Miracle-dz/UniFAR-FTUs huggingface dataset:
InceptionCapsule: Inception-Resnet and CapsuleNet with self-attention for medical image Classification

Initial weighting is significant in deep neural networks because the random selection of weights produces different outputs and increases the probability of overfitting and underfitting. On the other hand, vector-based approaches to extract vector features need rich vectors for more accurate classification. The InceptionCapsule approach is presented to alleviate these two problems. This approach uses transfer learning and the Inception-ResNet model to avoid random selection of weights, which takes initial weights from ImageNet. It also uses the output of Inception middle layers to generate rich vectors. Extracted vectors are given to a capsule network for learning, which is equipped with an attention technique. Kvasir data and BUSI with the GT dataset were used to evaluate this approach. This model was able to achieve 97.62 accuracies in 5-class classification and also achieved 94.30 accuracies in 8-class classification on Kvasir. In the BUSI with GT dataset, the proposed approach achieved accuracy=98.88, Precision=95.34, and F1-score=93.74, which are acceptable results compared to other approaches in the literature.
On Recurrence Axioms

The Recurrence Axiom for a class $\mathcal{P}$ of \pos\ and a set $A$ of parameters is an axiom scheme in the language of ZFC asserting that if a statement with parameters from $A$ is forced by a poset in $\mathcal{P}$, then there is a ground containing the parameters and satisfying the statement. The tightly super-$C^{(\infty)}$-$\mathcal{P}$-Laver generic hyperhuge continuum implies the Recurrence Axiom for $\mathcal{P}$ and $\mathcal{H}(2^{\aleph_0})$. The consistency strength of this assumption can be decided thanks to our main theorems asserting that the minimal ground (bedrock) exists under a tightly $\mathcal{P}$-generic hyperhuge cardinal $\kappa$, and that $\kappa$ in the bedrock is genuinely hyperhuge, or even super $C^{(\infty)}$ hyperhuge if $\kappa$ is a tightly super-$C^{(\infty)}$-$\mathcal{P}$-Laver generic hyperhuge definable cardinal. The Laver Generic Maximum (LGM), one of the strongest combinations of axioms in our context, integrates practically all known set-theoretic principles and axioms in itself, either as its consequences or as theorems holding in (many) grounds of the universe. For example, double plus version of Martin's Maximum is a consequence of LGM while Cicho\'n's Maximum is a phenomenon in many grounds of the universe under LGM.
A spatiotemporal epidemiological investigation of the impact of environmental change on the transmission dynamics of Echinococcus spp. in Ningxia Hui Autonomous Region, China

Echinococcoses are parasitic diseases of major public health importance globally. According to recent estimates, the geographical distribution of Echinococcus spp. infections is expanding and becoming an emerging and re-emerging problem in several regions of the world. Using geographic information systems and remote sensing technologies, we explored the role of environmental factors in determining the spatiotemporal variation of human echinococcosis risk in Ningxia Hui Autonomous Region, which is a hyper-endemic province for echinococcoses. Data on echinococcosis cases were obtained from a hospital-based retrospective survey undertaken between 1 January 1994 and 31 December 2013. Land cover maps of the region were created using a range of high-quality remote sensing images with approximately 30-meter spatial resolution. Data on climatic factors were obtained from 11 local meteorological stations. The inverse distance weighted interpolation method was applied to obtain data for those locations where there were no meteorological observations locally available. Bayesian spatiotemporal conditional autoregressive models were developed at the township-level to quantify the relationship between monthly cases of human echinococcoses and environmental factors. The results of the study indicate that the land cover patterns and landscape characteristics in the province were greatly altered during the study period. We also found a significant spatial heterogeneity in the prevalence of human echinococcoses across the province. Models revealed significant positive association between human cystic echinococcosis infection and mean temperature in winter (lag-10), and also negative associations between human alveolar echinococcosis and moving averages calculated for bareland, mean temperature and mean temperature in winter. Environmental determinants of human echinococcoses can be used to develop and refine initiatives local and national initiatives to reduce the medical, social and economic burden of the infection.
Data-driven algorithm design using neural networks with applications to branch-and-cut

