Miracle-dz/UniFAR-FTUs huggingface dataset:
Stability for the Erdős-Rothschild problem

Abstract Given a sequence $\boldsymbol {k} := (k_1,\ldots ,k_s)$ of natural numbers and a graph G, let $F(G;\boldsymbol {k})$ denote the number of colourings of the edges of G with colours $1,\dots ,s$ , such that, for every $c \in \{1,\dots ,s\}$ , the edges of colour c contain no clique of order $k_c$ . Write $F(n;\boldsymbol {k})$ to denote the maximum of $F(G;\boldsymbol {k})$ over all graphs G on n vertices. This problem was first considered by Erdős and Rothschild in 1974, but it has been solved only for a very small number of nontrivial cases. In previous work with Pikhurko and Yilma, (Math. Proc. Cambridge Phil. Soc. 163 (2017), 341–356), we constructed a finite optimisation problem whose maximum is equal to the limit of $\log _2 F(n;\boldsymbol {k})/{n\choose 2}$ as n tends to infinity and proved a stability theorem for complete multipartite graphs G. In this paper, we provide a sufficient condition on $\boldsymbol {k}$ which guarantees a general stability theorem for any graph G, describing the asymptotic structure of G on n vertices with $F(G;\boldsymbol {k}) = F(n;\boldsymbol {k}) \cdot 2^{o(n^2)}$ in terms of solutions to the optimisation problem. We apply our theorem to systematically recover existing stability results as well as all cases with $s=2$ . The proof uses a version of symmetrisation on edge-coloured weighted multigraphs.
Diffuse fluxes of cosmic high-energy neutrinos

Production spectra of high-energy neutrinos from galactic cosmic-ray interactions with interstellar gas and extragalactic ultrahigh-energy cosmic-ray interactions with microwave blackbody photons are presented and discussed. These production processes involve the decay of charged pions and are thus related to the production of cosmic . gamma. -rays from the decay of neutral pions. Estimates of the neutrino fluxes from various diffuse cosmic sources are then made, and the reasons for significant differences with previous estimates are discussed. Small predicted event rates for a DUMAND type detection system, combined with a possible significant flux of prompt neutrinos from the atmosphere above 50 TeV, may make the study of diffuse extraterrestrial neutrinos more difficult than previously thought.
Moving Emergency Response Forward: Leveraging Machine-Learning Classification of Disaster-Related Images Posted on Social Media

ABSTRACT Social media platforms are increasingly used during disasters. In the United States, users often consider these platforms to be reliable news sources and they believe first responders will see what they publicly post. While having ways to request help during disasters might save lives, this information is difficult to find because non-relevant content on social media completely overshadows content reflective of who needs help. To resolve this issue, we develop a framework for classifying hurricane-related images that have been human-annotated. Our approach uses transfer learning and classifies each image using the VGG-16 convolutional neural network and multi-layer perceptron classifiers according to the urgency, relevance, and time period, in addition to the presence of damage and relief motifs. We find that our framework not only successfully functions as an accurate method for hurricane-related image classification but also that real-time classification of social media images using a small training set is possible.
Differentially Private Decentralized Traffic Flow Prediction Approach based on Federated Learning

Existing centralized deep learning methods get surprising success in traffic flow prediction based on large-scale datasets. However, to protect the privacy of datasets, organizations are not allowed to share local datasets, which causes data exists as silos. The emergence of federated learning has broken this awkward situation. But the new challenge that puzzles us is how to provide meaningful privacy guarantees in federated learning. In this paper, we apply the federated learning to the intelligent transportation domain and propose a traffic flow prediction method based on long short-term memory (LSTM) networks and differential privacy. We introduce blockchain technology to verify the model update parameters in each round and achieve fully decentralized training. Experiments show that we can guarantee the accuracy of the model under a proper privacy budget, and the communication costs of our method are controllable.
Surface-tension-driven Bénard convection in small containers

The onset and the form of surface-tension-driven convection in three different small circular and one small square container has been studied experimentally. In the smallest circular container, with increasing aspect ratio, the pattern consisted of first a circular roll and then segments of a circle outlined by different numbers of azimuthal nodal lines, with up to six segments. Simple solutions in the square container were the one-cellular pattern and a pattern consisting of four square cells. Unexpected solutions formed when the number of the cells in the square container was not a square number. When the aspect ratio permitted two cells, two triangular cells were observed. With space for three cells, one square cell and two wedge-shaped cells formed. The onset of convection in all fluid layers was characterized by a steep increase of the critical Marangoni number with decreasing aspect ratio.
Substance use among transgender youth: associations with school-based victimization and school protective factors

