Miracle-dz/UniFAR-FTUs huggingface dataset:
The Amazing $3^n$ Theorem and its even more Amazing Proof [Discovered by Xavier G. Viennot and his \'Ecole Bordelaise gang]

The most amazing (at least to me) result in Enumerative Combinatorics is Dominique Gouyou-Beauchamps and Xavier Viennot's theorem that states that the number of so-called directed animals with compact source (that are equivalent, via Viennot's beautiful concept of heaps, to towers of dominoes, that I take the liberty of renaming xaviers) with n+1 points equals 3^n. This amazing result received an even more amazing proof by Jean B\'etrema and Jean-Guy Penaud. Both theorem and proof deserve to be better known! Hence this article, that is also accompanied by a comprehensive Maple package this http URL that implements everything (and much more)
Dissociative electron attachment on H2S: energy and angular distributions of H- ions

The authors have studied energy and angular distributions of H- ions produced by dissociative attachment on H2S in the 4.5-9 eV region by means of an electron impact spectrometer. H- formation proceeds via two distinct reactions. For the first one, the HS fragments, associated with the H- ions, are weakly vibrationally excited and the fraction of H- ions associated with the excited HS fragments increases with the incident electron energy. It is also shown that the H- ions associated with HSv=0 and those associated with HSv=1 fragments have different angular distributions. In the energy range of the second reaction, the HS fragments are in the ground vibrational state. In both cases, the HS fragments are found to be rotationally excited.
On the Accuracy of Numerical Differentiation Using High-Gain Observers and Adaptive Input Estimation

The ability to control a system is often enhanced by feeding back the derivatives of sensor signals, such as estimates of velocity and acceleration when only position is measured. Within this context, signal differentiation must be performed causally, that is, using only current and past data and with minimal computational latency. This paper formulates causal differentiation as a sampled-data input-estimation problem, where the plant is a cascade of integrators. Adaptive input estimation based on retrospective-cost optimization is considered, where the innovations from the Kalman filter is used to drive the online adaptation. Using backward-difference differentiation (BDD) as a baseline comparison, high-gain observers (HGO) with bilinear discretization and retrospective cost input estimation (RCIE) are applied to harmonic signals under various noise levels for single and double differentiation. These methods are then applied to experimental position data of a small rover for estimating its velocity and acceleration. Neither method uses information about the noise statistics, and no analog or digital filtering is used for noise suppression.
Enhancement of Iris Recognition System using Deep learning

With the growing demand for security and accurate personal authentication, as well as the new dimensions in security issues confronting the world today, a dependable and secure authentication system is essential. The iris has become a popular biometric technique because it is a highly protected internal organ of the body. Furthermore, it is very hard to surgically change it without causing significant damage to the iris. The total accuracy of the iris recognition system is determined by the iris segmentation process's performance. This paper presents a deep learning technique for improving iris segmentation performance. The proposed method employs an efficient deep learning technique (SegNet), which performs joint semantic segmentation of ocular qualities (iris and pupil) with greater accuracy in unconstrained scenarios. These difficult circumstances limit the performance and dependability of ocular segmentation structures. Segmentation begins by denoising the pristine image with a deep convolutional neural network to address these issues (DCNN). The semantic segmentation of the iris and pupil is then accomplished with the help of a densely connected fully convolutional encoder-decoder network. Finally, for feature extraction and classification, the proposed system is implemented using a pre-trained convolutional neural network (Alex Net). The iris recognition system's performance is evaluated using five public databases: IITD, iris databases CASIA-Iris-V1, CASIA-Iris-V2 device 1, CASIA-Iris-V2 device 2, and MMU iris database. The results show that the proposed system has a high accuracy rate of 94.08 percent, 84 percent, 97.31 percent, 100 percent, and 97.7 percent, respectively and has time of execution of less than or equal to 2 minutes
Physical Effects of Extra Dimension and Concomitant Map between Photons and Gravitons in Randall-Sundrum Brane-World Scenario