Data-driven algorithm design is a paradigm that uses statistical and machine learning techniques to select from a class of algorithms for a computational problem an algorithm that has the best expected performance with respect to some (unknown) distribution on the instances of the problem. We build upon recent work in this line of research by introducing the idea where, instead of selecting a single algorithm that has the best performance, we allow the possibility of selecting an algorithm based on the instance to be solved. In particular, given a representative sample of instances, we learn a neural network that maps an instance of the problem to the most appropriate algorithm {\em for that instance}. We formalize this idea and derive rigorous sample complexity bounds for this learning problem, in the spirit of recent work in data-driven algorithm design. We then apply this approach to the problem of making good decisions in the branch-and-cut framework for mixed-integer optimization (e.g., which cut to add? ). In other words, the neural network will take as input a mixed-integer optimization instance and output a decision that will result in a small branch-and-cut tree for that instance. Our computational results provide evidence that our particular way of using neural networks for cut selection can make a significant impact in reducing branch-and-cut tree sizes, compared to previous data-driven approaches.
Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective

Time series Anomaly Detection (AD) plays a crucial role for web systems. Various web systems rely on time series data to monitor and identify anomalies in real time, as well as to initiate diagnosis and remediation procedures. Variational Autoencoders (VAEs) have gained popularity in recent decades due to their superior de-noising capabilities, which are useful for anomaly detection. However, our study reveals that VAE-based methods face challenges in capturing long-periodic heterogeneous patterns and detailed short-periodic trends simultaneously. To address these challenges, we propose Frequency-enhanced Conditional Variational Autoencoder (FCVAE), a novel unsupervised AD method for univariate time series. To ensure an accurate AD, FCVAE exploits an innovative approach to concurrently integrate both the global and local frequency features into the condition of Conditional Variational Autoencoder (CVAE) to significantly increase the accuracy of reconstructing the normal data. Together with a carefully designed"target attention"mechanism, our approach allows the model to pick the most useful information from the frequency domain for better short-periodic trend construction. Our FCVAE has been evaluated on public datasets and a large-scale cloud system, and the results demonstrate that it outperforms state-of-the-art methods. This confirms the practical applicability of our approach in addressing the limitations of current VAE-based anomaly detection models.
Grover-QAOA for 3-SAT: Quadratic Speedup, Fair-Sampling, and Parameter Clustering

The SAT problem is a prototypical NP-complete problem of fundamental importance in computational complexity theory with many applications in science and engineering; as such, it has long served as an essential benchmark for classical and quantum algorithms. This study shows numerical evidence for a quadratic speedup of the Grover Quantum Approximate Optimization Algorithm (G-QAOA) over random sampling for finding all solutions to 3-SAT problems (All-SAT). G-QAOA is less resource-intensive and more adaptable for 3-SAT and Max-SAT than Grover's algorithm, and it surpasses conventional QAOA in its ability to sample all solutions. We show these benefits by classical simulations of many-round G-QAOA on thousands of random 3-SAT instances. We also observe G-QAOA advantages on the IonQ Aria quantum computer for small instances, finding that current hardware suffices to determine and sample all solutions. Interestingly, a single-angle-pair constraint that uses the same pair of angles at each G-QAOA round greatly reduces the classical computational overhead of optimizing the G-QAOA angles while preserving its quadratic speedup. We also find parameter clustering of the angles. The single-angle-pair protocol and parameter clustering significantly reduce obstacles to classical optimization of the G-QAOA angles.
DS-MS-TCN: Otago Exercises Recognition with a Dual-Scale Multi-Stage Temporal Convolutional Network

