Miracle-dz/UniFAR-FTUs huggingface dataset:
random walk with probability distribution for SAT with long clauses

Focused random walk (FRW) is one of the most influential paradigm of stochastic local search (SLS) algorithms for the propositional satisfiability (SAT) problem. Recently, an interesting probability distribution (PD) strategy for variable selection was proposed and has been successfully used to improve SLS algorithms, resulting in state-of-the-art solvers. However, most solvers based on the PD strategy only use polynomial function (PoF) to handle the exponential decay and are still unsatisfactory in dealing with medium and huge k -SAT instances at and near the phase transition. The present paper is focused on handling all k -SAT instances with long clauses. Firstly, an extensive empirical study of one state-of-the-art FRW solver WalkSATlm on a wide range of SAT problems is presented with the focus given on fitting the distribution of the break value of variable selected in each step, which turns out to be a Boltzmann function. Using theses case studies as a basis, we propose a pseudo normal function (PNF) to fit the distribution of the break value of variable selected, which is actually a variation of the Boltzmann function. In addition, a new tie-breaking flipping (TBF) strategy is proposed to prevent the same variable from being flipped in consecutive steps. The PNF based PD strategy combined with the TBF strategy lead to a new variable selection heuristic named PNF-TBF. The PNF-TBF heuristic along with a variable allocation value (Vav) function are used to significantly improve ProbSAT, a state-of-the-art SLS solver, leads to a new FRW algorithm dubbed PNFSat, which achieves the state-of-the-art performance on a broad range of huge random 7-SAT instance near the phase transition as demonstrated via the extensive experimental studies. Some further improved versions on top of PNFSat are presented respectively, including PNFSat_alt, which achieves the state-of-the-art performance on the medium 7-SAT instances at the phase transition; PN&PoFSat, which achieves the state-of-the-art performance on a broad range of random 5-SAT benchmarks; as well as an integrated version of these three algorithms, named PDSat, which achieves the state-of-the-art performances on all huge and medium random k -SAT instances with long clauses as demonstrated via the comparative studies using different benchmarks.
The distribution of faults and fractures and their importance in accommodating extensional strain at Kimmeridge Bay, Dorset, UK

Abstract The spatial distribution of extensional strain in interbedded mudstones and carbonates from around Kimmeridge Bay in southern England is examined using a variety of line samples. Normal faults and tensile fractures (veins) from the same deformation event show displacements ranging over 6 orders of magnitude. The relative contribution of these structures to the overall extension varies, with large faults (>10 m heave) accommodating about 65%, smaller faults (1–10 m heave) about 25% and veins less than 10% of the overall extension. The heterogeneity of fracture density and strain can be quantified from cumulative plots by applying a non-parametric method based on Kuiper's test. Both the degree and statistical significance of strain heterogeneity can be determined and are shown to be scale-dependent. Thin veins accommodate a fairly constant background strain across the region, whilst thick veins and small faults take up localized higher strains in damage zones around larger faults. Fault-strain is relatively homogenously distributed across the region. The faults and veins do not share the same scaling relationship. Thus, this study shows that it is not possible to simply extrapolate fracture frequencies and strain from fault scale to vein scale, and that the heterogeneity of extensional strain is scale dependent.
Signal Characterization in Terms of Sinusoidal and Non-sinusoidal Components

This paper addresses the problem of signal characterization in terms of sinusoidal and non-sinusoidal components. A first measure of sinusoidality is reviewed. Drawbacks of this sinusoidal estimator are investigated and solutions proposed. Estimation of sinusoidality on non-stationary signal is then made on a pre-processed signal. A phase derived sinusoidality measure and the use of Re-estimated Spectra are introduced which allow deriving very precise and local characteristics. Finally, this characterization is used in a new synthesis scheme using Additive and PSOLA synthesis.
Promoting Sustainable Development in the 21st Century: The Role of Marketing Education

