Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives

We consider a discrete optimization based approach for learning sparse classifiers, where the outcome depends upon a linear combination of a small subset of features. Recent work has shown that mixed integer programming (MIP) can be used to solve (to optimality) $\ell_0$-regularized problems at scales much larger than what was conventionally considered possible in the statistics and machine learning communities. Despite their usefulness, MIP-based approaches are significantly slower compared to relatively mature algorithms based on $\ell_1$-regularization and relatives. We aim to bridge this computational gap by developing new MIP-based algorithms for $\ell_0$-regularized classification. We propose two classes of scalable algorithms: an exact algorithm that can handle $p\approx 50,000$ features in a few minutes, and approximate algorithms that can address instances with $p\approx 10^6$ in times comparable to fast $\ell_1$-based algorithms. Our exact algorithm is based on the novel idea of \textsl{integrality generation}, which solves the original problem (with $p$ binary variables) via a sequence of mixed integer programs that involve a small number of binary variables. Our approximate algorithms are based on coordinate descent and local combinatorial search. In addition, we present new estimation error bounds for a class of $\ell_0$-regularized estimators. Experiments on real and synthetic data demonstrate that our approach leads to models with considerably improved statistical performance (especially, variable selection) when compared to competing toolkits.
Thermodynamics in $f(R,\mathcal{L})$ Theories

In this paper we study the recently proposed $f(R,\mathcal{L})$ theories from a thermodynamic point of view. The uniqueness of these theories lies in the fact that the spacetime curvature is coupled to the baryonic matter instead of exotic matter (in the form of scalar field). We investigate the viability of these theories from the point of view of the thermodynamic stability of the models. We consider several models of $f(R,\mathcal{L})$ theories where baryonic matter is coupled with spacetime curvature both minimally and non-minimally. Various thermodynamic quantities like entropy, enthalpy, internal energy, Gibbs free energy, etc. are computed and using their allowed ranges various model parameters are constrained. The study gives us an idea about the viability of these theories from a thermodynamic point of view.
The Kodaira Problem for Kähler Spaces with Vanishing First Chern Class

Abstract Let X be a normal compact Kähler space with klt singularities and torsion canonical bundle. We show that X admits arbitrarily small deformations that are projective varieties if its locally trivial deformation space is smooth. We then prove that this unobstructedness assumption holds in at least three cases: if X has toroidal singularities, if X has finite quotient singularities and if the cohomology group ${\mathrm {H}^{2} \! \left ( X, {\mathscr {T}_{X}} \right )}$ vanishes.
The Deepest Radio Observations of Nearby SNe Ia: Constraining Progenitor Types and Optimizing Future Surveys

We report deep radio observations of nearby Type Ia supernovae (SNe Ia) with the electronic Multi-Element Radio Linked Interferometer Network and the Australia Telescope Compact Array. No detections were made. With standard assumptions for the energy densities of relativistic electrons going into a power-law energy distribution and the magnetic field strength (ϵe = ϵB = 0.1), we arrive at upper limits on mass-loss rate for the progenitor system of SN 2013dy (SN 2016coj, SN 2018gv, SN 2018pv, SN 2019np) of , where vw is the wind speed of the mass loss. To SN 2016coj, SN 2018gv, SN 2018pv, and SN 2019np we add radio data for 17 other nearby SNe Ia and model their nondetections. With the same model as described, all 21 SNe Ia have . We compare those limits with the expected mass-loss rates in different single-degenerate progenitor scenarios. We also discuss how information on ϵe and ϵB can be obtained from late observations of SNe Ia and the youngest SN Ia remnant detected in radio, G1.9+0.3, as well as stripped-envelope core-collapse SNe. We highlight SN 2011dh and argue for ϵe ≈ 0.1 and ϵB ≈ 0.0033. Finally, we discuss strategies to observe at radio frequencies to maximize the chance of detection, given the time since explosion, the distance to the SN, and the telescope sensitivity.
Visual Simplified Characters' Emotion Emulator Implementing OCC Model

In this paper, we present a visual emulator of the emotions seen in characters in stories. This system is based on a simplified view of the cognitive structure of emotions proposed by Ortony, Clore and Collins (OCC Model). The goal of this paper is to provide a visual platform that allows us to observe changes in the characters' different emotions, and the intricate interrelationships between: 1) each character's emotions, 2) their affective relationships and actions, 3) The events that take place in the development of a plot, and 4) the objects of desire that make up the emotional map of any story. This tool was tested on stories with a contrasting variety of emotional and affective environments: Othello, Twilight, and Harry Potter, behaving sensibly and in keeping with the atmosphere in which the characters were immersed.
On the Smith classes, the van Kampen obstruction and embeddability of $[3]*K$