The Otago Exercise Program (OEP) represents a crucial rehabilitation initiative tailored for older adults, aimed at enhancing balance and strength. Despite previous efforts utilizing wearable sensors for OEP recognition, existing studies have exhibited limitations in terms of accuracy and robustness. This study addresses these limitations by employing a single waist-mounted Inertial Measurement Unit (IMU) to recognize OEP exercises among community-dwelling older adults in their daily lives. A cohort of 36 older adults participated in laboratory settings, supplemented by an additional 7 older adults recruited for at-home assessments. The study proposes a Dual-Scale Multi-Stage Temporal Convolutional Network (DS-MS-TCN) designed for two-level sequence-to-sequence classification, incorporating them in one loss function. In the first stage, the model focuses on recognizing each repetition of the exercises (micro labels). Subsequent stages extend the recognition to encompass the complete range of exercises (macro labels). The DS-MS-TCN model surpasses existing state-of-the-art deep learning models, achieving f1-scores exceeding 80% and Intersection over Union (IoU) f1-scores surpassing 60% for all four exercises evaluated. Notably, the model outperforms the prior study utilizing the sliding window technique, eliminating the need for post-processing stages and window size tuning. To our knowledge, we are the first to present a novel perspective on enhancing Human Activity Recognition (HAR) systems through the recognition of each repetition of activities.
Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning

As a marriage between offline RL and meta-RL, the advent of offline meta-reinforcement learning (OMRL) has shown great promise in enabling RL agents to multi-task and quickly adapt while acquiring knowledge safely. Among which, Context-based OMRL (COMRL) as a popular paradigm, aims to learn a universal policy conditioned on effective task representations. In this work, by examining several key milestones in the field of COMRL, we propose to integrate these seemingly independent methodologies into a unified information theoretic framework. Most importantly, we show that the pre-existing COMRL algorithms are essentially optimizing the same mutual information objective between the task variable $\boldsymbol{M}$ and its latent representation $\boldsymbol{Z}$ by implementing various approximate bounds. Based on the theoretical insight and the information bottleneck principle, we arrive at a novel algorithm dubbed UNICORN, which exhibits remarkable generalization across a broad spectrum of RL benchmarks, context shift scenarios, data qualities and deep learning architectures, attaining the new state-of-the-art. We believe that our framework could open up avenues for new optimality bounds and COMRL algorithms.
SELECTION RESPONSE FOR INCREASED GRAIN YIELD IN TWO HIGH OIL MAIZE SYNTHETICS

Selection for increased oil level in maize showed the increase was associated with decrease in starch concentration, kernel weight, and grain yield. The study was conducted with the objectives: (1) to evaluate response to six cycles for increased grain yield in the high oil maize Alexho Elite (AE: 60-90 g kg-1 oil concentration) and Ultra High Oil (UHO: 100-140 g kg-1 oil concentration) using inbred tester B73; (2) to measure responses to selection for increased grain yield with changes in yield components; and (3) to determine a suitable tester. Previously the two synthetics had been selected for oil concentration. After six cycles, the six genotypes i.e. AE C0, AE C3, AE C6, UHO C0, UHO C3, and UHO C6 were testcrossed to B73, LH185, and LH202 inbreds (40 g kg-1 oil concentration) to a total of 18 testcrosses. Two field experiments were used to evaluate selection in AE and UHO testcrosses. The study showed selection using inbred tester B73 in AE and UHO was effective in increasing grain yield of AE testcrosses without changing (i.e. decreasing) oil and protein concentrations. AE testcrosses produced higher grain yield and greater selection response for grain yield than UHO testcrosses. LH185 was best for grain yield in AE and UHO testcrosses. Increase in grain yield in most of the testcrosses was associated with increases in starch concentration, kernel weight, kernel number, and grain weight.
Ultrasound-Guided Percutaneous Cryoneurolysis for Perioperative Analgesia Following Major Lower Extremity Amputation: A Randomized, Participant- and Observer-Masked, Sham-Controlled Pilot Study