A b s t r a c t he relevance of acquiring viable skills capable of placing an individual T compliant to contemporary societal dynamics and expectations prompted an investigation into promoting sustainable development in st the 21 century: The role of marketing education. The study adopted the descriptive survey design. The population of the study was all the 105 marketing students (the 75 2017/2018 final year and 30 Postgraduates) in Ignatius Ajuru University of Education, Rumuolumeni, Port Harcourt. A sample of 60 (43 final years and 17 Postgraduates students) respondents were selected for the study using proportional stratified sampling technique. A self-structured instrument titled “Promoting Sustainable Development Role of Marketing Education Questionnaire” (PSDROMEQ) was used for study. Data were analyzed using Analysis of Variance (ANOVA). The study revealed that the acquisition of: sales promotion skills statistically and significantly enhanced graduate's poverty reduction (F5, 54=3.474, p<05) and managerial skills statistically and significantly enhanced graduate's wealth creation (F9, st 50=42.016, p<05) in 21 century Rivers State. The study recommended among others, that: adequate human capital development be provided to enhance Business Education graduate's poverty reduction. Alongside, graduates should acquire the managerial skills that will help them place value and use ICT skills during financial and marketing transactions in their self-employment ventures.
University of Birmingham Quality of working environment and corporate financial distress

: This study examines the impacts of the quality of working environment and its components on corporate financial distress. Employing a unique dataset of firm-level data from 41 countries over the period of 2012-2018, we find that a better working environment is related to a higher level of financial soundness. Particularly, firms which have better training and career development policies are less likely to take excessive risks. Further examination suggests that the quality of working environment tends to affect corporate financial risk by influencing firms’ cash holding policies.
Production of perovskite-like La

Synopsis - Ferromagnetic and perovskite-like thin films (< 1 µ m) of La 1-x Ca x MnO 3+ δ have been routinely prepared by heat treatment of an amorphous La-Ca-Mn precursor. The precursor was electrodeposited cathodically in the absence of oxygen and water onto polished silver substrates from a non-aqueous solution of the components’ nitrates. Analysis by X-Ray diffraction and SQUID magnetometry shows these materials exhibit the appropriate structural and magnetic phases indicative of Colossal Magnetoresistance.
Journal of Health Economics

Islamophobia has increased in the last years, in part, due to terrorist attacks perpetrated by ji-hadist groups. This phenomenon might be a source of stress, being particularly problematic for pregnant (Muslim) women. We examine how stress generated by the 2017 Catalonia (Spain) attacks aﬀected the health of newborns whose mothers are from a Muslim country (as the per-petrators). We use a diﬀerence-in-diﬀerences-in-diﬀerences model comparing newborns whose mothers come from a Muslim country and are residing in a municipality directly aﬀected by the attacks, to other newborns, before-after the attacks. Results show that the share of low-birth-weight babies and deliveries with complications raise signiﬁcantly by 23.77%, and 13.02%. We document a signiﬁcant increase in Islamophobia and in emotional distress in our group of interest. We conclude that Islamophobia-related stress is possibly one of the channels aﬀecting health at birth.
The Eﬀects of Corrugated Steel Fiber on the Properties of Ultra-High Performance Concrete of Diﬀerent Strength Levels