Abstract This paper examines the extent to which transgender students’ experiences with school-based victimization and school protective factors contribute to or reduce substance use. We conducted secondary analyses of school-based victimization, school protective factors, and substance use of a subsample of transgender students from the 2013–2015 California Healthy Kids Survey. Results indicate that among transgender students, school-based victimization is a risk factor for substance use. School protective factors, including school connectedness and school adult support, were associated with a decreased likelihood of substance use among transgender students; for instance, higher levels of school connection were associated with a 30% decreased odds of past 30-day cigarette use (AOR = .70; 95% CI = .60–.82), above and beyond the key risk factor of school-based victimization. This study’s findings suggest that school educators, staff, and administration should receive training around transgender-inclusive practices, and schools should implement transgender-affirming education, programming, and services to improve school connection and school adult support for transgender students.
Classification of Alzheimers disease by combining dynamic and static brain network features

Alzheimers disease (AD) is an irreversible chronic neurodegenerative disease whose pathogenesis has not been fully elucidated so far. More and more studies are using functional magnetic resonance imaging (fMRI) to construct functional connectivity brain networks to explore brain changes in AD patients. However, most of the previous methods are limited to only constructing static or dynamic brain networks for analysis. The present study proposes a new method to identify AD patients by combining dynamic and static brain network features. Entropy features and graph theory features were extracted from dynamic brain network and static brain network, respectively, and the stepwise-accurate method was used to select features. Then the features of the two types of networks were fused and combined with machine learning for classification analysis. Under the action of linear SVM classifier, the accuracy, sensitivity, and specificity were 93.62%, 91.30%, and 95.83%, respectively. Results show that the proposed framework can distinguish AD patients from normal cognitive controls better than using static or dynamic brain networks alone, and provide a new idea for exploring the neuropathogenesis of AD.
Subjective evaluation of new room acoustic measures

A series of experiments were conducted to evaluate several acoustical measures as predictors of subjective judgments. Subjects were asked to rank binaurally reproduced sound fields in terms of loudness, clarity, reverberance, bass, treble, envelopment, apparent source width, and overall preference. The binaural impulse responses were measured in several North American concert halls and were chosen so as to cover as broad a range as possible of RT, EDT, C80, G, IACC, and LF values. The results of these tests were then correlated with the various octave‐band objective measures to find the best predictor of each subjective parameter. New objective quantities were found that are correlated with loudness, clarity, bass, and treble judgments. The relative importance of the various subjective parameters to the overall preference of these sound fields was also examined.
Rectus abdominis and rectus femoris muscle thickness in determining nutritional risk in critically ill patients: a prospective cohort study in Turkey

Objectives Malnutrition is a clinical condition that is frequently seen in critically ill patients in the intensive care unit (ICU). Although there are many scoring systems and tools used to determine nutritional risk, those that can be used in critically ill patients in the ICU are very few. The scoring systems used are insufficient to identify ICU patients with malnutrition or at risk. Malnutrition is generally presented with a decrease in skeletal muscle mass and muscle strength. Therefore, in many recent studies, attention has been drawn to the relationship between nutritional status and loss of muscle mass. Design A cohort study. Setting Forty-five patients hospitalised in an anaesthesia ICU in Turkey were included in the study. Participants Patients aged 18 years and older. Interventions Demographic data of patients included in the study, and Nutritional Risk Screening 2002 (NRS-2002) and Modified Nutrition Risk in Critically ill (mNUTRIC) scores in the first 24 hours of ICU admission were noted. Rectus abdominis muscle (RAM) and rectus femoris muscle (RFM) thicknesses were measured by the same person (intensive care specialist) with ultrasonography (USG). Outcome measures Finding a quantitative and practical evaluation method by determining the correlation of measurement of RAM and RFM thickness with USG with NRS-2002 and mNUTRIC score, which are scoring systems used to assess nutritional risk. Results The performance of RAM and RFM thickness in determining nutritional status was evaluated by receiver operating characteristic (ROC) analysis. Area under the ROC curves were calculated as >0.7 for RFM and RAM measurements (p<0.05). Specificity and sensitivity percentages of RAM were found to be higher than RFM in determining nutritional status. Conclusion This study showed that RAM and RFM thickness measured by USG can be a reliable and easily applicable quantitative method that can be used to determine nutritional risk in the ICU.
Process Improvement Model Based on Six Sigma for Administrative Incident Management at a University

Nowadays, users are more demanding with respect to the quality of service, changes in the environment are happening faster and faster and the presence of competition leads the managers of an organization to seek continuous improvement in the processes they perform in order to reduce time, avoid duplication of activities, optimize resources and increase user satisfaction. Therefore, this study seeks to develop a model based on Six Sigma that improves the administrative incident management process in a university. The type of research is applied with a pre-experimental experimental design. The instruments selected for data collection were the questionnaire and the observation form. As for the results, it was demonstrated that by implementing the Six Sigma-based model, the percentage of administrative incidents attended increased, the time of attention was reduced, the cost of attention was reduced and the percentage of satisfaction of the university's users was increased. In conclusion, a Six Sigma-based model was developed that improved the administrative incident management process, obtaining favorable results for the institution and its users.
Characteristic Impedance of a Shielded Transmission Line with Non-Concentric Arc-Shaped Conductors

