Miracle-dz/UniFAR-FTUs huggingface dataset:
Synchronization and Diversity of Solutions

A central computational problem in the realm of automata theory is the problem of determining whether a finite automaton A has a synchronizing word. This problem has found applications in a variety of subfields of artificial intelligence, including planning, robotics, and multi-agent systems. In this work, we study this problem within the framework of diversity of solutions, an up-and-coming trend in the field of artificial intelligence where the goal is to compute a set of solutions that are sufficiently distinct from one another. We define a notion of diversity of solutions that is suitable for contexts were solutions are strings that may have distinct lengths. Using our notion of diversity, we show that for each fixed r ∈ N, each fixed finite automaton A, and each finite automaton B given at the input, the problem of determining the existence of a diverse set {w1,w2, . ,wr} ⊆ L(B) of words that are synchronizing for A can be solved in polynomial time. Finally, we generalize this result to the realm of conformant planning, where the goal is to devise plans that achieve a goal irrespectively of initial conditions and of nondeterminism that may occur during their execution.
Remark on Robin problem for Poisson equation

The two basic boundary value problems for the Poisson equation are the Dirichlet and the Neumann problems. Their related fundamental solutions are the harmonic Green and Neumann functions. A linear combination of these two boundary value problems is the Robin problem. The related fundamental solution, the Robin function, can be chosen as an interpolation between the Green and the Neumann functions. For plane domains this was done in [Begehr H, Vaitekhovich T. Modified harmonic Robin functions. Complex Variables Elliptic Equ. 2013;58:483–496] with complex notation. While the Dirichlet problem is unconditionally solvable this is in general not the case for the other ones. In explicit form, however, this was shown only for the unit disc. Here the solvability condition is given for any admissible plane domain. Proper alteration of the fundamental solution can make the solvability condition disappear. This is shown explicitly for the case of the upper half of the unit disc .
Improved Input and State Estimation for Linear Stochastic Systems with Direct Feedthrough

This paper is concerned with the problem of joint input and state estimation for linear stochastic systems with direct feedthrough. Based on the fact that each unknown input between any two time steps is always bounded, a novel improved algorithm is proposed. Compared with existing results, this algorithm can effectively enhance estimation accuracy. Moreover, the stability of the algorithm is also discussed. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed approach.
Optical feedback interferometry applied to visualize an acoustic stationary wave

This article presents a novel technique to acquire and visualize two-dimensional images of dynamic changes of acoustic pressure in the case of a stationary acoustic wave. This method uses optical feedback interferometry sensing with a near-infrared laser diode. The stationary acoustic wave is generated using two piezoelectric transducers of 40 kHz facing each other, dynamic changes in acoustic pressure are measured in a 100 mm x 100 mm acoustic propagation field whose refractive index is variable along the optical path of the laser from the laser diode to a distant mirror and vice-versa. The image system records an image of 100 x 100 pixels of the acoustic pressure variation.
Safety Verification of Nonlinear Systems with Bayesian Neural Network Controllers

Bayesian neural networks (BNNs) retain NN structures with a probability distribution placed over their weights. With the introduced uncertainties and redundancies, BNNs are proper choices of robust controllers for safety-critical control systems. This paper considers the problem of verifying the safety of nonlinear closed-loop systems with BNN controllers over unbounded-time horizon. In essence, we compute a safe weight set such that as long as the BNN controller is always applied with weights sampled from the safe weight set, the controlled system is guaranteed to be safe. We propose a novel two-phase method for the safe weight set computation. First, we construct a reference safe control set that constraints the control inputs, through polynomial approximation to the BNN controller followed by polynomial-optimization-based barrier certificate generation. Then, the computation of safe weight set is reduced to a range inclusion problem of the BNN on the system domain w.r. t. the safe control set, which can be solved incrementally and the set of safe weights can be extracted. Compared with the existing method based on invariant learning and mixed-integer linear programming, we could compute safe weight sets with larger radii on a series of linear benchmarks. Moreover, experiments on a series of widely used nonlinear control tasks show that our method can synthesize large safe weight sets with probability measure as high as 95% even for a large-scale system of dimension 7.
Treatment of Nodular Lymphocyte-Predominant Hodgkin Lymphoma: Where Do We Stand? Where Do We Go?

