Miracle-dz/UniFAR-FTUs huggingface dataset:
HeteRSGD: Tackling Heterogeneous Sampling Costs via Optimal Reweighted Stochastic Gradient Descent

One implicit assumption in current stochastic gradient descent (SGD) algorithms is the identical cost for sampling each component function of the finite-sum objective. However, there are applications where the costs differ substantially, for which SGD schemes with uniform sampling invoke a high sampling load. We investigate the use of importance sampling (IS) as a cost saver in this setting, in contrast to its traditional use for variance reduction. The key ingredient is a novel efficiency metric for IS that advocates low sampling costs while penalizing high gradient variances. We then propose HeteRSGD, an SGD scheme that performs gradient sampling according to optimal probability weights stipulated by the metric, and establish theories on its optimal asymptotic and finite-time convergence rates among all possible IS-based SGD schemes. We show that the relative efficiency gain of Het-eRSGD can be arbitrarily large regardless of the problem dimension and number of components. Our theoretical results are validated numerically for both convex and nonconvex problems.
Effect of organizational commitment on employee turnover intention using old generation commercial banks in Ilorin – Nigeria

The regulatory and competitive dynamics in the Nigerian Banking industry compelled Old Generation Banks (OGBs) in Nigeria to alter their Human Resource policies and practices, which may affect the banks’ Human Resource outcomes when investigated. Thus, this study interrogated whether employees’ organizational commitment exerts significant effect on turnover intentions in OGBs in Ilorin metropolis. OGB employees were randomly sampled and surveyed using structured questionnaire. Research data were analyzed using basic descriptive statistics, while the research hypotheses were tested using regression at 5-percent significant level. Accordingly, the study confirmed that organizational commitment exerted significant influence on employees’ turnover intention in OGBs in Ilorin. However, only normative commitment is a strong predictor of employee turnover intention. The study, therefore, recommends that the OGBs’ Human Resource executives should not only sustain but upgrade normative commitment indicators (training and development and related investment on banks’ personnel) so as to redu ce turnover intentions among the bankers and avoid accompanying costs of positive turnover intention in order .
DÁNH GIÁ TÁC ĐỘNG CỦA VÓN TRÍ TUỆ đến hiệu quả tài chính của các doanh nghiệp Việt Nam

Resource-based theory have supported the importance of intangible resources, or intellectual capital of a com ­ pany. Intangible resources gradually replace the role of tangible resources which are increasingly limited and eas ­ ily replaced in the knowledge economy. If organizations take advantage of intellectual capital management and practices, they are expected to bring economic benefits to the company. Therefore, this study is the original Value Add Intellectual Cofficient (VAIC) ofPulic (2000) as well as the adjusted VAIC model to examine the impact of in ­ tellectual capital on financial performance of companies listed on the Vietnamese stock market from 2015 to 2019. Intellectual capital is evaluated according to the overall quantity as well as each component in the intellectual capi ­ tal structure, includes human capital, structural capital, and relational capital. The financial performance includes ROA, ROE and market to book value ratio. We pay special attention to dealing with the endogeneity problem through the systematic GMM estimation of Blundell and Bond (1998). All results support the hypothesis about the positive impact of intellectual capital as well as its components on firm performance. However, the level of impact can be different in each IC component depending on the level of knowledge, showing that IC identification and management is also one of the focuses in the goal of adding value to the organization.
Differentiable Heightfield Path Tracing with Accelerated Discontinuities