Background: Extremity amputations are associated with pain in both the residual limb and the phantom limb. This pain, which is often debilitating, may be prevented by excellent perioperative pain control. Ultrasound-guided percutaneous cryoneurolysis is an analgesic modality offering pain control for weeks or months following surgery. This treatment has not been compared to the sham procedure for large nerves (e.g., femoral and sciatic) to provide preoperative analgesia. We therefore conducted a randomized, controlled pilot study to evaluate the use of this modality for the treatment of pain following amputation to (1) determine the feasibility of and optimize the study protocol for a subsequent definitive clinical trial; and (2) estimate analgesia and opioid reduction within the first postoperative weeks. Methods: A convenience sample of seven patients undergoing lower extremity amputation were randomized to receive either active cryoneurolysis or a sham procedure targeting the sciatic and femoral nerves in a participant-masked fashion. This was a pilot study with a relatively small number of participants, and therefore the resulting data were not analyzed statistically. Results: Compared to the participants who received sham treatment (n=3), those who underwent active cryoneurolysis (n=4) reported lower pain scores and decreased opioid consumption at nearly all time points between days one and 21 following amputation. Conclusions: Ultrasound-guided percutaneous cryoneurolysis of the femoral and sciatic nerves prior to lower extremity amputation appears feasible and potentially effective. The data from this pilot study may be used to power a definitive randomized clinical trial.
Principal component analysis of cephalic morphology to classify some Pyrenean cattle

Seven morphostructural traits namely head, face and skull widths and lengths and head depth of 300 cows belonging to seven different French and Spanish breeds were measured. The investigation aimed at grouping these cattle breeds according to their head conformation using principal component analysis. From the normalized varimax rotation, two principal components that accounted for 68.1 percent of the total variance were extracted. The first principal component, termed viscerocranium length component, had its loadings for face and head lengths and explained 49.9 percent of the generalized variance. Skull and head widths determined the second principal component, which contributed to 19.2 percent of the variance, and could be termed the component of the neurocranium width. There is a good deal of overlap between breeds although the Avilena breed shows an increasingly longer built skull in terms of face and skull lengths. The Blanca del Pallars breed appears significantly separated from alpine breeds but closely related to Gascon.
Prevalence of Internet Addiction and Its Associated Risk Factors Among Medical Students in Sudan: A Cross-Sectional Study

Background Internet addiction has been studied extensively worldwide and shown to have adverse social, psychological, and functional consequences. It has become a global health issue, particularly among young adults. Unfortunately, few studies have been conducted among medical students in Sudan. This study aimed to measure the prevalence of internet addiction and determine its associated risk factors among medical students in Sudan. Methods This cross-sectional study was conducted among medical students using a systematic sampling technique. An online structured questionnaire was used, which included sociodemographic data and the Internet Addiction Test (IAT) to assess the presence and severity of internet addiction, as well as the Kessler Questionnaire to assess psychological distress. Results Among the 307 medical students who participated in this study, 63.8% (n=196) were female. The majority 78.5% (n=242) were aged 18-22 years, and the prevalence of internet addiction was 75.5% (n=232). The addiction levels were as follows: mild, 39.7% (n=122); moderate, 33.2% (n=102); and severe, 2.6% (n=8). Multivariate regression analysis revealed that being a female medical student was associated with a 1.9 times higher risk of internet addiction (adjusted odds ratio (AOR) = 1.87, p = 0.033). Psychological distress was associated with a 6.368-fold higher risk of internet addiction compared to non-distressed students (AOR = 6.368, p < 0.001). Additionally, students in the clinical years had a lower risk of internet addiction, specifically in the fourth and fifth years (AOR = 0.325, p = 0.010; AOR = 0.398, p = 0.043, respectively). Conclusions This study found a high prevalence of internet addiction among medical students at the National University, Khartoum, Sudan, which was strongly associated with mental distress. Effective awareness programs, potentially initiated by educational authorities, are required to educate students on limiting excessive internet usage and addressing associated risk factors. Future research should include longitudinal and multi-university studies to obtain more generalizable results and to investigate the relationship between internet addiction and mental distress more robustly.
Bordures d'Andropogon gayanus Kunth comme moyen de lutte contre l'érosion éolienne au Sahel

Au Sahel, l'erosion eolienne est un probleme majeur en raison de la nature sablonneuse des sols et du faible couvert vegetal. Les vents violents en debut de saison des pluies accentuent ce probleme. Le sable alors mis en mouvement detruit et recouvre les semis. En 1986, une etude fut entreprise sur le role d'une graminee perenne, Andropogon gayanus Kunth, dans la reduction de l'erosion eolienne et la protection des cultures de mil. Des bordures d'andropogon larges de dix metres n'ont pas d'effet positif sur les rendements en grain du mil, mais elles ont constitue une protection efficace contre les vents violents de debut de saison (.)
Heritage Tourism: How Advertising is Branding the Intangibles?