Simple Summary Nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL) is a rare lymphoma entity accounting for ≈5% of all Hodgkin lymphoma (HL) cases. Since pathological characteristics and clinical presentation of NLPHL differ from classical HL and often resemble indolent B-cell non-Hodgkin lymphoma (B-NHL), nodular lymphocyte-predominant B-cell lymphoma has been proposed as an alternative name. In recent years, NLPHL has increasingly been treated with B-NHL-directed approaches, although more data are available on HL-directed treatment strategies. Overall, the outcomes of NLPHL patients are excellent, and the excess mortality in comparison with the general population is low. Future research in NLPHL will thus aim to determine strategies to reduce treatment intensity whenever possible. The definition of the optimal use of HL-directed and B-NHL-directed approaches in this disease is also pending. Abstract Nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL) is a rare B cell-derived lymphoma entity accounting for ≈5% of all Hodgkin lymphoma (HL) cases. In recent decades, patients with newly diagnosed NLPHL have usually been treated very similarly to classical HL (cHL). The 10-year overall survival rates with HL-directed approaches are in excess of 90%. However, pathological and clinical characteristics of NLPHL resemble indolent B-cell non-Hodgkin lymphoma (B-NHL) in some aspects. Thus, nodular lymphocyte-predominant B-cell lymphoma has been proposed as an alternative name, and the use of B-NHL-directed treatment strategies has become more common in NLPHL despite limited data. Given the often indolent clinical course of NLPHL, even in the case of relapse, the majority of patients with disease recurrence do not require high-dose chemotherapy and autologous stem cell transplantation but are treated sufficiently with low-intensity approaches such as single-agent anti-CD20 antibody treatment. The establishment of novel prognostic scores for NLPHL patients may optimize risk group and treatment allocation in newly diagnosed and relapsed disease.
Differentiable Sparse Optimal Control

This letter develops a framework for differentiating sparse optimal control inputs with respect to cost parameters. The difficulty lies in the non-smoothness induced by a sparsity-enhancing regularizer. To avoid this, we identify the optimal inputs as a unique zero point of a function using the proximal technique. This enables us to characterize the differentiability and employ the implicit function theorem. We also demonstrate the effectiveness of our approach using a numerical example of inverse optimal control.
Achievement goal viewed from parental involvement

Parent involvement is a concept which includes children’s education. Research and theories suggest that parent involvement can predict children’s achievement goal. The aims of this research is to know whether parent involvement can predict the children’s achievement goal. Yogyakarta high-school students (185 males and 379 females) were selected to fill in two questionnaires which assessed both their perceptions of parent involvement and the students’ achievement goals. SEM analyses showed that the model was fit to explain the correlation between parent involvement and achievement goal. Results of this study revealed that parent involvement is a predictor of students’ achievement goals. Parent involvement had an indirect effect on mastery goals and performance goals also in approach and avoidance. Parent involvements’ effect on performance is higher than the effect on mastery. Parent involvements’ effect on approach is higher than the effect on avoidance. Implications of these findings for students’ achievement goal and academic achievement are discussed.
The Effectiveness of Dialectical Behavior Therapy on Emotional Processing Defects and Impulsivity of Soldiers Aged 18 to 20 Years with High-risk Behaviors

