Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Job Search Skills: A Field Experiment on Online Employment Assistance

Finding a job requires effective search skills to engage successfully with employers with vacancies. In a field experiment, we test a website that supplements such search skills by providing editable resume and cover letter templates as well as tips on how to look and apply for jobs. Exposure to the website was randomized among about 2,700 job seekers in Australia. The intervention increased job-finding rates, particularly among job seekers aged 35-50 (up to 8 percentage points), with larger effects for women within this age group (up to 10 percentage points). The quality of job matches improved too.
Exploring the diversity of flies (Diptera)

ABSTRACT Flies (Diptera) are an important but underappreciated part of our planet's biodiversity. With over 124,000 described species, and countless more awaiting discovery, they are one of the most diverse groups of organisms on Earth. This series of ten papers explores the diversity of Diptera. Several authors describe the diversity of dipteran lifestyles and behaviours, both as larvae and adults. They also reveal the various roles that these animals play in the ecological interactions of the planet—countless numbers of flies feed on plants, control pest arthropods (including other flies! ), break down rotting vegetation and excrement, pollinate flowers, provide food for other species, and of course, spread diseases. Indeed, because of their role as vectors of disease, flies have almost single-handedly prevented the economic development of countries in tropical Africa and South America. But flies are used in positive ways by humans, too, and several authors describe their use in forensic science, molecular research, and even as “main attractions” in the tourism industry. The intent of this series of papers is to encourage a broader interest in Diptera that, ideally, will lead to further research and conservation efforts.
Progress in Neuropsychopharmacology & Biological Psychiatry

Recently, objective and automated methods for the diagnosis of post-traumatic stress disorder (PTSD) have attracted increasing attention. However, previous studies on machine-learning-based diagnosis of PTSD with resting-state electroencephalogram (EEG) have reported poor accuracies of as low as 60%. Here, a Riemannian geometry-based classi ﬁ er, the Fisher geodesic minimum distance to the mean (FgMDM), was employed for PTSD classi ﬁ cation for the ﬁ rst time. Eyes-closed resting-state EEG data of 39 healthy individuals and 42 PTSD patients were used for the analysis. EEG source activities in 148 cortical regions were parcellated based on the Destrieux atlas
Human Strategic Steering Improves Performance of Interactive Optimization

A central concern in an interactive intelligent system is optimization of its actions, to be maximally helpful to its human user. In recommender systems for instance, the action is to choose what to recommend, and the optimization task is to recommend items the user prefers. The optimization is done based on earlier user's feedback (e.g. "likes" and "dislikes"), and the algorithms assume the feedback to be faithful. That is, when the user clicks "like," they actually prefer the item. We argue that this fundamental assumption can be extensively violated by human users, who are not passive feedback sources. Instead, they are in control, actively steering the system towards their goal. To verify this hypothesis, that humans steer and are able to improve performance by steering, we designed a function optimization task where a human and an optimization algorithm collaborate to find the maximum of a 1-dimensional function. At each iteration, the optimization algorithm queries the user for the value of a hidden function f at a point x, and the user, who sees the hidden function, provides an answer about f(x). Our study on 21 participants shows that users who understand how the optimization works, strategically provide biased answers (answers not equal to f(x)), which results in the algorithm finding the optimum significantly faster. Our work highlights that next-generation intelligent systems will need user models capable of helping users who steer systems to pursue their goals.
Synthesis of Heterocycle-linked Thioureas and Their Inhibitory Activities of NO Production in LPS Activated Macrophages

A series of thioureas were synthesized as inhibitors of NO production in lipopolysaccharide-activated macrophages. We investigated the effect of lipophilic moiety and N-substituents of the thioureas on the activity. Phenoxazine and carbazole-containing derivatives revealed higher activity than indole-containing thioureas. The appropriate spacer between lipophilic tail and thiourea head and methyl substituent at N3 position of thiourea brought beneficial effect on the inhibition of NO production. Among prepared compounds, phenoxazine-containing derivative 2a was the most potent with 2.32 μM of IC 50 value. RT-PCR analysis suggested that the prepared thioureas inhibited NO production through the suppression of iNOS mRNA expression.
Energy mu-Calculus: Symbolic Fixed-Point Algorithms for omega-Regular Energy Games

