Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Language Models with Knowledge Graph Embeddings

Most NLP tasks use word embeddings to improve performance. Breakthroughs like ELMo (Peters et al. 2018) and BERT (Devlin et al. 2018) have shown that state of the art results can be achieved in many NLP tasks through good language models, even without a task-speciﬁc architecture. Word vectors have been a simple, popular, and effective language model for years. Methods for generating these word vectors typically use unsupervised learning based on the context in which each word is used within the greater corpus. We propose new method of generating these word vectors. We use knowledge embeddings extracted from knowledge bases like Freebase and WordNet (Bordes et al. 2013) as a starting point, and introduce syntactic information captured from existing language models. By incorporating knowledge directly into the word embedding we aim to improve the task of nat-ural language inference, similar to those achieved by applying knowledge bases to machine reading (Yang and Mitchell 2019). The new embeddings are judged primarily on their perfermance on the natural language inference model HBMP (Talman, Yli-Jyr¨a, and Tiedemann 2019). This performance is compared to that of Glove (Pennington, Socher, and Man-ning 2014), with the same architecture. No improvement was found to accuracy, however further steps to achieve the desired improvement are well deﬁned.
Electric Field and SAR Reduction in High Impedance RF Arrays by Using High Permittivity Materials for 7T MR Imaging

Higher frequencies and shorter wavelengths present significant design issues at ultra-high fields, making multi-channel array setup a critical component for ultra-high field MR imaging. The requirement for multi-channel arrays, as well as ongoing efforts to increase the number of channels in an array, are always limited by the major issue known as inter-element coupling. This coupling affects the current and field distribution, noise correlation between channels, and frequency of array elements, lowering imaging quality and performance. To realize the full potential of UHF MRI, we must ensure that the coupling between array elements is kept to a minimum. High-impedance coils allow array systems to completely realize their potential by providing optimal isolation while requiring minimal design modifications. These minor design changes, which demand the use of low capacitance on the conventional loop to induce elevated impedance, result in a significant safety hazard that cannot be overlooked. High electric fields are formed across these low capacitance lumped elements, which may result in higher SAR values in the imaging subject, depositing more power and, ultimately, providing a greater risk of tissue heating-related injury to the human sample. We propose an innovative method of utilizing high-dielectric material to effectively reduce electric fields and SAR values in the imaging sample while preserving the B1 efficiency and inter-element decoupling between the array elements to address this important safety concern with minimal changes to the existing array design comprising high-impedance coils.
iDesigner: A High-Resolution and Complex-Prompt Following Text-to-Image Diffusion Model for Interior Design

With the open-sourcing of text-to-image models (T2I) such as stable diffusion (SD) and stable diffusion XL (SD-XL), there is an influx of models fine-tuned in specific domains based on the open-source SD model, such as in anime, character portraits, etc. However, there are few specialized models in certain domains, such as interior design, which is attributed to the complex textual descriptions and detailed visual elements inherent in design, alongside the necessity for adaptable resolution. Therefore, text-to-image models for interior design are required to have outstanding prompt-following capabilities, as well as iterative collaboration with design professionals to achieve the desired outcome. In this paper, we collect and optimize text-image data in the design field and continue training in both English and Chinese on the basis of the open-source CLIP model. We also proposed a fine-tuning strategy with curriculum learning and reinforcement learning from CLIP feedback to enhance the prompt-following capabilities of our approach so as to improve the quality of image generation. The experimental results on the collected dataset demonstrate the effectiveness of the proposed approach, which achieves impressive results and outperforms strong baselines.
Entanglement generation via single-qubit rotations in a teared Hilbert space

