Miracle-dz/UniFAR-FTUs huggingface dataset:
Semigroups with fixed multiplicity and embedding dimension

Given $m\in \mathbb{N},$ a numerical semigroup with multiplicity $m$ is called packed numerical semigroup if its minimal generating set is included in $\{m,m+1,\ldots, 2m-1\}. $ In this work, packed numerical semigroups are used to built the set of numerical semigroups with fixed multiplicity and embedding dimension, and to create a partition in this set. Moreover, Wilf's conjecture is checked in the tree associated to some packed numerical semigroups.
Guided Reinforcement Learning: A Review and Evaluation for Efficient and Effective Real-World Robotics [Survey]

Recent successes aside, reinforcement learning (RL) still faces significant challenges in its application to the real-world robotics domain. Guiding the learning process with additional knowledge offers a potential solution, thus leveraging the strengths of data- and knowledge-driven approaches. However, this field of research encompasses several disciplines and hence would benefit from a structured overview.
An Integrated Hardware Prototype for Monitoring Gas leaks, Fires, and Remote control via Mobile Application

—Liquefied petroleum gas (LPG) is used in a wide range of applications such as home and industrial appliances, vehicles, and refrigerators. However, leakage of gas can have a dangerous and toxic effect on humans and other living organisms. In this paper, an IoT based system is employed for this purpose to monitor gas leakage, detect flames, and alert users. The MQ-5 gas sensor was used to understand the concentration level of a closed volume of gas, while the infrared flame sensor was used to detect the spread of fire in this study. The proposed system has the capacity to detect fire and gas leaks as well as take additional action to lower gas concentration by air ventilation with exhausted fan and put out fires with fire extinguisher. The suggested approach will contribute to increasing safety, lowering the mortality toll, and minimizing harm to the environment. Overall system is implemented with IOT cloud-based remote controls to prevent gas leakage by using android application in response to individual feedback or feed-forward commands. The controller used here is Arduino Uno Rev3 SMD. This study provides design approaches to both software and hardware.
CHANDRA OBSERVATIONS OF SNR RCW 103

We analyze three Chandra observations, with a combined exposure time of 99 ks, of the Galactic supernova remnant (SNR) RCW 103, a young SNR, with no previous clear detections of metal-rich ejecta. Based on our imaging and spectral analyses of these deep Chandra data, we find evidence for metal-rich ejecta emission scattered throughout the remnant. X-ray emission from the shocked ejecta is generally weak, and the shocked circumstellar medium (CSM) is a largely dominant component across the entire remnant. The CSM component shows abundances of ∼0.5 solar, while Ne, Mg, Si, S, and Fe abundances of the ejecta are up to a few times solar. Comparison of these ejecta abundances with yields from supernova nucleosynthesis models suggests, together with the existence of a central neutron star, a progenitor mass of ∼18–20 M ⊙ ? > , though the Fe/Si ratios are larger than predicted. The shocked CSM emission suggests a progenitor with high mass-loss rate and subsolar metallicity.
Higher amalgamation properties in stable theories.

For a complete, stable theory $T$ we construct, in a reasonably canonical way, a related stable theory $T^*$ which has higher independent amalgamation properties over the algebraic closure of the empty-set. The theory $T^*$ is an algebraic cover of $T$ and we give an explicit description of the finite covers involved in the construction of $T^*$ from $T$. This follows an approach of E. Hrushovski. If $T$ is almost strongly minimal with a $0$-definable strongly minimal set, then we show that $T^*$ has higher amalgamation over any algebraically closed subset.
Domain shape dependence of semiclassical corrections to energy

Stationary solution of a one-dimensional sine-Gordon system is embedded in a multidimensional theory with an explicitly finite domain in the added spatial dimensions. Semiclassical corrections to energy are calculated for a static kink solution with emphasis on the impact of the scale of the domain as well as the choice of boundary conditions on the results for a rectangular cross-section.
QoS-Aware Resource Allocation for Green Sensor-as-a-Service Provisioning in Vehicular Multi-Sensor-Cloud