We investigate, in Randall-Sundrum braneworld scenario, the relationship between perturbations of gravitational and electromagnetic waves in a black hole neighboorhood, proposing an extra-dimensional braneworld extension of the traditional formalism. Mutual transformations of electromagnetic and gravitational fields due to the strong fields in Reissner-Nordstrom black holes are analyzed from an effective 5-dimensional Randall-Sundrum braneworld perspective.
A Scalable Machine Learning-based Ensemble Approach to Enhance the Prediction Accuracy for Identifying Students at-Risk

—Among the educational data mining problems, the early prediction of the students' academic performance is the most important task, so that timely and requisite support may be provided to the needy students. Machine learning techniques may be used as an important tool for predicting low-performers in educational institutions. In the present paper, five single-supervised machine learning techniques have been used, including Decision Tree, Naïve Bayes, k-Nearest-Neighbor, Support Vector Machine, and Logistic Regression. To analyze the effect of an imbalanced dataset, the performance of these algorithms has been checked with and without various resampling methods such as Synthetic Minority Oversampling Technique (SMOTE), Borderline SMOTE, SVM-SMOTE, and Adaptive Synthetic (ADASYN). The Random hold-out method and GridSearchCV were used as model validation techniques and hyper-parameter tuning respectively. The results of the present study indicated that Logistic Regression is the best performing classifier with every balanced dataset generated using all of the four resampling techniques and also achieved the highest accuracy of 94.54% with SMOTE. Furthermore, to improve the prediction results and to make the model scalable, the most suitable classifier was integrated with the help of bagging, and a well-accepted accuracy of 95.45% was achieved.
Work Experience in Moderating the Independence, Competence, and Motivation of Internal Auditors on the Effectiveness of Internal Control

Economic developments in Indonesia will have an impact on the growth of companies. Therefore, we need a system that can secure the company's assets and encourage business efficiency. Management is obliged to be able to manage the company's activities properly as the implementation of the internal auditor's accountability to the public. This study aims to examine the effect of the role of independence, competence, and motivation on the effectiveness of the internal control system of consumer goods companies in North Sumatra. Testing research data using multiple linear regression analysis and residual tests for moderating variables. The results of this study indicate that the independence, competence, and motivation of internal auditors simultaneously affect the effectiveness of the internal control system. Partially, the independence and competence of internal auditors affect the effectiveness of internal control. This is in line with the research hypothesis, but the motivation of internal auditors does not partially affect the effectiveness of internal controls. Meanwhile, work experience is not proven to moderate (strengthen or weaken) the relationship between the independence, competence, and motivation of internal auditors and the effectiveness of internal controls.
A Simple Test of the ‘Risk Class Hypothesis’

Abstract In this paper we formally test the assumption of equal intra-industry systematic business risk, which we term the ‘risk class hypothesis'. For the purposes of our analysis we define risk classes around four-digit SIC stock groupings. In general the ‘risk class hypothesis' is not supported across our sample of US stocks. While this finding does not mean that all applications of the ‘risk class' approach are inevitably flawed, it does suggest that in future we should think very carefully before blindly invoking such risk equality assumptions.
Patient Satisfaction and Clinical Effects of Platelet-Rich Plasma on Pattern Hair Loss in Male and Female Patients