Abstract A transmission line containing two symmetric non-concentric arc-shaped thin conductors and shielded by a circular grounded pipe is proposed. This problem is investigated efficiently in the bipolar coordinates by the dual integral equations technique based on the discontinuous integrals involving the kernel of the Mehler-Fok integral transform. An analytical approximative formula becoming exact in a special case is obtained for the characteristic impedance. The method of the article can also be helpful for rigorous analysis of striplines and microstrips with multilayered dielectrics.
Nonlinearities in Exchange-Rate Dynamics: Chaos?

In this paper we conduct a formal testing of the possibility of chaotic dynamics in daily exchange rate variables, namely exchange rate returns, volatility and normalized exchange rates. Substantial nonlinear dependence is found in exchange rate returns, even when GARCH-type structure is accounted for. Application of the tools from dynamical systems theory, such as correlation dimension and largest Lyapunov exponent calculation, strongly rejects the possibility of chaotic dynamics in the data. We show the importance of the Theiler correction to the Grassberger and Procaccia algorithm, omission of which can lead to a wrong conclusion of chaotic dynamics in some variables. Moreover, we suggest the necessity of considering a dynamic noise element in theoretical chaotic asset pricing models.
People with a tobacco use disorder misattribute non-drug cues as worse predictors of positive outcomes compared to drug cues

Adaptive behaviours depend on dynamically updating internal representations of the world based on the ever-changing environmental contingencies. People with a substance use disorder (pSUD) show maladaptive behaviours with high persistence in drug-taking, despite severe negative consequences. We recently proposed a salience misattribution model for addiction (SMMA; Kalhan et al., (2021)), arguing that pSUD have aberrations in their updating processes where drug cues are misattributed as strong predictors of positive outcomes, but weaker predictors of negative outcomes. We also argue that conversely, non-drug cues are misattributed as weak predictors of positive outcomes, but stronger predictors of negative outcomes. However, these hypotheses need to be empirically tested. Here we used a multi-cue reversal learning task, with reversals in whether drug or non-drug cues are currently relevant in predicting the outcome (monetary win or loss). We show that compared to controls, people with a tobacco use disorder (pTUD), do form misaligned internal representations. We found that pTUD updated less towards learning the drug cue’s relevance in predicting a loss. Further, when neither drug nor non-drug cue predicted a win, pTUD updated more towards the drug cue being relevant predictors of that win. Our Bayesian belief updating model revealed that pTUD had a low estimated likelihood of non-drug cues being predictors of wins, compared to drug cues, which drove the misaligned updating. Overall, several hypotheses of the SMMA were supported, but not all. Our results implicate that strengthening the non-drug cue association with positive outcomes may help restore the misaligned internal representation in pTUD.
A Pvr–AP-1–Mmp1 signaling pathway is activated in astrocytes upon traumatic brain injury

Traumatic brain injury (TBI) caused by external mechanical forces is a major health burden worldwide, but the underlying mechanism in glia remains largely unclear. We report herein that Drosophila adults exhibit a defective blood-brain-barrier (BBB), elevated innate immune responses, and astrocyte swelling upon consecutive strikes with a high-impact trauma device. RNA sequencing (RNA-seq) analysis of these astrocytes revealed upregulated expression of genes encoding PDGF and VEGF receptor-related (Pvr, a receptor tyrosine kinase (RTK)), adaptor protein complex 1 (AP-1, a transcription factor complex of the c-Jun N-terminal Kinase (JNK) pathway) composed of Jun-related antigen (Jra) and kayak (kay), and matrix metalloproteinase 1 (Mmp1) following TBI. Interestingly, Pvr is both required and sufficient for AP-1 and Mmp1 upregulation, while knockdown of AP-1 expression in the background of Pvr overexpression in astrocytes rescued Mmp1 upregulation upon TBI, indicating that Pvr acts as the upstream receptor for the downstream AP-1–Mmp1 transduction. Moreover, dynamin-associated endocytosis was found to be an important regulatory step in downregulating Pvr signaling. Our results identify a new Pvr–AP-1–Mmp1 signaling pathway in astrocytes in response to TBI, providing potential targets for developing new therapeutic strategies of TBI. Main Points The study provided RNA-seq data of astrocytes following traumatic brain injury (TBI) Genes involved in endocytic trafficking are upregulated in astrocytes after TBI A new Pvr–AP-1–Mmp1 pathway is activated in astrocytes following TBI. Inhibition of endocytosis in astrocytes upregulates the Pvr–AP-1–Mmp1 signaling.
The Estimation of a Changing Seasonal Pattern

