Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial Transfer Attacks With Unknown Data and Class Overlap

The ability to transfer adversarial attacks from one model (the surrogate) to another model (the victim) has been an issue of concern within the machine learning (ML) community. The ability to successfully evade unseen models represents an uncomfortable level of ease toward implementing attacks. In this work we note that as studied, current transfer attack research has an unrealistic advantage for the attacker: the attacker has the exact same training data as the victim. We present the first study of transferring adversarial attacks focusing on the data available to attacker and victim under imperfect settings without querying the victim, where there is some variable level of overlap in the exact data used or in the classes learned by each model. This threat model is relevant to applications in medicine, malware, and others. Under this new threat model attack success rate is not correlated with data or class overlap in the way one would expect, and varies with dataset. This makes it difficult for attacker and defender to reason about each other and contributes to the broader study of model robustness and security. We remedy this by developing a masked version of Projected Gradient Descent that simulates class disparity, which enables the attacker to reliably estimate a lower-bound on their attack's success.
Signature and escape of highly fractionated plasma in an active region

Accurate forecasting of space weather requires knowledge of the source regions where solar energetic particles (SEP) and eruptive events originate. Recent work has linked several major SEP events in 2014, January, to specific features in the host active region (AR 11944). In particular, plasma composition measurements in and around the footpoints of hot, coronal loops in the core of the active region were able to explain the values later measured in situ by the Wind spacecraft. Due to important differences in elemental composition between SEPs and the solar wind, the magnitude of the Si/S elemental abundance ratio emerged as a key diagnostic of SEP seed population and solar wind source locations. We seek to understand if the results are typical of other active regions, even if they are not solar wind sources or SEP productive. In this paper, we use a novel composition analysis technique, together with an evolutionary magnetic field model, in a new approach to investigate a typical solar active region (AR 11150), and identify the locations of highly fractionated (high Si/S abundance ratio) plasma. Material confined near the footpoints of coronal loops, as in AR 11944, that in this case have expanded to the AR periphery, show the signature, and can be released from magnetic field opened by reconnection at the AR boundary. Since the fundamental characteristics of closed field loops being opened at the AR boundary is typical of active regions, this process is likely to be general.
A special case of Vu's conjecture: Coloring nearly disjoint graphs of bounded maximum degree

A collection of graphs is \textit{nearly disjoint} if every pair of them intersects in at most one vertex. We prove that if $G_1, \dots, G_m$ are nearly disjoint graphs of maximum degree at most $D$, then the following holds. For every fixed $C$, if each vertex $v \in \bigcup_{i=1}^m V(G_i)$ is contained in at most $C$ of the graphs $G_1, \dots, G_m$, then the (list) chromatic number of $\bigcup_{i=1}^m G_i$ is at most $D + o(D)$. This result confirms a special case of a conjecture of Vu and generalizes Kahn's bound on the list chromatic index of linear uniform hypergraphs of bounded maximum degree. In fact, this result holds for the correspondence (or DP) chromatic number and thus implies a recent result of Molloy, and we derive this result from a more general list coloring result in the setting of `color degrees' that also implies a result of Reed and Sudakov.
Results from the Baksan Experiment on Sterile Transitions (BEST).

The Baksan Experiment on Sterile Transitions (BEST) was designed to investigate the deficit of electron neutrinos ν_{e} observed in previous gallium-based radiochemical measurements with high-intensity neutrino sources, commonly referred to as the "gallium anomaly," which could be interpreted as evidence for oscillations between ν_{e} and sterile neutrino (ν_{s}) states. A 3.414-MCi ^{51}Cr ν_{e} source was placed at the center of two nested Ga volumes and measurements were made of the production of ^{71}Ge through the charged current reaction, ^{71}Ga(ν_{e},e^{-})^{71}Ge, at two average distances. The measured production rates for the inner and the outer targets, respectively, are [54. 9_{-2.4}^{+2.5}(stat)±1.4(syst)] and [55. 6_{-2.6}^{+2.7}(stat)±1.4(syst)] atoms of ^{71}Ge/d. The ratio (R) of the measured rate of ^{71}Ge production at each distance to the expected rate from the known cross section and experimental efficiencies are R_{in}=0.79±0.05 and R_{out}=0.77±0.05. The ratio of the outer to the inner result is 0.97±0.07, which is consistent with unity within uncertainty. The rates at each distance were found to be similar, but 20%-24% lower than expected, thus reaffirming the anomaly. These results are consistent with ν_{e}→ν_{s} oscillations with a relatively large Δm^{2} (>0.5 eV^{2}) and mixing sin^{2}2θ (≈0.4).
The First Retrieval of a Substellar Subdwarf: A Cloud-free SDSS J125637.13–022452.4

