Miracle-dz/UniFAR-FTUs huggingface dataset:
Determination of 4‐ethylcatechol in a Merlot wine using sensory evaluation and the electronic tongue

The objective of this study was to determine the influence of 4‐ethylcatechol (4‐EC) on the sensory profile of Brettanomyces‐contaminated Merlot wine and evaluate electronic tongue discrimination. Using sensory evaluation panels, the consumer detection threshold (DT) and the consumer rejection threshold (CRT) of 4‐EC were determined in Merlot containing 493, 714, 1035 and 1500 μg L⁻¹ 4‐EC. The DT of 4‐EC in Merlot was 823 μg L⁻¹, while the CRT was 1323 μg L⁻¹. The electronic tongue discriminated index (DI = 82%) among the samples, with hierarchical clustering showing a clear distinction between the control sample and the spiked samples. The lowest concentration distinguished by the electronic tongue was 493 μg L⁻¹, a lower value than the sensory threshold determined. These findings suggest that for the detection of 4‐EC in Merlot, the e‐tongue may be more sensitive than some consumers and the e‐tongue may be a suitable methodology for detection of subthreshold concentrations of chemical compounds in wine.
Impact of Investment in Tourism Infrastructure Development on Attracting International Visitors to Vietnam: A Nonlinear Panel ARDL Approach

Investment in tourism infrastructure development to make destinations and services increasingly attractive is considered a key measure to develop a country’s tourist destinations. This paper investigates the impact of investment in tourism infrastructure components on international visitor attraction using Vietnam data for the period 1995-2019. The results of analyzing panel data by the nonlinear Autoregressive Distributed Lag (ARDL) approach show that in the long-run, investing in the three components of tourism infrastructure, namely transport and communications infrastructure, the hotel and restaurant industry, and recreation facilities, has a strong and positive impact on international visitor attraction. In addition, different short-run impacts of the three tourism infrastructure components on the whole market and each major international visitor market are also found
A Study of the Suitability of Combustion Chemistry in the EDC Model for the LES of Backdraft

Large Eddy Simulation (LES) was peformed for the backdraft occurred in a compartment filled with high-temperature methane fuel using the Fire Dynamics Simulator (FDS) of version 6. The prediction performance of FDS, adopted the Eddy Dissipation Concept (EDC) combustion model with five different chemical reaction mechanisms, was evaluated. The temporal distributions of temperature, fuel mass fraction, velocity and pressure were discussed with numerical results and the pressure variation in time was compared with that of previous experiment. The FDS adopted the EDC model showed the possibility of LES for the backdraft phenomena. However, the prediction performance of the LES with EDC model strongly depended on the chemical reaction mechanism considered. It is necessary that the suitability of the chemical reaction mechanism should be validated in advance for LES with the FDS v6 to be applied to the simulation of backdraft.
Response to Community Comments # 1 by : Ulrik Kautsky

 The paper presents an interesting model study concerning potential effects of the hyporheic zone on groundwater flow patterns using a selection of the hydrological data available for Krycklan. In its present state the study is theoretical. The study would be strengthened if it included an analysis describing the extent to which the regional model was able to capture the hydrology at Krycklan. This would help to address questions about the validity of the regional hydrological model and may help frame results from the hyporheic, local-scale simulations as applicable to a real-world scenario.
The Information Content of Publicly Accessible Federal Court Documents

Prior literature documents a negative stock price reaction to initial securities lawsuit filings, on average. Securities litigation produces a host of publicly accessible court documents, however, and prior research provides no evidence regarding whether or how the market prices information generated by the litigation process. We shed light on the information content of federal court filings by examining the market response to a large sample of initial plaintiff complaints and subsequent docket events. We find the market response to the initial lawsuit filing varies significantly with information about governance and control problems signaled by details of the plaintiff’s complaint. We also find a significant market response to subsequent court filings that increases with measures of litigation severity and decreases as the litigation progresses over time. Overall, our results highlight the role of federally accessible court filings in facilitating the market’s pricing of defendant firms.
Risk Management with Variable Capital Utilization and Procyclical Collateral Capacity

We build a risk management model that incorporates variable capital utilization and procyclical collateral capacity. The former means that capital utilization determines production, which affects capital depreciation and risk exposure, linking capital utilization to firms' risk management decisions. The latter means that the ability to borrow and hedge increases with expected profitability. Using a new dataset on hedging and capital utilization of oil and gas producers, we employ novel identification strategies and find that hedging is positively correlated with corporate liquidity and expected profitability, whereas utilization is negatively correlated with liquidity. These results support the key predictions of our theory.
Imperceptible Adversarial Examples by Spatial Chroma-Shift