This work addresses the problem of optimal resource allocation for provisioning green Sensor-as-a-Service (Se-aaS) in the presence of multiple sensor-cloud service providers (SCSPs) and vehicle owners (VOs) having heterogeneous sensors. To ensure high service availability, low carbon footprint, and optimal profit distribution, optimal usage of the available resources while maintaining optimal contribution of VOs in the multi-sensor-cloud (MSC) market is necessary. Additionally, a single SCSP may lose revenue due to the absence of compatible sensor-equipped vehicles (SVs) to serve user requests. To address these issues, we propose FRAME, a game-theoretic resource allocation scheme, for vehicular MSC. Initially, we use a single-leader-multiple-followers Stackelberg game to decide the optimal set of SVs for serving a request while ensuring high QoS, low energy consumption, and optimal profit distribution among the SCSPs and VOs. Further, to address the inadequacy of resources, we propose a collaboration scheme using the expected utility theory for multiple SCSPs and choose the optimal SCSP to serve the requests. We evaluate the performance of FRAME theoretically and experimentally and compare it with benchmark schemes. Simulation results depict a 4.14-27.3% increase in served requests, a 14.61-22.83% decrease in energy consumption, and optimal profit distribution among the SCSP and VOs.
Determination of the Glycemic Index Values of the Turkey-Specific Ice Cream

Objective: The glycemic index (GI) is defined as the effect of consumed food on blood glucose relative to the reference food. The aim of this study is to determine the GI value of “Atatürk Orman Çiftliği (AOÇ)” ice cream, which is unique to our country. Method: This study included 18 adult healthy individuals with normal body mass index (BMI) values (18.5-24.9 kg/m²) (Female/Male: 10/8). Individuals participating in the study were invited to the our outpatient clinic on different days for two weeks to consume 2 test foods (AOC ice cream) and 2 reference foods (glucose) after 12 hours of fasting. Venous blood samples were taken at 0, 15, 30, 45, 60, 90 and 120 minutes after consumption of reference and test foods and glucose values were recorded. The glycemic index value of the test food was calculated by multiplying by 100 the ratio of the sum of the area under the curve obtained from the test food to the sum of the area under the curve obtained from the reference food. Results: The median values of the area under the curve of the reference food and the test food were 2360.88 (483.75-4132.5) and 475.95 (44.88-1980), respectively (p<0.001). The Glycemic index value of the test foods was 21.46 (4.08-409.3) according to the reference food. Conclusion: In this study, the glycemic index value of the Turkey-specific “Atatürk Orman Çiftliği” ice cream was found to be 21,46 (4.08-409.3) (Low). These types of foods with a low glycemic index may be preferred to increase the dietary compatibility of the patient with type 2 diabetes, especially at snack meals.
A Malicious Domains Detection Method Based on File Sandbox Traffic

With the recent increasing number of malicious cyber activities using domain names as attack vectors, malicious domains must be detected and blocked in order to combat cyber attackers. However, current studies of malicious domains detection are limited to Domain Name System (DNS) traffic features or character features, which ignore the associations of malware and malicious domain in the detection. In this paper, we propose a malicious domains detection approach based on domain relationship features extracted from real sandbox traffic. We construct heterogeneous graphs based on sandbox traffic and use the Relational Graph Convolutional Network (RGCN) to build detection models to extract inter-node relationship features. Experiments were conducted using data extracted from real sandbox traffic, and our approach achieved an accuracy of 87.11%. The experimental results demonstrate the effectiveness of using relationship features extracted from sandbox traffic for malicious domains detection.
Fisher–Hartwig determinants, conformal field theory and universality in generalised XX models

We discuss certain quadratic models of spinless fermions on a 1D lattice, and their corresponding spin chains. These were studied by Keating and Mezzadri in the context of their relation to the Haar measures of the classical compact groups. We show how these models correspond to translation invariant models on an infinite or semi-infinite chain, which in the simplest case reduce to the familiar XX model. We give physical context to mathematical results for the entanglement entropy, and calculate the spin–spin correlation functions using the Fisher–Hartwig conjecture. These calculations rigorously demonstrate universality in classes of these models. We show that these are in agreement with field theoretic and renormalization group arguments that we provide.
Strings in Yang-Mills-Higgs theory coupled to gravity