We present the first retrieval analysis of a substellar subdwarf, SDSS J125637.13−022452.4 (SDSS J1256−0224), using the Brewster retrieval code base. We find SDSS J1256−0224 is best fit by a cloud-free model with an ion (neutral H, H−, and electron) abundance corresponding to Fe/Hion=−1.5 . However, this model is indistinguishable from a cloud-free model with Fe/Hion=−2.0 and a cloud-free model with Fe/Hion=−1.5 assuming a subsolar carbon-to-oxygen ratio. We are able to constrain abundances for H2O, FeH, and CrH, with an inability to constrain any carbon-bearing species likely due to the low metallicity of SDSS J1256−0224. We also present an updated spectral energy distribution (SED) and semiempirical fundamental parameters. Our retrieval- and SED-based fundamental parameters agree with the Baraffe low-metallicity evolutionary models. From examining our “rejected” models (those with ΔBIC > 45), we find that we are able to retrieve gas abundances consistent with those of our best fitting model. We find the cloud in these poorer fitting “cloudy” models is either pushed to the bottom of the atmosphere or made optically thin.
The longest edge in discrete and continuous long-range percolation

We consider both a discrete and continuous variant of long-range percolation on $\mathbb{R}^d$ in which an edge between two points at distance $r$ is present with probability $\bar{g}(r)$. We elucidate yet unrevealed properties these models exhibit, namely by investigating the longest edge with at least one endpoint within some finite observation window, as the volume of this window tends to infinity. We show that the length of the latter in the continuous model, after normalizing by some appropriate centering and scaling sequences, asymptotically behaves like one of each of the three extreme value distributions, depending on choices of the probability $\bar{g}(r)$. In the discrete variant the results are more intriguing as we encounter subtle differences to the continuous model. In particular, our results highlight an unexpected behavior of the longest edge in the Fr\'echet case when $d=1$ and $\alpha=2$ in discrete long-range percolation. Parts of the proof employ the Chen-Stein method, a general result on Poisson approximation related to Stein's method and Palm calculus.
Revisit Geophysical Imaging in A New View of Physics-informed Generative Adversarial Learning

Seismic full waveform inversion (FWI) is a powerful geophysical imaging technique that produces high-resolution subsurface models by iteratively minimizing the misfit between the simulated and observed seismograms. Unfortunately, conventional FWI with least-squares function suffers from many drawbacks such as the local-minima problem and computation of explicit gradient. It is particularly challenging with the contaminated measurements or poor starting models. Recent works relying on partial differential equations and neural networks show promising performance for two-dimensional FWI. Inspired by the competitive learning of generative adversarial networks, we proposed an unsupervised learning paradigm that integrates wave equation with a discriminate network to accurately estimate the physically consistent models in a distribution sense. Our framework needs no labelled training data nor pretraining of the network, is flexible to achieve multi-parameters inversion with minimal user interaction. The proposed method faithfully recovers the well-known synthetic models that outperforms the classical algorithms. Furthermore, our work paves the way to sidestep the local-minima issue via reducing the sensitivity to initial models and noise.
The TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI). I. Dry Cases—The Fellowship of the GCMs

With the commissioning of powerful, new-generation telescopes such as the James Webb Space Telescope (JWST) and the ground-based Extremely Large Telescopes, the first characterization of a high molecular weight atmosphere around a temperate rocky exoplanet is imminent. Atmospheric simulations and synthetic observables of target exoplanets are essential to prepare and interpret these observations. Here we report the results of the first part of the TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI) project, which compares 3D numerical simulations performed with four state-of-the-art global climate models (ExoCAM, LMD-Generic, ROCKE-3D, Unified Model) for the potentially habitable target TRAPPIST-1e. In this first part, we present the results of dry atmospheric simulations. These simulations serve as a benchmark to test how radiative transfer, subgrid-scale mixing (dry turbulence and convection), and large-scale dynamics impact the climate of TRAPPIST-1e and consequently the transit spectroscopy signature as seen by JWST. To first order, the four models give results in good agreement. The intermodel spread in the global mean surface temperature amounts to 7 K (6 K) for the N2-dominated (CO2-dominated) atmosphere. The radiative fluxes are also remarkably similar (intermodel variations less than 5%), from the surface (1 bar) up to atmospheric pressures ∼5 mbar. Moderate differences between the models appear in the atmospheric circulation pattern (winds) and the (stratospheric) thermal structure. These differences arise between the models from (1) large-scale dynamics, because TRAPPIST-1e lies at the tipping point between two different circulation regimes (fast and Rhines rotators) in which the models can be alternatively trapped, and (2) parameterizations used in the upper atmosphere such as numerical damping.
Transient critical regime for light near the three-dimensional Anderson transition

