mining approach based on quick propagation neural network to the daily energy demand forecasting of the next month for a Regulated Client of Peru. Because energy demand forecasting on holidays are more complex than in nonholidays a new special holidays codifying is presented in the data preparation phase that produce better forecasting results both in holidays and in the whole month than other possible holidays codifying. The results obtained by using the quick propagation neural network and the proposed holidays codifying are compared with those produced by using an ARIMA model. The comparative results show that quick prop- agation neural network with the proposed codifying form outperform the ARIMA model on holidays of July and August of 2005. Also the neu- ral network outperforms in average the ARIMA model in July 2005, a month that has two holidays.
Ambient intelligence for health: advances in vital signs and gait monitoring systems within mHealth environments

This chapter presents an overview about the application of Ambient Intelligence to Healthcare environments, and how the current use of mobile devices provides new opportunities and determines new research areas such as mHealth, focused on the use of mobile technologies to improve people quality life getting clinical benefits. In this sense, monitoring is an important branch today in which researchers are working. There are many systems to monitor several factors regarding health. In our case, three kinds of monitoring systems are detailed. The first approach describes a framework-based system to monitor several diseases like diabetes taking into account the most common factors. In the second case, we present the importance of long-term gait monitoring to detect frailty symptoms in early stages by developing software system and hardware infrastructures based on a variety of sensors. Finally, we detail an analysis tool to measure the level of performance of Instrumental Activities of Daily Living (IADL) of elderly people at home. Likewise, this tool provides some functionalities to assess level stress and quality of life of caregivers by conducting questionnaires. Besides the description of the systems, we detail the evaluations carried out in each. We show the results according to system characteristics, usability, functionality and deployment of features among others.
Convergence Analysis of Deep Residual Networks

Various powerful deep neural network architectures have made great contribution to the exciting successes of deep learning in the past two decades. Among them, deep Residual Networks (ResNets) are of particular importance because they demonstrated great usefulness in computer vision by winning the first place in many deep learning competitions. Also, ResNets were the first class of neural networks in the development history of deep learning that are really deep. It is of mathematical interest and practical meaning to understand the convergence of deep ResNets. We aim at characterizing the convergence of deep ResNets as the depth tends to infinity in terms of the parameters of the networks. Toward this purpose, we first give a matrix-vector description of general deep neural networks with shortcut connections and formulate an explicit expression for the networks by using the notions of activation domains and activation matrices. The convergence is then reduced to the convergence of two series involving infinite products of non-square matrices. By studying the two series, we establish a sufficient condition for pointwise convergence of ResNets. Our result is able to give justification for the design of ResNets. We also conduct experiments on benchmark machine learning data to verify our results.
Integrating User and Item Reviews in Deep Cooperative Neural Networks for Movie Ranking Prediction

User evaluations include a signiﬁcant quantity of information across online platforms. This information source has been neglected by the majority of existing recommendation systems, despite its potential to ease the sparsity issue and enhance the quality of suggestions. This work presents a deep model for concurrently learning item attributes and user behaviour from review text. Deep Cooperative Neural Network (DeepCoNN) is the suggested model consisting of two parallel neural networks connected in their ﬁnal layers. One of the networks focuses on learning user behaviour from reviews submitted by the user, while the other network learns item attributes from user reviews. On top, a shared layer is added to connect these two networks. Similar to factorization machine approaches, the shared layer allows latent factors acquired for people and things to interact with each other. On a number of datasets, DeepCoNN surpasses all baseline recommendation systems, according to experimental ﬁndings.
Learning quantum graph states with product measurements

We consider the problem of learning N identical copies of an unknown n-qubit quantum graph state with product measurements. These graph states have corresponding graphs where every vertex has exactly d neighboring vertices. Here, we detail an explicit algorithm that uses product measurements on multiple identical copies of such graph states to learn them. When n ≫ d and N = O(d log(1/ϵ) + d2 log n), this algorithm correctly learns the graph state with probability at least 1 – ϵ. From channel coding theory, we find that for arbitrary joint measurements on graph states, any learning algorithm achieving this accuracy requires at least Ω(log(1/ϵ) + d log n) copies when $d = o\left( {\sqrt n } \right)$. We also supply bounds on N when every graph state encounters identical and independent depolarizing errors on each qubit.
KASAM: Spline Additive Models for Function Approximation