We propose an efficient yet simple protocol to generate arbitrary symmetric entangled states with only global single-qubit rotations in a teared Hilbert space. The system is based on spin-1/2 qubits in a resonator such as atoms in an optical cavity or superconducting qubits coupled to a metal microwave resonator. By sending light or microwave into the resonator, it induces AC Stark shifts on particular angular-momentum eigenstates (Dicke states) of qubits. Then we are able to generate barriers that hinder transitions between adjacent Dicke states and tear the original Hilbert space into pieces. Therefore, a simple global single-qubit rotation becomes highly non-trivial, and thus generates entanglement among the many-body system. By optimal control of energy shifts on Dicke states, we are able to generate arbitrary symmetric entangled states. We also exemplify that we can create varieties of useful states with near-unity fidelities in only one or very few steps, including W states, spin-squeezed states (SSS), and Greenberger-Horne-Zeilinger (GHZ) states. Particularly, the SSS can be created by only one step with a squeezing parameter $\xi_R^2\sim1/N^{0.843}$ approaching the Heisenberg limit (HL). Our finding establishes a way for universal entanglement generations with only single-qubit drivings where all the multiple-qubit controls are integrated into simply switching on/off microwave. It has direct applications in the variational quantum optimizer which is available with existing technology.
Analysis of Coding Gain Due to In-Loop Reshaping

Reshaping, a point operation that alters the characteristics of signals, has been shown capable of improving the compression ratio in video coding practices. Out-of-loop reshaping that directly modifies the input video signal was first adopted as the supplemental enhancement information~(SEI) for the HEVC/H.265 without the need of altering the core design of the video codec. VVC/H.266 further improves the coding efficiency by adopting in-loop reshaping that modifies the residual signal being processed in the hybrid coding loop. In this paper, we theoretically analyze the rate-distortion performance of the in-loop reshaping and use experiments to verify the theoretical result. We prove that the in-loop reshaping can improve coding efficiency when the entropy coder adopted in the coding pipeline is suboptimal, which is in line with the practical scenarios that video codecs operate in. We derive the PSNR gain in a closed form and show that the theoretically predicted gain is consistent with that measured from experiments using standard testing video sequences.
Can helicity modulus be defined for boundary conditions with finite twist?

We study the response of a two-dimensional classical XY model to a finite (non-infinitesimal) twist of the boundary conditions. We use Monte Carlo simulations to evaluate the free energy difference between periodic and twisted-periodic boundary conditions and find deviations from the expected quadratic dependence on the twist angle. Consequently, the helicity modulus (spin-stiffness) shows a non-trivial dependence on the twist angle. We show that the deviation from the expected behavior arises because of a degeneracy due to the chirality of the spin-waves which leads to an additional entropy contribution in the quasi-long-range ordered phase. We give an improved prescription for the numerical evaluation of the helicity modulus and resolve some open questions related to anti-periodic boundary conditions. We also discuss applications to discrete spin systems and some experimental scenarios where boundary conditions with finite twist are necessary.
A Review on Malware Analysis by using an Approach of Machine Learning Techniques

In the Internet age, malware (such as viruses, trojans, ransomware, and bots) has posed serious andevolving security threats to Internet users. To protect legitimate users from these threats, anti-malware softwareproducts from different companies, including Comodo, Kaspersky, Kingsoft, and Symantec, provide the majordefense against malware. Unfortunately, driven by the economic benefits, the number of new malware sampleshas explosively increased: anti-malware vendors are now confronted with millions of potential malware samplesper year. In order to keep on combating the increase in malware samples, there is an urgent need to developintelligent methods for effective and efficient malware detection from the real and large daily sample collection.One of the most common approaches in literature is using machine learning techniques, to automatically learnmodels and patterns behind such complexity, and to develop technologies to keep pace with malware evolution.This survey aims at providing an overview on the way machine learning has been used so far in the context ofmalware analysis in Windows environments. This paper gives an survey on the features related to malware filesor documents and what machine learning techniques they employ (i.e., what algorithm is used to process the inputand produce the output). Different issues and challenges are also discussed.
Comparing pain intensity rating scales in acute postoperative pain: boundary values and category disagreements