We investigate the problem of accelerating a physically-based differentiable renderer for heightfields based on path tracing with global illumination. On a heightfield with 1 million vertices (1024 × 1024 resolution), our differentiable renderer requires only 4 ms per sample per pixel when differentiating direct illumination, orders of magnitude faster than most existing general 3D mesh differentiable renderers. It is well-known that one can leverage spatial hierarchical data structures (e.g., the maximum mipmaps) to accelerate the forward pass of heightfield rendering. The key idea of our approach is to further utilize the hierarchy to speed up the backward pass—differentiable heightfield rendering. Specifically, we use the maximum mipmaps to accelerate the process of identifying scene discontinuities, which is crucial for obtaining accurate derivatives. Our renderer supports global illumination. we are able to optimize global effects, such as shadows, with respect to the geometry and the material parameters. Our differentiable renderer achieves real-time frame rates and unlocks interactive inverse rendering applications. We demonstrate the flexibility of our method with terrain optimization, geometric illusions, shadow optimization, and text-based shape generation.
Analysis of factors affecting primary school students’ performance

—This paper assesses the impact of gender, preschool and medium of the class in influencing academic results of primary students in government schools, with special attention given to performance in oral tests. The data used for the analysis was gathered by the Chennai chapter of Asha, an NGO working to improve the education of rural and urban schools in Tamil Nadu, India. They conducted oral and written assessments in about 90 primary government schools in Tamil Nadu in the subjects of Math, English, and Tamil. The data collected from these schools comprised of student’s age, height, weight, parent’s educational qualification and details about their preschool. Apart from these, details about the school were gathered from a website hosted and maintained by the National University of Educational Planning and Administration (NUEPA). This included school details such as the number of students, teachers and their qualifications, the facilities, etc. The results show that the medium of the class, the type of preschool attended and the gender of a student have a correlation with the performance while factors such as BMI, student-teacher ratio and school infrastructure quality have a small or negligible correlation with the performance.
Green synthesis of silver nanoparticles using Pistacia palaestina (Boiss). extract: Evaluation of in vivo wound healing activity

: This paper conducts green synthesis of silver nanoparticles using Pistacia palaestina fruit extract and studies the effects of the fabrication conditions on the size of fabricated nanoparticles during the synthesis process and the evaluation of its wound healing activity in vivo . Methods: P. palaestina extract has been used to reduce silver nitrate solution to AgNPs. The synthesized AgNPs were characterized using UV-Vis spectroscopy, Dynamic Light Scattering (DLS), Atomic Force Microscopy (AFM), and Scanning Electron Microscopy (SEM). The fabricated AgNPs were prepared as an ointment (1% w/w) and evaluated for wound healing activity using an incision wound model on rats. The time of healing was determined and compared with a positive control (gentamicin ointment 0.1%) and a negative control. Results: The fabricated AgNPs have a dark brown color, and their absorption peak was at 430 nm. The obtained particles have an average diameter of 36 nm by SEM, 55.64 nm by DLS, and 100 nm by AFM. AFM and SEM images conﬁrmed the spherical shape of prepared AgNPs. The optimal formula for preparing AgNPs was by using fruit extract (pH 7) at a ratio of (1:9) at 35°C for 24 h. The prepared AgNPs ointment (1%) accelerated wound healing compared to a negative control group, as the time of healing was 9 ± 1.41, 12 ± 1.73, and 17 ± 2.00 days, respectively. Conclusion: It is concluded that P. palaestina extract can be utilized for fabricating AgNPs without any additions, and they have wound healing effect in experimental rats.
Optimization-based machine learning and data mining

