Miracle-dz/UniFAR-FTUs huggingface dataset:
Finite Precision Analysis for an FPGA-based NILM Event-Detector

Most Non-Intrusive Load Monitoring (NILM) techniques often require a correct detection of the events that occur in the mains, in order to carry out a correct identification of the corresponding appliances. For that performance, event-detectors are normally based on signals, such as voltages and currents, acquired by a smart meter at the entrance of the household. In this work, a finite precision analysis is performed for an event detector implemented on a System-on-Chip (SoC) based on a Field-Programmable Gate Array (FPGA) for NILM applications. The proposal employs an integrated circuit (ADE9153A) to perform the signal acquisition at 4 ksamples/s, higher than those used in common smart meters installed at homes nowadays. The purpose of the finite precision analysis is to reduce the hardware resource consumption of the FPGA, while the precision of the event detector is maintained. To validate the design, a comparison is carried out in terms of accuracy and specificity against previous works, as well as a comparison of the event detector using finite precision (fixed-point) and single-precision floating-point resolution. Finally, the hardware resource consumption obtained for the proposed architecture is discussed
Advancement in the diagnosis of mitochondrial diseases

Mitochondrial diseases are multi-systemic, heterogeneous groups of diseases that are associated with various neuromuscular problems, cardiovascular disorders, metabolic syndrome, cancer, and obesity. Mitochondrial diseases are due to mutations in mitochondrial DNA or nuclear DNA that can affect the assembly of the mitochondrial components and mitochondrial function. Typically, mitochondrial diseases can be inherited through an autosomal dominant, autosomal recessive or X-linked pattern of inheritance. To date, there are more than 100 mitochondrial diseases identified. However, clinical phenotype heterogeneity is a huge problem for the diagnosis of mitochondrial diseases, as patients with the same mutations exhibit different clinical symptoms. Also, the heteroplasmy/homoplasmy conditions complicate the diagnosis process. Here, in this review, we discuss these challenges and problems in mitochondrial disease diagnosis, focusing on the mutational profile of both primary and secondary mitochondrial diseases. We also review the utilization of next-generation technology and multi-omics strategy to improve the diagnosis. The discussion addresses the current evidence of those applications and the challenges that need some improvement for better diagnosis yield.
Interleukin-6 is associated with tryptophan metabolism and signs of depression in individuals with carbohydrate malabsorption

The aim of the present study was to investigate possible associations between interleukin-6 (IL-6), interferon-gamma (INF-γ), tumor necrosis factor-alpha (TNF-α), lactoferrin and lipopolysaccharide binding protein (LBP) with TRP metabolism and signs of depression in a large cohort of outpatients referred for carbohydrate malabsorption testing. Serum concentrations of IL-6, INF-γ, TNF-α, lactoferrin, LBP, tryptophan (TRP), kynurenine (KYN) and kynuric acid were determined in 250 adults referred for lactose and fructose malabsorption testing. All participants filled out the Beck Depression Inventory (BDI). Serum IL-6 levels were positively correlated with the BDI score (p = 0.001, ρ = 0.205) and indicators of TRP metabolism (KYN/TRP ratio, KYN) (P-values < 0.05, ρ = 0.176 and 0.136). Ninety-five individuals with a BDI score > 13 showed significantly higher IL-6 serum levels (1.7 [1.0 - 2.8] vs. 1.1 [0.8 - 1.7] pg/mL, p < 0.001) compared to 115 individuals with a BDI score ≤ 13. LBP showed a positive correlation with the KYN/TRP ratio (p = 0.005, ρ = 0.177). IL-6 and LBP were associated with indicators of TRP metabolism. IL-6 was found to be linked to signs of depression. Individuals with the presence of depressive symptoms showed higher serum IL-6 levels compared to individuals without depressive symptoms.
The effect of silicone gel versus contractubex gel on the upper-extremity postsurgical scars: A randomized, double-blinded, controlled trial