: This article describes the inﬂuence of corrugated steel ﬁber on the mechanical properties and fracture energy of Ultra-High Performance Concretes (UHPC) of various strength levels. Three UHPC formulations with compressive strengths of 143, 152, and 177 MPa were tested. The following parameters for the formulations without ﬁber and those containing 2% steel ﬁber by volume were determined: compressive strength, spli�ing tensile strength, ﬂexural strength, modulus of elasticity, Poisson’s ratio and critical stress intensity factor. From the axial tensile test results, the following parameters were obtained: the cracking stress, tensile strength, and fracture energy of Ultra-High Performance Fiber Reinforced Concrete (UHPFRC) of diﬀerent strength levels. With the introduction of steel ﬁber, an increase in all the investigated parameters is observed regardless of the strength of the concrete matrix. The most remarkable inﬂuence the ﬁber has on the spli�ing tensile strength, ﬂexural strength and critical stress intensity coeﬃcient, the increase is up to 1.6–3.2 times. There was a slight increase in compressive strength and elastic modulus—up to 5.0–7.4% depending on the composition. Poisson’s ratio was equal to 0.2 regardless of the strength of the concrete matrix and the presence of steel ﬁber. Based on the test results, equations were proposed to predict the properties of UHPC and UHPFRC depending on the water–cement ratio, silica fume content, cement compressive strength and the volumetric content of corrugated steel ﬁber. The calculated and experimental values showed good convergence with a correlation coeﬃcient in the range of 0.885–0.997.
Cache-Efﬁcient Dynamic Programming MDP Solver

Automated planning research often focuses on developing new algorithms to improve the computational performance of planners, but effective implementation can also play a signiﬁcant role. Hardware features such as memory hierarchy can yield substantial running time improvements when optimized. In this paper, we propose two state-reordering techniques for the Topological Value Iteration (TVI) al-gorithm. Our ﬁrst technique organizes states in memory so that those belonging to the same Strongly Connected Component (SCC) are contiguous, while our second technique optimizes state value propagation by reordering states within each SCC. We analyze existing planning algorithms with respect to their cache efﬁciency and describe domain characteristics which can provide an advantage to each of them. Empirical results show that, in many instances, our new algorithms, called eTVI and eiTVI, run several times faster than traditional VI, TVI, LRTDP and ILAO* techniques.
Edinburgh Research Explorer Distributed and dynamic intracellular organization of extracellular information

Although cells respond speciﬁcally to environments, how environmental identity is encoded intracellularly is not understood. Here we study this organization of information in budding yeast by estimating the mutual information between environmental transitions and the dynamics of nuclear translocation for ten transcription factors. Our method of estimation is general, scalable, and based on decoding from single cells. The dynamics of the transcription factors are necessary to encode the highest amounts of extracellular information, and we show that information is transduced through two channels: generalists (Msn2/4, Tod6 and Dot6, Maf1, and Sfp1) can encode the nature of multiple stresses but only if stress is high; specialists (Hog1, Yap1, and Mig1/2) encode one particular stress, but do so more quickly and for a wider range of magnitudes. In particular, Dot6 encodes almost as much information as Msn2, the master regulator of the environmental stress response. Each transcription factor reports diﬀerently, and it is only their collective behavior that distinguishes between multiple environmental states. Changes in the dynamics of the localization of transcription factors thus constitute a precise, distributed internal representation of extracellular change. We predict that such multi-dimensional representations are common in cellular decision-making.
Expression and activity of eIF6 trigger Malignant Pleural Mesothelioma growth in vivo

eIF6 is an antiassociation factor that regulates the availability of active 80S. Its activation is driven by the RACK1/PKCβ axis, in a mTORc1 independent manner. We previously described that eIF6 haploinsufficiency causes a striking survival in the Eμ-Myc mouse lymphoma model, with lifespans extended up to 18 months. Here we screen for eIF6 expression in human cancers. We show that Malignant Pleural Mesothelioma tumors (MPM) and a MPM cell line (REN cells) contain high levels of hyperphosphorylated eIF6. Enzastaurin is a PKC beta inhibitor used in clinical trials. We prove that Enzastaurin treatment decreases eIF6 phosphorylation rate, but not eIF6 protein stability. The growth of REN, in vivo, and metastasis are reduced by either Enzastaurin treatment or eIF6 shRNA. Molecular analysis reveals that eIF6 manipulation affects the metabolic status of malignant mesothelioma cells. Less glycolysis and less ATP content are evident in REN cells depleted for eIF6 or treated with Enzastaurin (Anti-Warburg effect). We propose that eIF6 is necessary for malignant mesothelioma growth, in vivo, and can be targeted by kinase inhibitors.
Enhanced cell migration and apoptosis resistance may underlie the association between high SERPINE1 expression and poor outcome in head and neck carcinoma patients