$\omega$-regular energy games, which are weighted two-player turn-based games with the quantitative objective to keep the energy levels non-negative, have been used in the context of verification and synthesis. The logic of modal $\mu$-calculus, when applied over game graphs with $\omega$-regular winning conditions, allows defining symbolic algorithms in the form of fixed-point formulas for computing the sets of winning states. In this paper, we introduce energy $\mu$-calculus, a multi-valued extension of the $\mu$-calculus that serves as a symbolic framework for solving $\omega$-regular energy games. Energy $\mu$-calculus enables the seamless reuse of existing, well-known symbolic $\mu$-calculus algorithms for $\omega$-regular games, to solve their corresponding energy augmented variants. We define the syntax and semantics of energy $\mu$-calculus over symbolic representations of the game graphs, and show how to use it to solve the decision and the minimum credit problems for $\omega$-regular energy games, for both bounded and unbounded energy level accumulations.
Quasi-particle functional Renormalisation Group calculations in the two-dimensional half-filled Hubbard model at finite temperatures

We present a highly parallelisable scheme for treating functional Renormalisation Group equations which incorporates a quasi-particle-based feedback on the flow and provides direct access to real-frequency self-energy data. This allows to map out the boundaries of Fermi-liquid regimes and to study the effect of quasi-particle degradation near Fermi liquid instabilities. As a first application, selected results for the two-dimensional half-filled perfectly nested Hubbard model are shown.
Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities

Remote sensing image scene classification, which aims at labeling remote sensing images with a set of semantic categories based on their contents, has broad applications in a range of fields. Propelled by the powerful feature learning capabilities of deep neural networks, remote sensing image scene classification driven by deep learning has drawn remarkable attention and achieved significant breakthroughs. However, to the best of our knowledge, a comprehensive review of recent achievements regarding deep learning for scene classification of remote sensing images is still lacking. Considering the rapid evolution of this field, this article provides a systematic survey of deep learning methods for remote sensing image scene classification by covering more than 160 papers. To be specific, we discuss the main challenges of remote sensing image scene classification and survey: first, autoencoder-based remote sensing image scene classification methods; second, convolutional neural network-based remote sensing image scene classification methods; and third, generative adversarial network-based remote sensing image scene classification methods. In addition, we introduce the benchmarks used for remote sensing image scene classification and summarize the performance of more than two dozen of representative algorithms on three commonly used benchmark datasets. Finally, we discuss the promising opportunities for further research.
Oblique Decision Trees from Derivatives of ReLU Networks

We show how neural models can be used to realize piece-wise constant functions such as decision trees. The proposed architecture, which we call locally constant networks, builds on ReLU networks that are piece-wise linear and hence their associated gradients with respect to the inputs are locally constant. We formally establish the equivalence between the classes of locally constant networks and decision trees. Moreover, we highlight several advantageous properties of locally constant networks, including how they realize decision trees with parameter sharing across branching / leaves. Indeed, only $M$ neurons suffice to implicitly model an oblique decision tree with $2^M$ leaf nodes. The neural representation also enables us to adopt many tools developed for deep networks (e.g., DropConnect (Wan et al., 2013)) while implicitly training decision trees. We demonstrate that our method outperforms alternative techniques for training oblique decision trees in the context of molecular property classification and regression tasks.
A Tight Lower Bound on Adaptively Secure Full-Information Coin Flip