Non-Abelian strings for an Einstein-Yang-Mills-Higgs theory are explicitly constructed. We consider N{sub f} Higgs fields in the fundamental representation of the U(1)xSU(N{sub c}) gauge group in order to have a color-flavor SU(N{sub c}) group remaining unbroken. Choosing a suitable ansatz for the metric, Bogomol'nyi-like first-order equations are found and rotationally symmetric solutions are proposed. In the N{sub f}=N{sub c} case, solutions are local strings and are shown to be truly non-Abelian by parametrizing them in terms of orientational collective coordinates. When N{sub f}>N{sub c}, the solutions correspond to semilocal strings which, beside the orientational degrees of freedom, acquire additional collective coordinates parametrizing their transverse size. The low-energy effective theories for the correspondent moduli are found, showing that all zero modes are normalizable in presence of gravity, even in the semilocal case.
Spatio-Temporal Variability of Malaria Incidence in the Health District of Kati, Mali, 2015–2019

Introduction: Despite the implementation of control strategies at the national scale, the malaria burden remains high in Mali, with more than 2.8 million cases reported in 2019. In this context, a new approach is needed, which accounts for the spatio-temporal variability of malaria transmission at the local scale. This study aimed to describe the spatio-temporal variability of malaria incidence and the associated meteorological and environmental factors in the health district of Kati, Mali. Methods: Daily malaria cases were collected from the consultation records of the 35 health areas of Kati’s health district, for the period 2015–2019. Data on rainfall, relative humidity, temperature, wind speed, the normalized difference vegetation index, air pressure, and land use–land cover were extracted from open-access remote sensing sources, while data on the Niger River’s height and flow were obtained from the National Department of Hydraulics. To reduce the dimension and account for collinearity, strongly correlated meteorological and environmental variables were combined into synthetic indicators (SI), using a principal component analysis. A generalized additive model was built to determine the lag and the relationship between the main SIs and malaria incidence. The transmission periods were determined using a change-point analysis. High-risk clusters (hotspots) were detected using the SatScan method and were ranked according to risk level, using a classification and regression tree analysis. Results: The peak of the malaria incidence generally occurred in October. Peak incidence decreased from 60 cases per 1000 person–weeks in 2015, to 27 cases per 1000 person–weeks in 2019. The relationship between the first SI (river flow and height, relative humidity, and rainfall) and malaria incidence was positive and almost linear. A non-linear relationship was found between the second SI (air pressure and temperature) and malaria incidence. Two transmission periods were determined per year: a low transmission period from January to July—corresponding to a persisting transmission during the dry season—and a high transmission period from July to December. The spatial distribution of malaria hotspots varied according to the transmission period. Discussion: Our study confirmed the important variability of malaria incidence and found malaria transmission to be associated with several meteorological and environmental factors in the Kati district. The persistence of malaria during the dry season and the spatio-temporal variability of malaria hotspots reinforce the need for innovative and targeted strategies.
A Convolutional Long Short-Term Memory-Based Neural Network for Epilepsy Detection From EEG

Epilepsy (EP) is a severe neurological disorder characterized by recurrent seizures, which increases the risk of death three times more than normal. Currently, electroencephalography (EEG) has emerged as a highly promising technique for the diagnosis of EP. The majority of current EEG-based EP detection research has employed a variety of deep-learning (DL)-based models, but most of the approaches suffer from poor generalizability, optimal design, and performance rates. To address these issues, this study aims to develop an efficient framework based on the deep spatiotemporal neural network called convolutional long short-term memory (ConvLSTM) for EP detection from EEG signals. In the proposed model, first standard 19-channel EEG data are selected and resampled at 256 Hz and then those signals are segmented into 3-s time frames. Afterward, the segmented data are fed as input to the ConvLSTM model for identifying epileptic patients from normal subjects. To generalize the proposed model, we have tested it on two different datasets with varying population sizes. We have used the five-fold cross-validation and leave-one-out cross-validation (LOOCV) schemes to eliminate the experiment’s biases. To further validate the proposed framework, we have carried out various ablation studies. The experimental results demonstrate that the proposed model outperforms the current state-of-the-art results for the studied datasets, making it suitable for use as an automated system for the diagnosis of EP.
D-Extract: Extracting Dimensional Attributes From Product Images

