Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed fixed resources exchanging particles: Phases of an asymmetric exclusion process connected to two reservoirs

We propose and study a conceptual one-dimensional model to explore how the combined interplay between fixed resources and particle exchanges between different parts of an extended system can affect the stationary densities in a current carrying channel connecting different parts of the system. To this end, we consider model composed of a totally asymmetric simple exclusion process (TASEP) in a lattice connecting two particle reservoirs without any internal dynamics but which can exchange particles, ensuring nonvanishing currents in the steady states. The total particle number in the system that defines the ``resources'' available, although is kept constant by the model dynamics, but can take any value independent of the model parameters that define the dynamical update rules. We show that the resulting phase diagrams of the model are parametrised by the parameters which define the various dynamical update rules together with the total available resources. These parameters can be tuned to make the density on the TASEP lane globally uniform or piecewise continuous, and can also control populations of the two reservoirs. In general, the phase diagrams are quite different from a TASEP with open boundaries. In the limit of large amount of resources, the phase diagrams become topologically identical to that for an open TASEP.
Estimating the Weight Enumerators of Reed-Muller Codes via Sampling

This paper develops an algorithmic approach for obtaining estimates of the weight enumerators of Reed-Muller (RM) codes. Our algorithm is based on a technique for estimating the partition functions of spin systems, which in turn employs a sampler that produces codewords according to a suitably defined Gibbs distribution. We apply our method to moderate-blocklength RM codes and derive approximate values of their weight enumerators. We observe that the rates of the weight enumerator estimates returned by our method are close to the true rates when these rates are either known or computable by brute-force search; in other cases, our computations provide provably robust estimates. As a byproduct, our sampling algorithm also allows us to obtain estimates of the weight spectra of RM codes. We illustrate our methods by providing estimates of the hitherto unknown weight enumerators of the RM$(11,5)$ code and the exact weight spectra of the RM$(10,3)$ and RM$(10,4)$ codes.
ACM MMSys 2024 Bandwidth Estimation in Real Time Communications Challenge

The quality of experience (QoE) delivered by video conferencing systems to end users depends in part on correctly estimating the capacity of the bottleneck link between the sender and the receiver over time. Bandwidth estimation for real-time communications (RTC) remains a significant challenge, primarily due to the continuously evolving heterogeneous network architectures and technologies. From the first bandwidth estimation challenge which was hosted at ACM MMSys 2021, we learned that bandwidth estimation models trained with reinforcement learning (RL) in simulations to maximize network-based reward functions may not be optimal in reality due to the sim-to-real gap and the difficulty of aligning network-based rewards with user-perceived QoE. This grand challenge aims to advance bandwidth estimation model design by aligning reward maximization with user-perceived QoE optimization using offline RL and a real-world dataset with objective rewards which have high correlations with subjective audio/video quality in Microsoft Teams. All models submitted to the grand challenge underwent initial evaluation on our emulation platform. For a comprehensive evaluation under diverse network conditions with temporal fluctuations, top models were further evaluated on our geographically distributed testbed by using each model to conduct 600 calls within a 12-day period. The winning model is shown to deliver comparable performance to the top behavior policy in the released dataset. By leveraging real-world data and integrating objective audio/video quality scores as rewards, offline RL can therefore facilitate the development of competitive bandwidth estimators for RTC.
Flat or crumpled: states of active symmetric membranes

We set up and study the hydrodynamic theory for active fluid and tethered membranes. We focus on those membranes which are inversion-symmetric. We show that such membranes are either described by appropriate linear hydrodynamic equations, which are exact in the asymptotic long wavelength limit, giving stable flat phases with positional quasi long range orders, or be linearly unstable, implying crumpling of the membranes. We argue that in a such an active membrane thermal noises dominate over any active noises, and use it to calculate the correlation functions of the undulation and in-plane displacements of the membrane in the stable case, and the associated correlation functions of the embedding bulk flow velocities.
AI-Powered Marketing: Transforming Consumer Engagement and Brand Growth

This paper explores the profound impact of artificial intelligence (AI) on marketing strategies across various sectors. AI has revolutionized marketing practices by enabling personalized customer experiences, enhancing data analytics, and optimizing advertising campaigns. The paper examines case studies from companies like Alibaba, Sephora, and Toyota, highlighting how they leverage AI to improve customer engagement and drive sales. Additionally, it discusses the components of AI marketing, including machine learning and big data analytics, and their role in bridging the gap between data collection and actionable insights. Furthermore, the paper delves into the implications of AI for different aspects of marketing, such as email marketing, advertising, chatbots, predictive analysis, and dynamic pricing. By synthesizing insights from a diverse range of sources, this paper provides a comprehensive overview of the transformative impact of AI on modern marketing practices.
Pulling back symmetric Riemannian geometry for data analysis