In a distributed coin-flipping protocol, Blum [ACM Transactions on Computer Systems '83], the parties try to output a common (close to) uniform bit, even when some adversarially chosen parties try to bias the common output. In an adaptively secure full-information coin flip, Ben-Or and Linial [FOCS '85], the parties communicate over a broadcast channel and a computationally unbounded adversary can choose which parties to corrupt along the protocol execution. Ben-Or and Linial proved that the $n$-party majority protocol is resilient to $O(\sqrt{n})$ corruptions (ignoring poly-logarithmic factors), and conjectured this is a tight upper bound for any n-party protocol (of any round complexity). Their conjecture was proved to be correct for single-turn (each party sends a single message) single-bit (a message is one bit) protocols Lichtenstein, Linial, and Saks [Combinatorica '89], symmetric protocols Goldwasser, Tauman Kalai, and Park [ICALP '15], and recently for (arbitrary message length) single-turn protocols Tauman Kalai, Komargodski, and Raz [DISC '18]. Yet, the question for many-turn protocols was left completely open. In this work we close the above gap, proving that no $n$-party protocol (of any round complexity) is resilient to $\omega(\sqrt{n})$ (adaptive) corruptions.
Generalized Knowledge Tracing: A Constrained Framework for Learner Modeling

Adaptive learning technology solutions often use a learner model to trace learning and make pedagogical decisions. The present research introduces a formalized methodology for specifying learner models, Generalized Knowledge Tracing, GKT, that consolidates many extant learner modeling methods. The strength of GKT is the specification of a symbolic notation system for alternative logistic regression models that is powerful enough to specify many extant models in the literature, as well as many new models. To demonstrate the generality of GKT, it was used to fit 12 models, some variants of well-known models and some newly devised, to 6 learning technology datasets. The results indicated that no single learner model was best in all cases, further justifying a broad approach that considers multiple learner model features and the learning context. To strengthen the applicability to learning technology, the models presented here avoid student-level fixed parameters, since these are difficult to acquire in practice. We argue that to be maximally applicable a learner model needs to adapt to student differences, rather than needing to be pre-parameterized with the level of each student's ability.
SModelS Database Update v1.2.3

We present an update of the SModelS database with simplified model results from 13 ATLAS and 10 CMS searches for supersymmetry at Run 2. This includes 5 ATLAS and 1 CMS analyses for full Run 2 luminosity, i.e. close to 140/fb of data. In total, 76 official upper limit and efficiency map results have been added. Moreover, 21 efficiency map results have been produced by us using MadAnalysis5, to improve the coverage of gluino-squark production. The constraining power of the new database, v1.2. 3, is compared to that of the previous release, v1.2. 2. SModelS v1.2. 3 is publicly available and can readily be employed for physics studies.
Lagrangian neural style transfer for fluids

Artistically controlling the shape, motion and appearance of fluid simulations pose major challenges in visual effects production. In this paper, we present a neural style transfer approach from images to 3D fluids formulated in a Lagrangian viewpoint. Using particles for style transfer has unique benefits compared to grid-based techniques. Attributes are stored on the particles and hence are trivially transported by the particle motion. This intrinsically ensures temporal consistency of the optimized stylized structure and notably improves the resulting quality. Simultaneously, the expensive, recursive alignment of stylization velocity fields of grid approaches is unnecessary, reducing the computation time to less than an hour and rendering neural flow stylization practical in production settings. Moreover, the Lagrangian representation improves artistic control as it allows for multi-fluid stylization and consistent color transfer from images, and the generality of the method enables stylization of smoke and liquids likewise.
Estimating road traffic impacts of commute mode shifts

This work considers the sensitivity of commute travel times in US metro areas due to potential changes in commute patterns, for example caused by events such as pandemics. Permanent shifts away from transit and carpooling can add vehicles to congested road networks, increasing travel times. Growth in the number of workers who avoid commuting and work from home instead can offset travel time increases. To estimate these potential impacts, 6-9 years of American Community Survey commute data for 118 metropolitan statistical areas are investigated. For 74 of the metro areas, the average commute travel time is shown to be explainable using only the number of passenger vehicles used for commuting. A universal Bureau of Public Roads model characterizes the sensitivity of each metro area with respect to additional vehicles. The resulting models are then used to determine the change in average travel time for each metro area in scenarios when 25% or 50% of transit and carpool users switch to single occupancy vehicles. Under a 25% mode shift, areas such as San Francisco and New York that are already congested and have high transit ridership may experience round trip travel time increases of 12 minutes (New York) to 20 minutes (San Francisco), costing individual commuters $1065 and $1601 annually in lost time. The travel time increases and corresponding costs can be avoided with an increase in working from home. The main contribution of this work is to provide a model to quantify the potential increase in commute travel times under various behavior changes, that can aid policy making for more efficient commuting.
Words Aren’t Enough, Their Order Matters: On the Robustness of Grounding Visual Referring Expressions

