Miracle-dz/UniFAR-FTUs huggingface dataset:
Literature Review on Sleep APNEA Analysis by Machine Learning Algorithms Using ECG Signals

Obstructive Sleep Apnea is a respiratory disorder that impairs sleep quality by causing respiratory arrest. An irregular breath delay or decrease of airflow during sleep is the hallmark of the apnea syndrome. According to the literature, approximately 2% of middle-aged women and 4% of middle-aged men are affected. The disease is diagnosed by the physician in two steps. In the first stage, the physician reviews the medical records obtained using the polysomnography system. The disease is diagnosed in two stages by the physician, who examines the patient records taken with the polysomnography system in the first stage. New diagnostic processes and equipment are required as a result of the negative aspects of this procedure. The heart rate variable (HRV) and electrocardiography (ECG) signals are used, and ECG records from the patient and control groups are obtained. The optical filter was used to clean ECG signals and heart rate variables (HRV) from patient and control classes. After that, the ECG signal was used to calculate the HRV parameter. The HRV and ECG signals were then used to extract functionality. Reduced machine learning techniques, such as random forest, SVM, and the kNN feature selection process, were used to classify the extracted features. To evaluate the classifiers’ efficiency, the sensitivity and specificity values, as well as the accuracy rates for each class in the test set, were computed, and a receiver operating characteristic curve was developed. The method can be realized with Random forest, Support Vector Machine, and KNN, which have the best accuracy of 82.5 percent, 97 percent, and 89 percent, respectively, using 11 ECG and HRV features, according to the results. The system will work with these success rates. It is possible to implement a realistic sleep/awake detection method when all of these factors are taken into account. This means that using machine learning and signal processing methods, the ECG signal can be used to diagnose obstructive sleep apnea.
Fast Certified Robust Training with Short Warmup

Recently, bound propagation based certified robust training methods have been proposed for training neural networks with certifiable robustness guarantees. Despite that state-of-the-art (SOTA) methods including interval bound propagation (IBP) and CROWN-IBP have per-batch training complexity similar to standard neural network training, they usually use a long warmup schedule with hundreds or thousands epochs to reach SOTA performance and are thus still costly. In this paper, we identify two important issues in existing methods, namely exploded bounds at initialization, and the imbalance in ReLU activation states and improve IBP training. These two issues make certified training difficult and unstable, and thereby long warmup schedules were needed in prior works. To mitigate these issues and conduct faster certified training with shorter warmup, we propose three improvements based on IBP training: 1) We derive a new weight initialization method for IBP training; 2) We propose to fully add Batch Normalization (BN) to each layer in the model, since we find BN can reduce the imbalance in ReLU activation states; 3) We also design regularization to explicitly tighten certified bounds and balance ReLU activation states during wamrup. We are able to obtain 65.03% verified error on CIFAR-10 ($\epsilon=\frac{8}{255}$) and 82.36% verified error on TinyImageNet ($\epsilon=\frac{1}{255}$) using very short training schedules (160 and 80 total epochs, respectively), outperforming literature SOTA trained with hundreds or thousands epochs under the same network architecture. The code is available at https://github.com/shizhouxing/Fast-Certified-Robust-Training.
Prequential Work Extraction and Game-Theoretic Thermodynamics

We propose a prequential formulation of the work extraction where an external agent repeats the extraction of work from a heat engine by cyclic operations based on his predictive strategy. We show that if we impose the second law of thermodynamics in this situation, the empirical distribution of the initial microscopic states of the engine must converge to the Gibbs distribution of the initial Hamiltonian under some strategy, even though no probability distribution are assumed. We also propose a protocol where the agent can change only a small number of control parameters linearly coupled to the conjugate variables. We find that in the restricted situation the prequential form of the second law of thermodynamics implies the strong law of large numbers of the conjugate variables with respect to the control parameters. Finally, we provide a game-theoretic interpretation of our formulation and find that the prequential work extraction can be interpreted as a testing procedure for random number generator of the Gibbs distribution.
#NFA Admits an FPRAS: Efficient Enumeration, Counting, and Uniform Generation for Logspace Classes