Product dimension is a crucial piece of information enabling customers make better buying decisions. E-commerce websites extract dimension attributes to enable customers filter the search results according to their requirements. The existing methods extract dimension attributes from textual data like title and product description. However, this textual information often exists in an ambiguous, disorganised structure. In comparison, images can be used to extract reliable and consistent dimensional information. With this motivation, we hereby propose two novel architecture to extract dimensional information from product images. The first namely Single-Box Classification Net-work is designed to classify each text token in the image, one at a time, whereas the second architecture namely Multi-Box Classification Network uses a transformer network to classify all the detected text tokens simultaneously. To attain better performance, the proposed architectures are also fused with statistical inferences derived from the product category which further increased the F1-score of the Single-Box Classification Network by 3.78% and Multi-Box Classification Network by ≈ 0.9%≈. We use distance super-vision technique to create a large scale automated dataset for pretraining purpose and notice considerable improvement when the models were pretrained on the large data before finetuning. The proposed model achieves a desirable precision of 91.54% at 89.75% recall and outperforms the other state of the art approaches by ≈ 4.76% in F1-score1.
An adaptive generalized multiscale discontinuous Galerkin method (GMsDGM) for high-contrast flow problems

In this paper, we develop an adaptive Generalized Multiscale Discontinuous Galerkin Method (GMs-DGM) for a class of high-contrast flow problems, and derive a-priori and a-posteriori error estimates for the method. Based on the a-posteriori error estimator, we develop an adaptive enrichment algorithm for our GMsDGM and prove its convergence. The adaptive enrichment algorithm gives an automatic way to enrich the approximation space in regions where the solution requires more basis functions, which are shown to perform well compared with a uniform enrichment. We also discuss an approach that adaptively selects multiscale basis functions by correlating the residual to multiscale basis functions (cf. [4]). The proposed error indicators are L2-based and can be inexpensively computed which makes our approach efficient. Numerical results are presented that demonstrate the robustness of the proposed error indicators.
Metastability of the contact process on fast evolving scale-free networks

We study the contact process in the regime of small infection rates on finite scale-free networks with stationary dynamics based on simultaneous updating of all connections of a vertex. We allow the update rates of individual vertices to increase with the strength of a vertex, leading to a fast evolution of the network. We first develop an approach for inhomogeneous networks with general kernel and then focus on two canonical cases, the factor kernel and the preferential attachment kernel. For these specific networks we identify and analyse four possible strategies how the infection can survive for a long time. We show that there is fast extinction of the infection when neither of the strategies is successful, otherwise there is slow extinction and the most successful strategy determines the asymptotics of the metastable density as the infection rate goes to zero. We identify the domains in which these strategies dominate in terms of phase diagrams for the exponent describing the decay of the metastable density.
DEVELOPMENT OF A SYSTEM OF FIBER-OPTIC SENSORS BASED ON FIBER BRAGG GRATINGS

The article herein considers testing and distant health monitoring of bridge structure in Kazakhstan, Almaty oblast, at river Issyk. Bridge on river Issyk represents a deck type construction with total length of 70 meters and width of 6 meters. Superstructure consists of 10-inch monolithic reinforced concrete plate and leans against at prestrained piled bends, each of which consists of 5 piles. Bragg smart tilted grating fiber-optic sensor for distant health monitoring is embedded beneath the bridge. There have been conducted static and dynamic bridge testing with loaded trucks and automobiles. The sensor was switched on to data collection system, constantly installed in-situ. Collected data, as well, analytical investigations prove, that current bridges specifications are conservative. Technology, having been developed in this work’s framework, will allow fulfill practical, economically effective and reliable systemic technical maintenance of bridge structures, and research will give unique possibility for future growth of the technology in Almaty oblast and in other regions of Kazakhstan.
Relating virtual knot invariants to links in $\mathbb{S}^{3}$