In this survey-research paper, we first introduce the theory of Smith classes of complexes with fixed-point free, periodic maps on them. These classes, when defined for the deleted product of a simplicial complex $K$, are the same as the embedding classes of $K$. Embedding classes, in turn, are generalizations of the van Kampen obstruction class for embeddability of a $d$-dimensional complex $K$ into the Euclidean $2d$-space. All of these concepts will be introduced in simple terms. Second, we use the theory introduced in the first part to relate the embedding classes (or the special Smith classes) of the the complex $[3]*K$ with the embedding classes of $K$. Here $[3]*K$ is the join of $K$ with a set of three points. Specifically, we prove that if the $m$-th embedding class of $K$ is non-zero, then the $(m+2)$-nd embedding class of $[3]*K$ is non-zero. We also prove some of the consequences of this theorem for the embeddability of $[3]*K$.
Existence analysis of a stationary compressible fluid model for heat-conducting and chemically reacting mixtures

The existence of large-data weak solutions to a steady compressible Navier–Stokes–Fourier system for chemically reacting fluid mixtures is proved. General free energies are considered satisfying some structural assumptions, with a pressure containing a γ-power law. The model is thermodynamically consistent and contains the Maxwell–Stefan cross-diffusion equations in the Fick–Onsager form as a special case. Compared to previous works, a very general model class is analyzed, including cross-diffusion effects, temperature gradients, compressible fluids, and different molar masses. A priori estimates are derived from the entropy balance and the total energy balance. The compactness for the total mass density follows from an estimate for the pressure in L p with p > 1, the effective viscous flux identity, and uniform bounds related to Feireisl’s oscillation defect measure. These bounds rely heavily on the convexity of the free energy and the strong convergence of the relative chemical potentials.
Inhibited Coagulation of Micron-size Dust Due to the Electrostatic Barrier

The collisional evolution of solid material in protoplanetary disks is a crucial step in the formation of planetesimals, comets, and planets. Although dense protoplanetary environments favor fast dust coagulation, there are several factors that limit the straightforward pathway from interstellar micron-size grains to pebble-size aggregates. Apart from the grain bouncing, fragmentation, and fast drift to the central star, a notable limiting factor is the electrostatic repulsion of like-charged grains. In this study we aim at theoretical modeling of the dust coagulation coupled with the dust charging and disk ionization calculations. We show that the electrostatic barrier is a strong restraining factor to the coagulation of micrometer-size dust in dead zones of the disk (where the turbulence is suppressed). While the sustained turbulence helps to overcome the electrostatic barrier, low fractal dimensions of dust aggregates can potentially block their further coagulation even in this case. Coulomb repulsion may keep a significant fraction of small dust in the disk atmosphere and outer regions.
Semiclassical estimates for pseudodifferential operators and the Muskat problem in the unstable regime

Abstract We obtain new semiclassical estimates for pseudodifferential operators with low regular symbols. Such symbols appear naturally in a Cauchy Problem related to recent weak solutions to the unstable Muskat problem constructed via convex integration. In particular, our new estimates reveal the tight relation between the speed of opening of the mixing zone and the regularity of the interphase.
Fourier Transform Approach to Machine Learning III: Fourier Classification

We propose a Fourier-based learning algorithm for highly nonlinear multiclass classification. The algorithm is based on a smoothing technique to calculate the probability distribution of all classes. To obtain the probability distribution, the density distribution of each class is smoothed by a low-pass filter separately. The advantage of the Fourier representation is capturing the nonlinearities of the data distribution without defining any kernel function. Furthermore, contrary to the support vector machines, it makes a probabilistic explanation for the classification possible. Moreover, it can treat overlapped classes as well. Comparing to the logistic regression, it does not require feature engineering. In general, its computational performance is also very well for large data sets and in contrast to other algorithms, the typical overfitting problem does not happen at all. The capability of the algorithm is demonstrated for multiclass classification with overlapped classes and very high nonlinearity of the class distributions.
Efficient Bitruss Decomposition for Large-scale Bipartite Graphs

Cohesive subgraph mining in bipartite graphs becomes a popular research topic recently. An important structure k-bitruss is the maximal cohesive subgraph where each edge is contained in at least k butterflies (i.e., (2,2)-bicliques). In this paper, we study the bitruss decomposition problem which aims to find all the k-bitrusses for k ≥ 0. The existing bottom-up techniques need to iteratively peel the edges with the lowest butterfly support. In this peeling process, these techniques are time-consuming to enumerate all the supporting butterflies for each edge. To relax this issue, we first propose a novel online index — the BE-Index which compresses butterflies into k-blooms (i.e., (2,k)-bicliques). Based on the BE-Index, the new bitruss decomposition algorithm BiT-BU is proposed, along with two batch-based optimizations, to accomplish the butterfly enumeration of the peeling process in an efficient way. Furthermore, the BiT-PC algorithm is devised which is more efficient against handling the edges with high butterfly supports. We theoretically show that our new algorithms significantly reduce the time complexities of the existing algorithms. Also, we conduct extensive experiments on real datasets and the results demonstrate that our new techniques can speed up the state-of-the-art techniques by up to two orders of magnitude.
Diatomic Rovibronic Transitions as Potential Probes for Proton-to-Electron Mass Ratio Across Cosmological Time

