Miracle-dz/UniFAR-FTUs huggingface dataset:
Pd-Modified Cu-Zn Catalysts for Methanol Synthesis from CO2/H2 Mixtures: Catalytic Structures and Performance

The effect of palladium incorporation on the performance of a CuO–ZnO catalyst for methanol synthesis by hydrogenation of carbon dioxide is studied. Three different catalysts are prepared: the reference CuO-ZnO (CZ), and two Pd-based CuO–ZnO catalysts, PCZ-CP and PCZ-SP, which are prepared by co-precipitation and sequential precipitation, respectively. The PCZ-CP system appears to be almost inactive for methanol synthesis, whereas the PCZ-SP catalyst increases the methanol yield [mol MeOH/(h kgcat)] with respect to the base CZ catalyst. The marked decrease in the catalytic activity of the PCZ-CP catalyst is explained in terms of the loss in copper surface area. The effect of Pd is not capable of overcoming this significant loss in copper surface area. The enhanced methanol yield for PCZ-SP is not due to additional active palladium sites, but to a synergetic effect of Pd on the active Cu sites. The increase in the amount of hydrogen consumed (H2/M) during copper oxide reduction for PCZ-SP suggests that an H2-spillover mechanism is responsible for the increase in the methanol yield. This research reveals the importance of the methodology used for the incorporation of palladium. The results show that the precipitation order has a remarkable influence on the properties of the active phases and, consequently, on the catalytic performance for the hydrogenation of CO2 to methanol.
IRS-Assisted Active Device Detection

This paper studies intelligent reflecting surface (IRS) assisted active device detection. Since the locations of the devices are a priori unknown, optimal IRS beam alignment is not possible and a worst-case design for a given coverage area is developed. To this end, we propose a generalized likelihood ratio test (GLRT) detection scheme and an IRS phase-shift design that minimizes the worst-case probability of misdetection. In addition to the proposed optimization-based phase-shift design, we consider two alternative suboptimal designs based on closed-form expressions for the IRS phase shifts. Our performance analysis establishes the superiority of the optimization-based design, especially for large coverage areas. Furthermore, we investigate the impact of scatterers on the proposed line-of-sight based design using simulations.
Effect of Pilates Vs.yoga on Balance, Cognition and Core Strength in Elderly

Background: In older people, falls have become a major health issue. People aged 65and above are moreprone for most frequent accidents which can lead to injury related hospitalisation. Elderly people have manyserious health issues and they have several disorders affecting their body at the same time. Pilates is kind ofexercise that uses a combination of muscle that increases power, stretches muscle and also concentrate onbreathing to develop strength of the trunk muscles and restore muscle balance for physical fitness. Yoga isthe active engagement between the mind and body. Yoga focuses specifically on what is exactly happening isthe body and where the body is actually moving in space which increases both awareness and proprioception.Objective: To study the effect of Pilates on balance, cognition and core strength. To study the effect of Yogaon balance, cognition and core strength. To compare the effect of Pilates Vs. Yoga on balance, cognition andcore strength in elderly. Method: There were two groups Pilates and Yoga. 30 subjects were sequentiallydivided in both groups. The treatment was given for 3 days a week for 4 weeks. The outcome measures weretaken at the beginning and at the end of 4th week. Result: The Pilates group is having statistically significantimprovement in balance . The Yoga group is having statistically significant improvement in cognition. Thecore strength is clinically significant in Pilates group. Conclusion: This study concluded that Pilates andYoga both are equally effective in improving cognition, balance and core strength at the end of 4 weeksprotocol, yoga group showed marked increase in cognition whereas Pilates group showed marked increasein balance and core strength.
Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine Translation