Introduction Hair loss is a widespread condition in both genders. Over the past decade, platelet-rich plasma (PRP) has become a common treatment for hair loss. Our goal was to analyze patient satisfaction and the clinical effects of PRP on male and female pattern hair loss. Methods Over a period of 12 months, we treated a total of 56 patients for androgenetic alopecia with PRP. All of these patients were included in this study; 15 cc of whole blood was spun in an ACP double syringe (Arthrex Inc., Naples, Forida) for five minutes. The yielded 5 to 6 cc of PRP were then injected into the scalp. Forty-nine patients were treated with a series of three injections at monthly intervals, three patients with a series of four injections, two patients with a series of five injections, one patient was treated with a series of seven injections, and one patient with a series of eight injections. Follow-ups were conducted one month after the last treatment. A self-drawn questionnaire was used to assess the satisfaction and clinical results from the patient's and the clinician's perspectives. Results The average age was 41 years (20-68 years). Fifty-seven percent of all patients were male and 43% female. In total, the patients were satisfied with the treatment results. The average score was 7.29 on a scale from 0 to 10. The clinician's rating was similar (6.46). Moreover, with an average score of 8 on a scale from 0 to 10, it is very likely that the patients will recommend PRP treatments to friends. The probability of occurrence of clinical effects among the entire study population was reported as follows: improvement in hair density (patient‘s rating: 64%; clinician's rating: 46%), thickness (38%; 45%), quality (46%; 54%), sheen/lustre (27%; 21%), new hair growth (57%; 68%), less hair loss (48%; 20%), other positive effects (5%, 2%), no effects (4%; 4%), negative effects (0%; 0%). Conclusion Our study revealed encouraging results for the treatment of male and female pattern hair loss with PRP. The autologous treatment was rated with high satisfaction scores and can be considered a safe and effective treatment modality.
Multiuser Detection for Time-Frequency-Packed Systems

We consider time-frequency-packed systems in an additive white Gaussian noise scenario. With respect to systems employing time packing, thus introducing intentional intersymbol interference only, a further improvement of the spectral efficiency is obtained by packing the adjacent carriers closer in frequency. The adoption of a multiuser detector can allow coping with the resulting intentional intercarrier interference. The optimal detector for time-frequency-packed systems has a complexity that increases exponentially with the number of interferers in both time and frequency dimensions and thus becomes unmanageable. We propose a suboptimal receiver, which is derived by using the framework based on factor graphs and the sum-product algorithm, with a complexity that is linear in the number of adjacent carriers and takes also advantage of a multi-user channel shortener to further reduce the complexity. We assess the performance by means of computer simulations and show that, when compared with other suboptimal receivers available in the literature and with the optimal detector, the proposed receiver results very promising in terms of trade-off between performance and computational complexity.
Object Tracking in Satellite Videos: Correlation Particle Filter Tracking Method With Motion Estimation by Kalman Filter

Object tracking in satellite videos faces various challenges such as target occlusion, target rotation, and background clutter. This study proposes a Correlation particle filter (CPF) algorithm with motion estimation (ME) for object tracking in satellite videos. The tracker, called correlation particle Kalman filter (CPKF), combines the strengths of the correlation, particle, and Kalman filters. Compared with the existing tracking methods based on correlation filters, the proposed tracker has three major advantages: 1) particle sampling, and ME build robustness against partial and complete occlusion; 2) color histogram model makes it robust to target rotation; and 3) fusion of multiple feature response maps effectively handles background clutter and low contrast. The experimental results demonstrate that the proposed tracking algorithm performs better than the state-of-the-art methods.
A SURVEY OF H2O, CO2, AND CO ICE FEATURES TOWARD BACKGROUND STARS AND LOW-MASS YOUNG STELLAR OBJECTS USING AKARI

We present near-infrared spectroscopic observations of 19 molecular clouds made using the AKARI satellite, and the data reduction pipeline written to analyze those observations. The 2.5–5 μm spectra of 30 objects—22 field stars behind quiescent molecular clouds and 8 low-mass young stellar objects in cores—were successfully extracted using the pipeline. Those spectra are further analyzed to calculate the column densities of key solid phase molecular species, including H2O, CO2, CO, and OCN−. The profile of the H2O ice band is seen to vary across the objects observed and we suggest that the extended red wing may be an evolutionary indicator of both dust and ice mantle properties. The observation of 22 spectra with fluxes as low as < 5 mJy toward background stars, including 15 where the column densities of H2O, CO, and CO2 were calculated, provides valuable data that could help to benchmark the initial conditions in star-forming regions prior to the onset of star formation.
$p$-adic families of automorphic forms over some unitary Shimura varieties

