Miracle-dz/UniFAR-FTUs huggingface dataset:
Lifted Reasoning for Combinatorial Counting

Combinatorics math problems are often used as a benchmark to test human cognitive and logical problem-solving skills. These problems are concerned with counting the number of solutions that exist in a specific scenario that is sketched in natural language. Humans are adept at solving such problems as they can identify commonly occurring structures in the questions for which a closed-form formula exists for computing the answer. These formulas exploit the exchangeability of objects and symmetries to avoid a brute-force enumeration of all possible solutions. Unfortunately, current AI approaches are still unable to solve combinatorial problems in this way. This paper aims to fill this gap by developing novel AI techniques for representing and solving such problems. It makes the following five contributions. First, we identify a class of combinatorics math problems which traditional lifted counting techniques fail to model or solve efficiently. Second, we propose a novel declarative language for this class of problems. Third, we propose novel lifted solving algorithms bridging probabilistic inference techniques and constraint programming. Fourth, we implement them in a lifted solver that solves efficiently the class of problems under investigation. Finally, we evaluate our contributions on a real-world combinatorics math problems dataset and synthetic benchmarks.
Coupling between plate vibration and acoustic radiation

Abstract A detailed numerical investigation of the coupling between the vibration of a flexible plate and the ensuing acoustic radiation is described. The non-linear Euler equations are used to describe the acoustic fluid, while the non-linear yon Karman plate equations are used to describe the plate response. The plate is excited by a time harmonic, normally incident acoustic source, the amplitude of which is varied. We compute and describe both linear and non-linear time periodic vibrations as well as aperiodic responses. In each case, we also describe the acoustic radiation pattern resulting from the plate vibration. We find that for low excitation levels, so that the plate response is essentially linear, the effect of acoustic coupling is negligible. However, as the level of the loading increases, the radiated pressure from a plate undergoing non-linear or chaotic vibrations is found to propagate non-linearly into the far-field. The non-linearity due to wave propagation is much weaker than that due to the plate vibrations. As the acoustic wave propagates into the far field, the relative difference in level between the fundamental and its harmonics and subharmonics decreases with distance.
The Impact of Digital Financial Inclusion and Bank Competition on Bank Stability in Sub-Saharan Africa

The last few years have witnessed a rapid development in digital finance that may threaten the manner in which traditional financial services are being used. It opens up new opportunities for low-income groups and small businesses that have limited or no access to formal financial services. Thus, digital financial inclusion plays a vital role in boosting a country’s financial inclusion, fulfilling some sustainable development goals and achieving higher economic growth. This study builds on a new measure of digital financial inclusion to examine the impact of digital financial inclusion and bank competition on bank stability in Sub-Saharan Africa for the period 2014 to 2020 using the two-step System Generalised Method of Moments. An index of digital financial inclusion, z-score, Herfindahl–Hirschman Index (HHI), and non-performing loans were used as data variables. The study findings reveal that digital financial inclusion has a significant positive relationship with bank stability (z-score) and a negative relationship with non-performing loans. The study also found a significant negative effect of bank competition (HHI) on bank stability in line with the competition-fragility view. Policymakers should ensure digital financial literacy for all since it feeds into bank stability and also reduces bank insolvency. They should also find ways of enhancing bank competition which reduces non-performing loans and bank insolvency. On practical implications, the study calls for strategic measures to preserve bank stability, such as complementing digital financial inclusion with financial literacy and enhancing bank competition.
Myoclonic epilepsy with ragged red fibers syndrome associated with mitochondrial 3302A>G mutation in the MT‑TL1 gene: A case report