The data scarcity in low-resource languages has become a bottleneck to building robust neural machine translation systems. Fine-tuning a multilingual pre-trained model (e.g., mBART (Liu et al., 2020)) on the translation task is a good approach for low-resource languages; however, its performance will be greatly limited when there are unseen languages in the translation pairs. In this paper, we present a continual pre-training (CPT) framework on mBART to effectively adapt it to unseen languages. We first construct noisy mixed-language text from the monolingual corpus of the target language in the translation pair to cover both the source and target languages, and then, we continue pre-training mBART to reconstruct the original monolingual text. Results show that our method can consistently improve the fine-tuning performance upon the mBART baseline, as well as other strong baselines, across all tested low-resource translation pairs containing unseen languages. Furthermore, our approach also boosts the performance on translation pairs where both languages are seen in the original mBART's pre-training. The code is available at https://github.com/zliucr/cpt-nmt.
The "Young" and "Reverse" Dichotomy of Polynomials

A ``flip-and-reversal" involution arising in the study of quasisymmetric Schur functions provides a passage between what we term ``Young" and ``reverse" variants of bases of polynomials or quasisymmetric functions. Building on this perspective, which has found recent application in the study of q-analogues of combinatorial Hopf algebras and generalizations of dual immaculate functions, we develop and explore Young analogues of well-known bases for polynomials. We prove several combinatorial formulas for the Young analogue of the key polynomials, show that they form the generating functions for left keys, and provide a representation-theoretic interpretation of Young key polynomials as traces on certain modules. We also give combinatorial formulas for the Young analogues of Schubert polynomials, including their crystal graph structure. We moreover determine the intersections of (reverse) bases and their Young counterparts, further clarifying their relationships to one another.
Contrastive Conditional Transport for Representation Learning

Contrastive learning (CL) has achieved remarkable success in learning data representations without label supervision. However, the conventional CL loss is sensitive to how many negative samples are included and how they are selected. This paper proposes contrastive conditional transport (CCT) that deﬁnes its CL loss over dependent sample-query pairs, which in practice is realized by drawing a random query, randomly selecting positive and negative samples, and contrastively reweighting these samples according to their distances to the query, exerting a greater force to both pull more distant positive samples towards the query and push closer negative samples away from the query. Theoretical analysis shows that this unique contrastive reweighting scheme helps in the representation space to both align the positive samples with the query and reduce the mutual information between the negative sample and query. Extensive large-scale experiments on standard vision tasks show that CCT not only consistently outperforms existing methods on benchmark datasets in contrastive representation learning, but also provides interpretable contrastive weights and latent representations. PyTorch code will be provided.
Compact Dual Microwave/Millimeter-Wave Planar Shared-Aperture Antenna for Vehicle-to-Vehicle/5G Communications

This paper presents a compact dual-port dual-frequency planar shared-aperture antenna with large frequency ratio. It consists of a microwave magneto-electric (ME) dipole antenna and a millimeter-wave parallel-plate resonator antenna (PPRA). It has two vertical conducting walls that form an upper-band parallel-plate resonator and meanwhile provide a magnetic source to the lower-band ME dipole. The design is a shared-aperture structure and is therefore very compact. For demonstration, a dual-frequency antenna simultaneously covering the 5.9-GHz vehicle-to-vehicle (V2V) band (5.855–5.925 GHz) and the 28-GHz 5G band (27.5–28.35 GHz) was designed, fabricated, and measured. Good agreement between the measured and simulated results was found. The lower and upper bands have the measured 10-dB impedance bandwidths of 41.6% (5.30–8.08 GHz) and 6.66% (27.15–29.02 GHz), and measured peak antenna gains of 5.80 dBi and 8.46 dBi, respectively. The antenna can be used on vehicular platforms for providing V2V communications and 5G millimeter-wave connections simultaneously.
Performance Analysis of RIS-Aided Systems With Practical Phase Shift and Amplitude Response

Reconfigurable intelligent surfaces (RISs) have drawn significant attention due to their capability of controlling the radio environment and improving the system performance. In this paper, we study the performance of an RIS-assisted single-input single-output system over Rayleigh fading channels. Differently from previous works that assume a constant reflection amplitude, we consider a model that accounts for the intertwinement between the amplitude and phase response, and derive closed-form expressions for the outage probability and ergodic capacity. Moreover, we obtain simplified expressions under the assumption of a large number of reflecting elements and provide tight upper and lower bounds for the ergodic capacity. Finally, the analytical results are verified by using Monte Carlo simulations.
ANTI-HYPERGLYCEMIC EFFECT OF DIFFERENT FRACTIONS OF ANNONA RETICULATA LEAF