High SERPINE1 expression is a common event in head and neck squamous cell carcinoma (HNSCC); however, whether it plays a role in determining clinical outcome remains still unknown. We studied SERPINE1 as a prognostic marker in two HNSCC patient cohorts. In a retrospective study (n = 80), high expression of SERPINE1 was associated with poor progression-free (p = 0.022) and cancer-specific (p = 0.040) survival. In a prospective study (n = 190), high SERPINE1 expression was associated with poor local recurrence-free (p = 0.022), progression-free (p = 0.002) and cancer-specific (p = 0.006) survival. SERPINE1 expression was identified as an independent risk factor for progression-free survival in patients treated with chemo-radiotherapy or radiotherapy (p = 0.043). In both patient cohorts, high SERPINE1 expression increased the risk of metastasis spread (p = 0.045; p = 0.029). The association between SERPINE1 expression and survival was confirmed using the HNSCC cohort included in The Cancer Genome Atlas project (n = 507). Once again, patients showing high expression had a poorer survival (p < 0.001). SERPINE1 over-expression in HNSCC cells reduced cell proliferation and enhanced migration. It also protected cells from cisplatin-induced apoptosis, which was accompanied by PI3K/AKT pathway activation. Downregulation of SERPINE1 expression had the opposite effect. We propose SERPINE1 expression as a prognostic marker that could be used to stratify HNSCC patients according to their risk of recurrence.
A 14-bit 1-GS/s Pipelined-SAR ADC in 28-nm CMOS with Foreground and Background Calibration

A 14-bit 1-GS/s Pipelined-Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC) in 28-nm CMOS is presented in this paper. In the initial phase, a pair of SAR ADCs employing the Time-Interleaved (TI) technique are employed to attain the necessary sampling rate. A multi-comparators structure is used to increase the speed and improve the mismatch. We have implemented a foreground calibration technique to address capacitor mismatch, optimizing its processing logic to enhance the chip’s speed. The interstage operational amplifier adopts a two-stage structure. Some structural changes are presented to optimize its bandwidth and phase margin. A background calibration method for gain error is implemented. In the subsequent phase, we employ four SAR ADCs utilizing the TI method. The outcomes demonstrate that the ADC delivers a Signal-to-Noise and Distortion Ratio (SNDR) of 65.58 dB and a Spurious-Free Dynamic Range (SFDR) of 72.12 dB at a 1-GS/s sampling rate without calibration, and 69.02 dB SNDR and 81.26 dB SFDR at the same 1-GS/s sampling rate with calibration. The core layout occupies an area of 0.6 mm2 and consumes 225.02 mW of power.
Comparison of 1T1R and 1C1R ReRAM Arrays

The rise of memristors with potential applications in memory has attracted wide interests. Memristors are typically assembled in crossbar arrays with data bits encoded by the resistance of individual cells. The 1T1R cell structure is the most popular for memristive memory as it eliminates sneak path currents. The transistor not only allows for flexible selection of memory cells but also facilitates the programming for computing-in-memory applications. In this paper, we replace the selector in the 1T1R configuration with a capacitor to form a selectorless and passive combination of ReRAM structure. Moreover, we evaluate the merits of the two structures in SkyWater 130nm CMOS technology by comparing the writing technique, power consumption, switching speed, and memory density. Furthermore, we compared the complexity of the readout method between 1T1R and 1C1R for a memory application. This work shows that the 1C1R configuration is a promising memory structure that consumes less energy, switches faster, has higher density, and has a simpler readout method when compared to 1T1R.
Prefab-Net: Segmentation of prefabricated building from satellite imagery with deep neural network