A 37-year-old woman presented with proximal limb weakness, an unstable gait, tiredness and paroxysmal jitters. Neurological examination showed decreased deep tendon reflexes and positive signs indicating damage to the cerebellum. The patient's children reported no symptoms but were found to have the mitochondrial 3302A>G mutation in the mitochondrially encoded tRNA-Leu (UUA/G) 1 gene. The patient presented with increased blood lactic acid and lactic acid dehydrogenase levels, myopathy-related limb muscle electromyographic activities, ragged red fibers (RRFs), cytochrome oxidase-negative muscle fibers and mitochondrial 3302A>G mutation. Inverted lactic acid peaks in the basal ganglia, an atrophied cerebellum and multiple electroencephalographic spike waves were also observed. Therefore, myoclonic epilepsy with RRFs syndrome with the 3302A>G mutation was considered.
Composition, Antibacterial Efficacy, and Anticancer Activity of Essential Oil Extracted from Psidium guajava (L.) Leaves

Essential oils (EO) are used as a natural remedy to treat various chronic diseases, although clinical evidence is lacking. In this context, we have endeavored to measure the percentage of chemical composition and biological efficacy of Psidium guajava (guava) leaf essential oil in treating oral infections and oral cancer. The essential oil obtained from hydrodistillation of P. guajava L. leaves was analyzed by gas chromatography–mass spectrometry (GC–MS). The activities of selected oral pathogens Candida albicans (C. albicans) and Streptococcus mutans (S. mutants) were studied in vitro and in silico. MTT assay was used to test for anticancer activity against human oral epidermal carcinoma (KB). GC–MS showed that the main components of PGLEO were limonene (38.01%) and β-caryophyllene (27.98%). Minimum inhibitory concentrations (MICs) of 0.05–0.1% were demonstrated against C. albicans and S. mutans. Antimicrobial activity against C. albicans and S. mutans, as shown by molecular linkage analysis, revealed that the main metabolites, limonene and β-caryophyllene, potentially inhibited the receptors of C. albicans and S. mutans. PGLEO showed significant (p < 0.001) anticancer activity (45.89%) at 200 µg/mL compared to doxorubicin (47.87%) with an IC50 value of 188.98 µg/mL. The outcomes of the present study suggest that PGLEO has promising antimicrobial and anticancer activities and could be a useful source for developing a natural therapeutic agent for oral infections and oral cancer.
Two-Step Optogalvanic Spectroscopy of Neutral Barium: Observation and Interpretation of the Even Levels Above the 6s Ionization Limit Between 5.2 and 7 eV

Even parity levels of neutral barium above the 6s ionization limit have been investigated by using a two-step pulsed laser excitation with optogalvanic detection. From the metastable states, 5d6s, populated by a d.c. discharge in a heat-pipe vapour, we reach the 5dnl even Rydberg series with J values ranging from 0 to 5, the 6p2 1S0 state and the low-lying members (n = 7 and 8) of the 6pnp series above the 5d limits. The 5dnd configurations (18 levels for each n member) are observed from n = 8 up to n = 45 using optogalvanic spectra recorded from five intermediate 5d6p levels: 3P1,20, 3F3,40 and 1P10. These eighteen series are observed for the first time. Fine structure is resolved up to n = 25 and has been analyzed using Slater-Condon parametric calculations. In addition, the 5d3/2ns J = 1 and 5d5/2ns J = 3 series are observed up to n = 38 and 42 respectively, as well as sharp resonances interpreted as 5d3/2ng7/2 J = 2 and 5d5/2ng J = 1, 2 series up to n = 27 and n = 28 respectively. In conclusion, the energies of 636 new levels between 6s and 5d limits are given with their term and configuration identifications. Above the 5d5/2 limit, broad resonances observed from the 5d6p 1P10, 6s7p 1P10 and 3P0,1,20 intermediate levels have been identified with levels of the 6p7p and 6p8p configurations using parametric calculations based on the Slater-Condon theory.
Use of Minimum-energy Modes for Modal-active Optics Corrections of Thin Meniscus Mirrors