Background: Faulty emotional strategies are one of the most crucial indicators of dangerous behavior. Objectives: This study aimed to evaluate the effectiveness of dialectical behavior therapy (DBT) on emotional processing defects and impulsivity of soldiers aged 18 to 20 years with high-risk behaviors. Methods: The method of the present study was quasi-experimental with a pre-test-post-test design. The statistical population of this study included all soldiers aged 18 to 20 years referred to Valiasr Medical Center in Tehran in 2020. The research sample included 30 soldiers with high-risk behaviors who were selected purposefully and randomly assigned to two groups (15 people in the experimental group and 15 people in the control group). To collect data, the high-risk behaviors Scale (IARS), Barrett's Impulsiveness scale, and the Toronto Alexithymia Scale (TAS-20) was used. The experimental group underwent ten sessions of DBT, and the control group did not receive any treatment. Data were analyzed using multivariate analysis of covariance and SPSS-23 software. The significance level of the tests was considered 0.05. Results: The results of this study showed that the intervention and control groups had statistically significant differences in terms of emotional processing (P < 0.01) and impulsivity (P < 0.05) after the intervention of dialectical behavior therapy. Conclusions: Based on the findings of the present study, it can be concluded that dialectical behavioral therapy can be used along with other treatments to reduce the problems of soldiers with high-risk behaviors.
Development of Character Recognition Model Inspired by Visual Explanations

Deep neural networks (DNNs) currently constitute the best-performing artificial vision systems. However, humans are still better at recognizing many characters, especially distorted, ornamental, or calligraphic characters compared with the highly sophisticated recognition models. Understanding the mechanism of character recognition by humans may give some cues for building better recognition models. However, the appropriate methodological approach to using these cues has not been much explored for developing character recognition models. Therefore, this paper tries to understand the process of character recognition by humans and DNNs by generating visual explanations for their respective decisions. We have used eye tracking to assay the spatial distribution of information hotspots for humans via fixation maps. We have proposed a gradient-based method for visualizing the reasoning behind the model's decision through visualization maps and have proved that our method is better than the other class activation mapping methods. Qualitative comparison between visualization maps and fixation maps reveals that both model and humans focus on similar regions in character in the case of correctly classified characters. However, when the focused regions are different for humans and model, the characters are typically misclassified by the latter. Hence, we propose to use the fixation maps as a supervisory input to train the model that ultimately results in improved recognition performance and better generalization. As the proposed model gives some insights about the reasoning behind its decision, it can find applications in fields, such as surveillance and medical applications, where explainability helps to determine system fidelity.
Molecular modeling of transmembrane delivery of paclitaxel by shock waves with nanobubbles

The development of advanced delivery strategies for anticancer drugs that can permeate through cellular membranes is urgently required for biomedical applications. In this work, we investigated the dynamic transmembrane behavior of paclitaxel (PTX), a powerful anticancer drug, under the combined impact of shock waves and nanobubbles, by using atomistic molecular dynamics simulations. Our simulations show that the PTX molecule experiences complicated motion modes during the action process with the membrane, as a consequence of its interplay with the lipid bilayer and water, under the joint effect of the shock wave and nanobubble. Moreover, it was found that the transmembrane movement of PTX is closely associated with the conformation changes of PTX, as well as the structural changes of the membrane (e.g., compression and poration in membrane). The nanobubble collapse induced by the shock wave, the proper PTX location with respect to the nanobubble, and a suitable nanobubble size and shock impulse are all n.
Delay-dependent stability criterion for uncertain discrete time systems in presence of actuator saturation

In this paper, the idea of triple Lyapunov functional is applied for controller design of discrete time systems subjected to various nonlinearities like actuator saturation, time varying delay and uncertainties. The performance of the system considered has been examined using H∞ technique in presence of external disturbance. A novel augmented triple Lyapunov-Krasovskii functional is applied with reciprocal convex approach to achieve the stability conditions, which are based on Linear Matrix Inequality. Saturation nonlinearity is tackled using less conservative convex hull method. Numerical examples are provided to illustrate the effectiveness of the proposed criterion.
Pilon fractures: Consensus and controversy