We construct some $n$-dimensional eigenvarieties for finite slope overconvergent eigenforms over some unitary Shimura varieties with signature $(1,n-1)\times(0,n)\times\cdots\times(0,n)$ by adapting Andreatta-Iovita-Pilloni's method. We also show that there are some Galois pseudo-characters over our eigenvarieties by studying analytic continuation of finite slope eigenforms over these Shimura varieties.
Climate Change Vulnerability Assessment in Mangrove-Dependent Communities of Manoka Island, Littoral Region of Cameroon

This study was conducted on Manoka Island (Littoral Region of Cameroon) with the aim of analyzing climate change vulnerability and local adaptation strategies based on the local community's perceptions and biophysical evidence. We used household surveys, focus group discussions, field observation, GIS, and remote sensing to collect data on variables of exposure, sensitivity, and adaptive capacity. Historical changes in rainfall and temperature, mangrove cover, and the occurrence of extreme climatic events were used as indicators of exposure. Property losses and income structure were used as indicators of sensitivity, while human, natural, social, financial, and physical assets represented adaptive capacity. 89 households were interviewed in the nine settlements of the island. Results show that Manoka Island is experiencing irregular rainfall patterns (with average annual values deviating from the mean by −1.9 to +1.8 mm) and increasing temperature (with annual values deviating from the mean by −1.2 to +3.12). The dynamics of the coastline between 1975 and 2017 using EPR show average setbacks of more than ±3 m/year, with erosion levels varying depending on the period and location. The number of households perceiving extreme climatic events like seasonal variability, flood, and rain storm was higher. From respondents' perception, housing and health are the sectors most affected by climate change. The reported high dependence of households on fishing for income, their overall low livelihood diversification, and their poor access to climate information reported by 65% of respondents portray their poor adaptive capacity. Local response initiatives are ineffective and include among others constructing buildings on stilts and using car wheels to counter the advancement of seawater inland. The study concludes that households on Manoka Island are vulnerable to the impacts of climate change. Income diversification, mangrove reforestation, the development of sustainable supply chains for wood fuel, and sustainable fish smoking devices are the main pathways for adaptation planning in this area.
Global Stabilization of Polynomial Systems Using Equilibrium-Independent Dissipativity

This work presents a proof that the existence of a sum of squares (SOS) decomposition for a closed-loop (asymptotic) stability condition valid around the origin is equivalent, for input-affine polynomial systems, to the existence of an SOS certificate for a strict dissipation inequality subject to a certain equality constraint. In addition, a new sufficient condition for feedback asymptotic stabilization of nonlinear systems around nonzero equilibria is proposed using the notion of equilibrium-independent dissipativity (EID). Then, this new condition is applied to polynomial systems, specifically. It is proved in this article that the aforementioned SOS certificate for strict dissipativity exists, with a strongly convex energy function, if and only if a certain equilibrium-independent strict dissipativity (EISD) condition also has an SOS decomposition. Consequently, not only the origin, but any assignable equilibrium point can be globally stabilized by an appropriate state feedback. As the systems considered are polynomial, semidefinite programming (SDP) can be used to solve this global stabilization problem. A numerical example provides further evidence on the usefulness of the proposed strategy.
Property valuation problems and market context – evidence from Kenya

ABSTRACT Property valuation problems such as valuation inaccuracies/variations, client influence, and the use of heuristics persist despite efforts by professional bodies and researchers to improve the practice of valuation worldwide. While some studies attribute the above problems to limited information, most studies link these problems to valuer misconduct. Previous studies in developing countries mirror those of developed nations despite the different property market environments in the two nations; they place little emphasis on market-related problems. Further, there is limited evidence on why valuation problems persist in developing countries. The main objective of this study is to establish why valuation problems in Kenya persist despite efforts to minimise them. The study utilised a quantitative research design involving a survey and experiment of registered and practicing valuers in Kenya. Findings indicate that the above valuation problems persist because of the nature of the valuation environment in Kenya, characterised by limited information. This is typical of many other developing countries. The study makes a critical contribution to knowledge as it builds on the existing literature by providing additional empirical support on why valuation problems persist while introducing appropriate measures to address them.
A Problem of the QCD Axion in Supergravity