ABSTRACT Context: Several species of the genus Annona were reported to have hypoglycemic properties and this makes Annona reticulata Linn. (Annonaceae) an interesting plant for investigating its anti-hyperglycemic potential. Objective: Different fractions prepared from hydro-alcoholic extract of A. reticulata leave were investigated for their blood glucose lowering effect on Streptozotocin (STZ) induced hyperglycemic rats. Methods: Ethyl acetate, methanol, and residual fractions (at dose level of 100 mg/kg by oral route) prepared from the hydro-alcoholic extract of A. reticulata leave were administered for 14 consecutive days to STZ induced hyperglycemic rats for evaluation of their anti-hyperglycemic potential. Anti-hyperglycemic potential was assessed by observation of a decrease in fasting blood glucose level. Results: The studies revealed that ethyl acetate fraction decreased the blood glucose level of hyperglycemic rats from 447.67 to 234.17 mg/dL and is significant (p<0.001) when compared with diabetic control group. The residual fraction and methanolic fraction decreased blood glucose level from 417.83 to 402.50 mg/dL and 432.33 to 371.67 mg/dL respectively but not significant when compared with the diabetic control group. Standard drug metformin (dose 300 mg/kg) reduced the blood glucose level from 447.33 to 219.50 mg/dL. Discussion: Ethyl acetate fraction at tested dose level was capable not only to control the elevated blood glucose level but also able to attenuate certain secondary parameters associated with STZ induced hyperglycemia. Conclusion: This study suggested that the ethyl acetate fraction prepared from hydro-alcoholic extract of A. reticulata leave exhibit potential antihyperglycemic property in the tested experimental models and should be investigated further. Keywords: Streptozotocin, Diabetes, Dyslipidemia.
Palmar Dermatoglyphics and Idiopathic Epilepsy – A Systematic Review

Dermatoglyphics is the science which deals with the study of dermal ridge configuration on the digits, palmsand soles. Etymologically this term is harmonious blend of two words Derma – skin; Glyphe – carve. It givesthe impression that something has been carved out of the skin. The entire human body is clothed with theskin which happens to be the largest and most important organ of the body. However, the skin on the ventralsides of the hands and the plantar sides of feet is exclusively designed and is corrugated with the ridgesand configurations which are functionally useful as they help in the grasping without which the objectswould easily slip away from the hands. Dermatoglyphic traits are genetically determined. Dermatoglyphicabnormalities are due to genetic or other factors that express their effect before the end of 5th month of foetaldevelopment. The permanency of finger patterns, the extreme variability from one individual to the otherand easy analysis are some of the reasons for its wide application in a variety of conditions. Abnormality inthe genetic configurations of parents is inherited by children and is reflected in the dermatoglyphic pattern.Hence dermatoglyphic study proves to be a very useful, easily applicable, inexpensive, indispensable toolas an indicator in the diagnosis of hereditary diseases in patients. The etiology of the epilepsies allows aclassification of syndrome features into two groups – idiopathic or cryptogenic epilepsy, which has isolatedprimary symptoms without apparent cause and is probably hereditary and finger print configurations areinherited with an embryonic origin common to nervous system. Their attractions indicate pleiotropic effectsof the genotype responsible for enaphalographic irregularity and convulsive seizures.
Diurnal and Seasonal Results of the Schumann Resonance Observatory in Sierra De Filabres, Spain