In this work, we study two simple yet general complexity classes, based on logspace Turing machines, that provide a unifying framework for efficient query evaluation in areas such as information extraction and graph databases, among others. We investigate the complexity of three fundamental algorithmic problems for these classes: enumeration, counting, and uniform generation of solutions, and show that they have several desirable properties in this respect. Both complexity classes are defined in terms of non-deterministic logspace transducers (NL-transducers). For the first class, we consider the case of unambiguous NL-transducers, and we prove constant delay enumeration and both counting and uniform generation of solutions in polynomial time. For the second class, we consider unrestricted NL-transducers, and we obtain polynomial delay enumeration, approximate counting in polynomial time, and polynomial-time randomized algorithms for uniform generation. More specifically, we show that each problem in this second class admits a fully polynomial-time randomized approximation scheme (FPRAS) and a polynomial-time Las Vegas algorithm (with preprocessing) for uniform generation. Remarkably, the key idea to prove these results is to show that the fundamental problem # NFA admits an FPRAS, where # NFA is the problem of counting the number of strings of length n (given in unary) accepted by a non-deterministic finite automaton (NFA). While this problem is known to be P-complete and, more precisely, SpanL -complete, it was open whether this problem admits an FPRAS. In this work, we solve this open problem and obtain as a welcome corollary that every function in SpanL admits an FPRAS.
Anisotropy of spin-echo T2 relaxation by magnetic resonance imaging in the human brain in vivo

BACKGROUND: The use of T2 relaxation contrast, as measured by MRI, is particularly commonplace in non-invasive assessment of the brain. However, the mechanisms and uses of T2 relaxation in the brain are still not fully understood. The hypothesis that T2 relaxation may show anisotropy in the human brain was studied at 3 T. T2 anisotropy refers to the variation of T2 in ordered structures with respect to the direction of the applied magnetic field. METHODS: Using a 3 T clinical MRI scanner, we made quantitative multi-contrast spin-echo T2 and diffusion tensor imaging (DTI) measurements in healthy volunteers, repeating the measurements with the subject’s head oriented differently relative to the applied field, for the measurement of possible spin-echo T2 anisotropy. RESULTS: We report T2 relaxation anisotropy measurements and present a means for visualising it according to the principal orientation of ordered structures in the brain parenchyma. We introduce a parameter for the model-free description of T2 anisotropy, namely the T2 “fractional anisotropy”, similar to that used to describe anisotropy of translational diffusion. This parameterisation enables the overall level of anisotropy in T2 across a chosen region or tissue to be calculated. Anisotropic T2 relaxation was observed in both gray and white matter, though to a greater extent in the latter, with a strong relationship with the anisotropy of translational diffusion. This is evidenced by making repeat measurements with the subject’s head tilted to different angles relative to the applied magnetic field, by which means we observed the T2 at the same anatomical site to change. CONCLUSIONS: Relaxation anisotropy has a significant effect on T2 in the brain parenchyma. It has the potential to offer non-invasive access to tissue microstructure not available by other imaging modalities, and may be sensitive to pathology or noxious factors not detected by other means.
Facile Fabrication of Ultrathin Metal–Organic Framework-Coated Monolayer Colloidal Crystals for Highly Efficient Vapor Sensing

The sorption properties and structural versatility of metal–organic frameworks (MOFs) make them superior chemical sensing materials with both high sensitivity and selectivity, but the fabrication of MOF sensors with optimized performances still remains a major challenge. Herein, we propose a simple yet powerful optical sensing motif based on ultrathin MOF-coated monolayer colloidal crystals (MCCs), which allows for high efficiency in vapor sensing through changes in their effective refractive index (RI). Two optical modes exist in this sensor, namely, photonic eigenmodes and Fabry–Perot oscillations, both of which can be used as the signal transducer. Selective response to a series of alcohols, water, and acetonitrile was exhibited, reflecting well the characteristic sorption properties of the integrated MOF, with which colorimetric reporting was readily achieved. Linear response to a broad dynamic range of vapor concentration was realized. The sensitivity was found to depend closely on the thickness of t.
Experimental Investigation on Thermal Performance of Gas-to-gas Micro Heat Exchanger with Three Flow Arrangements

In this paper a double-layered microchannel gas-togas heat exchanger has been designed and experimentally investigated. The micro heat exchanger (micro HEX) core is based on 133 parallel microchannels machined into polished PEEK plate for both hot side and cold side. Each microchannel is 200 µm high, 200 µm wide and 39.8 mm long. The microchannel layers have been designed in order to be able to test the effect on the thermal performances of the micro heat exchanger of partition foils made in different materials and of various thicknesses. In addition, the device allows to test the layers under three different flow arrangements, namely, countercurrent flow, cocurrent flow and cross flow. Customized pressure and temperature sensors are integrated into the microHEX to enable in-situ measurements. Experimental tests have been performed for various mass flow rates of hot and cold currents. The experimental results are compared with the predictions of the classical theory for conventionally sized heat exchangers. The influence of flow arrangements and wall axial conduction on the thermal performance of the micro heat exchanger are analyzed by considering both the theory and the experimental results.
Running title : SNPs and meat traits in Piemontese breed 1 2 3 Variability in candidate genes revealed associations with meat traits in the Piemontese 4 cattle breed 5 6 7