Laura A. Cobus, 2, ∗ Georg Maret, and Alexandre Aubry † Institut Langevin, ESPCI Paris, CNRS, PSL University, 1 rue Jussieu, F-75005 Paris, France Dodd-Walls Centre for Photonic and Quantum Technologies, New Zealand and Department of Physics, University of Auckland, Private Bag 92109, Auckland, New Zealand Fachbereich Physik, Universität Konstanz, D-78457 Konstanz, Germany (Dated: September 24, 2021)
Modulating internal transition kinetics of responsive macromolecules by collective crowding.

Packing and crowding are used in biology as mechanisms to (self-)regulate internal molecular or cellular processes based on collective signaling. Here, we study how the transition kinetics of an internal "switch" of responsive macromolecules is modified collectively by their spatial packing. We employ Brownian dynamics simulations of a model of Responsive Colloids, in which an explicit internal degree of freedom-here, the particle size-moving in a bimodal energy landscape self-consistently responds to the density fluctuations of the crowded environment. We demonstrate that populations and transition times for the two-state switching kinetics can be tuned over one order of magnitude by "self-crowding. " An exponential scaling law derived from a combination of Kramers' and liquid state perturbation theory is in very good agreement with the simulations.
Mixed-supervised segmentation: Confidence maximization helps knowledge distillation

standard pseudo-mask generation, and argue that the former should be preferred over the latter for leveraging information from unlabeled pixels. We evaluate the eﬀectiveness of the proposed formulation through a series of quantitative and qualitative experiments using two publicly available datasets. Results demonstrate that our method signiﬁcantly outperforms other strategies for semantic segmentation within a mixed-supervision framework, as well as recent semi-supervised approaches. Moreover, in line with recent observations in clas-siﬁcation, we show that the branch trained with reduced supervision and guided by the top branch largely outperforms the latter. Our code is publicly available: https
Actionable Conversational Quality Indicators for Improving Task-Oriented Dialog Systems

Automatic dialog systems have become a mainstream part of online customer service. Many such systems are built, maintained, and improved by customer service specialists, rather than dialog systems engineers and computer programmers. As conversations between people and machines become commonplace, it is critical to understand what is working, what is not, and what actions can be taken to reduce the frequency of inappropriate system responses. These analyses and recommendations need to be presented in terms that directly reflect the user experience rather than the internal dialog processing. This paper introduces and explains the use of Actionable Conversational Quality Indicators (ACQIs), which are used both to recognize parts of dialogs that can be improved and to recommend how to improve them. This combines benefits of previous approaches, some of which have focused on producing dialog quality scoring while others have sought to categorize the types of errors the dialog system is making. We demonstrate the effectiveness of using ACQIs on LivePerson internal dialog systems used in commercial customer service applications and on the publicly available LEGOv2 conversational dataset. We report on the annotation and analysis of conversational datasets showing which ACQIs are important to fix in various situations. The annotated datasets are then used to build a predictive model which uses a turn-based vector embedding of the message texts and achieves a 79% weighted average f1-measure at the task of finding the correct ACQI for a given conversation. We predict that if such a model worked perfectly, the range of potential improvement actions a bot-builder must consider at each turn could be reduced by an average of 81%.
Informative and Representative Triplet Selection for Multilabel Remote Sensing Image Retrieval

Learning the similarity between remote sensing (RS) images forms the foundation for content-based RS image retrieval (CBIR). Recently, deep metric learning approaches that map the semantic similarity of images into an embedding (metric) space have been found very popular in RS. A common approach for learning the metric space relies on the selection of triplets of similar (positive) and dissimilar (negative) images to a reference image called an anchor. Choosing triplets is a difficult task particularly for multilabel RS CBIR, where each training image is annotated by multiple class labels. To address this problem, in this article, we propose a novel triplet sampling method in the framework of deep neural networks (DNNs) defined for multilabel RS CBIR problems. The proposed method selects a small set of the most representative and informative triplets based on two main steps. In the first step, a set of anchors that are diverse to each other in the embedding space is selected from the current minibatch using an iterative algorithm. In the second step, different sets of positive and negative images are chosen for each anchor by evaluating the relevancy, hardness, and diversity of the images among each other based on a novel strategy. Experimental results obtained on two multilabel benchmark archives show that the selection of the most informative and representative triplets in the context of DNNs results in: 1) reducing the computational complexity of the training phase of the DNNs without any significant loss on the performance and 2) an increase in learning speed since informative triplets allow fast convergence. The code of the proposed method is publicly available at https://git.tu-berlin.de/rsim/image-retrieval-from-triplets.
Health Workers’ Practice Towards Smoking Cessation Intervention Based on 5A’s Model and Associated Factors in Public Hospitals, Hadiya Zone, Southern Ethiopia

