Miracle-dz/UniFAR-FTUs huggingface dataset:
Regularized online DR-submodular optimization

The utilization of online optimization techniques is prevalent in many fields of artificial intelligence, enabling systems to continuously learn and adjust to their surroundings. This paper outlines a regularized online optimization problem, where the regularizer is defined on the average of the actions taken. The objective is to maximize the sum of re-wards and the regularizer value while adhering to resource constraints, where the reward function is assumed to be DR-submodular. Both concave and DR-submodular regularizers are analyzed. Con-cave functions are useful in describing the impar-tiality of decisions, while DR-submodular functions can be employed to represent the overall effect of decisions on all relevant parties. We have developed two algorithms for each of the concave and DR-submodular regularizers. These algorithms are easy to implement, efficient, and produce sublinear regret in both cases. The performance of the proposed algorithms and regularizers has been verified through numerical experiments in the context of online joke recommendation and internet advertising.
ASSOCIATION OF CT-ESTIMATED LUMBAR BONE MINERAL DENSITY AND FATTY INFILTRATION OF PSOAS MUSCLE IN A YOUNG AND MIDDLE AGE POPULATION

Osteoporosis is a disease of reduced bone density and increased bone fragility. Fractures due to osteoporosis cause global health problem, especially in the elderly population (1). Dual-energy xray absorptiometry (DXA) is the most preferred method for measuring bone mineral density (BMD) due to the simplicity of the technique and low doses of radiation exposure (2). However, DXA can be inaccurate in cases of severe degeneration of the spine, scoliosis or after spine surgery, which is not the case with computerized tomography (CT) measurements, because itocan measure the trabecular part of the bone (3). In recent decades, the idea of opportunistic CT estimation of BMD has been broadly introduced, showing an excellent correlation with DXA (4, 5). Each CT scan, performed for other indication, contains unused valuable information which could greatly aid in the detection of reduced bone density. The information can be obtained without any additional procedures, medicalscosts, or additional radiation exposure. Also, CT is an excellent method for the assessment of changes in skeletal muscles due to precise differentiation and measurement of muscle attenuation values expressed in Hounsfield Units (HU), which are an indicator of muscle fat infiltration (6). Skeletal muscles and bones share common embryological origin, and mass and strength of both tissues decline with age, which is manifested by an accumulation of adipose tissue in both cases (7). The main feature of muscle and bone aging process and fatty infiltration on CT is a decrease in HU. The psoas major muscle is one of the most important muscles that covers the spine. So far, it has been confirmed that the psoas muscle size was associated with acute and chronic low back pain, and with many other conditions, such as sarcopenia and loss of global muscle mass (8, 9). In addition to the fact that fatty infiltration results in reduced muscle strength and function, it is also associated with an increased risk of bone fractures (8-10).
Effect of Biscuits Supplemented with Cinnamon, Fennel, and Anise Powders on the Quality of Vital Functions in Aged Rats

The objective of this work was to study the impact of 5% of cinnamon, fennel, and anise powder in biscuits on the quality of vital functions in 30 aged female rats, divided into 6 groups (5 rats) as following, group (1) young control group (-ve) fed on basal diet, group (2) elder control group (+ve) fed on basal diet, group (3) fed on wheat biscuit and the other groups 4, 5 and 6 were fed on cinnamon, fennel, and anise biscuits respectively during the experimental period (8 weeks). The results indicated that groups fed on biscuits supplemented with cinnamon, fennel, and anise powders showed significantly higher levels of serum HDLc , T.Bil, SOD, CAT,GSH ,calcium and hormones progesterone, and osteocalcin, while showed significantly lower level of serum TG, TC, LDL-c , VLDL-c , AST, ALT, ALP , creatinine, urea, uric acid, MDA, hormones LH, PTH, testosterone, comparing with elder control group (+ve). Therefore, the study recommends eating products that contain cinnamon, fennel and anise powder because they have antioxidant properties and can help treat cholesterol, liver, kidney and female hormones for the elderly.
Better Replacement for TTS Naturalness Evaluation

