Miracle-dz/UniFAR-FTUs huggingface dataset:
Community Microgrid Planning Considering Building Thermal Dynamics

In this paper, the building thermal dynamic characteristics are introduced in the community microgrid (MG) planning model. The proposed planning model is formulated as a mixed integer linear programming (MILP) which seeks to determine the optimal deployment strategy for various distributed energy resources (DER). The objective is to minimize the annualized cost of community MG, including investment cost in DER, operation cost for dispatchable fuel-generators (DFG), energy storage system (ESS) degradation cost, energy purchasing and peak demand charge at PCC, customer discomfort cost due to the room temperature deviation and load curtailment cost. Given the slow thermal dynamic characteristics of buildings, the heating, ventilation and air-conditioning (HVAC) system in the proposed model is treated as a demand side management (DSM) component, whose dispatch commands are provided by the central MG controller. Numerical results based on a community MG comprising of 20 residential buildings demonstrate the effectiveness of the proposed planning model and the benefits of introducing the building thermal dynamic model.
Katrina, Rita, and the Failure of Imagination

mericans have had to deal with disaster preparedness for decades, anticipating events such as industrial accidents,power plant meltdowns,natural disasters, and nuclear attacks. Since September 11, 2001, the nation has spent unprecedented resources, created new federal, state, and local agencies, and greatly intensified efforts to prepare for catastrophes. But when Hurricane Katrina hit, we were unable to respond in an organized and comprehensive manner. This does not bode well for how we would be able to respond to significant terrorism attacks or a large-scale industrial accident. We still lack the capacity to imagine what would or could actually happen in a catastrophic scenario. Scientists and engineers had warned for more than a decade that the levees in New Orleans would fail and that the
Endomorphisms of the symmetric 2-rig of finite sets

Let $\widehat{\mathbb{F}\mathbb{S}et}$ be the groupoid of finite sets and bijections between them equipped with the canonical symmetric rig category structure given by the disjoint union and the cartesian product of finite sets. We prove that the category (in fact, groupoid) of endomorphisms of $\widehat{\mathbb{F}\mathbb{S}et}$ is equivalent to the terminal category, thus providing some evidence that $\widehat{\mathbb{F}\mathbb{S}et}$ is the right categorical analog of the commutative rig $\mathbb{N}$ of nonnegative integers. This is shown using a particular semistrict skeletal version of $\widehat{\mathbb{F}\mathbb{S}et}$ for which the endomorphisms can be described very explicitly.
Temporal Network Sampling

Temporal networks representing a stream of timestamped edges are seemingly ubiquitous in the real-world. However, the massive size and continuous nature of these networks make them fundamentally challenging to analyze and leverage for descriptive and predictive modeling tasks. In this work, we propose a general framework for temporal network sampling with unbiased estimation. We develop online, single-pass sampling algorithms and unbiased estimators for temporal network sampling. The proposed algorithms enable fast, accurate, and memory-efficient statistical estimation of temporal network patterns and properties. In addition, we propose a temporally decaying sampling algorithm with unbiased estimators for studying networks that evolve in continuous time, where the strength of links is a function of time, and the motif patterns are temporally-weighted. In contrast to the prior notion of a $\bigtriangleup t$-temporal motif, the proposed formulation and algorithms for counting temporally weighted motifs are useful for forecasting tasks in networks such as predicting future links, or a future time-series variable of nodes and links. Finally, extensive experiments on a variety of temporal networks from different domains demonstrate the effectiveness of the proposed algorithms.
Phase Noise in Modular Millimeter Wave Massive MIMO