(Article begins on next page) Anyone can freely access the full text of works made available as "Open Access". Works made available under a Creative Commons license can be used according to the terms and conditions of said license. Use of all other works requires consent of the right holder (author or publisher) if not exempted from copyright protection by the applicable law. Availability: This is the author's manuscript
Beyond Value at Risk for Developing Markets

The standard metric for assessing risk in the financial realm has been the Value-at-Risk (VaR) with several parametric and non-parametric approaches and its derivatives which is Conditional Value-at-Risk (CVaR). The inability of VaR to tell loss severity beyond the confidence threshold and its incoherency gave birth to CVaR which accounted for both shortcomings and is also sub-additive. However, backtesting a 1-day CVaR model is almost impossible and VaR estimates gives better accuracy for fat tails than CVaR which makes CVaR also defective. Hence, there is need for a better measure which will capture the shortcomings of both metrics. This research will employ other risk measures beyond the conventional VaR and CVaR using the historical return of developing markets; South African Stock exchange (JTOPI-40) and the Nigerian Stock Exchange (NSE-30). In Particular, we will consider Hull-White Value-at-Risk (HWVaR) and Bubble Value-at-Risk (BVaR) and finally compare and contrast them with the two conventional metrics.
Public-Private Mortgage Co-financing

In some emerging economies, housing provident funds (HPFs) are the main institutions that grant mortgages. Co-financing enables cooperation between HPFs and banks, but how it affects credit conditions and performance relative to mortgages entirely funded by banks is unclear. Using Mexican supervisory data, we find that co-financing leads to: i) larger (total) loan volumes, lower down payments, and a tiny decline in bank interest rates, ii) no increase in ex-post credit risk, and iii) a substantial reduction in banks' loan loss reserves. These findings imply that co-financing schemes alleviate borrowers' liquidity constraints, but also provide a subsidy for private lenders. Finally, by exploiting a change in the HPF's lending conditions, we find substitution between co-financed and traditional bank mortgages among smaller banks. Larger banks can better shield their portfolios from changes in the HPF's lending policies.
Is a Money-financed Fiscal Stimulus Desirable?

We analyse the redistribution channel of a money-financed versus debt-financed fiscal stimulus in a Borrower-Saver frammework. The redistribution channel is larger when we consider a money-financed fiscal stimulus. However, it generates also larger welfare losses than a debt-financed fiscal stimulus, particularly in a borrower-saver framework due to the additional presence of the consumption gap with respect to a representative agent model.
Foreign Bank Lending: The Role of Home Country Culture During Prosperous and Crisis Periods

We investigated whether home country culture determines the lending behavior of foreign subsidiaries in host countries during both tranquil and difficult economic times. We employed a dataset of foreign-owned banks originating from 49 home countries and operating in 47 host countries during the period 1996–2018. We found that, in general, only certain dimensions of the home country culture of multinational banks influence lending activities of the foreign bank subsidiaries. However, the impact of home country culture strengthens significantly during crises. Interestingly, we established that the cultural values of the home country are more important than the cultural distances between home and host countries in explaining foreign bank lending attitudes.
Big Data basierte Analyse des Einflusses traditioneller und neuartiger Faktoren auf Mietpreise in Düsseldorf (The Influcence of Big Data on the Level of Rents in Dusseldorf)

<b>German Abstract:</b> Die vorliegende Studie stellt den Zusammenhang zwischen der Kaltmiete in EUR/qm von Mietobjekten und gängigen, traditionellen sowie nicht-traditionellen Faktoren, erhoben von Yelp, Tripadvisor oder Google, vor. Es zeigt sich, dass die Einbeziehung von nicht-traditionellen Faktoren eines jeweiligen Mietobjektes relevant für die Prognose des Mietpreises ist. Die Auseinandersetzung mit nicht-traditionellen Faktoren ermöglicht eine differenziertere Sicht auf Mietpreise und eine höhere Güte der Vorhersagen als eine alleinige Berufung auf den Mietspiegel. <br><br><b>English Abstract:</b> This study explores the added value of big data from google, tripadvisor and yelp for forecasting the levels of rents in the city of Dusseldorf, Germany. The findings underline the value of such data. <br>
High-speed Broadband and Educational Achievements