This article presents the Schumann resonance (SR) variations, obtained in the extreme low frequency (ELF) signal observatory of Sierra de Filabres belonging to the University of Almería Spain, in operation since 2011. This station has two large and sensitive orthogonal sensors operating between 1 and 100 Hz. A 24 bit low-noise, high-resolution system processes the signal. The Sierra de Filabres ELF station is operating to offer valuable information on SRs’ activity in this area. This system allows us to have a broader range of comparison patterns to study correlations with other phenomena. The information presented in this work deals with the diurnal and seasonal variations of the first six modes of the SRs in 2016 and 2017. The graphs have been obtained through Lorentzian functions. We explain the diurnal variation based on the position and distance to the most important lightning hotspots and demonstrate its relation with the first mode captured in our observatory. We introduce a new yearly evolution method based on the resonance frequency through the most representative lightning activity hours in one year. This article is supported by the comparison between our signals and the global thunderstorm center’s activity.
Food quality assurance of crude palm oil: a review on toxic ester feedstock

Palm oil, the commodity produced mainly in Indonesia and Malaysia, is widely used for deep-frying of fast food and food derivatives. European and American markets of palm oil are affected by the concern of the toxicity potential from monochloropropanediol esters (MCPDE) and glycidyl ester (GE) that are undesirably produced from monoacylglycerol (MAG), diacylglycerol (DAG) and chlorine in refineries. Improvement of oil palm plantation, fruit harvest and oil extraction process in palm oil mills is necessary before the refinery process so that hydrolysis reactions that produce MAG and DAG and chlorine contamination can be minimized in crude palm oil (CPO). This review focuses on the quality control currently employed in the mills especially in managing free fatty acid (FFA) formation as the indicator of the hydrolysis reactions along with other quality control parameters and the reduction of chlorine content.
Summary of Target Detection Algorithms

In recent years, the soaring development of CNN has facilitated the maturity of the Computer Vision Algorithm. This paper will briefly introduce some representative Target Detection Algorithm, and systematically analyze the underlying problems, the modified methods, and the prospective direction of the algorithm in accordance with its merits and demerits. It is generally divided into a single-stage detection model and a double-stage detection model in terms of whether candidate areas need to be extracted during target detection for further tasks. Featured with scale in the double-stage detection model, the algorithm is divided into single-scale detection and multi-scale detection based on whether it can appropriately integrate with a network structure, which enhances the accuracy of the network model towards small targets. Meanwhile, it can also be divided into anchor-base and anchor-free in a single-stage detection model on the basis of the anchor bolt. A predictable development of the Target Detection Algorithm will show to us in the future.
The Influence of Media Exposure, Managerial Ownership, and the Indonesia Sustainability Reporting Award on Corporate Social Responsibility (CSR) Disclosure

This paper aimed to study whether media exposure, classified as positive media exposure and negative media exposure, managerial ownership, and the Indonesia Sustainability Reporting Award influenced the disclosure of Corporate Social Responsibility (CSR). This study’s subjects were companies listed on the Indonesian Stock Exchange that published sustainability reports. The study involved 23 samples of companies observed for over two years (2017-2018), so we had 46 samples in total; these samples were chosen through purposive sampling. This study employed multiple regression analyses to examine the influence of those variables on CSR disclosure. Based on the analysis results, positive media exposure and ISR Award positively influenced CSR disclosure. In contrast, managerial ownership and negative media exposure had no significant effect on CSR disclosure.
Endothelialization of an ePTFE vessel prosthesis modified with an antithrombogenic fibrin/heparin coating enriched with bound growth factors

Early and late thrombosis remain the most frequent reasons for the failure of synthetic cardiovascular grafts. Long-term hemocompatibility of implanted synthetic grafts can be achieved if a natural living endothelium is formed over its blood-contacting surface. Here we present a modification of a standard expanded polytetrafluorethylene (ePTFE) vessel prosthesis by a controlled preparation of a fibrin mesh enriched with covalently bound heparin and noncovalently bound vascular endothelial growth factor (VEGF) and fibroblast growth factor (FGF). Compared to a bare prosthesis, the coated prosthesis showed excellent antithrombogenic properties after contact with heparinized fresh human blood. Human umbilical vein endothelial cells seeded on the inner surface of the coated prosthesis formed a confluent layer in 5 days, whereas only small colonies of cells were scattered on the bare prosthesis. Viability of the cells was promoted mainly by FGF immobilized on the coating. These findings suggest that the coating may prevent acute thrombus formation and support the self-endothelialization of an implanted ePTFE vascular graft in vivo.
MetaKernel: Learning Variational Random Features With Limited Labels