Abstract For thin meniscus mirrors the modes which correct arbitrary wave-front aberrations in the most efficient way are derived. These modes are similar to the free-vibration modes of the mirror. The relationship between the two types of modes is shown. The most important features such as orthogonality and energy of the modes are discussed. This modal approach is applied to a few practical problems in connection with the primary mirrors of the Very Large Telescope of the European Southern Observatory.
Convergence study of message passing in arbitrary continuous Bayesian networks

Pearl's traditional message passing algorithm developed in 1980s is the first exact inference algorithm for Bayesian networks (BNs). Although it originally was developed for discrete polytree networks only, it has been used widely in networks with loops by providing approximate solutions. In such case, messages propagated in the loops are not exact and this method is called loopy belief propagation. Loopy propagation usually converges and when it converges, it provides good approximate solutions. However, when dealing with arbitrary continuous Bayesian networks, message representations and manipulations need special cares because continuous vairables may have arbitrary distributions and their dependency relationships could be nonlinear. In this paper, we propose a loopy message passing mechanism for arbitrary continuous Bayesian networks called Unscented Message Passing (UMP-BN). UMP-BN combines Pearl's algorithm and an efficient nonlinear transformation technique called Unscented Transformation to provide estimates of the first two moments of the posterior distributions for any hidden continuous variable. We study its convergence properties by investigating various typical situations with different networks. The numerical experiments show promising results.
Tumor therapy with heavy charged particles

Nuclear science has contributed significantly to the development of tumor therapy with heavy charged particles. Interest evolved for neutron therapies in the forties because of the increased radiobiological effectiveness (RBE) compared to photon irradiation. The development of more powerful proton and heavy ion accelerators with higher energies or higher intensities, made new particles for radiation therapy available. Pions, protons, light ions, from helium up to silicon were studied in view of precision dose delivery and increased RBE. Without the parallel development of new diagnostic techniques such as computer tomography (CT) and positron emission tomography (PET) the rapid development would not have been possible. Heavy-charged particle therapy has now come into a consolidation phase. Hospital-based facilities are built by industry, and research institutes focus on refinements in dose delivery and treatment planning, as well as systems for monitoring dose delivery and for dose distribution verification.
POPULATION GENETIC ANALYSIS OF BRAZILIAN PEACH

Peach has great economic and social importance in Brazil. Diverse sources of germplasm were used to introduce desirable traits in the Brazilian peach breeding pool, composed mainly by local selections and accessions selected from populations developed by the national breeding programs, adapted to subtropical climate, with low chill requirement, as well as accessions introduced from several countries. In this research, we used SSR markers, selected by their high level of polymorphism, to access genetic diversity and population structure of a set composed by 204 peach selected genotypes, based on contrasting phenotypes for valuable traits in peach breeding. A total of 80 alleles were obtained, giving an average of eight alleles per locus. In general, the average value of observed heterozygosity (0.46) was lower than the expected heterozygosity (0.63). STRUCTURE analysis assigned 162 accessions splitted into two subpopulations based mainly on their flesh type: melting (96) and non-melting (66) flesh cultivars. The remaining accessions (42) could not be assigned under the 80% membership coefficient criteria. Genetic variability was greater in melting subpopulation compared to non-melting. Additionally, 55% of the alleles present in the breeding varieties were also present in the founder varieties, indicating that founding clones are well represented in current peach cultivars and advanced selections developed. Overall, this study gives a first insight of the peach genetic variability available and evidence for population differentiation (structure) in this peach panel to be exploited and provides the basis for genome-wide association studies.
The Fear of Missing Out and Usage Intensity of Social Media

Social media make adolescents easy to connect with others; thus, it can change the way of communication in this modern era. Adolescents often observe and compare their lives with others’ lives through social media, which could lead to decreasing level of life satisfaction. Fear of Missing Out (FoMO) is the fear of losing another person or group moments where the subject does not involve. This study aims to examine the association between the influence of FoMO on the usage intensity of Social Media by adolescents. Participants were 200 adolescents aged 15 – 18 who use social media of whom 83 male and 117 female in Mojokerto, East Java, Indonesia. The technique of sampling uses quota sampling. Participants respond to the standardized measure of FoMO scale and Usage intensity of Social media scale in which the result indicated that there is influence or a positive relationship between FoMO on usage intensity of social media(R= .550*). The data were analyzed using simple linear regression analysis of Statistical Package for Social Science for Windows 21.0 version. This study result explains that FoMO and Usage Intensity of Social Media are more experienced by males rather than females.
Theoretical analysis of joint hierarchical modulation and physical layer network coding for next passive optical networks