Geometric interpretations of some virtual knot invariants are given in terms of invariants of links in $\mathbb{S}^3$. Alexander polynomials of almost classical knots are shown to be specializations of the multi-variable Alexander polynomial of certain two-component boundary links of the form $J \sqcup K$ with $J$ a fibered knot. The index of a crossing, a common ingredient in the construction of virtual knot invariants, is related to the Milnor triple linking number of certain three-component links $J \sqcup K_1 \sqcup K_2$ with $J$ a connected sum of trefoils or figure-eights. Our main technical tool is virtual covers. This technique, due to Manturov and the first author, associates a virtual knot $\upsilon$ to a link $J \sqcup K$, where $J$ is fibered and $\text{lk}(J,K)=0$. Here we extend virtual covers to all multicomponent links $L=J \sqcup K$, with $K$ a knot. It is shown that an unknotted component $J_0$ can be added to $L$ so that $J_0 \sqcup J$ is fibered and $K$ has algebraic intersection number zero with a fiber of $J_0 \sqcup J$. This is called fiber stabilization. It provides an avenue for studying all links with virtual knots.
Decoupling Optimization of the Three-Coil Coupler for IPT System Featuring High Efficiency and Misalignment Tolerance

The efficiency of an inductive power transfer (IPT) system decreases significantly with misalignment. This article proposes a parameter optimization method for the three-coil coupler (TCC) to obtain high system efficiency and misalignment tolerance. The magnetic flux distribution of the TCC is compared with the conventional two-coil coupler, based on which the reason for high misalignment tolerance of the TCC is clarified. To reduce the optimization time, the concepts of primary self-inductance decoupling and mutual inductance decoupling are introduced. An IPT prototype using inductor-capacitor-capacitor/series compensation topology and the optimal TCC was built to verify the validity of the proposed optimization method. The radii of the transmitting and receiving pads of the TCC are 210 and 130 mm, respectively, and the power transfer distance is 100 mm. The system efficiency is 90.106% in aligned case and 85.446% in the most misaligned case (misalignments in X-, Y-, and Z-axis are 75, 100, and 50 mm, respectively). Compared to the output voltage in aligned case, the output voltage in the most misaligned case is only decreased by 39.26%.
TRUST AS PERFORMANCE

It is argued that trust is a performative kind and that the evaluative normativity of trust is a special case of the evaluative normativity of performances generally. The view is shown to have advantages over competitor views, e.g., according to which good trusting is principally a matter of good believing (e.g., Hieronymi, 2008; McMyler, 2011), or good affect (e.g., Baier, 1986; Jones, 1996), or good conation (e.g., Holton, 1994). Moreover, the view can be easily extended to explain good (and bad) distrust , where the latter is understood as aimed (narrow-scoped) forbearance from trusting. The over-arching framework—which assimilates the evaluative norms of trusting (and distrusting) to performance-theoretic norms—supplies us with an entirely new lens to view traditional philosophical problems about what is involved in trusting and distrusting well and badly, and thus, places our capacity to make progress on problems in this area on a new footing.
Banach space actions and L2-spectral
gap

Żuk proved that if a finitely generated group admits a Cayley graph such that the Laplacian on the links of this Cayley graph has a spectral gap $> \frac{1}{2}$, then the group has property (T), or equivalently, every affine isometric action of the group on a Hilbert space has a fixed point. We prove that the same holds for affine isometric actions of the group on a uniformly curved Banach space (for example an $L^p$-space with $1 1-\varepsilon$. This criterion applies to random groups in the triangular density model for densities $> \frac{1}{3}$. In this way, we are able to generalize recent results of Druţu and Mackay to affine isometric actions of random groups on uniformly curved Banach spaces. Also, in the setting of actions on $L^p$-spaces, our results are quantitatively stronger, even in the case $p=2$. This naturally leads to new estimates on the conformal dimension of the boundary of random groups in the triangular model. Additionally, we obtain results on the eigenvalues of the $p$-Laplacian on graphs, and on the spectrum and degree distribution of Erdős-Renyi graphs.