This paper investigates the effect of oscillator phase noise on a multiuser millimeter wave (mmWave) massive MIMO uplink as we scale up the number of base station antennas, fixing the load factor, defined as the ratio of the number of simultaneous users to the number of base station antennas. We consider a modular approach in which the base station employs an array of subarrays, or “tiles. ” Each tile supports a fixed number of antennas, and can therefore be implemented using a separate radio frequency integrated circuit (RFIC), with synchronization across tiles accomplished by employing a phased locked loop in each tile to synthesize an on-chip oscillator at the carrier frequency by locking on to a common lower frequency reference clock. Assuming linear minimum mean squared error (LMMSE) multiuser detection, we provide an analytical framework that can be used to specify the required power spectral density (PSD) mask for phase noise for a target system performance. Our analysis for the phase noise at the output of the LMMSE receiver indicates two distinct effects: self-noise for each user which is inversely proportional to the number of tiles, and cross-talk between users which is insensitive to the number of tiles, and is proportional to the load factor. These analytical predictions, verified by simulations for a 140 GHz system targeting a per-user data rate of 10 Gbps, show that tiling is a robust approach for scaling. Numerical results for our proposed design approach yield relatively relaxed specifications for phase noise PSD masks.
Byte-Pair Encoding for Text-to-SQL Generation

Neural sequence-to-sequence models provide a competitive approach to the task of mapping a question in natural language to an SQL query, also referred to as text-to-SQL generation. The Byte-Pair Encoding algorithm (BPE) has previously been used to improve machine translation (MT) between natural languages. In this work, we adapt BPE for text-to-SQL generation. As the datasets for this task are rather small compared to MT, we present a novel stopping criterion that prevents overfitting the BPE encoding to the training set. Additionally, we present AST BPE, which is a version of BPE that uses the Abstract Syntax Tree (AST) of the SQL statement to guide BPE merges and therefore produce BPE encodings that generalize better. We improved the accuracy of a strong attentive seq2seq baseline on five out of six English text-to-SQL tasks while reducing training time by more than 50% on four of them due to the shortened targets. Finally, on two of these tasks we exceeded previously reported accuracies.
Amamentação e o seio feminino: uma análise sob a ótica da sexualidade e dos direitos reprodutivos

A pesar del conocimiento sobre el amamantamiento, reunido por cientificos y profesionales de la salud, a lo largo del siglo XX, el destete precoz persistio en este periodo como una causa importante para el aumento de la morbilidad y la mortalidad infantil. Desde el ano 1980, las politicas publicas en pro del amamantamiento empezaron a ser desarrolladas, principalmente en los paises de tercer mundo. Sin embargo, las mujeres principales actoras en el proceso del amamantamiento, muchas veces no son escuchadas en el momento de cumplir la funcion de amamantar. Este estudio aporta algunas reflexiones sobre el amamantamiento, enfocando la sexualidad y los derechos reproductivos de las mujeres en la cultura occidental. Esta dirigido a los profesionales de la Salud que trabajan con las mujeres en el ciclo gravido puerperal, para permitirles comprender y apoyar a las mujeres en sus decisiones sobre el proceso de amamantamiento.
Aleatoric and Epistemic Uncertainty in Machine Learning: A Tutorial Introduction

The notion of uncertainty is of major importance in machine learning and constitutes a key element of machine learning methodology. In line with the statistical tradition, uncertainty has long been perceived as almost synonymous with standard probability and probabilistic predictions. Yet, due to the steadily increasing relevance of machine learning for practical applications and related issues such as safety requirements, new problems and challenges have recently been identified by machine learning scholars, and these problems may call for new methodological developments. In particular, this includes the importance of distinguishing between (at least) two different types of uncertainty, often refereed to as aleatoric and epistemic. In this paper, we provide an introduction to the topic of uncertainty in machine learning as well as an overview of hitherto attempts at handling uncertainty in general and formalizing this distinction in particular.
Lesioni nodulari epatiche come inusuale manifestazione di mieloma multiplo in progressione: descrizione di un caso clinico