Text-To-Speech (TTS) systems are commonly evaluated along two main dimensions: intelligibility and naturalness. While there are clear proxies for intelligibility measurements such as transcription Word-Error-Rate (WER), naturalness is not nearly so well defined. In this paper, we present the results of our attempt to learn what aspects human listeners consider when they are asked to evaluate the “naturalness” of TTS systems. We conducted a user study similar to common TTS evaluations and at the end asked the subject to define the sense of naturalness that they had used. Then we coded their answers and statistically analysed the distribution of codes to create a list of aspects that users consider as part of naturalness. We can now provide a list of suggested replacement questions to use instead of a single oblique notion of naturalness.
Enhanced Effect of Metal Sulfide Doping (MgS-TiO2) Nanostructure Catalyst on Photocatalytic Reduction of CO2 to Methanol

The conversion of CO2 gas from the global emission to methanol can be a route to look at in addressing greenhouse gas (GHG) issues. Photocatalysis has been attracting attention in the conversion of CO2 to methanol, as it is seen to be one of the most viable, economic, and sustainable strategies. The biggest hindrance to the use of metal oxide photocatalysts was the poisoning by sulfur content in the CO2 gas feedstock. Therefore, in the development of photocatalysts using metal oxide-based additives, the metal needs to be in the form of metal sulfides to avoid catalyst poisoning due to the presence of H2S. The magnesium sulfide-based TiO2 (MgS-TiO2) photocatalyst has not been synthesized and studied for its photocatalytic potential. In this study, a novel MgS-TiO2 photocatalyst was synthesized using a combination of wet impregnation and hydrothermal method and characterized to determine the physical and chemical properties of the photocatalyst. Characterization results have shown the presence of MgS on the native TiO2 photocatalyst. The optimization of MgS-TiO2 formulation was conducted, wherein the MgS and TiO2 ratio of 0.5 wt % has been shown to give the highest methanol yield of 229.1 μmol/g·h. The photocatalytic parameter optimization results showed that temperature and catalyst loading were the most important factors that impacted the photocatalytic process. In contrast, reaction time had the least significant effect on the CO2 photocatalytic reduction to methanol. This concludes that the MgS-TiO2 photocatalyst has potential and can be used for the photocatalytic reduction of CO2 to methanol.
Phase-shifted Adversarial Training (Supplementary Material)

Motivated by the examination of F-principle [Xu et al., 2020], we use the filtering method to analyze the behavior of the neural networks in adversarial training. The idea is to split the frequency domain into two parts, i.e., low-frequency and high-frequency parts. However, the Fourier transform for high-dimensional data requires high computational costs and large memory footprints. As an alternative, we use the Fourier transform of a Gaussian function Ĝ. Let the original dataset be {xj , yj} j=0 , and the network output for xj be Tj . The low frequency part of the training dataset can be derived by y j = 1 Cj N−1 ∑
Asymptotic behavior of solutions of ut=Δum as m→∞

On etudie le comportement asymptotique des solutions u=u (m) de: u t =Δu m dans R n ×(0, ∞), u(X, o)=f(x) (X∈R n ), f≥0, f∈L 1 (R n ), quand m→∞. Sous l'hypothese que f est de profil etoile, on etablit que u (∞) ≡lem m→∞ u (m) existe et est donnee par u (∞) =u( ∞) (x)=1 si x∈A et=f(x) si x/∈A ou A est l'ensemble de non coincidence de la solution de l'inegalite variationnelle −Δw≥f−1, w≥0, (Δw+f−1)w=0 p.p. dans R n
Pollen and Flora as Bioindicators in Assessing the Status of Polluted Sites: The Case Study of the Mantua Lakes (SIN “Laghi di Mantova e Polo Chimico”; N Italy)