Astrophysical molecular spectroscopy is an important method of searching for new physics through probing the variation of the proton-to-electron mass ratio, μ, with existing constraints limiting variation to a fractional change of less than 10−17per year. To improve on this constraint and therefore provide better guidance to theories of new physics, new molecular probes will be useful. These probes must have spectral transitions that are observable astrophysically and have different sensitivities to variation in the proton-to-electron mass ratio. Here, we concisely detail how the set of potential molecular probes and promising sensitive transitions is constrained based on how the frequency and intensity of these transitions align with available telescopes. Our detailed investigation focuses on rovibronic transitions in astrophysical diatomic molecules, using the spectroscopic models of 11 diatomics to identify sensitive transitions and probe how they generally arise in real complex molecules with many electronic states and fine structure. While none of the 11 diatomics investigated have sensitive transitions likely to be astrophysically observable, we have found that at high temperatures (1000K) five of these diatomics have a significant number of low intensity sensitive transitions arising from an accidental near-degeneracy between vibrational levels in the ground and excited electronic states. This insight enables screening of all astrophysical diatomics as potential probes of proton-to-electron mass variation, with CN, CP, SiN and SiC being the most promising candidates for further investigation for sensitivity in rovibronic transitions.
The future of computing beyond Moore’s Law

Moore’s Law is a techno-economic model that has enabled the information technology industry to double the performance and functionality of digital electronics roughly every 2 years within a fixed cost, power and area. Advances in silicon lithography have enabled this exponential miniaturization of electronics, but, as transistors reach atomic scale and fabrication costs continue to rise, the classical technological driver that has underpinned Moore’s Law for 50 years is failing and is anticipated to flatten by 2025. This article provides an updated view of what a post-exascale system will look like and the challenges ahead, based on our most recent understanding of technology roadmaps. It also discusses the tapering of historical improvements, and how it affects options available to continue scaling of successors to the first exascale machine. Lastly, this article covers the many different opportunities and strategies available to continue computing performance improvements in the absence of historical technology drivers. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.
Internet and Mobile Applications in Work Life and Private Life of Digital Marketers - Methodology of Research

Abstract The goal of the article is to declare and describe the methodology of research about Internet and mobile applications in work life and private life of digitals marketers. This article is a reflection on the research methods used, their adequacy and potential results that the research team should achieve during the research. The article is the justification for the selected research method (both quantitative and qualitative), describes the research group. The authors, based on the pilot study, also make some conclusions, which will then be verified using subsequent - more extensive and implemented on a larger scale research tools.
The VES KM: a pathway for protein folding in vivo

Abstract While according to the thermodynamic hypothesis, protein folding reproducibility is ensured by the assumption that the native state corresponds to the minimum of the free energy in normal cellular conditions, here, the VES kinetic mechanism for folding in vivo is described according to which the nascent chain of all proteins is helical and the first and structure defining step in the folding pathway is the bending of that initial helix around a particular amino acid site. Molecular dynamics simulations are presented which indicate both the viability of this mechanism for folding and its limitations in the presence of a Markovian thermal bath. An analysis of a set of protein structures formed only of helices and loops suggests that bending sites are correlated with regions bounded, on the N-side, by positively charged amino acids like Lysine and Histidine and on the C-side by negatively charged amino acids like Aspartic acid.
A NEW FRONTIER IN HEALTH TECHNOLOGY: The Role of SMS Text-Based Messaging as a Smoking Cessation Intervention

In the past century, technology has played an increasingly important role in the delivery of health. Traditionally, health technology has been defined by instruments that have actively aided in the prevention, diagnosis and treatment of disease. Now, however, the focus of health technology has become centered on the transmission, processing, and storing of health information to decrease costs and improve resource allocation. Mobile health (mHealth) is an emerging component of health information technology (HIT) that utilizes mobile devices to deliver health care. Short messaging service (SMS) texting has gained increased attention as an affordable, ubiquitous form of mHealth to promote smoking cessation. Research in this area has been limited, but is growing rapidly. However, the ability of such interventions to facilitate long-term behavioral change remains unknown. Nonetheless, SMS text-based interventions contain a great deal of promise, and their potential health gains continue to be investigated.
Collective expression: how robotic swarms convey information with group motion