Extramedullary localizations during the course of multiple myeloma (MM) are rare. They can arise in any tissue and their presence has been associated with more aggressive disease. Particularly atypical appear liver involvement in living patient. Here we describe a case in whom investigation of liver nodules by biopsy revealed hepatic localization during advanced MM. Pathological hepatic involvements found in MM reported in post-mortem studies are more prevalent compared with those on living patients; therefore a critical evaluation of these circumstances is required in order to understand if those associations are actually unusual as reported previously.
Autonomous Industrial Management via Reinforcement Learning: Self-Learning Agents for Decision-Making - A Review

Industry has always been in the pursuit of becoming more economically efficient and the current focus has been to reduce human labour using modern technologies. Even with cutting edge technologies, which range from packaging robots to AI for fault detection, there is still some ambiguity on the aims of some new systems, namely, whether they are automated or autonomous. In this paper we indicate the distinctions between automated and autonomous system as well as review the current literature and identify the core challenges for creating learning mechanisms of autonomous agents. We discuss using different types of extended realities, such as digital twins, to train reinforcement learning agents to learn specific tasks through generalization. Once generalization is achieved, we discuss how these can be used to develop self-learning agents. We then introduce self-play scenarios and how they can be used to teach self-learning agents through a supportive environment which focuses on how the agents can adapt to different real-world environments.
Equivalence tests for binary efficacy-toxicity responses

Clinical trials often aim to compare a new drug with a reference treatment in terms of efficacy and/or toxicity depending on covariates such as, for example, the dose level of the drug. Equivalence of these treatments can be claimed if the difference in average outcome is below a certain threshold over the covariate range. In this paper we assume that the efficacy and toxicity of the treatments are measured as binary outcome variables and we address two problems. First, we develop a new test procedure for the assessment of equivalence of two treatments over the entire covariate range for a single binary endpoint. Our approach is based on a parametric bootstrap, which generates data under the constraint that the distance between the curves is equal to the pre-specified equivalence threshold. Second, we address equivalence for bivariate binary (correlated) outcomes by extending the previous approach for a univariate response. For this purpose we use a 2-dimensional Gumbel model for binary efficacy-toxicity responses. We investigate the operating characteristics of the proposed approaches by means of a simulation study and present a case study as an illustration.
Paradigm shifts in cardioprotection research: the importance of the MPTP as a therapeutic target

This editorial refers to an article by D.J. Hausenloy et al . [12][1] published in Cardiovascular Research in 2002 (see [Box 1][2]). It is accompanied by an editorial by M. Ruiz-Meana, pp. 157–159, this issue, as part of this Spotlight on Landmark Papers in Cardiovascular Research . Our interest in the mitochondrial permeability transition pore (MPTP) as a target for cardioprotection was first aroused by a seminar given at University College London by John Lemasters and during one of our weekly journal clubs, back in early 2001. The research article which attracted our attention was by two pioneers in MPTP research, Fabio Di Lisa and Paulo Bernardi at the University of Padova.1 In their experimental study, they had demonstrated that pre-treating isolated perfused rat hearts with the immunosuppressant agent cyclosporin-A (CsA) could decrease myocardial injury (measured by lactate dehydrogenase release) following an episode of acute lethal ischaemia and reperfusion injury (IRI) and that the cardioprotective effect was associated with inhibition of MPTP opening at the time of myocardial reperfusion. At that time, our research group had been keen to discover novel therapeutic agents which were able to prevent lethal myocardial reperfusion injury and limit myocardial infarct (MI) size when administered at the onset of myocardial reperfusion. In addition, we were keen to unravel the mechanistic pathways underlying the phenomenon of ischaemic preconditioning (IPC),2 in which the heart could be rendered resistant to an episode of acute lethal ischaemia and reperfusion by preconditioning it with one or more brief cycles of non-lethal ischaemia and reperfusion. Therefore, the suggestion that the opening of the MPTP at the onset of reperfusion was a critical mediator of lethal myocardial reperfusion injury made it an obvious target for investigation. This was the first time our research group had come across the MPTP, and so we … [1]: #ref-12 [2]: #F1
The C-Band All-Sky Survey: total intensity point-source detection over the northern sky