Data sets tend to live in low-dimensional non-linear subspaces. Ideal data analysis tools for such data sets should therefore account for such non-linear geometry. The symmetric Riemannian geometry setting can be suitable for a variety of reasons. First, it comes with a rich mathematical structure to account for a wide range of non-linear geometries that has been shown to be able to capture the data geometry through empirical evidence from classical non-linear embedding. Second, many standard data analysis tools initially developed for data in Euclidean space can also be generalised efficiently to data on a symmetric Riemannian manifold. A conceptual challenge comes from the lack of guidelines for constructing a symmetric Riemannian structure on the data space itself and the lack of guidelines for modifying successful algorithms on symmetric Riemannian manifolds for data analysis to this setting. This work considers these challenges in the setting of pullback Riemannian geometry through a diffeomorphism. The first part of the paper characterises diffeomorphisms that result in proper, stable and efficient data analysis. The second part then uses these best practices to guide construction of such diffeomorphisms through deep learning. As a proof of concept, different types of pullback geometries -- among which the proposed construction -- are tested on several data analysis tasks and on several toy data sets. The numerical experiments confirm the predictions from theory, i.e., that the diffeomorphisms generating the pullback geometry need to map the data manifold into a geodesic subspace of the pulled back Riemannian manifold while preserving local isometry around the data manifold for proper, stable and efficient data analysis, and that pulling back positive curvature can be problematic in terms of stability.
Integrated SWIPT Receiver with Memory Effects: Circuit Analysis and Information Detection

Wireless power transfer has been proposed as a key technology for the foreseen machine type networks. A main challenge in the research community lies in acquiring a simple yet accurate model to capture the energy harvesting performance. In this work, we focus on a half-wave rectifier and based on circuit analysis we provide the actual output of the circuit which accounts for the memory introduced by the capacitor. The provided expressions are also validated through circuit simulations on ADS. Then, the half-wave rectifier is used as an integrated simultaneous wireless information and power transfer receiver where the circuit's output is used for decoding information based on amplitude modulation. We investigate the bit error rate performance based on two detection schemes: (i) symbol-by-symbol maximum likelihood (ML); and (ii) ML sequence detection (MLSD). We show that the symbol period is critical due to the intersymbol interference induced by circuit. Our results reveal that MLSD is necessary towards improving the error probability and achieving higher data rates.
Unprotected 4G/5G Control Procedures at Low Layers Considered Dangerous

Over the years, several security vulnerabilities in the 3GPP cellular systems have been demonstrated in the literature. Most studies focus on higher layers of the cellular radio stack, such as the RRC and NAS, which are cryptographically protected. However, lower layers of the stack, such as PHY and MAC, are not as thoroughly studied, even though they are neither encrypted nor integrity protected. Furthermore, the latest releases of 5G significantly increased the number of low-layer control messages and procedures. The complexity of the cellular standards and the high degree of cross-layer operations, makes reasoning about security non-trivial, and requires a systematic analysis. We study the control procedures carried by each physical channel, and find that current cellular systems are susceptible to several new passive attacks due to information leakage, and active attacks by injecting MAC and PHY messages. For instance, we find that beamforming information leakage enables fingerprinting-based localization and tracking of users. We identify active attacks that reduce the users' throughput by disabling RF front ends at the UE, disrupt user communications by tricking other connected UEs into acting as jammers, or stealthily disconnect an active user. We evaluate our attacks against COTS UEs in various scenarios and demonstrate their practicality by measuring current operators' configurations across three countries. Our results show that an attacker can, among other things, localize users with an accuracy of 20 meters 96% of the time, track users' moving paths with a probability of 90%, reduce throughput by more than 95% within 2 seconds (by spoofing a 39 bits DCI), and disconnect users.
Managing Marine Seascapes Through Community-based Conservation

In this multi-component exercise, you have been recruited as community scientists to analyze real-world data collected in Vatu-i-Ra Seascape using non-destructive diver operated video (DOV) methods. These videos were previously collected by the Wildlife Conservation Society in collaboration with local divers in Fiji. Students will quantitatively analyze and use this data to assess the fisheries management efficacy of tabu areas—a traditional Fijian approach to create no-take, Marine Protected Areas—as a method of promoting marine biodiversity and improving overall ecosystem health using metrics such as fish abundance and coral reef complexity. During this exercise you will also learn about the importance and cultural significance of tabu areas in relation to ecosystem health and human livelihoods. You will be directed on how to visualize the results and summarize their conclusions through a written report in the style of a scientific journal article. In the discussion section of your scientific journal article, you are encouraged to critically think about study limitations and discuss future research directions to expand the project.
Design optimization of hydraulic fracturing