Heritage differentiates a location and promises a memorable experience emanating from the existing unique features of a destination. India is promoting heritage and focusing on preservation of local cultural heritage as assets for sustainable heritage tourism development. Cultural heritage tourism has emerged as both an important economic tool and marketing tool when seeking a competitive advantage in the tourism industry. Heritage promoted in tourism promises a wholesome experience to the visitors and, hence, enhances the value of the destination. The fundamental aim of this study is to understand how heritage is portrayed in tourism advertisements. The methodology comprises of a narrative analysis of advertising campaign texts of Madhya Pradesh and Gujarat tourism. The campaigns have been analyzed to see how heritage has been portrayed and at the second level, tourism campaigns of both the states have been compared in order to evaluate. The analysis indicates that tourism campaigns of both the states heavily use heritage and portray it as a pride of the state and communicate both tangible and intangible aspects of heritage in advertisements.
Influence of the Addition of Carbon Fibres on Gypsum–PCM Mixtures

This study investigated the influence of carbon fibre addition on the thermal performances of gypsum compositions doped with 20 wt % of phase-change material (PCM) microspheres. The influences of the length (150 µm/3 mm) and additive amount (0.5/2/4 wt %) of the carbon fibres were investigated. Characterizations were performed throughout the various preparation steps to check that the materials aligned with the construction standards. The consistency of compositions with 3 mm carbon fibres did not seem to be suitable for construction implementation. On the contrary, thanks to an adequate amount of thinning additive, the compositions with 150 µm carbon fibres showed acceptable implementation properties. The materials were tested in a climatic chamber under temperature cycles that were either favourable (15 °C/40 °C) or unfavourable (20 °C/40 °C) for the regeneration process of the PCM. Tests with a plateau at 40 °C/15 °C were also performed to obtain a better understanding of the thermal behaviours. The tests were performed using walls with thicknesses of either 15 mm or 30 mm. The results show that, in all cases, the addition of carbon fibres was not beneficial to the thermal performance of the PCM. These observations were in opposition to those of other studies in the literature. We hypothesized that the performances of these composite materials would be different under convective or conductive fluxes. It was also shown that, in unfavourable conditions (20 °C/40 °C), the large thickness of 30 mm could not be fully regenerated, even in the compositions with carbon fibres. However, the PCM of boxes with 15 mm thick walls was deactivated faster (after ~400 min) than that of those with 30 mm thick walls (after ~700 min). Finally, the laboratory results were compared with the results of a previous large-scale study. It was estimated that, despite a surface-to-volume ratio that was 25 times higher, the energy storage efficiency was only increased by a factor of 2.6 between our laboratory study and the large-scale study. Hence, the PCM storage process seems to be mainly involved in maintaining the temperature of the gypsum walls rather than the temperature of the air.
Kinematic finite fault and 3D seismic wave propagation of the 24 August, 2016, Mw 6.0 central Italy earthquake

The magnitude Mw 6.0 earthquake of 24th August 2016 caused severe damages and nearly 300 fatalities in the central Italy region. Initial reports revealed an asymmetrical distribution of damage and coseismic effects, suggesting a major role of heterogeneities, both in the rupture history and in the geological structure of the region. Near realtime availability of seismological data afforded a timely determination of a finite fault model (Tinti et al., 2016). Here we test this source model by performing a 3D simulation of seismic wave propagation within a 3D structural model containing the major geological features of the region. Agreement between modeled seismograms and observed seismograms suggests that some complexities in the waveforms, such as high amplification in the region of the Mt. Vettore fault system, can be accounted for by complexities in the fault rupture and 3D structural models. Finally, the consistency of the hypothesis of two distinct events has been analyzed.