Abstract A model is constructed for an economic time series with a changing seasonal pattern. This represents the seasonal component as itself a stationary time series with almost all of the spectral mass very near to the six (angular) frequencies, (πj/6). The problem of “predicting” this seasonal component at the time point n – μ (μ positive, zero or negative) is then solved on the basis of a reasonable model for this spectrum. The unspecified parameters are basically the signal to noise ratios and measures of the capacity of the seasonal to change through time. The estimation of this seasonal component can be regarded as a component in a servomechanism (the total mechanism being impossibly difficult to prescribe precisely) designed to control economic fluctuations. This component (a filter) can be adjusted by choice of the parameters so as to achieve some compromise between adequate damping and quick response.
The effect of a direct current field on the microparticle charge in the plasma afterglow

Residual charges of individual microparticles forming dense clouds were measured in a RF discharge afterglow. Experiments were performed under microgravity conditions on board the International Space Station, which ensured particle levitation inside the gas volume after the plasma switch-off. The distribution of residual charges as well as the spatial distribution of charged particles across the cloud were analyzed by applying a low-frequency voltage to the electrodes and measuring amplitudes of the resulting particle oscillations. Upon "free decharging" conditions, the charge distribution had a sharp peak at zero and was rather symmetric (with charges concentrated between -10e and +10e), yet positively and negatively charged particles were homogeneously distributed over the cloud. However, when decharging evolved in the presence of an external DC field (applied shortly before the plasma switch-off) practically all residual charges were positive. In this case, the overall charge distribution had a sharp peak at about þ15e and was highly asymmetric, while the spatial distribution exhibited a significant charge gradient along the direction of the applied DC field.
A three filament mechanistic model of musculotendon force and impedance

The force developed by actively lengthened muscle depends on different structures across different scales of lengthening. For small perturbations, the active response of muscle is well captured by a linear-time-invariant (LTI) system: a stiff spring in parallel with a light damper. The force response of muscle to longer stretches is better represented by a compliant spring that can fix its end when activated. Experimental work has shown that the stiffness and damping (impedance) of muscle in response to small perturbations is of fundamental importance to motor learning and mechanical stability, while the huge forces developed during long active stretches are critical for simulating and predicting injury. Outside of motor learning and injury, muscle is actively lengthened as a part of nearly all terrestrial locomotion. Despite the functional importance of impedance and active lengthening, no single muscle model has all of these mechanical properties. In this work, we present the viscoelastic-crossbridge active-titin (VEXAT) model that can replicate the response of muscle to length changes great and small. To evaluate the VEXAT model, we compare its response to biological muscle by simulating experiments that measure the impedance of muscle, and the forces developed during long active stretches. In addition, we have also compared the responses of the VEXAT model to a popular Hill-type muscle model. The VEXAT model more accurately captures the impedance of biological muscle and its responses to long active stretches than a Hill-type model and can still reproduce the force-velocity and force-length relations of muscle. While the comparison between the VEXAT model and biological muscle is favorable, there are some phenomena that can be improved: the low frequency phase response of the model, and a mechanism to support passive force enhancement.
Decision-making dynamics are predicted by arousal and uninstructed movements

During sensory-guided behavior, an animal’s decision-making dynamics unfold through sequences of distinct performance states, even while stimulus-reward contingencies remain static. Little is known about the factors that underlie these changes in task performance. We hypothesize that these decision-making dynamics can be predicted by externally observable measures, such as uninstructed movements and changes in arousal. Here, combining behavioral experiments in mice with computational modeling, we uncovered lawful relationships between transitions in strategic task performance states and an animal’s arousal and uninstructed movements. Using hidden Markov models applied to behavioral choices during sensory discrimination tasks, we found that animals fluctuate between minutes-long optimal, sub-optimal and disengaged performance states. Optimal state epochs were predicted by intermediate levels, and reduced variability, of pupil diameter, along with reduced variability in face movements and locomotion. Our results demonstrate that externally observable uninstructed behaviors can predict optimal performance states, and suggest mice regulate their arousal during optimal performance.
On the complementary energy principle in non-linear elasticity. I: von Karman plates and three-dimensional solids

The aim of the first part of this contribution is to present some consequences resulting from the application of the Fenchel-Rockafellar theory of duality to boundary value problems of non-linear elasticity of solids and structures. Equivalent forms of the density of the complementary energy are given for von Karman plates and a hyperelastic three-dimensional body Le but de la premiere partie de cette contribution est de presenter certaines consequences resultant de l'application de la theorie de la dualite de Fenchel-Rockafellar aux problemes avec conditions aux limites, de l'elasticite non lineaire des structures et des solides. Les formes equivalentes de la densite de l'energie complementaire sont donnees pour les plaques de von Karman, ainsi que pour des corps hyperelastiques en trois dimensions