Novel approaches for six important problems in machine learning, and two methods for solving linear programs, are introduced. Each machine learning problem is addressed by formulating it as an optimization problem. By using results based on theorems of the alternative for linear or convex functions, we are able to incorporate prior knowledge into function approximations or classifiers generated by linear combinations of linear or nonlinear kernels. We will consider prior knowledge consisting of linear inequalities to be satisfied over multiple polyhedral regions, nonlinear inequalities to be satisfied over arbitrary regions, and nonlinear equalities to be satisfied over arbitrary regions. Each kind of prior knowledge leads to different formulations, each with certain advantages. In privacy-preserving classification, data is divided into groups belonging to different entities unwilling to share their privately-held data. By using a completely random matrix, we are able to construct public classifiers that do not reveal the privately held data, but have accuracy comparable to that of an ordinary support vector machine classifier based on the entire data. To address the problem of feature selection in clustering, we propose a modification of the objective function of a standard clustering algorithm which allows features to be eliminated. For feature selection in nonlinear kernel classification, we propose a mixed-integer algorithm which alternates between optimizing the continuous variables of an ordinary nonlinear support vector machine and optimizing integer variables which correspond to selecting or removing features from the classifier. We also propose a classifier based on proximity to two planes which are not required to be parallel. Finally, we tackle the multiple instance classification problem by formulating the problem as the minimization of a linear function subject to linear and bilinear constraints. In several of these problems, the solution we propose involves solving linear programs. We consider sufficient conditions which allow us to determine whether a solution of a linear program obtained by either of two algorithms is exact. Both of these two algorithms can be implemented using only a solver for linear systems of equations.
Factoring Ideals into Semiprime Ideals

Let D be an integral domain with 1 ≠ 0 . We consider “property SP” in D, which is that every ideal is a product of semiprime ideals. (A semiprime ideal is equal to its radical.) It is natural to consider property SP after studying Dedekind domains, which involve factoring ideals into prime ideals. We prove that a domain D with property SP is almost Dedekind, and we give an example of a nonnoetherian almost Dedekind domain with property SP.
SIMULATION-BASED ANALYSIS OF EVACUATION ELEVATOR ALLOCATION FOR A MULTI-LEVEL HOSPITAL EMERGENCY DEPARTMENT

Evacuation planning for hospital emergency departments is challenging because of the large number of patients with limited mobility due to severe illness. For trolley-ridden patients, elevators are often the only available mode for vertical evacuations. Thus, allocation of trolley-ridden patients to elevators is important to reduce vertical evacuation time with limited number of elevators. We developed a simulation model of vertical evacuation using elevators and applied the model to the future Singapore General Hospital emergency department as a case study. In the case study, we divided trolley-ridden patients into several groups based on their locations and evaluated the maximum evacuation time for various allocation setups. Simulation results show that evacuation on the lower level is sensitive to the allocation on the upper level. The overlapping utilization of the shared elevator by each level may lead to long queuing time at the lower levels and consequently increase the overall evacuation time. 978-1-6654-7661-4/22/$31.00 ©2022 IEEE 358 Su, Kwak, Pourghaderi, Lees, Tan, Loo, Chua, Quah, Cai, and Ong
The Intrinsic Statistical Properties and Correlations of Short Gamma-Ray Bursts

The intrinsic statistical properties and correlations of short gamma-ray bursts (SGRBs) have not been fully determined, due to the limitations of observations. In this paper, we compile a more extensive sample of 82 SGRBs with measured redshifts and present a comprehensive study of their intrinsic characteristics. We obtain the median values of the intrinsic duration (T 90,z), peak energy (E p,z), isotropic energy (E iso), and peak luminosity (L iso) as 0.47 s, 466 keV, 8.21 ×1050 erg, and 3.22 × 1051 erg s−1, respectively. We update the spectrum–energy correlations, and report Ep,z∝Eiso0.36±0.05 and Ep,z∝Liso0.33±0.04 , which further confirm the previous results that the E p,z–E iso correlations of SGRBs and long gamma-ray bursts (LGRBs) are different and that this correlation can be used to distinguish GRB types. We report for the first time that there is a tighter correlation between the isotropic energy of the prompt emission of SGRBs and the star formation rate of their host galaxies, which reads sSFR∝Eiso0.38±0.11 . Using the measured jet break time (t jet) of 11 SGRBs, we tentatively investigate the E iso–E p,z–t jet,z and L iso–E p,z–t jet,z correlations of SGRBs and find that three-parameter correlations of SGRBs also exist and are different from those of LGRBs. Based on the E iso–E p,z–t jet,z correlation, we estimate the t jet,z values of other SGRBs and calculate the opening angles of SGRBs. We find that the median value of the SGRB opening angle is 7. °5, which is larger than that of LGRBs.
Simulation of the N-MFSK Method Used for Laser-Induced Acoustic Communication