The approach to the problem of hydraulic fracturing design optimization is proposed which combines three modules: a fracture geometry module, an oil production module and an economic module. The approach is based on the genetic algorithm — the universal multivariate and multiobjective optimization method. The optimization process involves subsequent determination of the fracture geometry, calculation of the oil production depending on the number of fractures, and description of the investment–return cash flows. Iterated by the genetic algorithm for different set of input parameters, this approach allows determination of the optimal design of a multiply fractured well for a given set of geological and economical information. The results show that the proposed method is plausible and can be useful for achieving projected objectives.
Agronomic performance of dual-purpose Sorghum bicolor (L.) hybrids under water deficit

This study aimed to assess the agronomic performance of five sorghum hybrids irrigated with two replacement levels of crop evapotranspiration (ETc). The experimental design was randomized blocks in a 5x2 factorial scheme with four replications. The factors were dual-purpose sorghum hybrids (AGRI 002-E, AGRI G1, AGRI G2, BREVANT SS318 and BRS 58) and water replacement levels (50% and 100% of ETc). The replacement of 50% of ETc was defined as water deficit (WD). The experiment was conducted in a greenhouse, and sorghum was grown in pots. The mean comparison test showed that plants under WD condition had low values of height, stem diameter, leaf area, chlorophyll content, and a 37% reduction in shoot dry mass productivity. The analysis of principal components revealed that the correlations between the variables changed according to the water regimes. Under WD, water productivity, shoot dry mass, and leaf area index were highly correlated with each other and with the hybrid AGRI 002-E. The hybrid AGRI 002-E showed better performance under normal irrigation conditions, while under WD conditions, the hybrids BREVANT SS318 and BRS 658 also showed good performance. Hybrids with higher leaf area and water productivity performed better under water-scarcity conditions
Simplified modelling methods to design integrative lighting

Evaluating non-visual effects of light is crucial. Designers often use DIALux for this purpose even if it is intended for calculating photopic quantities exclusively. To verify its applicability in this context, two approaches for melanopic equivalent daylight illuminance calculation are compared against measured data. Measurements were performed for 21 scenarios obtained combining 7 wall colours and 3 CCTs. Method 1 adopts traditional DIALux simulations, in method 2 melanopic flux and reflectance replace the corresponding photopic quantities. Method 2 performs better in 71.43% of cases, especially for colours highly reflective in long wavelengths; the opposite occurs for colours most reflective in short wavelengths.
On the Approximation of Kernel functions

Various methods in statistical learning build on kernels considered in reproducing kernel Hilbert spaces. In applications, the kernel is often selected based on characteristics of the problem and the data. This kernel is then employed to infer response variables at points, where no explanatory data were observed. The data considered here are located in compact sets in higher dimensions and the paper addresses approximations of the kernel itself. The new approach considers Taylor series approximations of radial kernel functions. For the Gauss kernel on the unit cube, the paper establishes an upper bound of the associated eigenfunctions, which grows only polynomially with respect to the index. The novel approach substantiates smaller regularization parameters than considered in the literature, overall leading to better approximations. This improvement confirms low rank approximation methods such as the Nystr\"om method.
Assessing the Use of Social Media as a Source of Information Related to Dentistry in Saudi Arabia

The term social media usually refers to Internet tool that allows communication between individuals, and enables them to share information, opinion, photos, videos, and other content [1]. There are various kinds of social media platforms such as blogs like Tumbler and Blogger, social networks similar to Facebook, Twitter, MySpace and Google, professional networks such as LinkedIn, media sharing as YouTube and Flickr, collaborative projects like Wikipedia, and virtual gaming or social worlds as SecondLife and HumanSim [4-6]. The collaborative environment to which social media belongs represents a shift in technology and functionality from ‘Web 1.0’, in which static online content and applications were created and published by individuals, to ‘Web 2.0’, in which there is continuous modification and participation by all users [7]. Different social media attract different age groups, for example, blogging has become less popular among teens and young adults since 2006 and more popular within older adults [8,9]. Similarly, there are differences in social media use depending on gender, race/ethnicity, education, and income characteristics [10]. Social media, offering a public reservoir of information, may serve as a source of patient information to aid clinical care [2]. It is clear, though, that in addition to seeking out traditional sources of healthcare information, patients are increasingly active online [11]. In 2011, looking for healthcare information was the third most common online activity [12], and in September 2012, 72% of adult internet users sought support and medical information online [13]. In 2012, 67% of internet users were using social media for any purpose [14] and 26% were using it for health issues [13]. By transcending geographical boundaries, social media has facilitated extension of clinical care to distant patients [2]. Telemedicine, broadly defined as using telecommunication for patient care, has been around since the 1960s but has only recently experienced rapid growth thanks to new technologies [15]. Patients are increasingly active online, and they are frequent social media users for health purposes. In 2015, it has been Volume 8 Issue 7 2017
Chemical characteristics of hydrologically distinct cryoconite holes in coastal Antarctica