Pain intensity assessment scales are important in evaluating postoperative pain and guiding management. Different scales can be used for patients to self‐report their pain, but research determining cut points between mild, moderate and severe pain has been limited to studies with < 1500 patients. We examined 13,017 simultaneous acute postoperative pain ratings from 913 patients taken at rest and on activity, between 4 h and 48 h following surgery using both a verbal rating scale (no, mild, moderate or severe pain) and 0–100 mm visual analogue scale. We determined the best cut points on the visual analogue scale between mild and moderate pain as 35 mm, and moderate and severe pain as 80 mm. These remained consistent for pain at rest and on activity, and over time. We also explored the presence of category disagreements, defined as patients verbally describing no or mild pain scored above the mild/moderate cut point on the visual analogue scale, and patients verbally describing moderate or severe pain scored below the mild/moderate cut point on the visual analogue scale. Using 30 and 60 mm cut points, 1533 observations (12%) showed a category disagreement and using 35 and 80 mm cut points, 1632 (13%) showed a category disagreement. Around 1 in 8 simultaneous pain scores implausibly disagreed, possibly resulting in incorrect pain reporting. The reasons are not known but low rates of literacy and numeracy may be contributing factors. Understanding these disagreements between pain scales is important for pain research and medical practice.
An Average Cost Pricing Method on Electric Tradable Credits Scheme in Traffic Management

Considering the tradable credits’ characteristics of public goods, this paper incorporated public pricing method into the tradable credits pricing analysis. Also, in view of budget imbalance of marginal cost pricing, we tried to make a price for the tradable credit by using an average pricing method. This paper constructed an average pricing method of tradable credits by the angles of travel behavior and economics. The main work of this paper is listed as follows: (1) Consumers and producers of tradable credits in the traffic network are defined, and their mathematical constraints are given; (2) Under the tradable credits scheme, the production cost function of producers and demanding function of consumers are analyzed; (3) The mathematical description of average pricing is given; and (4) At last, the process of average pricing on tradable credits are reappeared by a simple numerical example, which average pricing’s efficiency on tradable credits. In a word, average cost pricing pushes the application of tradable credits scheme in urban traffic management forward, which has certain theoretical and practical significance to improve the urban traffic demand management.
Adaptive genetic variation to drought in a widely distributed conifer suggests a potential for increasing forest resilience in a drying climate

Summary Drought intensity and frequency are increasing under global warming, with soil water availability now being a major factor limiting tree growth in circumboreal forests. Still, the adaptive capacity of trees in the face of future climatic regimes remains poorly documented. Using 1481 annually resolved tree‐ring series from 29‐yr‐old trees, we evaluated the drought sensitivity of 43 white spruce (Picea glauca (Moench) Voss) populations established in a common garden experiment. We show that genetic variation among populations in response to drought plays a significant role in growth resilience. Local genetic adaptation allowed populations from drier geographical origins to grow better, as indicated by higher resilience to extreme drought events, compared with populations from more humid geographical origins. The substantial genetic variation found for growth resilience highlights the possibility of selecting for drought resilience in boreal conifers. As a major research outcome, we showed that adaptive genetic variation in response to changing local conditions can shape drought vulnerability at the intraspecific level. Our findings have wide implications for forest ecosystem management and conservation.
Heatwave breaks down the linearity between sun-induced fluorescence and gross primary production.

Sun-induced fluorescence in the far-red region (SIF) is increasingly used as a remote and proximal-sensing tool capable of tracking vegetation gross primary production (GPP). However, the use of SIF to probe changes in GPP is challenged during extreme climatic events, such as heatwaves. Here, we examined how the 2018 European heatwave (HW) affected the GPP-SIF relationship in evergreen broadleaved trees with a relatively invariant canopy structure. To do so, we combined canopy scale SIF measurements, GPP estimated from an eddy covariance tower, and active PAM fluorescence. The HW caused an inversion of the photosynthesis-fluorescence relationship at both the canopy and leaf scales. The highly nonlinear relationship was strongly shaped by nonphotochemical quenching (NPQ), i.e., a dissipation mechanism to protect from the adverse effects of high light intensity. During the extreme heat stress, plants experienced a saturation of NPQ, causing a change in the allocation of energy dissipation pathways towards SIF. Our results show the complex modulation of the NPQ-SIF-GPP relationship at an extreme level of heat stress, which is not completely represented in state-of-the-art coupled radiative transfer and photosynthesis models.
Inactivation of the entire Arabidopsis group II GH3s confers tolerance to salinity and water deficit

