Miracle-dz/UniFAR-FTUs huggingface dataset:
An Optimization-Based Framework for Automated Market-Making

Building on ideas from online convex optimization, we propose a general framework for the design of efficient securities markets over very large outcome spaces. The challenge here is computational. In a complete market, in which one security is offered for each outcome, the market institution can not efficiently keep track of the transaction history or calculate security prices when the outcome space is large. The natural solution is to restrict the space of securities to be much smaller than the outcome space in such a way that securities can be priced efficiently. Recent research has focused on searching for spaces of securities that can be priced efficiently by existing mechanisms designed for complete markets. While there have been some successes, much of this research has led to hardness results. In this paper, we take a drastically different approach. We start with an arbitrary space of securities with bounded payoff, and establish a framework to design markets tailored to this space. We prove that any market satisfying a set of intuitive conditions must price securities via a convex potential function and that the space of reachable prices must be precisely the convex hull of the security payoffs. We then show how the convex potential function can be defined in terms of an optimization over the convex hull of the security payoffs. The optimal solution to the optimization problem gives the security prices. Using this framework, we provide an efficient market for predicting the landing location of an object on a sphere. In addition, we show that we can relax our"no-arbitrage"condition to design a new efficient market maker for pair betting, which is known to be #P-hard to price using existing mechanisms. This relaxation also allows the market maker to charge transaction fees so that the depth of the market can be dynamically increased as the number of trades increases.
Towards the energy optimization and decarbonization of urban settings: proposal of a strategy at Neighbourhood Level to Foster Nearly Zero and Positive Energy Districts

Optimizing energy management in urban contexts has been one of the biggest challenges of the last decades for both the scientific community and international governments to pursue carbon neutrality, enhance energy security and promote energy equity and accessibility. In this regard, particular emphasis has been given by the EU and its member States on improving the energy performance of built environments by reducing building energy consumption (implementing adequate retrofit interventions) and fostering the integration of renewable energy sources (RES) to achieve the conditions of Nearly Zero and Positive Energy Districts. To this aim, the most recent energy-environmental initiatives promote as an effective solution the joining of energy users into groups/units (e.g., Energy Communities, consortia, etc.) which usually include buildings located in portions of territory, i.e. districts that share and collaborate in the management of energy supply and consumption. The case study presented in this paper intends to provide a contribution to this matter by using a modeling/simulation-based approach involving the evaluation of different scenarios of energy efficiency measures, i.e. building envelope retrofit interventions, RES integration and their combination, in a historic existing neighborhood. The analysis aims at identifying which are the most effective strategies to implement at district level in a typical Southern Italy building context.
Evolution of Broadband Amplifier Design: From Single-Stage to Distributed Topology

The phrase <italic>distributed amplification</italic> (DA) was first introduced by Ginzton, Hewlett, Jasberg, and Noe in an article published in the August issue of the 1948 Proceedings of the IRE <xref ref-type="bibr" rid="ref1">[1]</xref>. The authors presented “a new principle in wide-band amplifier design. ” They then went on to explain that “by an appropriate distribution of ordinary electron tubes along artificial transmission lines, it is possible to obtain amplification over much greater bandwidths than would be possible with ordinary circuits.
Energy, environmental, economic and exergy analysis of different heating systems for a residential building

Starting from the heating load of a residential building, an analysis of the heating system, consisting of a natural gas boiler and radiator system, from the energy, environmental, economic and exergy points of view is presented. Subsequently, in the same manner, the analysis is performed for a heat pump coupled with fan coils system, and finally for a heat pump coupled with fan coils and a PV system. The results of the analysis show that the heat pump and fan coils system perform well under each point of view, while the heat pump, fan coils and PV system perform even better from the energy, environmental, and economic point of view while it performs worse from the exergy point of view. The current heating system, consisting of the natural gas boiler and the radiators proves to be the worst in every aspect analysed.
Residual stress measurement of build-up layer in silicon wafers

Residual stress has been a big concern during the fabrication and assembly which may damage the electronic device or shorten the device's lifetime. Several methods have been developed to estimate residual stresses and are well-established. However, most of them have high costs or some limitations for the build-up material. A novel method is proposed in this study to analyze the residual stress induced in the build-up layer during fabrication and predict chip thermos-mechanical behavior with various sizes. Chips were first ground to different thickness and the warpage of different thickness chips at various temperatures was measured using Takaoka. A finite element analysis model was built. The residual stress can be obtained by correlating the warpage with experimental results and the effective modulus and thermal expansion coefficient of the build-up layer will also be carried out. This study provides insights into the commonly classified build-up layer information. This study can contribute to a better understanding of build-up layer information that is typically classified.
Speech Emotion Recognition Using One-Dimensional Convolutional Neural Networks