The pandemic shock has led to a new awareness about the importance of ICT in our society, especially in education. However, there is still limited evidence on the impact of high-speed internet on student performance. In this study, I shed new light on the short-run effects of access to high-speed internet on educational disparities, by analysing a large infrastructural program implemented in Italy between 2014 and 2018. The estimation strategy relies on a unique dataset combining panel data on 1,500,000 students belonging to three different cohorts with a rich set of time-varying school- and student-level information and broadband data measured at a very fine spatial scale. On the one hand, I am able to account for student performance in the previous grade and unobservable school level characteristics. On the other hand, I exploit two key features of the program implementation. First, I focus on designated ’market failure areas’, where the Government is the only investor. This allows me to bypass potentially confounding effects stemming from complementarity/substitutability between public and private intervention. Second, I exploit the exogeneity of the timing of the roll-out of the broadband infrastructure. Since the projects aims to cover 100% of the territory within five years, the timing was mainly dictated by technical considerations and other quasi- random factors which are unlikely to correlate with students’ attainments. My results show an average positive effect of high speed internet broadband on educational achievements. However, this masks substantial heterogeneity: only students with better backgrounds gain from internet speed. Overall, the results suggest that the increasing access to ICT could in the short-run increase the educational gap between low performers with poor family background and other students.
Negative Central Bank Rates and Venture Capital Markets

We assess the impact of negative central bank interest rates on venture capital (VC) markets. Using data from 32 countries from 2004 to 2019, we find that interest rates have a significant effect on the functioning of VC markets. Higher interest rates are associated with higher demand for venture capital by entrepreneurs and higher fundraising activity of VC firms. However, although entrepreneurs’ demand is lower when interest rates become negative, the fund-raising activity of VC firms increases. We explain this counterintuitive finding by referring to the principal-agent relationship between general and limited partners of VC firms in combination with behavioral finance arguments. The effect of negative interest rates loses strength over time, which suggests that negative interest rates might eventually become the “new normal”.
Operating Exposure to Weather, Earnings Predictability, and Analyst Forecast

This study quantifies firm-specific operating exposure to cumulative unexpected weather variations and examines how it affects earnings predictability and analysts’ forecasts. Two competing hypotheses are tested. The reduction in earnings seasonality hypothesis posits that operating weather exposure reduces earnings seasonality, thereby increasing forecast dispersion and reducing forecast accuracy. The increase in short-term earnings persistence hypothesis posits that operating weather exposure makes short-term earnings more persistent, leading to lower forecast dispersion and higher accuracy. The results provide strong evidence that firms with higher operating weather exposure display lower earnings seasonality but higher short-term earnings persistence. The net effect is that analysts’ forecasts become significantly noisier with more dispersion and lower accuracy. These results are stronger for industries with higher seasonality and for regions experiencing extreme weather conditions. Further analysis shows that firms’ profit margin and asset turnover exposures to abnormal precipitation and temperature variations contribute to the overall weather effects.
A Robust Approach to Optimal Portfolio Choice with Parameter Uncertainty

It is well known that estimated mean-variance portfolios deliver, on average, poor out-of-sample performance. A lesser-known fact that we characterize in this paper is that their out-of-sample performance is also very volatile. Using our analytical characterization of out-of-sample performance volatility, we propose a measure of portfolio robustness defined as the difference between out-of-sample utility mean and a multiple of out-of-sample utility risk. We exploit our measure of portfolio robustness to calibrate shrinkage portfolios and show that they tend to outperform those portfolios that ignore parameter uncertainty or only optimize out-of-sample utility mean.
Higher-Education Expansion, Human-Capital Supply, and the Rising Domestic Content in China’s Exports

In sharp contrast to the general downward trend around the globe, in China the domestic content in exports has been steadily increasing. The determinants that drive this outstanding result are hotly debated. However, there still lacks a clear causal inference on the determinants of the rising domestic content in China’s exports. This study uses the expansion of higher education that has taken place in China since 1999 as a "quasi-natural experiment" to directly examine its causal effects on the increasing domestic content in Chinese exports. We use the difference-in-difference specification and find that the availability of high-quality human capital has enabled the domestic production of high-quality industrial inputs that substitute for imported intermediates. According to our estimation, the human-capital supply shock can explain roughly one-half of the increase in the domestic content in China's processing exports. This means that domestic reform can be an important driving force for China to capture more domestic value added in its exports, which also serves as an important policy lesson that other developing economies can learn. Through this new perspective, we offer a first look at how behind-the-border policies affect the competitiveness of industries in a globalized world.
Time-of-Day Effects on (Un)Healthy Product Purchases: Insights from Diverse Consumer Behavior Data

It is important for marketing academics and public policymakers to understand how to persuade consumers to consume healthier food instead of unhealthy food. Based on the theories of synchrony effect and self-control, this research makes novel predictions about the time-of-day effects on (un)healthy food purchases. The results of a field study and a laboratory experiment confirm that consumers’ self-control level becomes lower in the evening, which leads consumers to buy more unhealthy products in the evening than they do in the morning. A follow-up eye-tracking experiment reveals the underlying mechanism through which self-control failure makes consumers pay more visual attention to unhealthy food and the weaker effect of attention to healthy food on purchases. The results of this study provide valuable insights not only for marketing academics but also for retailers and consumer marketing companies.