The development of deep learning techniques such as convolutional neural networks (CNN) has injected new vitality into the study of semantic segmentation. Benefiting from the development of new technology, the automatic and intelligent work of remote sensing image segmentation has also entered a new stage. With the further improvement of China’s urbanization rate, urban construction is also developing rapidly. One of the most important tasks in land surveillance is to monitor buildings above ground using remote sensing images. Prefabricated building is the most common type of illegal building in developing area. At present, supervision mainly relies on the traditional human-centered method, which is time-consuming and low in efficiency. Whether the deep learning methods can benefit the prefabricated building segmentation has not yet been tested. By taking the realistic demand into account, this paper proposes a customized neural network structure, which combines multiple refined modules and improves the feature extraction, especially for the prefabricated building segmentation task. Through the comparison with the baseline algorithm, the specifically refined model for the prefabricated building has shown better results in our experimental area, which proves the feasibility and superiority in practical application.
A Reconfigurable Analog Baseband for Multistandard Wireless Receivers in 22-nm CMOS

This paper presents a low noise, and high linearity reconfigurable receiver (RX) analog baseband (ABB) with tunable bandwidth (BW) and gain for multi-standard applications. The designed ABB consists of a programmable gain amplifier (PGA) and a second-order active RC low-pass filter (LPF) with cutoff frequency range from 0.7 MHz–10 MHz, whereas the gain could be tuned between 0 dB and 49 dB. The proposed ABB is implemented in 22 nm CMOS process. The post-simulation results show that the current consumption is 3.36 mA from 1 V supply and the area occupies 571×328 μm 2. The ABB achieves 42.8 dBm in-band third-order harmonic intercept point (IIP3). The spurious-free dynamic range (SFDR) and in-band total harmonic distortion (THD) is 83.2 dBc and is -83.1 dB, respectively. The input referred in-band integrated noise (IRN) is 144.8 μVrms . A digital DCOC is used to calibrate the output DC level.
Probing Silicon Carbide with Phase-Modulated Femtosecond Laser Pulses: Insights into Multiphoton Photocurrent

Wide bandgap semiconductors are widely used in photonic technologies due to their advantageous features, such as large optical bandgap, low losses, and fast operational speeds. Silicon carbide is a prototypical wide bandgap semiconductor with high optical nonlinearities, large electron transport, and a high breakdown threshold. Integration of silicon carbide in nonlinear photonics requires a systematic analysis of the multiphoton contribution to the device functionality. Here, multiphoton photocurrent in a silicon carbide photodetector is investigated using phase-modulated femtosecond pulses. Multiphoton absorption is quantified using a 1030 nm phase-modulated pulsed laser. Our measurements show that although the bandgap is less than the energy of three photons, only four-photon absorption has a significant contribution to the photocurrent. We interpret the four-photon absorption as a direct transition from the valance to the conduction band at the {\Gamma} point. More importantly, silicon carbide withstands higher excitation intensities compared to other wide bandgap semiconductors making it an ideal system for high-power nonlinear applications.
FoundLoc: Vision-based Onboard Aerial Localization in the Wild

Robust and accurate localization for Unmanned Aerial Vehicles (UAVs) is an essential capability to achieve autonomous, long-range flights. Current methods either rely heavily on GNSS, face limitations in visual-based localization due to appearance variances and stylistic dissimilarities between camera and reference imagery, or operate under the assumption of a known initial pose. In this paper, we developed a GNSS-denied localization approach for UAVs that harnesses both Visual-Inertial Odometry (VIO) and Visual Place Recognition (VPR) using a foundation model. This paper presents a novel vision-based pipeline that works exclusively with a nadir-facing camera, an Inertial Measurement Unit (IMU), and pre-existing satellite imagery for robust, accurate localization in varied environments and conditions. Our system demonstrated average localization accuracy within a $20$-meter range, with a minimum error below $1$ meter, under real-world conditions marked by drastic changes in environmental appearance and with no assumption of the vehicle's initial pose. The method is proven to be effective and robust, addressing the crucial need for reliable UAV localization in GNSS-denied environments, while also being computationally efficient enough to be deployed on resource-constrained platforms.