An integrated floristic and palynological approach was carried out at the site of national interest “Laghi di Mantova e Polo Chimico” to obtain an environmental assessment useful for monitoring polluted sites. The flora of highly contaminated sectors (area A and area B) was surveyed, and the floristic composition and ecological strategies of the species were compared with a control sector (area C). A total of 195 species were observed in the three sectors. Pollen preservation of six selected species was checked as a bioindicator of environmental health in the same sectors. Area A and area B likely share similar environmental pressures, including anthropogenic stressors such as pollution, geographical proximity and a similar set of habitats, leading to similarities in flora composition. Similarly, the incidence of pollen without cytoplasm is higher in area A (9.3%) and area B (7.6%) than in area C (2.5%). The floristic differences among the sectors and the quantity of empty or abnormal pollen, together with the CSR strategies adopted by the species, suggest that the effects of anthropogenic impact on local vegetation can be detected at both macroscopic and microscopic levels. The discovery of the protected species Narcissus pseudonarcissus in area C is noteworthy, which may be important in directing efforts towards the protection of plant communities in this sector.
The curse of (non)convexity: The case of an Optimization-Inspired Data Pruning algorithm

Data pruning consists of identifying a subset of the training set that can be used for training instead of the full dataset. This pruned dataset is often chosen to satisfy some desirable properties. In this paper, we leverage some existing theory on importance sampling with Stochastic Gradient Descent (SGD) to derive a new principled data pruning algorithm based on Lipschitz properties of the loss landscape. The goal is to identify a training subset that accelerates training (compared to random data pruning). We call this algorithm LiPrune . We illustrate cases where LiPrune outperforms existing methods and discuss the limitations and failures of this algorithm in the context of deep learning.
Automated Detection and Tracking of Low Latitude Plasma Bubbles Utilizing Global-Scale Observations of the Limb and Disk (GOLD) 135.6 nm Data

Plasma bubbles are regions of depleted plasma within the upper thermosphere/ionosphere that form during post-sunset hours near the magnetic equator. These structures tend to align with local geomagnetic field lines, extend upwards hundreds of kilometers along geomagnetic longitudes, and thousands of kilometers along geomagnetic latitudes. These large scale plasma density gradients can attenuate lower frequency radio waves, while small scale structures along the walls can interfere with centimeter scale wavelengths via Fresnel and Bragg scattering. Large scale statistical analysis of this phenomenon can further understanding of their occurrence and subsequent behavior. The current study utilizes Global-Scale Observations of the Limb and Disk (GOLD) 135.6 nm nightglow data from October 5, 2018 to September 30, 2022. GOLD has a unique perspective from geostationary orbit, allowing a constant and consistent view of nightglow and structures over the Americas and Atlantic. A plasma bubble detection method is developed and used to generate a database of plasma bubble occurrences. Occurrences are used to calculate plasma bubble drift speeds and separations. Clear seasonality in plasma bubble occurrence rate is evident. Overall occurrences peak during December solstice months and minimize during June solstice for longitudes seen by GOLD. Within GOLD’s field of view, higher occurrences are seen to the west during December solstice and east during June solstice. Plasma bubble drift speeds and separations show consistent distributions regardless of magnetic region, geographic region, season, or local time. This suggests plasma bubbles behave consistently and regularly once formed, at least on spatial scales seen by GOLD.
Efficient Multi-Horizon Learning for Off-Policy Reinforcement Learning

Value estimates at multiple timescales can help create advanced discounting functions and allow agents to form more effective predictive models of their environment. In this work, we investigate learning over multiple horizons concurrently for off-policy deep reinforcement learning using an efficient architecture that combines a deeper network with the crucial components of Rainbow, a popular value-based off-policy algorithm. We use an advantage-based action selection method and our proposed agent learns over multiple horizons simultaneously while using either an exponential or hyperbolic discounting function to estimate the advantage that guides the acting policy. We test our approach on the Procgen benchmark, a collection of procedurally-generated environments, to demonstrate the effectiveness of this approach, and specifically, to evaluate the agent’s performance in previously unseen scenarios.
It's Not Just About Money in Peer-To-Peer Accommodation: Examining Residents' Intentions Using Motivation-Opportunity-Ability Theory