We present a point-source detection algorithm that employs the second-order Spherical Mexican Hat wavelet filter (SMHW2), and use it on C-Band All-Sky Survey (C-BASS) northern intensity data to produce a catalogue of point sources. This catalogue allows us to cross-check the C-BASS flux-density scale against existing source surveys, and provides the basis for a source mask that will be used in subsequent C-BASS and cosmic microwave background (CMB) analyses. The SMHW2 allows us to filter the entire sky at once, avoiding complications from edge effects arising when filtering small sky patches. The algorithm is validated against a set of Monte Carlo simulations, consisting of diffuse emission, instrumental noise, and various point-source populations. The simulated source populations are successfully recovered. The SMHW2 detection algorithm is used to produce a $4.76\, \mathrm{GHz}$ northern sky source catalogue in total intensity, containing 1784 sources and covering declinations δ ≥ −10°. The C-BASS catalogue is matched with the Green Bank 6 cm (GB6) and Parkes-MIT-NRAO (PMN) catalogues over their areas of common sky coverage. From this we estimate the 90 per cent completeness level to be approximately $610\, \mathrm{mJy}$, with a corresponding reliability of 98 per cent, when masking the brightest 30 per cent of the diffuse emission in the C-BASS northern sky map. We find the C-BASS and GB6 flux-density scales to be consistent with one another to within approximately 4 per cent.
Intracranial Neoplasms in Lower Animals: Studies in the Incidence and Inheritability of Spontaneous Tumors in Mice: Twenty-ninth Report

Review of the Literature Either intracranial neoplasms are extremely rare in other animals than man, or they have been overlooked because of failure to examine the contents of the skull, for there are but a few meager reports of either primary or secondary tumors in birds or animals. In view of the frequency of intracranial tumors in man, it would seem probable that their apparent infrequency in other animals is due to oversight. For example, the recent valuable paper on spontaneous tumors of the rat by Bullock and Curtis (5) mentions no brain tumors among some 3,000 cases of tumor growth in rats, and states that “routine autopsies rarely included the brain and spinal cord” because of absence of clinical manifestations of this involvement. Review of the literature has brought to light the following facts concerning this subject. The only commonly observed intracranial growths are found in the lateral ventricles of horses, and, although usually designated as cholesteatoma or psammosarcoma, these probably are not neoplasms at all. Joest (18) states that small cholesteatomatous masses are to be found in and about the choroid plexus of the majority of old horses, and that they occasionally reach large size. They consist chiefly of cholesterol crystals with giant cells and other reactive cell types. He has described as a psammosarcoma of the brain in a cow a somewhat similar tumor, which produced no metastases and seemed to arise in the vessels (17).
Autonomous Control of a Quadrotor-Manipulator; Application of Extended State Disturbance Observer

In this work, the autonomous control of a quadrotor-manipulator unmanned aerial vehicle is treated using an extended dynamic model. Due to persistent aerodynamic disturbances and dynamic couplings, the control of a quadrotor-manipulator unit is cumbersome. Active disturbance rejection control (ADRC) is proposed for the cancellation of the undesirable dynamics while a PD controller is developed for set-point tracking. Parameters were tuned using Gradient Descent-Sequential Quadratic Programming in Simulink design optimization
Data assimilation in Agent-based models using creation and annihilation operators

Agent-based models are a powerful tool for studying the behaviour of complex systems that can be described in terms of multiple, interacting ``agents''. However, because of their inherently discrete and often highly non-linear nature, it is very difficult to reason about the relationship between the state of the model, on the one hand, and our observations of the real world on the other. In this paper we consider agents that have a discrete set of states that, at any instant, act with a probability that may depend on the environment or the state of other agents. Given this, we show how the mathematical apparatus of quantum field theory can be used to reason probabilistically about the state and dynamics the model, and describe an algorithm to update our belief in the state of the model in the light of new, real-world observations. Using a simple predator-prey model on a 2-dimensional spatial grid as an example, we demonstrate the assimilation of incomplete, noisy observations and show that this leads to an increase in the mutual information between the actual state of the observed system and the posterior distribution given the observations, when compared to a null model.
Theoretical Investigation of Composite Neural Network