Neural networks have been criticised for their inability to perform continual learning due to catastrophic forgetting and rapid unlearning of a past concept when a new concept is introduced. Catastrophic forgetting can be alleviated by specifically designed models and training techniques. This paper outlines a novel Spline Additive Model (SAM). SAM exhibits intrinsic memory retention with sufficient expressive power for many practical tasks, but is not a universal function approximator. SAM is extended with the Kolmogorov-Arnold representation theorem to a novel universal function approximator, called the Kolmogorov-Arnold Spline Additive Model - KASAM. The memory retention, expressive power and limitations of SAM and KASAM are illustrated analytically and empirically. SAM exhibited robust but imperfect memory retention, with small regions of overlapping interference in sequential learning tasks. KASAM exhibited greater susceptibility to catastrophic forgetting. KASAM in combination with pseudo-rehearsal training techniques exhibited superior performance in regression tasks and memory retention.
Neurochaos Feature Transformation and Classification for Imbalanced Learning

Learning from limited and imbalanced data is a challenging problem in the Artificial Intelligence community. Real-time scenarios demand decision-making from rare events wherein the data are typically imbalanced. These situations commonly arise in medical applications, cybersecurity, catastrophic predictions etc. This motivates the development of learning algorithms capable of learning from imbalanced data. Human brain effortlessly learns from imbalanced data. Inspired by the chaotic neuronal firing in the human brain, a novel learning algorithm namely Neurochaos Learning (NL) was recently proposed. NL is categorized in three blocks: Feature Transformation, Neurochaos Feature Extraction (CFX), and Classification. In this work, the efficacy of neurochaos feature transformation and extraction for classification in imbalanced learning is studied. We propose a unique combination of neurochaos based feature transformation and extraction with traditional ML algorithms. The explored datasets in this study revolve around medical diagnosis, banknote fraud detection, environmental applications and spoken-digit classification. In this study, experiments are performed in both high and low training sample regime. In the former, five out of nine datasets have shown a performance boost in terms of macro F1-score after using CFX features. The highest performance boost obtained is 25.97% for Statlog (Heart) dataset using CFX+Decision Tree. In the low training sample regime (from just one to nine training samples per class), the highest performance boost of 144.38% is obtained for Haberman's Survival dataset using CFX+Random Forest. NL offers enormous flexibility of combining CFX with any ML classifier to boost its performance, especially for learning tasks with limited and imbalanced data.
Co-spectral radius for countable equivalence relations

We define the co-spectral radius of inclusions $\mathcal{S}\leq \mathcal{R}$ of discrete, probability measure-preserving equivalence relations, as the sampling exponent of a generating random walk on the ambient relation. The co-spectral radius is analogous to the spectral radius for random walks on $G/H$ for inclusion $H\leq G$ of groups. For the proof, we develop a more general version of the 2-3 method we used in another work on the growth of unimodular random rooted trees. We use this method to show that the walk growth exists for an arbitrary unimodular random rooted graph of bounded degree. We also investigate how the co-spectral radius behaves for hyperfinite relations, and discuss new critical exponents for percolation that can be defined using the co-spectral radius.
Global Convergence of Hessenberg Shifted QR II: Numerical Stability

We develop a framework for proving rapid convergence of shifted QR algorithms which use Ritz values as shifts, in finite arithmetic. Our key contribution is a dichotomy result which addresses the known forward-instability issues surrounding the shifted QR iteration [Parlett and Le 1993]: we give a procedure which provably either computes a set of approximate Ritz values of a Hessenberg matrix with good forward stability properties, or leads to early decoupling of the matrix via a small number of QR steps. Using this framework, we show that the shifting strategy introduced in Part I of this series [Banks, Garza-Vargas, and Srivastava 2021] converges rapidly in finite arithmetic with a polylogarithmic bound on the number of bits of precision required, when invoked on matrices of controlled eigenvector condition number and minimum eigenvalue gap.
Bang-Bang Control Of A Tail-less Morphing Wing Flight

Bats' dynamic morphing wings are known to be extremely high-dimensional, and they employ the combination of inertial dynamics and aerodynamics manipulations to showcase extremely agile maneuvers. Bats heavily rely on their highly flexible wings and are capable of dynamically morphing their wings to adjust aerodynamic and inertial forces applied to their wing and perform sharp banking turns. There are technical hardware and control challenges in copying the morphing wing flight capabilities of flying animals. This work is majorly focused on the modeling and control aspects of stable, tail-less, morphing wing flight. A classical control approach using bang-bang control is proposed to stabilize a bio-inspired morphing wing robot called Aerobat. Robot-environment interactions based on horseshoe vortex shedding and Wagner functions is derived to realistically evaluate the feasibility of the bang-bang control, which is then implemented on the robot in experiments to demonstrate first-time closed-loop stable flights of Aerobat.
Role of Charge Content in the Two-Step Deswelling of Poly(N-isopropylacrylamide)-Based Microgels

