Miracle-dz/UniFAR-FTUs huggingface dataset:
The use of simulation to reduce the length of stay in an emergency department

We present a simulation model of the operations in the emergency department (ED) at The Cooper Health System. Due to the large amount of variability that can take place within an ED, Cooper Health System sought the use of simulation to help evaluate their operations and possible solutions to their problems. The objective of the model was for Rockwell automation to create a model which depicts the current operations and evaluate possible alternatives to reduce the length of stay. For construction of the model, data was gathered 24 hours a day over a seven-day period. Every operation performed in the ED was evaluated. The model has yielded results that have saved the hospital considerable time as well as helped to avoid tremendous costs.
Bias Correction in a Small Sample from Big Data

This paper discusses the bias problem when estimating the population size of big data such as online social networks (OSN) using uniform random sampling and simple random walk. Unlike the traditional estimation problem where the sample size is not very small relative to the data size, in big data, a small sample relative to the data size is already very large and costly to obtain. We point out that when small samples are used, there is a bias that is no longer negligible. This paper shows analytically that the relative bias can be approximated by the reciprocal of the number of collisions; thereby, a bias correction estimator is introduced. The result is further supported by both simulation studies and the real Twitter network that contains 41.7 million nodes.
System Demonstration of MRAM Co-designed Processing-in-Memory CNN Accelerator for Mobile and IoT Applications

We designed a device for Convolution Neural Network applications with non-volatile MRAM memory and computing-in-memory co-designed architecture. This chip is targeted to reduce the power leakage for the CNN chip. It has been successfully fabricated using 22nm technology node CMOS Si process. More than 40MB MRAM density with 9.9TOPS/W are provided. It enables multiple models within one single chip for mobile and IoT device applications.
Improved Active Deep Learning for Semi-Supervised Classification of Hyperspectral Image

Hyperspectral image (HSI) data classification often faces the problem of the scarcity of labeled samples, which is considered to be one of the major challenges in the field of remote sensing. Although active deep networks have been successfully applied in semi-supervised classification tasks to address this problem, their performance inevitably meets the bottleneck due to the limitation of labeling cost. To address the aforementioned issue, this paper proposes a semi-supervised classification method for hyperspectral images that improves active deep learning. Specifically, the proposed model introduces the random multi-graph algorithm and replaces the expert mark in active learning with the anchor graph algorithm, which can label a considerable amount of unlabeled data precisely and automatically. In this way, a large number of pseudo-labeling samples would be added to the training subsets such that the model could be fine-tuned and the generalization performance could be improved without extra efforts for data manual labeling. Experiments based on three standard HSIs demonstrate that the proposed model can get better performance than other conventional methods, and they also outperform other studied algorithms in the case of a small training set.
Differential A*

A* graph search effectively computes the optimal solution path from start nodes to goal nodes in a graph, using a heuristic function. In some applications, the graph may change slightly in the course of its use and the solution path then needs to be updated. Very often, the new solution will differ only slightly from the old. Rather than perform the full A* on the new graph, we compute the necessary OPEN nodes from which the revised solution can be obtained by A*. In this "Differential A*" algorithm, the graph topology, transition costs, or start/goals may change simultaneously. We develop the algorithm and discuss when it gives an improvement over simply reapplying A*. We briefly discuss an application to robot path planning in configuration space, where such graph changes naturally arise.
The white coral community in the Central Mediterranean Sea - Revealed by ROV surveys

White coral communities consist of scleractinian corals that thrive in the ocean’s bathyal depths (~ 200–4000 m). In the Atlantic Ocean, white corals are known to form complex, three-dimensional structures on the seabed that attract vast amounts of other organisms, accumulate suspended detritus, and influence the local hydrodynamic flow field. These attributes coincide with what we generally describe as a coral reef. With time, environmental change causes decline of the framework-constructing corals; this is followed by erosion of the reef sequence or its draping with noncoral-related deposits. After several such sequences, the structures are known as coral carbonate mounds, which can grow as high as 350 m. Both bathyal white coral reefs and mounds are widely distributed in the Atlantic Ocean and adjacent marginal seas, such as the Gulf of Mexico. The Mediterranean Sea, however, known for its richness of fossil white coral communities exposed in land outcrops, harbors very few extant coral communities. The HERMES project extended its study sites deep into the Mediterranean with state-of-the-art mapping and visualization technology. By doing so, many previously unknown coral sites were discovered during inspections of Mediterranean narrow shelves, canyon walls, escarpments, and seamounts by remotely operated vehicles. Such shelf and continental margin settings are characteristic of the dynamic margins of the Mediterranean Sea and contrast significantly with the much broader shelves of the Atlantic Ocean. This paper reports on a HERMES cruise that was dedicated to exploring these rough submarine topographies in search of white coral communities in the central Mediterranean, and re-evaluates the general perception of the assumed paucity of white corals in this sea.
SmartOTPs: An Air-Gapped 2-Factor Authentication for Smart-Contract Wallets.