Summary: Pilon fractures are complex injuries that require an individualized approach to treatment to avoid complications and achieve good outcomes. Staged open reduction internal fixation remains the gold standard for most cases to achieve anatomic articular reduction while minimizing soft tissue complications and infection. Careful preoperative planning based on computed tomography dictates the surgical approach for reduction. A subset of cases may be amenable to early definitive or provisional open reduction and internal fixation based on fracture pattern. In some cases of severe articular comminution where reconstruction is not possible, primary ankle arthrodesis may be a good alternative.
Review of Visual Measurement Methods for Metal Vaporization Processes in Laser Powder Bed Fusion

Laser powder bed fusion (LPBF) is of great importance for the visual measurement and analysis of the metallization process, which is the process of solid, liquid, and gas phase transformations of metal powders under high-energy laser irradiation due to the low boiling point/high saturated vapor pressure. Since the evaporation of metals involves the interaction of driving forces such as vapor back pressure, surface tension, and gravity, the movement of the melt pool is not stable. At the same time, it also produces vaporization products such as vapor plumes and sprays, which cause defects such as bubbles, porosity, lack of fusion, inclusions, etc., during the manufacturing process of the parts, affecting the performance and manufacturing quality of the parts. More and more researchers are using imaging technologies, such as high-speed X-ray, high-speed visible light cameras, and high-speed schlieren imaging, to perform noncontact visual measurements and analyses of the melt pool, vapor plume, and spatter during the metal evaporation process, and the results show that the metal evaporation process can be suppressed by optimizing the process parameters and changing the processing atmosphere, thereby reducing part defects and improving part performance and built part quality. This paper reviews the research on metal evaporation mechanisms and visual measurement methods of metal evaporation, then discusses the measures of metal evaporation, and finally summarizes and prospects the future research hotspots of LPBF technology, according to the existing scholars’ research on numerical simulation analysis and visual measurement methods of the metal evaporation process.
Two problems on cycles in random graphs

OF THE DISSERTATION Two Problems on Cycles in Random Graphs by Jacob D. Baron Dissertation Director: Jeff Kahn We prove three results. First, an old conjecture of Zs. Tuza says that for any graph G, the ratio of the minimum size, τ3(G), of a set of edges meeting all triangles to the maximum size, ν3(G), of an edge-disjoint triangle packing is at most 2. Disproving a conjecture of R. Yuster [40], we show that for any fixed, positive α there are arbitrarily large graphs G of positive density satisfying τ3(G) > (1− o(1))|G|/2 and ν3(G) < (1 + α)|G|/4. Second, write C(G) for the cycle space of a graph G, Cκ(G) for the subspace of C(G) spanned by the copies of Cκ in G, Tκ for the class of graphs satisfying Cκ(G) = C(G), and Qκ for the class of graphs each of whose edges lies in a Cκ. We prove that for every odd κ ≥ 3 and G = Gn,p, max p Pr(G ∈ Qκ \ Tκ)→ 0; so the Cκ’s of a random graph span its cycle space as soon as they cover its edges. For κ = 3 this was shown in [12]. Third, we extend the seminal van den Berg–Kesten Inequality [9] on disjoint occurrence of two events to a setting with arbitrarily many events, where the quantity of interest is the maximum number that occur disjointly. This provides a handy tool for bounding upper tail probabilities for event counts in a product probability space.
A Resonant Magnetic Probe Capable of Simultaneously Measuring Two Orthogonal Magnetic-Field Components

In this brief, a resonant magnetic probe with multi-component characteristics is proposed and printed on a four-layer PCB. The proposed resonant magnetic probe is made up of a pair of LC resonant circuits including open stubs as capacitors and shorted stubs as inductors, a hybrid-ring detection structure with two shorted vias, and two transmission lines loaded with $50\Omega $ SMA connectors. Two rows of via fences are introduced into the resonant magnetic probe to restrict the undesired electromagnetic mode. To demonstrate the rationality of the design, the simulated and measured results are given and compared. The corresponding results reveal that the proposed resonant magnetic probe not only has a high sensitivity at 1.575 GHz, but also simultaneously tests two orthogonal magnetic-field components.
Multi-Feature Learning via Hierarchical Match Kernel for Image Classification