ABSTRACT Cryoconite holes play a significant role in the nutrient cycling on glaciers and can be regarded as a storehouse of nutrients that are generated through microbial and photochemical activities. In this work, the chemical characteristics of hydrologically connected and isolated cryoconite holes from three geographically distinct regions of coastal Antarctica, namely Larsemann Hills, Amery Ice Shelf and central Dronning Maud Land were studied. Major ions (Na+, K+, Mg2+, Ca2+, Cl−, SO42− and NO3−) and total organic carbon in the hydrologically isolated, closed cryoconite holes showed significantly higher enrichment (6–26 times and 9 times, respectively) over the conservative tracer ion Cl− possibly due to sediment dissolution and microbial synthesis during isolation period. In contrast, depletion of major ions and organic carbon were observed in the open, hydrologically connected holes due to their discharge from the cryoconite holes through interconnected streams. This study suggests that the contribution of cryoconite holes to the nutrient and microbial transport to downstream environments may vary with the extent of hydrological connectivity by virtue of the fact that nutrients and organic carbon which accumulate in the isolated cryoconite holes during isolation could get washed to downstream environments in the event that they get connected through surface or subsurface melt channels.
Baseline surveys for coral reef community structure and demographics in Vatia and Faga'alu Bay, American Samoa

This report describes the results of baseline assessment surveys for coral reef benthic structure, coral community demographics, and health condition conducted in Vatia Bay and Faga‘alu Bay, American Samoa, by the Coral Reef Ecosystem Program (CREP) of NOAA’s Pacific Islands Fisheries Science Center (PIFSC), from 23 October to 15 November 2015. The work described was funded by the NOAA Coral Reef Conservation Program (CRCP) through two internal projects entitled: “ Comprehensive Baseline Assessment and Pilot test of Outcome Performance Measures in Faga’alu Bay, American Samoa”, awarded to Suzie Holst (NOAA National Ocean Service) and “Eutrophication Impacts on Coral Ecosystem Health in Vatia, American Samoa”, awarded to David Whitall (NOAA National Center for Coastal and Ocean Science).
Evaluation of Content Based Image Retrieval Systems Based on Color Feature

The inevitable growth in computer field brought a significant increase for storing large amount of images along with different kinds of complexity aspects. The need for efficient storage and retrieval of images has been recognized by having large image collections in the areas such as medicine, journalism, education, entertainment, advertisement, commerce, etc. The process of retrieving desired images from a large collection of images on the basis of features is referred as Content Based Image Retrieval (CBIR) Systems. This paper deals an assessment methodology for CBIR systems with the help of a set of distance metrics. This quantitative evaluation scheme consists of statistical models that provide an insight in assessing the capability of different CBIR systems. This model also serves as a reference for selecting particular CBIR system for any particular application domain.
MP2-based composite extrapolation schemes can predict core-ionization energies for first-row elements with coupled-cluster level accuracy

X-ray photoelectron spectroscopy (XPS) measures core-electron binding energies (CEBEs) to reveal element-specific insights into chemical environment and bonding. Accurate theoretical CEBE prediction aids XPS interpretation but requires proper modeling of orbital relaxation and electron correlation upon core-ionization. This work systematically investigates basis set selection for extrapolation to the complete basis set (CBS) limit of CEBEs from $\Delta$MP2 and $\Delta$CC energies across 94 K-edges in diverse organic molecules. We demonstrate that an alternative composite scheme using $\Delta$MP2 in a large basis corrected by $\Delta$CC-$\Delta$MP2 difference in a small basis can quantitatively recover optimally extrapolated $\Delta$CC CEBEs within 0.02 eV. Unlike $\Delta$CC, MP2 calculations do not suffer from convergence issues and are computationally cheaper, and, thus, the composite $\Delta$MP2/$\Delta$CC scheme balances accuracy and cost, overcoming limitations of solely using either method. We conclude by providing a comprehensive analysis of the choice of small and large basis sets for the composite schemes and provide practical recommendations for highly accurate (within 0.10-0.15 eV MAE) ab initio prediction of XPS spectra.
Enhancing Image Caption Generation Using Reinforcement Learning with Human Feedback

Research on generative models to produce human-aligned / human-preferred outputs has seen significant recent contributions. Between text and image-generative models, we narrowed our focus to text-based generative models, particularly to produce captions for images that align with human preferences. In this research, we explored a potential method to amplify the performance of the Deep Neural Network Model to generate captions that are preferred by humans. This was achieved by integrating Supervised Learning and Reinforcement Learning with Human Feedback (RLHF) using the Flickr8k dataset. Also, a novel loss function that is capable of optimizing the model based on human feedback is introduced. In this paper, we provide a concise sketch of our approach and results, hoping to contribute to the ongoing advances in the field of human-aligned generative AI models.