We point out that the QCD axion generally couples to all the gauge ﬁelds in nature through the Super-Weyl, K¨ahler and sigma-model anomalies in supergravity. If supersymmetry is dynamically broken by the hidden-sector gauge interactions, the axion potential receives corrections due to the instanton in the hidden sector. We show that the supersymmetry breaking models are tightly constrained for the Peccei-Quinn mechanism to successfully solve the strong CP problem. In particular, the gravity mediation turns out to be strongly disfavored.
Deep Learning Toolkit-Driven Equivalence Checking of Flow-Based Computing Systems

The processor-memory bottleneck inherent to von Neumann architectures has encouraged the development of alternative computing paradigms. One such paradigm is flow-based in-memory computing using nanoscale crossbars. Recent improvements to design automation tools has scaled the crossbar designs to over one million memristors and over one hundred input variables. However, the state-of-the-art verification method using graph reachability cannot verify the correctness of crossbar designs with more than twenty input variables. In this paper, we propose an equivalence checking technique that natively leverages existing deep learning infrastructure. We achieve this by observing an analogy between a memristor crossbar and recurrent neural networks (RNNs), which allows equivalence checking to be efficiently performed using neural network inference. Using benchmark circuits from the RevLib and MCNC suites, we show that our proposed method can, on average, verify the correctness of a design 166x faster than the state-of-the-art method.
Polar-Slotted ALOHA Over Slot Erasure Channel

To reduce the throughput loss caused by the slot erasure channel (SEC), we propose a polar slotted ALOHA (PSA) framework where the access procedure is decomposed into two-stage and jointly optimized under the slot polarization idea. In the first stage, guided by the slot polarization, a slot subset is constructed at each active user side and the receiver side. With an irregular degree distribution, each active user randomly selects slots from the slot subset to transmit the packet replicas. Subsequently, in the second stage, by using the packet-based polarization transform, the slotted packets are encoded by using a packet-level polar code. Correspondingly, at the receiver side, a packet-oriented successive cancellation (pSC) or pSC list decoding algorithm is performed to overcome the corruption caused by the SEC. And then, the success interference cancellation procedure is executed to recover the packet. Furthermore, for a given average transmission rate, the irregular degree distribution is optimized by searching the maximum within all feasible traffic load thresholds. Then, an upper bound of the polar-code rate for the PSA scheme is derived. Simulation results indicate that the proposed PSA scheme can achieve an improved throughput over the irregular repetition slotted ALOHA scheme for the SEC.
Current conduction mechanisms in Pr2O3/oxynitride laminated gate dielectrics

Metal-oxide-semiconductor capacitors with Pr2O3/oxynitride laminated gate dielectrics were fabricated. The current transportation of Al/Pr2O3/SiON/n-Si devices was studied at temperatures ranging from 300 to 400 K. The dominant conduction mechanism at low electric field ( 2 MV/cm) is the Poole–Frenkel emission in which the trap energy level determined from Arrhenius plot is about 0.56±0.01 eV.
On the origin of bonding in metals: lithium as a case study

The bonding in metals is analysed within the framework of the PATMOS (Perturbed AToms in MOlecules and Solids) model. The electronic binding energy per atom is written as a sum of a distortion energy of the atoms and the partitioned interaction energy comprising Coulombic, exchange and correlation terms. The adopted physical model of the infinite system, is spherical embedding of the atoms of the reference unit cell. Correlation energies are calculated by second-order Møller-Plesset and second-order Epstein-Nesbet perturbation theory. The binding energy of lithium solid is calculated for 16 nearest neighbour distances from 4.0 to 10.0 Bohr. Electron correlation is of paramount importance for the binding energy. The calculated cohesive energy is $ (0.0571\pm 0.004) $ (0.0571±0.004) Hartree comparing with experimental value 0.0599 Hartree. The bonding picture is characterised by slightly expanded atomic orbitals. GRAPHICAL ABSTRACT