Image classification is an important task in computer vision. The methods based on spatial information generally employ some low-level features for image classification, such as gray scale, color, texture and location. It is difficult for vision system to understand and the single feature is too limited to obtain correct classification results. In this paper, an algorithm based on multi-kernel feature learning is proposed and used for image classification. First, the kernel function is used to produce a kernel descriptor, which aggregates the pixel attributes into patch-level features; Then, through the multi-kernel learning, these descriptors are further aggregated to obtain hierarchical multi-feature descriptors; Finally, the label of each image is given by the fusion strategy of on multi-classifiers, which effectively utilizes the advantages of multi-kernel learning and takes the complementary among the classifiers into account. The experimental results show that the proposed method is efficient in promoting the classification results.
Map-based cloning and CRISPR/Cas9-based editing uncover BoNA1 as the causal gene for the no-anthocyanin-accumulation phenotype in curly kale (Brassica oleracea var. sabellica)

Abstract Brassica oleracea comprises several important vegetable and ornamental crops, including curly kale, ornamental kale, cabbage, broccoli, and others. The accumulation of anthocyanins, important secondary metabolites valuable to human health, in these plants varies widely and is responsible for their pink to dark purple colors. Some curly kale varieties lack anthocyanins, making these plants completely green. The genetic basis of this trait is still unknown. We crossed the curly kale inbred line BK2019 (without anthocyanins) with the cabbage inbred line YL1 (with anthocyanins) and the Chinese kale inbred line TO1000 (with anthocyanins) to generate segregating populations. The no-anthocyanin trait was genetically controlled by a recessive gene, bona1. We generated a linkage map and mapped bona1 to a 256-kb interval on C09. We identified one candidate gene, Bo9g058630, in the target genomic region; this gene is homologous to AT5G42800, which encodes a dihydroflavonol-4-reductase-like (DFR-like) protein in Arabidopsis. In BK2019, a 1-bp insertion was observed in the second exon of Bo9g058630 and directly produced a stop codon. To verify the candidate gene function, CRISPR/Cas9 gene editing technology was applied to knock out Bo9g058630. We generated three bona1 mutants, two of which were completely green with no anthocyanins, confirming that Bo9g058630 corresponds to BoNA1. Different insertion/deletion mutations in BoNA1 exons were found in all six of the other no-anthocyanin kale varieties examined, supporting that independent disruption of BoNA1 resulted in no-anthocyanin varieties of B. oleracea. This study improves the understanding of the regulation mechanism of anthocyanin accumulation in B. oleracea subspecies.
Comparison of the Richardson and BCS models of superconductivity based on calculations of ground state energies

Kuusela, Petri Comparison of the Richardson and BCS models of superconductivity based on calculations of ground state energies Master’s thesis Department of Physics, University of Jyväskylä, 2017, 50 pages. Superconductivity remains an active area of research because there still is no comprehensive understanding of the phenomenon despite all the possibilities it offers. In this thesis I go through the basics of two models for superconductivity, the BCS model and the Richardson model. The BCS theory is the first and most used successful microscopic theory of superconductivity. Richardson model is a less used model which gives the exact eigenstates of the reduced BCS Hamiltonian. I calculate the ground state energies for both the reduced and the full BCS Hamiltonian for both models. This is done for a general paired state with fixed number of electrons as well. For this state I find that the difference between the full and reduced Hamiltonian energies depends only on the number of electrons, and thus conclude that it does not matter for comparison between such states which Hamiltonian is used. I find that in a system with equally spaced energies and in the free-electron three-dimensional system the ground state energies are very close to each other, with the Richardson model ground state energy being lower. From this I infer that the BCS model is a good description of these systems. The ground state energies of a two-level system however differs considerably, with the Richardson model ground state energy being significantly lower. This is an indication that the BCS model is not suitable for describing this system.