Laser-induced acoustic (LIA) technology is a possible way of communicating from an air platform to an underwater target. The present paper describes a modulation method of n-multiple frequency shift keying (n-MFSK) used for improving the communication rate of LIA technology. The thermal expansion mechanism was selected as a way of generating an acoustic signal by the pulse laser in this work. Thus, the technology of pulsed repetition frequency (PRF) can be used to realize n-MFSK modulation by controlling laser frequency and allocating periods in the time domain. The results of numerical simulation show that n-MFSK modulation can improve the communication rate and band utilization compared with the traditional n-frequency shift keying (n-FSK) modulation.
Optimal Design of Antennas based on CNN and BLS

Surrogate can greatly reduce the computing cost of full-wave electromagnetic simulation software in antenna design, and neural networks as a kind of surrogate have become very popular in recent years, among which the Convolutional Neural Network (CNN) is a very hot topic due to its better feature extraction and generalization capability. In this paper, Broad Learning System (BLS) is introduced to address the problems that CNN may fall into with local optimization and overfitting when dealing with antenna data. It provides a learning method to increase nodes in width for regression prediction, which has good generalization ability. Specifically, in antenna design, feature extraction is first applied using the convolution and pooling of CNN. The original dataset is combined with the extracted feature data to realize data augmentation, and then the BLS is used for regression training. Compared with the traditional deep CNN, this model uses lightweight convolutional layers, which is promising in solving the problem of the network falling into local optimum or overfitting. The simulation results show that the accuracy reaches 93.91% and 92.34% respectively on the Abalone benchmark and antenna data, proving the effectiveness of the proposed model in this paper.
Resource Modeling in Business Process Simulation

Business Process (BP) models address the specification of the flow of events and activities, along with the dependencies of activities on resources. BP models are often analyzed by using simulation-based approaches. This paper focuses on resource modeling for BP modeling and simulation, by first introducing the most important concepts and discussing how resources are modeled in the standard BP modeling notation (i.e., BPMN) and in the area of Discrete Event Simulation. Then, the paper presents two newer BP modeling and simulation approaches, namely the Object Event Modeling and Simulation with the Discrete Event Process Modeling Notation (DPMN) based on the JavaScript-based simulation framework OESjs, and Performability-enabled BPMN (PyBPMN), with the Java-based simulation framework eBPMN. A simple but effective case study dealing with a pizza service process is used to illustrate the main features of the presented approaches.
Employability attributes of hospitality graduates and expectations of hotel managers

Employers worldwide place the responsibility of ensuring that graduates are employable on institutions of higher learning. This study was conducted to determine the employability attributes of hospitality management students from private Higher education institutions. The study adopted a qualitative research approach using semi-structured interviews. Purposive, judgement sampling was used to gather hotel managers’ thoughts, knowledge and opinions. The findings suggest that hotel managers require eight knowledge sub-domains, and seven skill sub-domains, and a wide range of competencies from hospitality graduates from private Higher education institutions. However, private higher education institutions, hotel managers, hospitality graduates and human resources personnel have a great responsibility to ensure that graduates are competent, competitive and valuable and can make meaningful contributions to the hotel industry. Data was only gathered from hotel managers from 4 and 5-star hotels, therefore the findings cannot be generalised to all hotels in South Africa. Consequently, the findings cannot be generalised to all the tertiary institutions in South Africa. Suggestions are made that can assist higher education institutions, hotel managers, hospitality management graduates, students and future researchers, particularly now in this time of globalisation, knowledge economy, and rise of technological advancements.
Spectral and energy efficiency optimization of non-concurrent two-way relaying