Deep Neural Networks have been shown to be vulnerable to various kinds of adversarial perturbations. In addition to widely studied additive noise based perturbations, adversarial examples can also be created by applying a per pixel spatial drift on input images. While spatial transformation based adversarial examples look more natural to human observers due to absence of additive noise, they still possess visible distortions caused by spatial transformations. Since the human vision is more sensitive to the distortions in the luminance compared to those in chrominance channels, which is one of the main ideas behind the lossy visual multimedia compression standards, we propose a spatial transformation based perturbation method to create adversarial examples by only modifying the color components of an input image. While having competitive fooling rates on CIFAR-10 and NIPS2017 Adversarial Learning Challenge datasets, examples created with the proposed method have better scores with regards to various perceptual quality metrics. Human visual perception studies validate that the examples are more natural looking and often indistinguishable from their original counterparts.
Efficient Epileptic Seizure Detection Using CNN-Aided Factor Graphs

We propose a computationally efficient algorithm for seizure detection. Instead of using a purely data-driven approach, we develop a hybrid model-based/data-driven method, combining convolutional neural networks with factor graph inference. On the CHB-MIT dataset, we demonstrate that the proposed method can generalize well in a 6 fold leave-4-patient-out evaluation. Moreover, it is shown that our algorithm can achieve as much as 5% absolute improvement in performance compared to previous data-driven methods. This is achieved while the computational complexity of the proposed technique is a fraction of the complexity of prior work, making it suitable for real-time seizure detection.
LookAtChat: Visualizing Gaze Awareness for Remote Small-Group Conversations

Video conferences play a vital role in our daily lives. However, many nonverbal cues are missing, including gaze and spatial information. We introduce LookAtChat, a web-based video conferencing system, which empowers remote users to identify gaze awareness and spatial relationships in small-group conversations. Leveraging real-time eye-tracking technology available with ordinary webcams, LookAtChat tracks each user's gaze direction, identifies who is looking at whom, and provides corresponding spatial cues. Informed by formative interviews with 5 participants who regularly use videoconferencing software, we explored the design space of gaze visualization in both 2D and 3D layouts. We further conducted an exploratory user study (N=20) to evaluate LookAtChat in three conditions: baseline layout, 2D directional layout, and 3D perspective layout. Our findings demonstrate how LookAtChat engages participants in small-group conversations, how gaze and spatial information improve conversation quality, and the potential benefits and challenges to incorporating gaze awareness visualization into existing videoconferencing systems.
The z ∼ 2 [O iii] Luminosity Function of Grism-selected Emission-line Galaxies

Upcoming missions such as Euclid and the Nancy Grace Roman Space Telescope (Roman) will use emission-line-selected galaxies to address a variety of questions in cosmology and galaxy evolution in the z > 1 universe. The optimal observing strategy for these programs relies on knowing the number of galaxies that will be found and the bias of the galaxy population. Here we measure the [O iii] λ5007 luminosity function for a vetted sample of 1951 m J+JH+H < 26 galaxies with unambiguous redshifts between 1.90 < z < 2.35, which were selected using Hubble Space Telescope (HST)/WFC3 G141 grism frames made available by the 3D-HST program. These systems are directly analogous to the galaxies that will be identified by the Euclid and Roman missions, which will utilize grism spectroscopy to find [O iii] λ5007-emitting galaxies at 0.8 ≲ z ≲ 2.7 and 1.7 ≲ z ≲ 2.8, respectively. We interpret our results in the context of the expected number counts for these upcoming missions. Finally, we combine our dust-corrected [O iii] luminosities with rest-frame ultraviolet star formation rates to present a new calibration of the star formation rate density associated with 1.90 < z < 2.35 [O iii]-emitting galaxies. We find that these grism-selected galaxies contain roughly half of the total star formation activity at z ∼ 2.
Exploring Visualization Implementation Challenges Faced by D3 Users Online

Visualization languages help to standardize the process of designing effective visualizations, one of the most prominent being D3. However, few researchers have analyzed at scale how users incorporate these languages into existing visualization programming processes, i.e., implementation workflows . In this paper, we present a new method for evaluating visualization languages. Our method emphasizes the experiences of users as observed through the online communities that have sprouted to facilitate public discussion and support around visualization languages. We demonstrate our method by analyzing D3 implementation workflows and challenges discussed on Stack Overflow. Our results show how the visualization community may be limiting its understanding of users’ visualization implementation challenges by ignoring the larger context in which languages such as D3 are used. Based on our findings, we suggest new research directions to enhance the user experience with visualization languages. All our data and code are available at: https://osf.io/fup48/? view_only=2cdecfd00f1048a98064b381bde27c18. CCS Concepts: • Human-centered computing → Empirical studies in visualization ; Visualization design and evaluation methods
Experimental Study of the Rate of Evaporation from Porous Media of Different Matrices