Visual referring expression recognition is a challenging task that requires natural language understanding in the context of an image. We critically examine RefCOCOg, a standard benchmark for this task, using a human study and show that 83.7% of test instances do not require reasoning on linguistic structure, i.e., words are enough to identify the target object, the word order doesn’t matter. To measure the true progress of existing models, we split the test set into two sets, one which requires reasoning on linguistic structure and the other which doesn’t. Additionally, we create an out-of-distribution dataset Ref-Adv by asking crowdworkers to perturb in-domain examples such that the target object changes. Using these datasets, we empirically show that existing methods fail to exploit linguistic structure and are 12% to 23% lower in performance than the established progress for this task. We also propose two methods, one based on contrastive learning and the other based on multi-task learning, to increase the robustness of ViLBERT, the current state-of-the-art model for this task. Our datasets are publicly available at https://github.com/aws/aws-refcocog-adv.
Relaxant Action of Azulene‐1‐carboxamidine Derivative N1, N1‐dimethyl‐N2‐(2‐pyridylmethyl)‐5‐isopropyl‐3,8‐dimethylazulene‐1‐carboxamidine (HNS‐32) in Pig Coronary Artery

HNS-32 (N1N1-dimethyl-N2-(2-pyridylmethyl)-5-isopropyl-3,8-dimethylazulene-1-carbox-amidine) (CAS 186086-10-2) is a newly synthesized compound with an azulene structure within the molecule. The coronary relaxant action of HNS-32 was investigated pharmaco-mechanically on isolated pig coronary artery. The effects of HNS-32 were compared with diltiazem, a Ca2+-channel blocker. HNS-32 inhibited sustained contractions evoked by high KCl, prostaglandin F2α, a thromboxane A2 mimetic (U46619) and endothelin-1 in a concentration-dependent manner. The potency of HNS-32 to inhibit these contractions was 5- to 40-times lower than diltiazem. HNS-32 also diminished phasic contractions induced by acetylcholine, histamine and 5-hydroxytryptamine. Addition of excess Ca2+ counteracted HNS-32-induced inhibition of high KCl-induced contraction only by approximately 10% whereas it restored diltiazem-induced inhibition by about 50%. Suppression of the contractile response to a phorbol ester (phorbol 12,13-dibutyrate) by HNS-32 was approximately 40%. HNS-32 prevents coronary contractions produced by a wide variety of spasmogens. Although inhibitions of L-type Ca2+ channels and protein kinase C may be partly responsible for HNS-32 action, some direct action on the contractile systems seems to be involved in the coronary relaxation by HNS-32.
A Concise Yet Effective Model for Non-Aligned Incomplete Multi-View and Missing Multi-Label Learning

In reality, learning from multi-view multi-label data inevitably confronts three challenges: missing labels, incomplete views, and non-aligned views. Existing methods mainly concern the first two and commonly need multiple assumptions to attack them, making even state-of-the-arts involve at least two explicit hyper-parameters such that model selection is quite difficult. More toughly, they will fail in handling the third challenge, let alone addressing the three jointly. In this paper, we aim at meeting these under the least assumption by building a concise yet effective model with just one hyper-parameter. To ease insufficiency of available labels, we exploit not only the consensus of multiple views but also the global and local structures hidden among multiple labels. Specifically, we introduce an indicator matrix to tackle the first two challenges in a regression form while aligning the same individual labels and all labels of different views in a common label space to battle the third challenge. In aligning, we characterize the global and local structures of multiple labels to be high-rank and low-rank, respectively. Subsequently, an efficient algorithm with linear time complexity in the number of samples is established. Finally, even without view-alignment, our method substantially outperforms state-of-the-arts with view-alignment on five real datasets.
A Comparative Study of Image Quality Assessment Models through Perceptual Optimization