Summary Indole‐3‐acetic acid (IAA) controls a plethora of developmental processes. Thus, regulation of its concentration is of great relevance for plant performance. Cellular IAA concentration depends on its transport, biosynthesis and the various pathways for IAA inactivation, including oxidation and conjugation. Group II members of the GRETCHEN HAGEN 3 (GH3) gene family code for acyl acid amido synthetases catalysing the conjugation of IAA to amino acids. However, the high degree of functional redundancy among them has hampered thorough analysis of their roles in plant development. In this work, we generated an Arabidopsis gh3.1,2,3,4,5,6,9,17 (gh3oct) mutant to knock out the group II GH3 pathway. The gh3oct plants had an elaborated root architecture, showed an increased tolerance to different osmotic stresses, including an IAA‐dependent tolerance to salinity, and were more tolerant to water deficit. Indole‐3‐acetic acid metabolite quantification in gh3oct plants suggested the existence of additional GH3‐like enzymes in IAA metabolism. Moreover, our data suggested that 2‐oxindole‐3‐acetic acid production depends, at least in part, on the GH3 pathway. Targeted stress‐hormone analysis further suggested involvement of abscisic acid in the differential response to salinity of gh3oct plants. Taken together, our data provide new insights into the roles of group II GH3s in IAA metabolism and hormone‐regulated plant development.
SnO2/SnS heterojunction anchoring on CMK-3 mesoporous network improves the reversibility of conversion reaction for lithium/sodium ions storage

Tin oxide-based (SnO2) materials show high theoretical capacity for lithium and sodium storage benefiting from a double-reaction mechanism of conversion and alloying reactions. However, due to the limitation of the reaction thermodynamics and kinetics, the conversion reaction process of SnO2 usually shows irreversibility, resulting in serious capacity decay and hindering the further application of the SnO2 anode. Herein, SnO2/SnS heterojunction was anchored on the surface and inside of CMK-3 by in situ synthesis method, forming a stable 3D structural material (SnO2/SnS@CMK-3). The electrochemical properties of SnO2/SnS@CMK-3 composite show high capacity and reversible conversion reaction, which was attributed to the synergistic effect of CMK-3 and SnO2/SnS heterojunction. To further investigate the influence of the heterojunction on the reversibility of the conversion reaction, the Gibbs free energy (ΔG) was calculated using density functional theory. The results show that SnO2/SnS heterojunction has a closer to zero ΔG for lithium/sodium ion batteries compared to SnO2, indicating that the heterojunction enhances the reversibility of the conversion reaction in chemical reaction thermodynamics. Our work provides insights into the reversibility of the conversion reaction of SnO2-based materials, which is essential for improving their electrochemical performance.
Orthogonality of a new family of $q$-Sobolev type polynomials

In this work, we introduce and construct specific $q$-polynomials that are desired from the well-established families of $q$-orthogonal polynomials, namely little $q$-Jacobi polynomials and $q$-Laguerre polynomials, respectively. We examine these newly constructed $q$-polynomials and observe that they possess integral representations of little $q$-Jacobi polynomials and $q$-Laguerre polynomials. These polynomials solve a third-order $q$-difference equation and display an unconventional four-term recurrence relation. This unique recurrence relation makes us categorize them as $q$-Sobolev-type orthogonal polynomials. This motivation leads to defining the general Sobolev-type orthogonality for $q$-polynomials. Special cases of these polynomials are also explored and discussed. Furthermore, we delve into the behavior of these $q$-orthogonal polynomials of Sobolev type as the parameters approach $1$. We also examine their zeros and interlacing properties.
Modeling and Design of a Double-Sided Linear Motor with Halbach Array for Low-Vibration and High-Acceleration Applications