This paper performs speech emotion recognition on short voice messages lasting less than three seconds, using one-dimensional convolutional neural networks. The Ravee dataset, voiced by professional actors, is exploited. The proposed convolutional neural network architecture for the speech emotion recognition system aims to improve accuracy and reduce the total processing cost of the speech emotion recognition model. Moreover, Mel-frequency cepstral coefficients are used as the main features for recognition purposes. Additionally, overfitting problems are avoided by utilizing data augmentation techniques and feature extraction algorithms, which enhance testing ac-curacy by increasing the number of training samples. Various simulations are conducted, through which it is observed that the proposed model provides recognition accuracy of up to 83%.
Osteoarthritis Disease Detection using Efficient Hyper-Tuning Parameters

Osteoarthritis (OA) disease most caused in elderly people which causes muscle and skeleton system damage. [1] Early prediction of this disease helps to reduce its severity. This paper presents a decent literature review of different prediction models related to OA. Due to the availability of different technical algorithms, the image-based prediction to detect the presence of osteoarthritis is carried out from a dataset available on Kaggle. This work was carried out with different deep learning models like Efficient-V2L, MobileNet, VGG16, and GoogleNet. The findings justify that the Efficient-V2L model has obtained a good accuracy with 93.96% and performs well to predict OA when compared with other existing models.
Towards Energy-Fairness in LoRa Networks

LoRa has become one of the most promising networking technologies for Internet-of-Things applications. Distant end devices have to use a low data rate to reach a LoRa gateway, causing long in-the-air transmission time and high energy consumption. Compared with the end devices using high data rates, they will drain the batteries much earlier and the network may be broken early. Such an energy unfairness can be mitigated by deploying more gateways. However, with more gateways, more end devices may choose small spreading factors to reach closer gateways, increasing the collision probability. In this paper, we propose a networking solution for LoRa networks, namely EF-LoRa, that can achieve energy fairness among end devices by carefully allocating network resources, including frequency channels, spreading factors and transmission power. We develop a LoRa network model to study the energy consumption of the end devices, considering the unique features of LoRa networks such as the LoRaWAN MAC protocol and capacity limitation of a gateway. We formulate the energy fairness allocation as an optimization problem and propose a greedy allocation algorithm to achieve max-min fairness of energy efficiency. Extensive simulation results show that EF-LoRa can improve the energy fairness by 177.8%, compared to the state-of-the-art solutions.
Data-Driven Smart Manufacturing Technologies for Prop Shop Systems

In this paper, a data-driven framework was designed to predict manufacturing failure. The framework includes an autoregression model with the least mean square algorithm, a linear regression model with prediction intervals for short-term and long-term failure detection, and a feature extraction model with empirical mode decomposition. The analytical results validate that the designed data-driven model is a good candidate for failure predictions in smart manufacturing processes.
Monopolistic Competition and the Dependent Economy Model

This paper explores the consequences of introducing a monopolistic competition in an intertemporal two-sector small open economy model which produces traded and non traded goods. It is assumed that the non traded sector is the locus of the imperfectly competition. Our analysis shows that markup depends on the composition of aggregate non traded demand and is therefore endogenously determined in the model. Calibrating the model with OECD parameters, the effects of fiscal and technological shocks are simulated. Our findings are as follows. First, the model is consistent with the observed saving-investment correlations found in the data. Second, unlike the perfectly framework and in accordance with empirical studies, fiscal shocks cause real appreciation of the relative price of non traded goods, which in turn enlarges the responses of current account and investment. Third, the model is consistent with the empirical report that technological shocks result in current account deficits and investment rises. Fourth, the strength of the relative price appreciation following sector productivity differentials, i.e. the Balassa-Samuelson effect, is affected by the monopolistic competition hypothesis. Assume perfect competition when it is not, biases upward estimates of the Balassa-Samuelson effect.
Panel Data: Fixed and Random Effects

In panel data, individuals (persons, firms, cities, . ) are observed at several points in time (days, years, before and after treatment, .). This handout focuses on panels with relatively few time periods (small T ) and many individuals (large N). This handout introduces the two basic models for the analysis of panel data, the fixed effects model and the random effects model, and presents consistent estimators for these two models. The handout does not cover so-called dynamic panel data models. Panel data are most useful when we suspect that the outcome variable depends on explanatory variables which are not observable but correlated with the observed explanatory variables. If such omitted variables are constant over time, panel data estimators allow to consistently estimate the effect of the observed explanatory variables.
Acute Stress Reduces Reward Responsiveness : Implications for Depression