Two-way relaying (TWR) enables two source nodes to exchange data in two-channel uses and is shown to be spectrally-efficient than one-way relaying (OWR), which requires fourchannel uses for this data exchange. TWR assumes that a node wants to exchange data with its partner node. If TWR is incorporated in cellular networks, it will assume that a user also wants to exchange data with the base station (BS). In cellular networks, a user, however, does not usually exchange data with the BS. Consider a transmit-only user (TU) who is uploading a large file to a cloud, or a user RU, who is downloading a multimedia data from a server. We see that neither TU nor RU exchanges data with the BS, and consequently, the BS cannot serve them using TWR. The BS, however, can serve them using one-way relaying, but will require four-channel uses, i.e., two each for TU and RU. In the non-concurrent TWR (ncTWR) protocol considered in the literature, the BS can serve both TU and RU in two-channel uses, but the RU now experiences interference from the TU transmit data. In the first part of the thesis, we consider an orthogonal frequnecy division (OFDM) based ncTWR system, and design an overhearing-based interference cancellation scheme for the RU. It collects side-information by overhearing the TU transmit signal, and uses that to cancel the interference. We then maximize the spectral efficiency (SE) of this system by allocating power across the OFDM subbands jointly at the TU, BS, and the relay nodes. We use successive convex approximation approach to approximate the non-convex sum-rate maximization as a convex geometric program (GP). The algorithm is shown to outperform several other state-of-the-art techniques. In the second part of the thesis, we maximize the global energy efficiency (GEE) of the aforementioned ncTWR system using quadratic transformation (QT)-based optimization. We show that the proposed approach outperforms GP-based sum-rate maximization, one-way relaying, equal and random power allocation. We also show that the GEE reduces with increase in the number of subbands. This happens due to increase in circuit power consumption with the increase in number of subbands. In the last part of the thesis, we extend the system model to MIMO AF ncTWR with a single subcarrier. We first construct a novel transceiver using GSVD, which reduces the end-to-end MIMO channels into triangular channels. This enables the decoding of transmit symbols using successive interference cancellation. We maximize the system SE by proposing
T Nanonetworking in the Terahertz Band and Beyond From Nano-Bio Interfaces to Quantum Communications

After describing some innovative applications of this technology, the fundamental physical properties and capabilities of nano-transceivers and nano-antennas at optical and terahertz frequencies are reviewed. The main phenomena affecting the propagation of the generated signals are then presented both from the wave and particle perspectives. starting from the hardware capabilities and the channel peculiarities, tailored communication and networking solutions are discussed, all defining a roadmap to transform fundamental research into socially-meaning-ful applications.
Entropy-based Training Methods for Scalable Neural Implicit Sampler

Efficiently sampling from un-normalized target distributions is a fundamental problem in scientific computing and machine learning. Traditional approaches like Markov Chain Monte Carlo (MCMC) guarantee asymptotically unbiased samples from such distributions but suffer from computational inefficiency, particularly when dealing with high-dimensional targets, as they require numerous iterations to generate a batch of samples. In this paper, we propose an efficient and scalable neural implicit sampler that overcomes these limitations. Our sampler can generate large batches of samples with low computational costs by leveraging a neural transformation that directly maps easily sampled latent vectors to target samples without the need for iterative procedures. To train the neural implicit sampler, we introduce two novel methods: the KL training method and the Fisher training method. The former minimizes the Kullback-Leibler divergence, while the latter minimizes the Fisher divergence. By employing these training methods, we effectively optimize the neural implicit sampler to capture the desired target distribution. To demonstrate the effectiveness, efficiency, and scalability of our proposed samplers, we evaluate them on three sampling benchmarks with different scales. These benchmarks include sampling from 2D targets, Bayesian inference, and sampling from high-dimensional energy-based models (EBMs). Notably, in the experiment involving high-dimensional EBMs, our sampler produces samples that are comparable to those generated by MCMC-based methods while being more than 100 times more efficient, showcasing the efficiency of our neural sampler. We believe that the theoretical and empirical contributions presented in this work will stimulate further research on developing efficient samplers for various applications beyond the ones explored in this study.