Background: Wound healing is a process that has three overlapping inflammatory, proliferative, and reconstruction phases. Silicone gel and Contractubex (onion extract gel) are two main topical agents used for the prevention and treatment of hypertrophic scars. This clinical trial study aimed to evaluate the efficacy and safety of onion extract and silicone gel on hypertrophic scars of the upper extremity and compare it to non-treated patients. Methods: This randomized, double-blind, parallel, clinical trial was done on 120 male patients who had undergone upper extremity sharp injury repair. Patients were assigned to three groups, i.e., Silicone gel (Kelo-cortTM; WA, USA) (group 1; 40 patients), Onion extract (ContractubexTM, Merz Pharma, Frankfurt, Germany) gel (group 2; 40 patients), and No Intervention (group 3; 40 patients), using drawing sealed envelopes and a computer-based table of randomization. Data were recorded using the Vancouver scale in each visit by two surgeons who were blinded to the study groups. IBM SPSS Statistics for Windows, Version 22.0. (Armonk, NY: IBM Corp). Data were compared using the ANOVA test. A P-value of less than 0.05 was considered statistically significant. Results: Vascularity (p=0.200), pliability (p=0.058), pigmentation (p=0.701), and height (p=0.438) as subjective scar parameters were approximately similar in post-upper extremity sharp injury wound hypertrophic scar among the three groups. Conclusion: Vascularity, pliability, pigmentation and height, as subjective scar parameters, were not statistically different in post-upper extremity sharp injury wound hypertrophic scar among the groups. Even though onion extract gel and Silicone gel show preventive effects in the literature, especially in burns wounds, further studies are recommended to be conducted to prove the topical effects of above-mentioned gels in patients.
Annotated checklist and type catalog of fish genera and species described from Taiwan

3 Introduction . 3 Materials and Methods . 4 Results . 4 Annotated checklist and type catalog of fish genera and species collected from Taiwan . 15 Questionable records in Taiwan . 63 Acknowledgements . 65 References . 65
Advantage of Quantum Theory over Nonclassical Models of Communication

Quantum correlations provide dramatic advantage over the corresponding classical resources in several communication tasks. However, a broad class of probabilistic theories exists that attributes greater success than quantum theory in many of these tasks by allowing supra‐quantum correlations in “space‐like” and/or “time‐like” paradigms. In this letter, a communication task involving three spatially separated parties is proposed where one party (verifier) aims to verify whether the bit strings possessed by the other two parties (terminals) are equal or not. This task is called authentication with limited communication, the restrictions on communication being: i) the terminals cannot communicate with each other, but (ii) each of them can communicate with the verifier through single use of channels with limited capacity. Manifestly, classical resources are not sufficient for perfect success of this task. Moreover, it is also not possible to perform this task with certainty in several nonclassical theories although they might possess stronger “space‐like” and/or “time‐like” correlations. Surprisingly, quantum resources can achieve the perfect winning strategy. The proposed task thus stands apart from all previously known communication tasks as it exhibits quantum advantage over other nonclassical strategies.
Corpora Compared: The Case of the Swedish Gigaword & Wikipedia Corpora

In this work, we show that the difference in performance of embeddings from differently sourced data for a given language can be due to other factors besides data size. Natural language processing (NLP) tasks usually perform better with embeddings from bigger corpora. However, broadness of covered domain and noise can play important roles. We evaluate embeddings based on two Swedish corpora: The Gigaword and Wikipedia, in analogy (intrinsic) tests and discover that the embeddings from the Wikipedia corpus generally outperform those from the Gigaword corpus, which is a bigger corpus. Downstream tests will be required to have a definite evaluation.
New Horizons Observations of the Cosmic Optical Background