Peer-to-Peer accommodation (P2PA) has drastically transformed the traditional form of tourism and has become a worldwide phenomenon. There are few studies that have investigated the determinants of participation in peer-based accommodation from the residents' perspective. This study fills this gap by identifying the factors that influence residents' intention to become P2PA hosts based on Motivation-Opportunity-Ability (MOA) theory. Adopting a quantitative approach, a pan-India survey was conducted to collect 230 valid responses. For analysis, both convergent and discriminant validity of the constructs using PLS-SEM was examined, along with the reliability of all the measurement scales. Empirical evidence validates that extrinsic motivation (economic benefits) does not have a direct impact whereas intrinsic motivation (social relationships); opportunity, ability, and trust have a significant impact on residents' intentions to host. Finally, the paper recommends strategies for the policymakers and platform service providers to encourage residents to take up hosting to foster sustainable consumption and build relationships with tourists.
STRONG - Structure Controllable Legal Opinion Summary Generation

We propose an approach for the structure controllable summarization of long legal opinions that considers the argument structure of the document. Our approach involves using predicted argument role information to guide the model in generating coherent summaries that follow a provided structure pattern. We demonstrate the effectiveness of our approach on a dataset of legal opinions and show that it outperforms several strong baselines with respect to ROUGE, BERTScore, and structure similarity.
Global sensitivity analysis using the ultra-low 1 resolution Energy Exascale Earth System Model 2

For decades, the Arctic has been warming at least twice as fast as the rest of the globe. As a first step towards quantifying parametric uncertainty in Arctic feedbacks, we perform a variance-based global sensitivity analysis (GSA) using a fully-coupled, ultra-low resolution (ULR) configuration of version 1 of the Department of Energy’s Energy Exascale Earth System Model (E3SMv1). The study randomly draws 139 realizations of ten model parameters spanning three E3SMv1 components (sea ice, atmosphere and ocean), which are used to generate 75 year long projections of future climate using a fixed pre-industrial forcing. We quantify the sensitivity of six Arctic-focused quantities of interest (QOIs) to these parameters using main effect, total effect and Sobol sensitivity indices computed with a Gaussian process emulator. A sensitivity index-based ranking of model parameters shows that the atmospheric parameters in the CLUBB (Cloud Layers Unified by Binormals) scheme have significant impact on sea ice status and the larger Arctic climate. We also use the Gaussian process emulator to predict the response of varying each variable when the impact of other parameters are averaged out. These results allow one to assess the non-linearity of a parameter’s impact on a QOI and investigate the presence of local minima encountered during the spin-up tuning process. Our study confirms the necessity of performing global analyses involving fully-coupled climate models, and motivates follow-on investigations in which the ULR model is compared rigorously to higher resolution configurations to confirm its viability as a lower-cost surrogate in fully-coupled climate uncertainty analyses.
Compact High Gain and Wideband Octagon Microstrip Yagi Antenna

ABSTRACT A new structure of microstrip Yagi antenna is proposed in this article in order to obtain a high gain and wide bandwidth. It is called Octagon Microstrip Yagi Antenna. The Octagon Microstrip Yagi Antenna has been simulated, optimized, constructed, and tested. According to simulated results, it appears that the simulated Octagon Microstrip Yagi Antenna has the ability to obtain 15.97% bandwidth and gain of 11.16 dB in a compact size of 70 mm × 75 mm. On the other hand, experimental results show that the fabricated Octagon Microstrip Yagi Antenna can achieve a gain of 11 dB with 13.8% bandwidth. It can be seen that there is a good agreement between simulation and measurement results. Hence, it gives evidence that the proposed antenna is capable of producing a high gain with a wide bandwidth in a smaller size as compared to the others.
Assessing the Effects of District-Level Segregation on Meritocratic Beliefs in Germany