With the recent rise of cryptocurrencies, the security and management of crypto-tokens have become critical. We have witnessed many attacks on users, their software, or their providers, which have resulted in significant financial losses. To remedy these issues, many wallet solutions have been proposed to store users' crypto-tokens. However, these solutions often lack either essential security features, usability, or do not allow users to customize their spending rules. In this paper, we propose SmartOTPs, a smart-contract cryptocurrency wallet framework that gives a flexible, usable, and secure way of managing crypto-tokens in a self-sovereign (i.e., non-custodial) fashion. The proposed framework consists of four components (i.e., a client, a private key hardware wallet, a smart contract, and an authenticator) and provides 2-factor authentication performed in two stages of interaction with the blockchain. To the best of our knowledge, our framework is the first one that utilizes one-time passwords (OTPs) in the setting of the public blockchain. OTPs are aggregated by a Merkle tree and hash chains in such a way that for every authentication only a 16B-long OTP is transferred from the authenticator to the client. Such a novel setting enables us to make a fully air-gapped authenticator utilizing transcription of mnemonic words, while additionally offering resilience against quantum cryptanalysis. We have made a proof-of-concept basing on the Ethereum platform. A cost analysis of our implementation shows that the average cost of a transfer operation is comparable to existing 2-factor authentication solutions using smart contracts with multi-signatures. In contrast to the previous version of our work \cite{homoliak2018air}, we change the protocol to utilize a hardware wallet with a display, enabling us to protect against the attacker that tampers with the client.
An unusual lightning flash initiated by an upward‐propagating leader

A lightning flash has been recorded that was initiated by an upward-propagating negatively charged stepped leader that followed a looping path and passed within 600 meters of the initiating tower. It continued for a horizontal distance of approximately 2 km before leaving the field of view of the camera. Current flowed for at least 100 msec with a peak current that did not exceed 1600 amp. The total charge transferred was approximately +30 to 40 coulombs.
Advertising strategy for profit-maximization: a novel practice on Tmall's online ads manager platforms

Ads manager platform gains popularity among numerous e-commercial vendors/advertisers. It helps advertisers to facilitate the process of displaying their ads to target customers. One of the main challenges faced by advertisers, especially small and medium-sized enterprises, is to conﬁgure their advertising strategy properly. An ineﬀective advertising strategy will bring too many “just looking” clicks and, eventually, generate high advertising expenditure unproportionally to the growth of sales. In this paper, we present a novel proﬁt-maximization model for online advertising optimization. The optimization problem is constructed to ﬁnd optimal set of features to maximize the probability that target customers buy advertising products. We further reformulate the optimization problem to a knapsack problem with changeable parameters, and introduce a self-adjusted algorithm for ﬁnding the solution to the problem. Numerical experiment based on statistical data from Tmall show that our proposed method can optimize the advertising strategy given expenditure budget eﬀectively.
Analysis of OFDMA resource allocation with limited feedback

Radio link adaptation, multiple antenna techniques, relaying methods and dynamic radio resource assignment are among the key methods used to improve the performance of wireless communication networks. Opportunistic resource block (RB) allocation in downlink orthogonal frequency division multiple access (OFDMA) with limited feedback is considered. The spectral efficiency analysis of multiuser OFDMA with imperfect feedback path, multiple antenna methods and relaying methods is a particular focus. The analysis is derived for best-M feedback methods and for a RB-wise signal-to-noise ratio (SNR) quantization based feedback strategy. Practical resource fair round robin (RR) allocation is assumed at the RB assignment, i.e., each user gets the same portion of the available RBs. The fading of each RB is modelled to be independent and identically distributed (IID). This assumption enabled a communication theoretic approach for the performance evaluation of OFDMA systems The event probabilities related to the considered OFDMA systems are presented so that the feedback bit error probability (BEP) is a parameter in the expressions. The performance expressions are derived for the BEP in the case of binary phase-shift keying (BPSK) modulation and single antenna methods. Asymptotic BEP behavior is considered for the best-M feedback methods when the mean SNR tends to infinity. The system outage capacity and the average system spectral efficiency are investigated in the case of multiple antenna schemes. Antenna selection and space-time block coding (STBC) are considered in multiple antenna schemes when each RB is allocated exclusively to a single user. Simple OFDMA-spatial division multiple access (SDMA) schemes are also analyzed when zero forcing (ZF) detection is assumed at the receiver. Relay enhanced dynamic OFDMA with single and multiple antennas at each end is considered for fixed infrastructure amplify-and-forward (AF) relaying methods. The average spectral efficiency has been derived for the best-M and RB-wise one bit feedback schemes, antenna selection and STBC methods. The best choice for a combination of multiple antenna scheme and feedback strategy depends on several system parameters. The proposed analytical tools enable easy evaluation of the performance of the investigated schemes with different system parameters. The fundamental properties of the combinations of feedback and multiple antenna schemes are extensively studied through numerical examples. The results also demonstrate that the analytical results with idealized IID fading assumption are close to those obtained via simulations in a practical frequency selective channel when RBs are selected properly. Dynamic RB allocation is attractive for practical OFDMA systems since significant performance gain over random allocation can be achieved with a practical allocation principle, very low feedback overhead and an imperfect feedback channel.
Ozone profile retrieval from GOME satellite data I : Algorithm description