Poly(N-isopropylacrylamide)-based microgels are soft colloids undergoing a Volume Phase Transition (VPT) close to ambient temperature. Although widely employed for fundamental research and application purposes, the modifications of the microgel internal structure occurring at the VPT are not yet completely understood, especially concerning the role of electrostatics. Here we study in detail, both experimentally and numerically, the effect of the addition of acrylic acid (AAc) co-monomer on the microgel deswelling process. By combining viscosimetry, light scattering and electrophoresis, we show that the progressive addition of AAc increases the microgel mass and suppresses the occurrence of the VPT, progressively shifting the microgel collapse to higher temperatures. Most importantly, it also highly enhances the two-step deswelling of these submicron-sized networks, so that the inner core collapses at temperatures always lower than those marking the transition of the outer corona. These results indicate that a net increase of the charge density mismatch between the bulk and the surface of the microgels takes place. Numerical simulations fully confirm this scenario and clarify the impact of the charge distribution on the two-step deswelling, with mobile counterions efficiently screening the charges within the inner core, while leaving more monomers ionized on the surface. Our work unambiguously shows how electrostatic interactions influence the behavior of thermosensitive microgels in aqueous environment close to the VPT.
Multi-Marginal Gromov-Wasserstein Transport and Barycenters

Gromov-Wasserstein (GW) distances are combinations of Gromov-Hausdorff and Wasserstein distances that allow the comparison of two different metric measure spaces (mm-spaces). Due to their invariance under measure- and distance-preserving transformations, they are well suited for many applications in graph and shape analysis. In this paper, we introduce the concept of multi-marginal GW transport between a set of mm-spaces as well as its regularized and unbalanced versions. As a special case, we discuss multi-marginal fused variants, which combine the structure information of an mm-space with label information from an additional label space. To tackle the new formulations numerically, we consider the bi-convex relaxation of the multi-marginal GW problem, which is tight in the balanced case if the cost function is conditionally negative definite. The relaxed model can be solved by an alternating minimization, where each step can be performed by a multi-marginal Sinkhorn scheme. We show relations of our multi-marginal GW problem to (unbalanced, fused) GW barycenters and present various numerical results, which indicate the potential of the concept.
Black Holes Decohere Quantum Superpositions

We show that if a massive body is put in a quantum superposition of spatially separated states, the mere presence of a black hole in the vicinity of the body will eventually destroy the coherence of the superposition. This occurs because, in effect, the gravitational field of the body radiates soft gravitons into the black hole, allowing the black hole to acquire"which path"information about the superposition. A similar effect occurs for quantum superpositions of electrically charged bodies. We provide estimates of the decoherence time for such quantum superpositions. We believe that the fact that a black hole will eventually decohere any quantum superposition may be of fundamental significance for our understanding of the nature of black holes in a quantum theory of gravity.
Binary Interaction Dominates Mass Ejection in Classical Novae

Recent observations suggest our understanding of mass loss in classical novae is incomplete, motivating a new theoretical examination of the physical processes responsible for nova mass ejection. In this paper, we perform hydrodynamical simulations of classical nova outflows using the stellar evolution code MESA. We find that, when the binary companion is neglected, white dwarfs with masses ≳0.8 M ⊙ successfully launch radiation-pressure-driven optically thick winds that carry away most of the envelope. However, for most of the mass-loss phase, these winds are accelerated at radii beyond the white dwarf’s Roche radius assuming a typical cataclysmic variable donor. This means that, before a standard optically thick wind can be formed, mass loss will instead be initiated and shaped by binary interaction. An isotropic, optically thick wind is only successfully launched when the acceleration region recedes within the white dwarf’s Roche radius, which occurs after most of the envelope has already been ejected. The interaction between these two modes of outflow—a first phase of slow, binary-driven, equatorially focused mass loss encompassing most of the mass ejection and a second phase consisting of a fast, isotropic, optically thick wind—is consistent with observations of aspherical ejecta and signatures of multiple outflow components. We also find that isolated lower-mass white dwarfs ≲0.8 M ⊙ do not develop unbound optically thick winds at any stage, making it even more crucial to consider the effects of the binary companion on the resulting outburst.
Virtual passengers for real car solutions: synthetic datasets

Strategies that include the generation of synthetic data are beginning to be viable as obtaining real data can be logistically complicated, very expensive or slow. Not only the capture of the data can lead to complications, but also its annotation. To achieve high-fidelity data for training intelligent systems, we have built a 3D scenario and set-up to resemble reality as closely as possible. With our approach, it is possible to configure and vary parameters to add randomness to the scene and, in this way, allow variation in data, which is so important in the construction of a dataset. Besides, the annotation task is already included in the data generation exercise, rather than being a post-capture task, which can save a lot of resources. We present the process and concept of synthetic data generation in an automotive context, specifically for driver and passenger monitoring purposes, as an alternative to real data capturing.
Balanced Allocations: The Heavily Loaded Case with Deletions