Evaporation and boiling from porous media has drawn a broad attention in many applications ranging from industrial, environmental to engineering and technologies. However, prediction of evaporation rates from porous media remains a challenge due to complex interactions between ambient conditions and the porous media properties such as complex geometry, material of the matrices composing porous medium, many length scales, surface phenomena, and effective transport coefficients. In this study, the evaporation rates of distilled water from porous media of different matrices were studied experimentally. Porous media were made up from spherical brass balls of different diameters of 2 mm, 3 mm, 4 mm and 5 mm. The temperature profiles across porous media, air temperature, humidity, and amount of water evaporated were measured in experiments. The effects of the porosity, the thickness of a porous layer, and particle size on the evaporation from porous media were investigated. Different materials of the porous matrices have allowed bringing to light the influence of the material properties. The experimental data show that the rate of evaporation of distilled water with brass balls is higher than that of only distilled water without a porous medium embedded into the water layer and does not change significantly after a steady state condition is achieved.
Understanding parents' perceptions of children's cybersecurity awareness in Norway

Children are increasingly using the internet nowadays. While internet use exposes children to various privacy and security risks, few studies have examined how parents perceive and address their children's cybersecurity risks. To address this gap, we conducted a qualitative study with 25 parents living in Norway with children aged between 10 to 15. We conducted semi-structured interviews with the parents and performed a thematic analysis of the interview data. The results of this paper include a list of cybersecurity awareness needs for children from a parental perspective, a list of learning resources for children, and a list of challenges for parents to ensure cybersecurity at home. Our results are useful for developers and educators in developing cybersecurity solutions for children. Future research should focus on defining cybersecurity theories and practices that contribute to children's and parents' awareness about cybersecurity risks, needs, and solutions.
Dynamic Relevance Learning for Few-Shot Object Detection

Expensive bounding-box annotations have limited the development of object detection task. Thus, it is necessary to focus on more challenging task of few-shot object detection. It requires the detector to recognize objects of novel classes with only a few training samples. Nowadays, many existing popular methods adopting training way similar to meta-learning have achieved promising performance, such as Meta R-CNN series. However, support data is only used as the class attention to guide the detecting of query images each time. Their relevance to each other remains unexploited. Moreover, a lot of recent works treat the support data and query images as independent branch without considering the relationship between them. To address this issue, we propose a dynamic relevance learning model, which utilizes the relationship between all support images and Region of Interest (RoI) on the query images to construct a dynamic graph convolutional network (GCN). By adjusting the prediction distribution of the base detector using the output of this GCN, the proposed model serves as a hard auxiliary classification task, which guides the detector to improve the class representation implicitly. Comprehensive experiments have been conducted on Pascal VOC and MS-COCO dataset. The proposed model achieves the best overall performance, which shows its effectiveness of learning more generalized features. Our code is available at https://github.com/liuweijie19980216/DRL-for-FSOD.
Regret Analysis of Learning-Based MPC with Partially-Unknown Cost Function

The exploration/exploitation trade-off is an inherent challenge in data-driven adaptive control. Though this trade-off has been studied for multi-armed bandits (MAB's) and reinforcement learning for linear systems; it is less well-studied for learning-based control of nonlinear systems. A significant theoretical challenge in the nonlinear setting is that there is no explicit characterization of an optimal controller for a given set of cost and system parameters. We propose the use of a finite-horizon oracle controller with full knowledge of parameters as a reasonable surrogate to optimal controller. This allows us to develop policies in the context of learning-based MPC and MAB's and conduct a control-theoretic analysis using techniques from MPC- and optimization-theory to show these policies achieve low regret with respect to this finite-horizon oracle. Our simulations exhibit the low regret of our policy on a heating, ventilation, and air-conditioning model with partially-unknown cost function.
Quantum logical entropy: fundamentals and general properties

Logical entropy gives a measure, in the sense of measure theory, of the distinctions of a given partition of a set, an idea that can be naturally generalized to classical probability distributions. Here, we analyze how this fundamental concept and other related definitions can be applied to the study of quantum systems with the use of quantum logical entropy. Moreover, we prove several properties of this entropy for generic density matrices that may be relevant to various areas of quantum mechanics and quantum information. Furthermore, we extend the notion of quantum logical entropy to post-selected systems.
Chaos Synchronization using Nonlinear Observers with applications to Cryptography