In recent years, researchers have grappled with the phenomenon that public demand for redistribution has not systematically increased in response to rising inequality. Meritocratic beliefs have been suggested as an explanation for this observation, because they can help legitimize inequalities. Past research has identified local-level inequality, segregation, or diversity as important factors for how these beliefs might be formed and maintained. Different theoretical approaches have been advanced and tested to determine the direction and extent of these effects, producing mixed results. We put these theories to the test by focusing on a country in which changes in the level of inequality have indeed been met with equal changes in justice perceptions: Germany. Furthermore, we broaden the scope by focusing on local segregation between different socioeconomic status groups, rather than income inequality. To do so, we utilize geocoded individual-level data from the German Socio-Economic Panel (SOEP), microm data, and relevant geospatial indicators from the INKAR database. We find some indication that residential segregation of status groups and isolation of high status groups is associated with less support of meritocratic beliefs, contradicting previous work. Additionally, we find evidence of urban–rural differences in the effects of segregation.
An Ethnobotanical Study of Wild Edible Plants in Tach Gayint District, South Gondar Zone, Amhara Region, Northwestern Ethiopia

The purpose of this study was to carry out an ethnobotanical and ethnopharmacological investigation on wild edible plants and their value to households in the Tach Gayint district of South Gondar Zone of northwestern Ethiopia. A total of 175 informants (56 women and 119 males) were interviewed for ethnobotanical data, and 25 of them were key informants. Data collection techniques included semistructured interviews, guided field walks, and focus group discussions. Quantitative analytical tools were employed for ethnobotanical methods including preference ranking and direct matrix ranking techniques to analyse the data. 36 species of wild edible plants have been identified in the study area. Of these plant species, shrubs account for 15 (42%), followed by herbs 13 (36%) and trees 8 (22%). Regarding the edible parts, fruits account for 19 (53%) followed by young shoots, leaves, and flowers, 4 (11%) for each. These plant species are eaten raw (86%) or cooked (14%) and most are collected by younger people for herding cattle. According to the preference ranking analysis, the fruit of Opuntia ficus-indica is the most preferred plant species because its sweet taste. Although Cordia africana, the most commonly used multipurpose wild edible plant species, is mostly exploited due to human activity, activities such as the production of charcoal, the gathering of firewood, the construction of homes, and the use of agricultural tools all played a significant role in the plant's eventual extinction. In the study area, agricultural expansion was the main cause of putting wild edible plants in danger. It is best to cultivate and manage edible plants in a backyard garden and to perform more research on popular edible plant species.
Purdue e-Pubs Purdue e-Pubs

— Linear systems are a widely used model for the control tasks of modern cyber physical systems around their stationary state(s), e.g., smart grids, remote health applications, and autonomous driving systems. Specifically, each sensor first compresses its own measurement and then sends it to the con­ troller. Due to the inevitable random communication delay, the controller needs to decide how to fuse the received information to compute the desired control action. Suppose a fusion center has received several measurements over time. One common belief is that the control decision should be made solely based on the latest measurement of each sensor while ignoring the older/stale measurements from the same sensor. This work shows that while such a strategy is optimal in a single-sensor environment, it can be strictly suboptimal for a multi-sensor system. Namely, if one properly fuses both the latest and outdated measurements from each of the sensors, one can strictly improve the underlying control system performance. The numerical evaluation shows that even at a very low communication rate of 8 bits per measurement per sensor, the proposed scheme achieves a state variance of only 5% away from the best possible achievable L2 norm. It is 15% better than the MMSE fusion scheme using exclusively the freshest measurements (while discarding outdated ones).