In order to fulfill the traffic localization, the network coding (NC) paradigm is a promising approach that can significantly improve the throughput, end-to-end (E2E) delay, and energy efficiency for passive optical network (PON). On the other hand, more recently, the hierarchical modulation (HM) incorporates with advanced digital signal processing (DSP) technology can optimize the total available capacity of PON. In this paper, in order to optimize the network performance, we propose a joint physical layer NC (PLNC) and HM in PON. Subsequently, we evaluate the bit error rate (BER) of MQAM modulated PLNC that uses the proposed mapping scheme. Afterwards, as an application, a rate adaptation scheme for the method of PLNC is proposed. Simulation results show that combined HM and PLNC mechanism has better BER in OFDM-PON than the conventional NC. Meanwhile, the rate-adaptive PLNC is advantageous in various scenarios.
Winter adverse driving dataset for autonomy in inclement winter weather

Abstract. The availability of public datasets with annotated light detection and ranging (LiDAR) point clouds has advanced autonomous driving tasks, such as semantic and panoptic segmentation. However, there is a lack of datasets focused on inclement weather. Snow and rain degrade visibility and introduce noise in LiDAR point clouds. In this article, summarize a 3-year winter weather data collection effort and introduce the winter adverse driving dataset. It is the first multimodal dataset featuring moderate to severe winter weather—weather that would cause an experienced driver to alter their driving behavior. Our dataset features exclusively events with heavy snowfall and occasional white-out conditions. Data are collected using high-resolution LiDAR, visible as well as near infrared (IR) cameras, a long wave IR camera, forward-facing radio detection and ranging, and Global Navigation Satellite Systems/Inertial Measurement Unit units. Our dataset is unique in the range of sensors and the severity of the conditions observed. It is also one of the only data sets to focus on rural and semi-rural environments. Over 36 TB of adverse winter data have been collected over 3 years. We also provide dense point-wise labels to sequential LiDAR scans collected in severe winter weather. We have labeled and will make available around 1000 sequential LiDAR scenes, amounting to over 7 GB or 3.6 billion labeled points. This is the first point-wise semantically labeled dataset to include falling snow.
A Physics-Informed Action Network for Transient Stability Preventive Control

This letter proposes a physics-informed action network (PIAN) for power system transient stability preventive control (TSPC). The network firstly renders deep learning to reduce the TSPC complexity. Unlike common data-driven methods that superficially imitate control experience, TSPC is then analytically embedded into the proposed PIAN network, so that to enforce the network to learn in-depth physical patterns. The well-learned PIAN enables highly generalized real-time decisions. Comparisons with one model-based and two data-driven baselines on the IEEE 39-bus system and the IEEE 145-bus system highlight that, the proposed method enables highly reliable control decisions, and beats the others in terms of decision efficiency and generalizability.
Phenotypic and Molecular Characterization of Imipenem Resistant pseudomonas Isolates