The goal of this survey paper is to provide an introduction to chaos synchronization using nonlinear observers and its applications in cryptography. I start with an overview of cryptography. Then, I recall the basics of chaos theory and how to use chaotic systems for cryptography, with an introduction to the problem of chaos synchronization. Then, I present the theory of non-linear observers, which is used for the synchronization of chaotic systems. I start with an explanation of the observability problem. Then, I introduce some of the classical observers: Kalman filter, Luenberger observer, Extended Kalman filter, Thau's observer, and High gain observer. I finish by introducing the more advanced observers: Adaptive observers, Unknown inputs observers, Sliding mode observers and ANFIS (Adaptive Neuro-Fuzzy Inference Systems) observers.
A momentum preserving frictional contact algorithm based on affine particle-in-cell grid transfers

An efficient and momentum conserving algorithm for enforcing contact between solid bodies is proposed. Previous advances in the material point method (MPM) led to a fast and simple, but potentially momentum violating, strategy for enforcing contact. This was achieved through a combination of velocity transfers between background and foreground grids, and a background grid velocity field update. We propose a modified strategy which ensures conservation of both linear and angular momentum with a novel use of the affine particle-in-cell (APIC) method. Two issues common to particle-in-cell based algorithms for contact are also addressed: material bodies tend to stick at a gap which is proportional to the grid spacing; and material points tend to stick together permanently when located within the same grid cell, making material rebound and friction challenging. We show that the use of APIC, combined with a grid transfer and momentum update algorithm results in contact being enforced at essentially zero gap. For the second issue, we propose a novel iterative scheme which allows particles interacting through the background grid to naturally separate after contact and enforce friction, while still satisfying momentum conservation.
Aspis: A Robust Detection System for Distributed Learning

State of the art machine learning models are routinely trained on large scale distributed clusters. Crucially, such systems can be compromised when some of the computing devices exhibit abnormal ( Byzantine ) behavior and return arbitrary results to the parameter server (PS). This behavior may be attributed to a plethora of reasons including system failures and orchestrated attacks. Existing work suggests robust aggregation and/or computational redundancy to alleviate the effect of distorted gradients. However, most of these schemes are ineffective when an adversary knows the task assignment and can judiciously choose the attacked workers to induce maximal damage. Our proposed method Aspis assigns gradient computations to worker nodes using a subset-based assignment which allows for multiple consistency checks on the behavior of a worker node. Examination of the calculated gradients and post-processing (clique-ﬁnding in an appropriately constructed graph) by the central node allows for efﬁcient detection and subsequent exclusion of adversaries from the training process. We prove the Byzantine resilience and detection guarantees of Aspis under weak and strong attacks and extensively evaluate the system on various large-scale training scenarios. The main metric for our experiments is the test accuracy for which we demonstrate signiﬁ-cant improvement of about 30% compared to many state-of-the-art approaches on the CIFAR-10 dataset. The corresponding reduction of the fraction of corrupted gradients ranges from 16% to 98%.
Saxl graphs of primitive affine groups with sporadic point stabilizers

Let $G$ be a permutation group on a set $\Omega$. A base for $G$ is a subset of $\Omega$ whose pointwise stabiliser is trivial, and the base size of $G$ is the minimal cardinality of a base. If $G$ has base size $2$, then the corresponding Saxl graph $\Sigma(G)$ has vertex set $\Omega$ and two vertices are adjacent if they form a base for $G$. A recent conjecture of Burness and Giudici states that if $G$ is a finite primitive permutation group with base size $2$, then $\Sigma(G)$ has the property that every two vertices have a common neighbour. We investigate this conjecture when $G$ is an affine group and a point stabiliser is an almost quasisimple group whose unique quasisimple subnormal subgroup is a covering group of a sporadic simple group. We verify the conjecture under this assumption, in all but ten cases.
Spatial statistics in star-forming regions: is star formation driven by column density alone?

Star formation is known to occur more readily where more raw materials are available. This is often expressed by a ‘Kennicutt–Schmidt’ relation where the surface density of young stellar objects (YSOs) is proportional to column density to some power, μ. The aim of this work was to determine if column density alone is sufficient to explain the locations of Class 0/I YSOs within Serpens South, Serpens Core, Ophiuchus, NGC 1333, and IC348, or if there is clumping or avoidance that would point to additional influences on the star formation. Using the O-ring test as a summary statistic, 95 per cent confidence envelopes were produced for different values of μ from probability models made using the Herschel column density maps. The YSOs were tested against four distribution models: the best estimate of μ for the region, μ = 0 above a minimum column density threshold, and zero probability elsewhere, μ = 1, and the power law that best represents the five regions as a collective, μ = 2.05 ± 0.20. Results showed that μ = 2.05 model was consistent with the majority of regions and, for those regions, the spatial distribution of YSOs at a given column density is consistent with being random. Serpens South and NGC 1333 rejected the μ = 2.05 model on small scales of ${\sim}0.15\, \mathrm{pc}$ which implies that small-scale interactions may be necessary to improve the model. On scales above 0.15 pc, the positions of YSOs in all five regions can be well described using column density alone.