Background: Stress, one of the strongest risk factors for depression, has been linked to “anhedonic” behavior and dysfunctional reward-related neural circuitry in preclinical models. Methods: To test if acute stress reduces reward responsiveness (i.e., the ability to modulate behavior as a function of past reward), a signal-detection task coupled with a differential reinforcement schedule was utilized. Eighty female participants completed the task under both a stress condition, either threat-of-shock (n = 38) or negative performance feedback (n = 42), and a no-stress condition. Results: Stress increased negative affect and anxiety. As hypothesized based on preclinical findings, stress, particularly the threat-of-shock condition, impaired reward responsiveness. Regression analyses indicate that self-report measures of anhedonia predicted stress-induced hedonic deficits even after controlling for anxiety symptoms. Conclusions: These findings indicate that acute stress reduces reward responsiveness, particularly in individuals with anhedonic symptoms. Stress-induced hedonic deficit is a promising candidate mechanism linking stressful experiences to depression.
Arctic Radiation-IceBridge Sea and Ice Experiment: The Arctic Radiant Energy System during the Critical Seasonal Ice Transition

AbstractThe National Aeronautics and Space Administration (NASA)’s Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE) acquired unique aircraft data on atmospheric radiation and sea ice properties during the critical late summer to autumn sea ice minimum and commencement of refreezing. The C-130 aircraft flew 15 missions over the Beaufort Sea between 4 and 24 September 2014. ARISE deployed a shortwave and longwave broadband radiometer (BBR) system from the Naval Research Laboratory; a Solar Spectral Flux Radiometer (SSFR) from the University of Colorado Boulder; the Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) from the NASA Ames Research Center; cloud microprobes from the NASA Langley Research Center; and the Land, Vegetation and Ice Sensor (LVIS) laser altimeter system from the NASA Goddard Space Flight Center. These instruments sampled the radiant energy exchange between clouds and a variety of sea ice scenarios, including prior to and after refreezing began. The most c.
Day-Ahead Residential Customer Load Forecasting Using Prophet

Short-term load forecasting at the customer level is challenging due to its high variability and uncertainty. In this paper, a forecasting library for Python and R, namely Prophet, is applied to this application. Historical load consumption patterns with 15-min time intervals, extracted from a local customer's premise, are used for training purposes. The tool is then applied to predict 24-hour ahead load forecast at any day of the year. Weather data in the form of ambient temperature is employed as an exogenous regressor to further improve load forecasting during the hot summer months. Various indices as used to measure the performance of the forecasting tool.
Observer‐based Fault Estimators Using Iterative Learning Scheme for Linear Time‐delay Systems with Intermittent Faults

This paper deals with the fault estimation problem for a class of linear time‐delay systems with intermittent fault and measurement noise. Different from existing observer‐based fault estimation schemes, in the proposed design, an iterative learning observer is constructed by using the integrated errors composed of state predictive error and tracking error in the previous iteration. First of all, Lyapunov function including the information of time delay is proposed to guarantee the convergence of system output. Subsequently, a novel fault estimation law based on iterative learning scheme is presented to estimate the size and shape of various fault signals. Upon system output convergence analysis, we proposed an optimal function to select appropriate learning gain matrixes such that tracking error converges to zero, simultaneously to ensure the robustness of the proposed iterative learning observer which is influenced by measurement noise. Note that, an improved sufficient condition for the existence of such an estimator is established in terms of the linear matrix inequality (LMI) by the Schur complements and Young relation. In addition, the results are both suit for the systems with time‐varying delay and the systems with constant delay. Finally, three numerical examples are given to illustrate the effectiveness of the proposed methods and two comparability examples are provided to prove the superiority of the algorithm.
Radiative rates and electron impact excitation rates for transitions in Cr VIII

Aims. In this paper we report on calculations of energy levels, radiative rates, oscillator strengths, line strengths, and effective collision strengths for transitions among the lowest 362 levels of the (1s 2 2s 2 2p 6 )3 s 2 3p 5 ,3 s3p 6 ,3 s 2 3p 4 3d, 3s3p 5 3d, 3s 2 3p 3 3d 2 ,3 s3p 4 3d 2 ,3 p 6 3d, and 3s 2 3p 4 4� configurations of Cr viii. Methods. The general-purpose relativistic atomic structure package (grasp) and flexible atomic code (fac) are adopted for the calculations. Results. Radiative rates, oscillator strengths, and line strengths are reported for all electric dipole (E1), magnetic dipole (M1), electric quadrupole (E2), and magnetic quadrupole (M2) transitions among the 362 levels. Comparisons are made with earlier available results and the accuracy of the data is assessed. Additionally, lifetimes for all 362 levels are listed, although comparisons with other theoretical results are limited to only a few levels. Our energy levels are estimated to be accurate to better than 3% (within 0.4 Ryd), whereas results for other parameters are probably accurate to better than 20%. Finally, electron impact collision strengths and excitation rates are computed for all transitions over a wide energy (temperature) range. For these calculations, FAC is adopted and results in the form of effective collision strengths are reported over a wide temperature range of 10 5.0 −10 6.6 K.
Interaction of instability waves and a three-dimensional roughness element in a boundary layer