We used existing data from the New Horizons Long-range Reconnaissance Imager (LORRI) to measure the optical-band (0.4 ≲ λ ≲ 0.9 μm) sky brightness within seven high–Galactic latitude fields. The average raw level measured while New Horizons was 42–45 au from the Sun is 33.2 ± 0.5 nW m−2 sr−1. This is ∼10× as dark as the darkest sky accessible to the Hubble Space Telescope, highlighting the utility of New Horizons for detecting the cosmic optical background (COB). Isolating the COB contribution to the raw total required subtracting scattered light from bright stars and galaxies, faint stars below the photometric detection limit within the fields, and diffuse Milky Way light scattered by infrared cirrus. We removed newly identified residual zodiacal light from the IRIS 100 μm all-sky maps to generate two different estimates for the diffuse Galactic light. Using these yielded a highly significant detection of the COB in the range 15.9 ± 4.2 (1.8 stat., 3.7 sys.) nW m−2 sr−1 to 18.7 ± 3.8 (1.8 stat., 3.3 sys.) nW m−2 sr−1 at the LORRI pivot wavelength of 0.608 μm. Subtraction of the integrated light of galaxies fainter than the photometric detection limit from the total COB level left a diffuse flux component of unknown origin in the range 8.8 ± 4.9 (1.8 stat., 4.5 sys.) nW m−2 sr−1 to 11.9 ± 4.6 (1.8 stat., 4.2 sys.) nW m−2 sr−1. Explaining it with undetected galaxies requires the assumption that the galaxy count faint-end slope steepens markedly at V > 24 or that existing surveys are missing half the galaxies with V < 30.
Real-Time Control over Wireless Networks

Industrial internet of Things (IIoT) are gaining popularity for use in large-scale applications such as oil-field management (e.g., $74\times 8$km$^2$ East Texas Oil-field), smart farming, smart manufacturing, smart grid, and data center power management. These applications require the wireless stack to provide a scalable, reliable, low-power and low-latency communication. To realize a predictable and reliable communication in a highly unreliable wireless environment, industrial wireless standards use a centralized wireless stack design. In a centralized wireless stack design, a central manager generates routes and a communication schedule for a multi-channel time division multiple access communication (TDMA) based medium access control (MAC). However, a centralized wireless stack design is highly energy consuming, not scalable, and does not support frequent changes to networks or workloads. To address these challenges, the following contributions are made in this dissertation: (1) A scalable and distributed routing algorithm for industrial IoT which generates graph routes, which offer a high degree of redundancy, (2) A local and online scheduling algorithm that is scalable, energy-efficient, and supports network/workload dynamics while ensuring reliability and real-time performance, (3) An approach to minimize latency for in-band integration of multiple low-power networks, (4) A fast and efficient test of schedulability that determines if an application meets the real-time performance requirement for given network topology, and (5) A distributed scheduling and control co-design that balances the control performance requirement and real-time performance for industrial IoT.
Fock and Hardy spaces: Clifford Appell case

In this paper, we study a specific system of Clifford–Appell polynomials and, in particular, their product. Moreover, we introduce a new family of quaternionic reproducing kernel Hilbert spaces in the framework of Fueter regular functions. The construction is based on a general idea which allows us to obtain various function spaces by specifying a suitable sequence of real numbers. We focus on the Fock and Hardy cases in this setting, and we study the action of the Fueter mapping and its range.
Quasi-many-body localization of interacting fermions with long-range couplings

A number of experimental platforms for quantum simulations of disordered quantum matter, from dipolar systems to trapped ions, involve degrees of freedom which are coupled by power-law decaying hoppings or interactions, yet the interplay of disorder and interactions in these systems is far less understood than in their short-ranged counterpart. Here we consider a prototype model of interacting fermions with disordered long-ranged hoppings and interactions, and use the flow equation approach to map out its dynamical phase diagram as a function of hopping and interaction exponents. We demonstrate that the flow equation technique is ideally suited to problems involving long-range couplings due to its ability to accurately simulate very large system sizes. We show that, at large on-site disorder and for short-range interactions, a transition from a delocalized phase to a quasi many-body localized (MBL) phase exists as the hopping range is decreased. This quasi-MBL phase is characterized by intriguing properties such as a set of emergent conserved quantities which decay algebraically with distance. Surprisingly we find that a crossover between delocalized and quasi-MBL phases survives even in the presence of long-range interactions.
Characterization of photon pairs generated by a silicon ring resonator under electrical self-pumping

We report on the generation of nonclassical states of light in a silicon ring resonator in a selfpumping scheme. The ring is inserted in a lasing cavity, for which it acts as a filter, so that the lasing always occurs within a selected ring resonance, without active stabilization of the resonance frequency. We show the emission of coincident photon pairs and study their correlation properties through the reconstruction of the measurements via stimulated emission.
Disentangling 3D Prototypical Networks For Few-Shot Concept Learning