In the 2-choice allocation problem, m balls are placed into n bins, and each ball must choose between two random bins $i,j\in[n]$ that it has been assigned to. It has been known for more than two decades, that if each ball follows the GREEDY strategy (i.e., always pick the less-full bin), then the maximum load will be $m/n+O(\log\log n)$ with high probability in n (and $m/n+O(\log m)$ with high probability in m). It has remained an open question whether the same bounds hold in the dynamic version of the same game, where balls are inserted/deleted with no more than m balls present at a time.We show that, somewhat surprisingly, these bounds do not hold in the dynamic setting: already on 4 bins, there exists a sequence of insertions/deletions that cause the GREEDY strategy to incur a maximum load of $m/4+\Omega(\sqrt{m})$ with probability $\Omega(1)$—this is the same bound that one gets in the single-choice allocation model where each ball is assigned to a random bin! This raises the question of whether any 2-choice allocation strategy can offer a strong bound in the dynamic setting. Our second result answers this question in the affirmative: we present a new strategy, called MODULATEDGREEDY, that guarantees a maximum load of $m/n+O(\log m)$, at any given moment, with high probability in m. We also show how to generalize ModulatedGreedy to obtain dynamic guarantees for the $(1+\beta)$-choice setting, and for the setting of balls-and-bins on a graph.Finally, we consider an extension of the dynamic setting in which balls can be reinserted after they are deleted, and where the pair i, j that a given ball uses is consistent across insertions. This seemingly small modification renders tight load balancing impossible: on 4 bins, any balls-and-bins strategy that is oblivious to the specific identities of balls being inserted/deleted must allow for a maximum load of $m/4+\mathrm{poly}(m)$ at some point in the first poly (m) insertions/deletions, with high probability in m. This is a remarkable departure from the m=n case where the maximum load of O(loglogn) holds independently of whether reinsertions are allowed or not.
Building demand response and control methods for smart grids: A review

Demand response provides a solution to the grid imbalance problems which restrict the use of renewable energy. Since buildings possess a high amount of flexible load, they could contribute to smart grid stability and achieve cost savings for buildings and the other participants in the smart grid. However, the research and application of building demand response are still at the beginning stage. Majority of the existing demand response studies are from the view point of the grid side, rather than the demand side. This article, therefore, provides an overview on the studies on building demand response from the view point of buildings at the demand side. It mainly consists of two parts: (1) an overview of different types of demand response programs, and the status of the demand response programs in several countries and regions; (2) a review on the control methods for demand response in commercial and residential buildings. This review intends to support the further development of demand response methods for future smart grid applications and their implementation in buildings.
SYMPLECTIC GEOMETRY AND TOEPLITZ OPERATORS ON CARTAN DOMAINS OF

Let us consider, for n ≥ 3, the Cartan domain D IV n of type IV. On the weighted Bergman spaces A 2 λ (D IV n ) we study the problem of the existence of commutative C ∗ -algebras generated by Toeplitz operators with special symbols. We focus on the subgroup SO( n ) × SO(2) of biholomorphisms of D IV n that ﬁx the origin. The SO( n ) × SO(2)-invariant symbols yield Toeplitz operators that generate commutative C ∗ -algebras, but commutativity is lost when we consider symbols invariant under a maximal torus or under SO(2). We compute the moment map µ SO(2) -action for the SO(2)-action on D IV n considered as a symplectic manifold for the Bergman metric. We prove that the space of symbols of the form a = f ◦ µ SO(2) , denoted by L ∞ (D IV n ) µ SO(2) , yield Toeplitz operators that generate commutative C ∗ -algebras. Spectral integral formulas for these Toeplitz operators are also obtained.
Goal-Guided Neural Cellular Automata: Learning to Control Self-Organising Systems

Inspired by cellular growth and self-organization, Neural Cellular Automata (NCAs) have been capable of"growing"artificial cells into images, 3D structures, and even functional machines. NCAs are flexible and robust computational systems but -- similarly to many other self-organizing systems -- inherently uncontrollable during and after their growth process. We present an approach to control these type of systems called Goal-Guided Neural Cellular Automata (GoalNCA), which leverages goal encodings to control cell behavior dynamically at every step of cellular growth. This approach enables the NCA to continually change behavior, and in some cases, generalize its behavior to unseen scenarios. We also demonstrate the robustness of the NCA with its ability to preserve task performance, even when only a portion of cells receive goal information.
From maximum force to the field equations of general relativity - and implications

There are at least two ways to deduce Einstein’s ﬁeld equations from the principle of maximum force c 4 / 4 G or from the equivalent principle of maximum power c 5 / 4 G . Tests in gravitational wave astronomy, cosmology, and numerical gravitation conﬁrm the two prin-ciples. Apparent paradoxes about the limits can all be resolved. Several related bounds arise. The limits illuminate the beauty, consistency and simplicity of general relativity from an unusual perspective.