The performance of objective image quality assessment (IQA) models has been evaluated primarily by comparing model predictions to human judgments. Perceptual datasets (e.g., LIVE and TID2013) gathered for this purpose provide useful benchmarks for improving IQA methods, but their heavy use creates a risk of overfitting. Here, we perform a large-scale comparison of perceptual IQA models in terms of their use as objectives for the optimization of image processing algorithms. Specifically, we evaluate eleven fullreference IQA models by using them as objective functions to train deep neural networks for four low-level vision tasks: denoising, deblurring, super-resolution, and compression. Extensive subjective testing on the optimized images allows us to rank the competing models in terms of their perceptual performance, elucidate their relative advantages and disadvantages for these tasks, and propose a set of desirable properties for incorporation into future IQA models.
Antioxidative Effect of Glucose Oxidase and Catalase in Mayonnaises of Different Oxidative Susceptibility. I. Product Trials

The antioxidative effect of the enzyme system glucose oxidase/catalase (GOX/CAT) in mayonnaises containing different amounts of fish oil stored at 25 °C was investigated. A complete block experiment was carried out with enzyme concentrations of 0, 400 and 800 units/kg, fish oil fractions of 0, 0.25 and 0.50 of the oil phase, and packaging of the mayonnaises in oxygen-permeable and nonpermeable packaging materials. From measurements of headspace oxygen, glucose, lipid hydroperoxides and volatile aldehydes it was concluded that GOX/CAT exerted an antioxidative effect. The enzyme concentration needed to obtain a definite degree of retardation of lipid oxidation was shown to increase with the lipid oxidation rate. This shows that the antioxidative mechanism is scavenging of oxygen by glucose oxidation in competition with the lipid oxidation reaction. In an additional storage experiment at 5 °C (with fewer factors) the antioxidative effect of GOX/CAT was confirmed by chemical analysis. An off-flavour of unknown cause was, however, observed to develop in the mayonnaises containing both fish oil and GOX/CAT.
Finite Volume approximation of a two-phase two fluxes degenerate Cahn-Hilliard model

We study a time implicit Finite Volume scheme for degenerate Cahn–Hilliard model proposed in [W. E and P. Palffy-Muhoray, Phys. Rev. E 55 (1997) R3844–R3846] and studied mathematically by the authors in [C. Cancès, D. Matthes and F. Nabet, Arch. Ration. Mech. Anal. 233 (2019) 837–866]. The scheme is shown to preserve the key properties of the continuous model, namely mass conservation, positivity of the concentrations, the decay of the energy and the control of the entropy dissipation rate. This allows to establish the existence of a solution to the nonlinear algebraic system corresponding to the scheme. Further, we show thanks to compactness arguments that the approximate solution converges towards a weak solution of the continuous problems as the discretization parameters tend to 0. Numerical results illustrate the behavior of the numerical model.
Interplay between k-core and community structure in complex networks

The organisation of a network in a maximal set of nodes having at least $k$ neighbours within the set, known as $k$-core decomposition, has been used for studying various phenomena. It has been shown that nodes in the innermost $k$-shells play a crucial role in contagion processes, emergence of consensus, and resilience of the system. It is known that the $k$-core decomposition of many empirical networks cannot be explained by the degree of each node alone, or equivalently, random graph models that preserve the degree of each node (i.e., configuration model). Here we study the $k$-core decomposition of some empirical networks as well as that of some randomised counterparts, and examine the extent to which the $k$-shell structure of the networks can be accounted for by the community structure. We find that preserving the community structure in the randomisation process is crucial for generating networks whose $k$-core decomposition is close to the empirical one. We also highlight the existence, in some networks, of a concentration of the nodes in the innermost $k$-shells into a small number of communities.