Few-shot learning deals with the fundamental and challenging problem of learning from a few annotated samples, while being able to generalize well on new tasks. The crux of few-shot learning is to extract prior knowledge from related tasks to enable fast adaptation to a new task with a limited amount of data. In this paper, we propose meta-learning kernels with random Fourier features for few-shot learning, we call MetaKernel. Specifically, we propose learning variational random features in a data-driven manner to obtain task-specific kernels by leveraging the shared knowledge provided by related tasks in a meta-learning setting. We treat the random feature basis as the latent variable, which is estimated by variational inference. The shared knowledge from related tasks is incorporated into a context inference of the posterior, which we achieve via a long-short term memory module. To establish more expressive kernels, we deploy conditional normalizing flows based on coupling layers to achieve a richer posterior distribution over random Fourier bases. The resultant kernels are more informative and discriminative, which further improves the few-shot learning. To evaluate our method, we conduct extensive experiments on both few-shot image classification and regression tasks. A thorough ablation study demonstrates that the effectiveness of each introduced component in our method. The benchmark results on fourteen datasets demonstrate MetaKernel consistently delivers at least comparable and often better performance than state-of-the-art alternatives.
Polynomial-Delay Enumeration of Large Maximal Matchings

Enumerating matchings is a classical problem in the field of enumeration algorithms. There are polynomial-delay enumeration algorithms for several settings, such as enumerating perfect matchings, maximal matchings, and (weighted) matchings in specific orders. In this paper, we present polynomial-delay enumeration algorithms for maximal matchings with cardinality at least given threshold $t$. Our algorithm enumerates all such matchings in $O(nm)$ delay with exponential space, where $n$ and $m$ are the number of vertices and edges of an input graph, respectively. We also present a polynomial-delay and polynomial-space enumeration algorithm for this problem. As a variant of this algorithm, we give an algorithm that enumerates all maximal matchings in non-decreasing order of its cardinality and runs in $O(nm)$ delay.
A Rate-Distortion Framework for Characterizing Semantic Information

A rate-distortion problem motivated by the consideration of semantic information is formulated and solved. The starting point is to model an information source as a pair consisting of an intrinsic state which is not observable, corresponding to the semantic aspect of the source, and an extrinsic observation which is subject to lossy source coding. The proposed rate-distortion problem seeks a description of the information source, via encoding the extrinsic observation, under two distortion constraints, one for the intrinsic state and the other for the extrinsic observation. The corresponding state-observation rate-distortion function is obtained, and a few case studies of Gaussian intrinsic state estimation and binary intrinsic state classification are studied.
Learning Gradient Fields for Molecular Conformation Generation

We study a fundamental problem in computational chemistry known as molecular conformation generation, trying to predict stable 3D structures from 2D molecular graphs. Existing machine learning approaches usually first predict distances between atoms and then generate a 3D structure satisfying the distances, where noise in predicted distances may induce extra errors during 3D coordinate generation. Inspired by the traditional force field methods for molecular dynamics simulation, in this paper, we propose a novel approach called ConfGF by directly estimating the gradient fields of the log density of atomic coordinates. The estimated gradient fields allow directly generating stable conformations via Langevin dynamics. However, the problem is very challenging as the gradient fields are roto-translation equivariant. We notice that estimating the gradient fields of atomic coordinates can be translated to estimating the gradient fields of interatomic distances, and hence develop a novel algorithm based on recent score-based generative models to effectively estimate these gradients. Experimental results across multiple tasks show that ConfGF outperforms previous state-of-the-art baselines by a significant margin.