We present neural architectures that disentangle RGB-D images into objects' shapes and styles and a map of the background scene, and explore their applications for few-shot 3D object detection and few-shot concept classification. Our networks incorporate architectural biases that reflect the image formation process, 3D geometry of the world scene, and shape-style interplay. They are trained end-to-end self-supervised by predicting views in static scenes, alongside a small number of 3D object boxes. Objects and scenes are represented in terms of 3D feature grids in the bottleneck of the network. We show that the proposed 3D neural representations are compositional: they can generate novel 3D scene feature maps by mixing object shapes and styles, resizing and adding the resulting object 3D feature maps over background scene feature maps. We show that classifiers for object categories, color, materials, and spatial relationships trained over the disentangled 3D feature sub-spaces generalize better with dramatically fewer examples than the current state-of-the-art, and enable a visual question answering system that uses them as its modules to generalize one-shot to novel objects in the scene.
Conversion of LSAT behavioral specifications to automata

The Logistics Specification and Analysis Tool (LSAT) is a model-based engineering tool used for manufacturing system design and analysis. Using a domain specific language, a system can be specified in LSAT. In this paper, a conversion method is presented to obtain the system behavior of an LSAT specification in automata structure.
Parametric Flatten-T Swish: An Adaptive Non-linear Activation Function For Deep Learning

QActivation function is a key component in deep learning that performs non-linear mappings between the inputs and outputs. Rectified Linear Unit (ReLU) has been the most popular activation function across the deep learning community. However, ReLU contains several shortcomings that can result in inefficient training of the deep neural networks, these are: 1) the negative cancellation property of ReLU tends to treat negative inputs as unimportant information for the learning, resulting in performance degradation; 2) the inherent predefined nature of ReLU is unlikely to promote additional flexibility, expressivity, and robustness to the networks; 3) the mean activation of ReLU is highly positive and leads to bias shift effect in network layers; and 4) the multilinear structure of ReLU restricts the non-linear approximation power of the networks. To tackle these shortcomings, this paper introduced Parametric Flatten-T Swish (PFTS) as an alternative to ReLU. By taking ReLU as a baseline method, the experiments showed that PFTS improved classification accuracy on SVHN dataset by 0.31%, 0.98%, 2.16%, 17.72%, 1.35%, 0.97%, 39.99%, and 71.83% on DNN-3A, DNN-3B, DNN-4, DNN-5A, DNN-5B, DNN-5C, DNN-6, and DNN-7, respectively. Besides, PFTS also achieved the highest mean rank among the comparison methods. The proposed PFTS manifested higher non-linear approximation power during training and thereby improved the predictive performance of the networks.
DROGON: A Trajectory Prediction Model based on Intention-Conditioned Behavior Reasoning

We propose a Deep RObust Goal-Oriented trajectory prediction Network (DROGON) for accurate vehicle trajectory prediction by considering behavioral intentions of vehicles in traffic scenes. Our main insight is that the behavior (i.e., motion) of drivers can be reasoned from their high level possible goals (i.e., intention) on the road. To succeed in such behavior reasoning, we build a conditional prediction model to forecast goal-oriented trajectories with the following stages: (i) relational inference where we encode relational interactions of vehicles using the perceptual context; (ii) intention estimation to compute the probability distributions of intentional goals based on the inferred relations; and (iii) behavior reasoning where we reason about the behaviors of vehicles as trajectories conditioned on the intentions. To this end, we extend the proposed framework to the pedestrian trajectory prediction task, showing the potential applicability toward general trajectory prediction.
Underspecification Presents Challenges for Credibility in Modern Machine Learning