In this paper, Amperian current and magnetic charge models of permanent magnets are integrated into a hybrid approach to develop a comprehensive analytical modeling for designing a slotless double-sided linear motor with an arbitrary Halbach array. Unlike the conventional methods that treat magnets as sources for Poisson's equations, the solution is reduced to Laplace's equations, with magnets being represented as boundary conditions. The proposed hybrid approach reduces the complexity of of the problem, requiring the solution for only two or three regions compared to a large number of regions if either Amperian currents or magnetic charges were utilized. The magnetic fields and potentials within distinct regions, along with machine quantities such as shear stress, force-angle characteristics, torque profile, attraction force, misalignment force, and back-EMF, are derived, comprehensively analyzed, and compared to FEM results for accuracy validation. Finally, a thorough sensitivity analysis and design consideration of a linear stage for high acceleration and low vibration applications is discussed.
ENJOYMENT OF PHYSICAL ACTIVITY AND PERCEPTION OF SUCCESS IN SPORTS HIGH SCHOOL STUDENTS

: Enjoyment as a regulator of intrinsic motivation represents an effective tool in fostering desired and positive attitudes towards physical activity. The main purpose of the study was to explore the relationship between enjoyment of physical activity and perceived success in students of sports high school students. The study was cross-sectional and we collected data from a random research sample of 111 participants (55 boys and 56 girls) aged 11-14 years. Two self-report questionnaires were used. To identify the enjoyment of physical activity, we used the Slovak version of the Physical Activity Enjoyment Scale (PACES). Perception of success was measured by the Slovak version of the Perception of Success Questionnaire (POSQ). W e revealed a weak but significant correlation between the age of the participants and their level of enjoyment of physical activity. We also identified higher perception of success in the team sports athletes.
The EEG complexity, information integration and brain network changes in minimally conscious state patients during general anesthesia

Objective. General anesthesia (GA) can induce reversible loss of consciousness. Nonetheless, the electroencephalography (EEG) characteristics of patients with minimally consciousness state (MCS) during GA are seldom observed. Approach. We recorded EEG data from nine MCS patients during GA. We used the permutation Lempel–Ziv complexity (PLZC), permutation fluctuation complexity (PFC) to quantify the type I and II complexities. Additionally, we used permutation cross mutual information (PCMI) and PCMI-based brain network to investigate functional connectivity and brain networks in sensor and source spaces. Main results. Compared to the preoperative resting state, during the maintenance of surgical anesthesia state, PLZC decreased (p < 0.001), PFC increased (p < 0.001) and PCMI decreased (p < 0.001) in sensor space. The results for these metrics in source space are consistent with sensor space. Additionally, node network indicators nodal clustering coefficient (NCC) (p < 0.001) and nodal efficiency (NE) (p < 0.001) decreased in these two spaces. Global network indicators normalized average path length ( Lave/Lr ) (p < 0.01) and modularity (Q) (p < 0.05) only decreased in sensor space, while the normalized average clustering coefficient ( Cave/Cr ) and small-world index ( σ ) did not change significantly. Moreover, the dominance of hub nodes is reduced in frontal regions in these two spaces. After recovery of consciousness, PFC decreased in the two spaces, while PLZC, PCMI increased. NCC, NE, and frontal region hub node dominance increased only in the sensor space. These indicators did not return to preoperative levels. In contrast, global network indicators Lave/Lr and Q were not significantly different from the preoperative resting state in sensor space. Significance. GA alters the complexity of the EEG, decreases information integration, and is accompanied by a reconfiguration of brain networks in MCS patients. The PLZC, PFC, PCMI and PCMI-based brain network metrics can effectively differentiate the state of consciousness of MCS patients during GA.