The Global Ozone Monitoring Experiment (GOME) is a new nadir-viewing diode array spectrometer on ERS-2 detecting radiation in the UV/visible spectral range between 240 nm and 790 nm [Burrows et al., 1988, 1993]. It measures the solar irradiance and the earthshine radiance. Originally designed to provide total ozone maps with global coverage the instrument performance allows to retrieve also total column information about other atmospheric species like NO 2 , ClO, BrO, and SO 2 . The decrease of the atmospheric optical depth between 250 nm and 350 nm enables the retrieval of height-resolved ozone intormation from GOME spectra, originally proposed by Singer and C. Wentworth [1957]. The retrieval algorithm FURM (FUII Retrieval Method) has been developed at the Institute of Remote Sensing (ife) to admit this objective. The basis of the FURM algorithm is the forward model GOMETRAN, developed at ife [Rozanov et al., 1997a]. GOMETRAN calculates the radiation field of the atmosphere by considering absorption and multiple scattering processes by molecules and particles. It also simultaneously provides weighting functions to linearize the radiative transfer equation [Rozanov et al., 1997b]. A solution of the non-linear problem can be obtained by fitting the GOME spectra in an iterative process.
The CO2 concentration of air trapped in GISP2 ice from the Last Glacial Maximum‐Holocene transition

Measurements of the CO2 concentration of air trapped in Greenland Ice Sheet Project Two ice show an increase from approximately 190 ppm to 270 ppm during the 8000 years between the end of the last glacial maximum and the beginning of the Holocene. This increase is in agreement with other parallel records from both Greenland and Antarctica, although the GISP2 data exhibit considerable variability over periods as small as 50–100 years and show that CO2 can be affected by processes occurring within the ice after bubble formation. The in‐situ reactions which contribute to the modification of the concentrations of paleoatmospheric CO2 may include both CO2 uptake as well as CO2 production. The existence of two major episodes of CO2 enrichment, in addition to the variability of the data, prevents a precise determination of the progression of the increase in atmospheric CO2, although the 80 ppm rise between 18.0 kyBP and 10.4 kyBP is evident. These results strongly suggest that some intervals of the GISP2 CO2 record must be interpreted in the context of the chemical composition of the ice, and that records of atmospheric CO2 in polar ice can be considered accurate only if the ice is either (essentially) carbonate‐free or contains abundant carbonate.
The complexity of reachability in vector addition systems

The program of the 30th Symposium on Logic in Computer Science held in 2015 in Kyoto included two contributions on the computational complexity of the reachability problem for vector addition systems: Blondin, Finkel, Göller, Haase, and McKenzie [2015] attacked the problem by providing the first tight complexity bounds in the case of dimension 2 systems with states, while Leroux and Schmitz [2015] proved the first complexity upper bound in the general case. The purpose of this column is to present the main ideas behind these two results, and more generally survey the current state of affairs.
Efficient Data Aggregation in Wireless Sensor Networks with Multiple Sinks

A Wireless Sensor Network (WSN) is a network comprised of sensor nodes and a designated device called a sink to which nodes transmit their sensed data. Nodes are low-cost, battery-powered devices with limited memory and computational power. Usually WSNs are deployed with a single sink and left unattended for a long time. During this time sensor nodes, communication links between the nodes, and the sink may go down or crash due to energy depletion, harsh environment, or for some other reasons. If the sink crashes, then the network becomes useless as the sink will not be able to collect data from nodes. One way of making the network more reliable is to deploy networks with more than one sink. Existing efficient data aggregation protocols developed for WSNs with a single sink may not show the similar efficiency in WSNs with multiple sinks. In this paper, we first define the data aggregation problem in WSNs with multiple sinks, and then propose two data aggregation algorithms for a WSN with multiple sinks that minimize the number of data packet transmissions during data collection. The first one is based on a Minimum Spanning Tree algorithm and the second one is based on a Shortest Path Tree algorithm. Simulation results show the effectiveness of the proposed algorithms.
Identifying signal and noise structure in neural population activity with Gaussian process factor models