ML models often exhibit unexpectedly poor behavior when they are deployed in real-world domains. We identify underspecification as a key reason for these failures. An ML pipeline is underspecified when it can return many predictors with equivalently strong held-out performance in the training domain. Underspecification is common in modern ML pipelines, such as those based on deep learning. Predictors returned by underspecified pipelines are often treated as equivalent based on their training domain performance, but we show here that such predictors can behave very differently in deployment domains. This ambiguity can lead to instability and poor model behavior in practice, and is a distinct failure mode from previously identified issues arising from structural mismatch between training and deployment domains. We show that this problem appears in a wide variety of practical ML pipelines, using examples from computer vision, medical imaging, natural language processing, clinical risk prediction based on electronic health records, and medical genomics. Our results show the need to explicitly account for underspecification in modeling pipelines that are intended for real-world deployment in any domain.
Agent-Based Computational Economics in Management Accounting Research: Opportunities and Difficulties

Agent-based computational economics (ACE) - while adopted comparably widely in other domains of managerial science - is a rather novel paradigm for management accounting research (MAR). This paper provides an overview of opportunities and difficulties that ACE may have for research in management accounting and, in particular, introduces a framework that researchers in management accounting may employ when considering ACE as a paradigm for their particular research endeavor. The framework builds on the two interrelated paradigmatic elements of ACE: a set of theoretical assumptions on economic agents and the approach of agent-based modeling. Particular focus is put on contrasting opportunities and difficulties of ACE in comparison to other research methods employed in MAR.
The type problem for Riemann surfaces via Fenchel–Nielsen parameters

A Riemann surface X$X$ is said to be of parabolic type if it does not support a Green's function. Equivalently, the geodesic flow on the unit tangent bundle of X$X$ (equipped with the hyperbolic metric) is ergodic. Given a Riemann surface X$X$ of arbitrary topological type and a hyperbolic pants decomposition of X$X$ , we obtain sufficient conditions for parabolicity of X$X$ in terms of the Fenchel–Nielsen parameters of the decomposition. In particular, we initiate the study of the effect of twist parameters on parabolicity. A key ingredient in our work is the notion of nonstandard half‐collar about a hyperbolic geodesic. We show that the modulus of such a half‐collar is much larger than the modulus of a standard half‐collar as the hyperbolic length of the core geodesic tends to infinity. Moreover, the modulus of the annulus obtained by gluing two nonstandard half‐collars depends on the twist parameter, unlike in the case of standard collars. Our results are sharp in many cases. For instance, for zero‐twist flute surfaces as well as for half‐twist flute surfaces with concave sequences of lengths our results provide a complete characterization of parabolicity in terms of the length parameters. It follows that parabolicity is equivalent to completeness in these cases. Applications to other topological types such as surfaces with infinite genus and one end (also known as the infinite Loch–Ness monster), the ladder surface, and abelian covers of compact surfaces are also studied.
On the Stability Properties and the Optimization Landscape of Training Problems with Squared Loss for Neural Networks and General Nonlinear Conic Approximation Schemes

We study the optimization landscape and the stability properties of training problems with squared loss for neural networks and general nonlinear conic approximation schemes. It is demonstrated that, if a nonlinear conic approximation scheme is considered that is (in an appropriately defined sense) more expressive than a classical linear approximation approach and if there exist unrealizable label vectors, then a training problem with squared loss is necessarily unstable in the sense that its solution set depends discontinuously on the label vector in the training data. We further prove that the same effects that are responsible for these instability properties are also the reason for the emergence of saddle points and spurious local minima, which may be arbitrarily far away from global solutions, and that neither the instability of the training problem nor the existence of spurious local minima can, in general, be overcome by adding a regularization term to the objective function that penalizes the size of the parameters in the approximation scheme. The latter results are shown to be true regardless of whether the assumption of realizability is satisfied or not. We demonstrate that our analysis in particular applies to training problems for free-knot interpolation schemes and deep and shallow neural networks with variable widths that involve an arbitrary mixture of various activation functions (e.g., binary, sigmoid, tanh, arctan, soft-sign, ISRU, soft-clip, SQNL, ReLU, leaky ReLU, soft-plus, bent identity, SILU, ISRLU, and ELU). In summary, the findings of this paper illustrate that the improved approximation properties of neural networks and general nonlinear conic approximation instruments are linked in a direct and quantifiable way to undesirable properties of the optimization problems that have to be solved in order to train them.