Abstract When faced with the need of implementing a decentralized behavior for a group of collaborating robots, strategies inspired from swarm intelligence often avoid considering the human operator, granting the swarm with full autonomy. However, field missions require at least to share the output of the swarm to the operator. Unfortunately, little is known about the users’ perception of group behavior and dynamics, and there is no clear optimal interaction modality for swarms. In this paper, we focus on the movement of the swarm to convey information to a user: we believe that the interpretation of artificial states based on groups motion can lead to promising natural interaction modalities. We implement a grammar of decentralized control algorithms to explore their expressivity. We define the expressivity of a movement as a metric to measure how natural, readable, or easily understandable it may appear. We then correlate expressivity with the control parameters for the distributed behavior of the swarm. A first user study confirms the relationship between inter-robot distance, temporal and spatial synchronicity, and the perceived expressivity of the robotic system. We follow up with a small group of users tasked with the design of expressive motion sequences to convey internal states using our grammar of algorithms. We comment on their design choices and we assess the interpretation performance by a larger group of users. We show that some of the internal states were perceived as designed and discuss the parameters influencing the performance.
Evaluation and Decision Making of Organization Quality Specific Immunity Based on MGDM-IPLAO Method

Abstract As the starting point, the immunity theory is introduced into the organization quality management, combined with the organization quality specific immunity system, the evaluation index system of organization quality specific immunity is designed. And the evaluation and multi-attribute group decision making model of organization quality specific immunity based on the immunity perspective is constructed by the method of multi-attribute group decision making of intuitive pure linguistic aggregation operators, empirical analysis is carried out by the research objects of relevant experts, representative and typical manufacturing enterprises, the empirical analysis results indicate that multi-attribute group decision making method of intuitive pure linguistic aggregation operators can choose and determine the optimization evaluation solution and the best decision making of partners, confirm the highest value over all partners for organization quality specific immunity system, the method of multi-attribute group decision making of intuitive pure linguistic aggregation operators has validity, feasibility and operability in evaluation and decision making of organization quality specific immunity. Empirical analysis results and conclusion have certain practical value, which provide new ideas to solve the problem of multi-attribute decision making of intuitive information mixed with pure linguistic information, and provides the basis for the effective selection of the best partners for manufacturing enterprises of the supply chain from the perspective of quality management.
Meeting with social robots like the cat-cucumber meeting? An integrated model of human-robot first contact. Psychological perspective.

Abstract People contacts with social robots, especially humanoid ones, are still rare. The vastness of research shows that we have cognitive relationships. Based on the studies in the field of social psychology, we propose an integrated theoretical model of developing cognitive category “social robot” and the mental model as a result of contact with the robot. We are discussing the consequences of this process, both social and personal, such as cognitive conflicts, unmet expectations and disappointments.
Polish nursing into the future – challenges and opportunities

Abstract Introduction. Polish nursing has experienced rapid and unprecedented evolutionary change. Recent and ongoing changes to preparatory nurse education have heralded a profound change for the profession. At the same time the nursing profession itself has experienced unprecedented development in terms of changing roles and expanded practice. While the growth of professional is ongoing there are many challenges to this in terms of staffing shortages, an aging workforce and changing illness trajectories. While a committed workforce, nurses are challenged to maintain quality standards in the context of these challenges. Aim. This paper explores the responses and responsibilities required of the profession to face ongoing changes in medicine, health and society as well as policy and professional changes. The leadership required to successfully steer and negotiate this ongoing transformation will be discussed.
A simplified sensorless speed control of permanent magnet synchronous motor using model reference adaptive system

Abstract A simplified sensorless speed control of permanent magnet synchronous motor (PMSM) using model reference adaptive system (MRAS) is presented. The MRAS is designed and incorporated in a complete closed loop PMSM control system fed by a three-phase inverter that utilizes a simplified hysteresis current control (HCC) to generate gating signals. Accurate rotor position, being essential in PMSM control, is estimated using MRAS rather encoders and resolvers which are explicit position sensors thereby eliminating the drawbacks of the traditional speed sensors in drive systems. The performance of this model is compared with an existing model that utilizes encoders and resolvers. Superior performance was obtained from this new model with MRAS. After initial starting transients, it is observed that the rotor speed for the model with MRAS settled to steady state at 0.10 seconds as against the model with speed sensor which attained steady state at 0.31 seconds. Torque response follows the same pattern to return to the load torque of 11Nm at steady state after starting. After speed reversal, the model with MRAS restored to steady state to track the negative speed command at 0.44 seconds. This is a superior performance compared to the model with speed sensor which settled at 0.60 seconds after speed reversal. The results have clearly shown the superiority of sensorless MRAS over traditional drive models with speed sensors. The software used for this research is MATLAB/Simulink 2017 model.