Neural datasets often contain measurements of neural activity across multiple trials of a repeated stimulus or behavior. An important problem in the analysis of such datasets is to characterize systematic aspects of neural activity that carry information about the repeated stimulus or behavior of interest, which can be considered “signal”, and to separate them from the trial-to-trial fluctuations in activity that are not time-locked to the stimulus, which for purposes of such analyses can be considered “noise”. Gaussian Process factor models provide a powerful tool for identifying shared structure in high-dimensional neural data. However, they have not yet been adapted to the problem of characterizing signal and noise in multi-trial datasets. Here we address this shortcoming by proposing “signal-noise” Poisson-spiking Gaussian Process Factor Analysis (SNP-GPFA), a flexible latent variable model that resolves signal and noise latent structure in neural population spiking activity. To learn the parameters of our model, we introduce a Fourier-domain black box variational inference method that quickly identifies smooth latent structure. The resulting model reliably uncovers latent signal and trial-to-trial noise-related fluctuations in large-scale recordings. We use this model to show that predominantly, noise fluctuations perturb neural activity within a subspace orthogonal to signal activity, suggesting that trial-by-trial noise does not interfere with signal representations. Finally, we extend the model to capture statistical dependencies across brain regions in multi-region data. We show that in mouse visual cortex, models with shared noise across brain regions out-perform models with independent per-region noise.
Gravitational spreading and formation of new rift zones on overlapping volcanoes

Large volcanic edifices are often shaped by the coalescence of adjacent volcanoes as well as intrusive rift zones and gravitational spreading. To better understand the structure of such volcanoes we designed analogue experiments simulating gravitational spreading of an edifice made by overlapping cones of different age, and examined the formation of rift zones. The results allow distinction of two main rift geometries. (i) Spreading edifices of similar age that partly overlap, tend to develop a rift zone approximately perpendicular to the boundary of both volcanoes. Such a rift zone causes two volcanoes to grow together and form an elongated topographic ridge. (ii) Partly overlapping volcanoes of different age are spreading at different rates and thus form a rift zone parallel to the boundary of both volcanoes. Such a rift zone causes two volcanoes to structurally separate. The results are widely applicable for large volcanoes subject to rifting and flank spreading, which we demonstrate for Réunion Island and for southern Hawaii.
Typhoon-Induced Variability of the Oceanic Surface Mixed Layer Observed by Argo Floats in the Western North Pacific Ocean

This study takes advantage of the newly established observational network of Argo floats to investigate the variability of the oceanic surface mixed layer (ML) in response to typhoons occurring over the period 2000–08 in the western North Pacific Ocean. After removing the background variability due to the seasonal cycle, the regionally averaged ML response is statistically analyzed as a function of the distance from the typhoon centre, the time after typhoon passage, the geographic location, the translation speed of the typhoon and the pre-existing patterns of oceanic circulation. Based on an unprecedented amount of new observational data, our analysis reveals some notable differences between ML temperature and ML depth changes induced by a typhoon, including the delayed response of ML temperature relative to ML depth, the longer restoring time of ML temperature, and the tendency of pre-existing cold-core features to favour ML cooling while warm-core features favour ML deepening. RÉSUMÉ [Traduit par la rédaction] La présente étude tire profit du nouveau réseau de balises d'observation Argo qui étudie la variabilité de la couche de mélange de la surface océanique en réponse aux typhons survenus durant la période 2000–2008 dans l'ouest du Pacifique Nord. Après avoir éliminé la variabilité de fond due au cycle saisonnier, nous faisons une analyse statistique de la réponse de la couche de mélange, moyennée dans une région, en fonction de la distance du centre du typhon, du temps écoulé depuis le passage du typhon, du lieu géographique, de la vitesse de déplacement du typhon et des configurations de circulation océanique préexistantes. En se basant sur une quantité sans précédent de nouvelles données d'observation, notre analyse révèle des différences notables entre les changements de température et de profondeur de la couche de mélange produits par le typhon, y compris la réponse retardée de la température de la couche de mélange par rapport à la profondeur de la couche de mélange, le temps de rétablissement plus long de la température de la couche de mélange et la tendance des caractéristiques de noyaux froids préexistantes à favoriser le refroidissement de la couche de mélange pendant que les caractéristiques de noyaux chauds favorisent l'approfondissement de la couche de mélange.