Background: Pseudomonas spp. is one of the most frequent nosocomial pathogen that is often difficult to treat due to a multi-drug resistant pattern to a wide range of antibiotics like carbapenems. Imipenem-resistant Pseudomonas aeruginosa (IRP) resulting from metallo-b-lactamases has been reported to be an important cause of nosocomial infection and is a critical therapeutic problem worldwide, especially in the case of bacteremia. Objectives: In this work we aimed to detect Carbapenemase and MBL production as well as molecular detection of bla IMP gene in impenem resistant nosocomial isolates of Pseudomonas spp. Material and Methods:A total number of 150 Pseudomonas isolates were collected from clinical specimens submitted to Ain Shams University microbiology Laboratory for culture and antibiotic susceptibility testing.We used the Imipenem E-test strips to determine the MIC of the IPM resistant isolates . Screening for Carbapenemase and Metallo-β-lactamase was done phenotypically using modified hodge test (MHT) and Imipenem-EDTA Double disk Synergy test (DDST), also molecular detection of blaIMP gene was performed. Results: It was found that 38 out of 150 isolates (25.4%) were resistant to impenem by disc diffusion method which confirmed by E test that showed only 27 out of 150 (18%) were resistant. Ten Out of 27 isolates (37%) were expressed blaIMP gene. However, The Modified Hodge Test Positive isolates were 13 out of 27 E-test resistant isolates (48.1%). While the DDST positive isolates were 21 out of 27 (77.8%) of E-test resistant isolates. Conclusion: Metallo-b-lactamases among imipenem-resistant Pseudomonas were detected in 37% by PCR. This number might have been higher if other genes were included. Phenotypic tests could be misleading when testing for metallo-b-lactamases. Polymerase Chain Reaction detection remains the gold standard. [Sherin A.S. ElMasry, Rania A. Ammar and Sally M. Saber. Phenotypic and Molecular Characterization of Imipenem Resistant pseudomonas Isolates. Life Sci J 2012; 9(2s): 377-383] (ISSN: 1097-8135). http://www.lifesciencesite. com. 42
Robust deep neural network surrogate models with uncertainty quantification via adversarial training

Surrogate models have been used to emulate mathematical simulators of physical or biological processes for computational efficiency. High‐speed simulation is crucial for conducting uncertainty quantification (UQ) when the simulation must repeat over many randomly sampled input points (aka the Monte Carlo method). A simulator can be so computationally intensive that UQ is only feasible with a surrogate model. Recently, deep neural network (DNN) surrogate models have gained popularity for their state‐of‐the‐art emulation accuracy. However, it is well‐known that DNN is prone to severe errors when input data are perturbed in particular ways, the very phenomenon which has inspired great interest in adversarial training. In the case of surrogate models, the concern is less about a deliberate attack exploiting the vulnerability of a DNN but more of the high sensitivity of its accuracy to input directions, an issue largely ignored by researchers using emulation models. In this paper, we show the severity of this issue through empirical studies and hypothesis testing. Furthermore, we adopt methods in adversarial training to enhance the robustness of DNN surrogate models. Experiments demonstrate that our approaches significantly improve the robustness of the surrogate models without compromising emulation accuracy.
Pressure calibration of diamond anvil Raman gauge to 310GPa

In order to develop an optical method for pressure determination in the multimegabar region, the first-order Raman spectra of diamond anvils were investigated at pressures up to 310GPa. The high-frequency edge of the Raman band, which corresponds to the Raman shift of the anvil culet due to the normal stress, was calibrated against the sample pressure derived from the equation of state of Pt. The obtained pressure dependence of the edge frequency demonstrates the reliability of this diamond anvil Raman gauge. Up to the maximum pressure of this study, the relation between Raman frequency and normal stress at the diamond anvil culet is formally similar to the equation of state of a hydrostatically compressed isotropic elastic body having a bulk modulus of K0=547(11)GPa and a pressure derivative of the bulk modulus K0′=3.75(20).
Analysis of aircraft sortie generation with the use of a fork-join queueing network model

This article presents an approximate analytical method for evaluating an aircraft sortie generation process. The process is modeled as a closed network of multiserver queues and fork-join nodes that allow concurrent service activities. The model uses a variation of mean value analysis (MVA) to capture the effect of mean service times, resource levels, and network topology on performance measures including resource utilizations and the overall sortie generation rate. The quality of the analytical approximation is demonstrated through comparison with simulation results. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44: 153–164, 1997