A composite neural network is a rooted directed acyclic graph combining a set of pre-trained and non-instantiated neural network models. A pre-trained neural network model is well-crafted for a specific task and with instantiated weights. is generally well trained, targeted to approximate a specific function. Despite a general belief that a composite neural network may perform better than a single component, the overall performance characteristics are not clear. In this work, we prove that there exist parameters such that a composite neural network performs better than any of its pre-trained components with a high probability bound.
Clotho: an Audio Captioning Dataset

Audio captioning is the novel task of general audio content description using free text. It is an intermodal translation task (not speech-to-text), where a system accepts as an input an audio signal and outputs the textual description (i.e. the caption) of that signal. In this paper we present Clotho, a dataset for audio captioning consisting of 4981 audio samples of 15 to 30 seconds duration and 24 905 captions of eight to 20 words length, and a baseline method to provide initial results. Clotho is built with focus on audio content and caption diversity, and the splits of the data are not hampering the training or evaluation of methods. All sounds are from the Freesound platform, and captions are crowdsourced using Amazon Mechanical Turk and annotators from English speaking countries. Unique words, named entities, and speech transcription are removed with post-processing. Clotho is freely available online 1.
Identity Document and banknote security forensics: a survey

Counterfeiting and piracy are a form of theft that has been steadily growing in recent years. Banknotes and identity documents are two common objects of counterfeiting. Aiming to detect these counterfeits, the present survey covers a wide range of anti-counterfeiting security features, categorizing them into three components: security substrate, security inks and security printing. respectively. From the computer vision perspective, we present works in the literature covering these three categories. Other topics, such as history of counterfeiting, effects on society and document experts, counterfeiter types of attacks, trends among others are covered. Therefore, from non-experienced to professionals in security documents, can be introduced or deepen its knowledge in anti-counterfeiting measures.
Multi-dimensional Features for Prediction with Tweets

With the rise of opioid abuse in the US, there has been a growth of overlapping hotspots for overdose-related and HIV-related deaths in Springfield, Boston, Fall River, New Bedford, and parts of Cape Cod. With a large part of population, including rural communities, active on social media, it is crucial that we leverage the predictive power of social media as a preventive measure. We explore the predictive power of micro-blogging social media website Twitter with respect to HIV new diagnosis rates per county. While trending work in Twitter NLP has focused on primarily text-based features, we show that multi-dimensional feature construction can significantly improve the predictive power of topic features alone with respect STI's (sexually transmitted infections). By multi-dimensional features, we mean leveraging not only the topical features (text) of a corpus, but also location-based information (counties) about the tweets in feature-construction. We develop novel text-location-based smoothing features to predict new diagnoses of HIV.
Spectral bounds of the regularized normalized Laplacian for random geometric graphs

In this work, we study the spectrum of the regularized normalized Laplacian for random geometric graphs (RGGs) in both the connectivity and thermodynamic regimes. We prove that the limiting eigenvalue distribution (LED) of the normalized Laplacian matrix for an RGG converges to the Dirac measure in one in the full range of the connectivity regime. In the thermodynamic regime, we propose an approximation for the LED and we provide a bound on the Levy distance between the approximation and the actual distribution. In particular, we show that the LED of the regularized normalized Laplacian matrix for an RGG can be approximated by the LED of the regularized normalized Laplacian for a deterministic geometric graph with nodes in a grid (DGG). Thereby, we obtain an explicit approximation of the eigenvalues in the thermodynamic regime.