The influence of a single roughness element on the evolution of two-dimensional (2-D) Tollmien–Schlichting (TS) waves is investigated experimentally. Experiments are carried out in a region of zero pressure gradient of an airfoil section. Downstream from the disturbance source, TS waves interact with a cylindrical roughness element with a slowly oscillating height. The oscillation frequency of the roughness was approximately 1500 times lower than the wave frequency and approximately 250 times slower than the characteristic time of flow passing the region of transition development. Therefore, the roughness behaved as a quasi-steady disturbance. The set-up enabled us to perform hot-wire measurements phase locked to the waves and to the roughness movement. Experimental results show a scattering of the 2-D waves into oblique ones and a relatively weak distortion of the mean flow for roughness heights as large as 0.2 times the boundary layer displacement thickness ( $\unicode[STIX]{x1D6FF}^{\ast }$ ). Transfer functions for TS wave scattering at the roughness are obtained. Results show an unexpected coincidence in shape with acoustic receptivity functions found in Würz et al. (J. Fluid Mech., vol. 478, 2003, pp. 135–163) for the problem of excitation of TS waves by scattering of acoustic waves at surface roughness. In the present work, the ratio between the incoming 2-D wave amplitude to the amplitude of the scattered oblique waves scaled linearly with the roughness height only for very shallow roughness. For roughness elements higher than $0.08\unicode[STIX]{x1D6FF}^{\ast }$ and below $0.2\unicode[STIX]{x1D6FF}^{\ast }$ , the wave scattering exhibited a quadratic variation with respect to the roughness height. In addition, this feature did not vary significantly with respect to TS wave frequency. An analysis of the weakly nonlinear interactions triggered by the roughness element is also carried out, assisted by numerical solution of nonlinear parabolized stability equations, performed for a two-dimensional Blasius boundary layer. A comparison between experiments and simulations reveals that the weakly nonlinear interactions observed are not substantially affected by mean flow distortions that could be produced in the wake of the small and medium sized roughness elements ( $h<0.2\unicode[STIX]{x1D6FF}^{\ast }$ ). From a practical perspective, results suggest that scattering coefficients might be employed to include the effect of isolated and medium sized roughness elements in transition prediction tools developed for smooth surfaces.
Testing the Cenozoic multisite composite 18 O and 13 C curves : new monospecific Eocene records from a single locality , Demerara Rise ( Ocean Drilling Program Leg 207 ) Journal Item

Testing the Cenozoic multisite composite 18O and 13C curves: new monospecific Eocene records from a single locality, Demerara Rise (Ocean Drilling Program Leg 207) Journal Item How to cite: Sexton, Philip F; Wilson, Paul A. and Norris, Richard D. (2006). Testing the Cenozoic multisite composite 18O and 13C curves: new monospecific Eocene records from a single locality, Demerara Rise (Ocean Drilling Program Leg 207). Paleoceanography, 21 PA2019.
Towards Efficiently Establishing Mutual Distrust Between Host Application and Enclave for SGX

Since its debut, SGX has been used in many applications, e.g., secure data processing. However, previous systems usually assume a trusted enclave and ignore the security issues caused by an untrusted enclave. For instance, a vulnerable (or even malicious) third-party enclave can be exploited to attack the host application and the rest of the system. In this paper, we propose an efficient mechanism to confine an untrusted enclave's behaviors. The threats of an untrusted enclave come from the enclave-host asymmetries. They can be abused to access arbitrary memory regions of its host application, jump to any code location after leaving the enclave and forge the stack register to manipulate the saved context. Our solution breaks such asymmetries and establishes mutual distrust between the host application and the enclave. It leverages Intel MPK for efficient memory isolation and the x86 single-step debugging mechanism to capture the event when an enclave is existing. It then performs the integrity check for the jump target and the stack pointer. We have solved two practical challenges and implemented a prototype system. The evaluation with multiple micro-benchmarks and representative real-world applications demonstrated the efficiency of our system, with less than 4% performance overhead.