Background The tobacco pandemic is one of the world’s most serious public health concerns, killing more than 8 million lives per year. The worst burden is in low- and middle-income countries. Unless appropriate action is taken, the burden may worsen. Health workers are among the actors to implement smoke cessation interventions. However, the level of intervention practices towards smoke cessation was not defined. Objective To determine health workers’ practice towards smoke cessation interventions based on 5A’s model and associated factors in public hospitals. Methods Facility-based cross-sectional design was used. All public hospitals found in the Hadiya zone were included in the study. From each public hospital, representative number of health workers, who fulfil inclusion criteria, were included in the study. A total of 323 sample size of health workers were used. A self-administered questionnaire was employed to collect data. Both descriptive and advanced analyses were performed using SPSS software version 20.0. In bivariable analysis, variables with p-value < 0.25 were considered for multivariable analysis. Finally, odds ratio with 95% CI and p-value < 0.05 were used to declare factors as significantly associated with the outcome variable. Results About 97% of health care workers had poor practice on smoking cessation interventions. About 7.4% (23) health workers reported as they were current smokers. Moreover, only 28% of health workers asked their patients about smoking status. Being females, having <10 years of service, training and having good knowledge was significantly associated with the outcome variable. Conclusion Majority of the healthcare workers surveyed did not provide smoking cessation interventions. Identified statistically significant factors with the practice of smoke cessation interventions in this study were sex, knowledge, training and service years. Therefore, strategies should be designed and implemented to improve and equip the health workers towards practising of smoke cessation interventions by instituting smoke cessation programs.
The LEAF questionnaire is a good screening tool for the identification of the Female Athlete Triad/Relative Energy Deficiency in Sport among young football players

Background It has been noticed that Female Athlete Triad (Fat) and Relative Energy Deficiency (Red-S) in Sport are characterized by the symptoms of impaired endocrine-metabolic function and bone health in female athletes. In addition, it may be evaluated with a qualitative tool, such as Low Energy Availability in Females questionnaire (LEAF-Q) and quantitative measurements: bone mineral density (BMD), resting energy expenditure (REE), body composition, 24-hour dietary recall. Methods The aim of this study was to assess the prevalence of Triad and Red-S using the LEAF-Q in youth female football players. Additionally, the difference in the BMD, body composition, REE and energy intake (EI) were assessed between the Triad/Red-S risk and not at-risk groups. Results Almost two thirds (64.7%) of participants are classified as being at-risk for the triad according to their LEAF-Q scores. There were no statistically significant differences (p > 0.05) between most of the values among children from the analyzed groups. There was a statistically significant difference (p < 0.001) between the EI values among girls from the two analyzed groups: at-risk (1,773.18 kcal ± 232.57) and not at-risk (2,054.00 kcal ± 191.39). Girls who did not meet the energy intake recommendations were 10.00 as likely to be in the Triad/Red-S risk group. Conclusion Early identification of Fat/Red-S symptoms by screening tools such as the LEAF questionnaire is important in protecting young athletes from long-term damage due to the progression of the risk factors associated with the Fat/Red-S.
In silico analysis enabling informed design for genome editing in medicinal cannabis; gene families and variant characterisation

Background Cannabis has been used worldwide for centuries for industrial, recreational and medicinal use, however, to date no successful attempts at editing genes involved in cannabinoid biosynthesis have been reported. This study proposes and develops an in silico best practices approach for the design and implementation of genome editing technologies in cannabis to target all genes involved in cannabinoid biosynthesis. Results A large dataset of reference genomes was accessed and mined to determine copy number variation and associated SNP variants for optimum target edit sites for genotype independent editing. Copy number variance and highly polymorphic gene sequences exist in the genome making genome editing using CRISPR, Zinc Fingers and TALENs technically difficult. Evaluation of allele or additional gene copies was determined through nucleotide and amino acid alignments with comparative sequence analysis performed. From determined gene copy number and presence of SNPs, multiple online CRISPR design tools were used to design sgRNA targeting every gene, accompanying allele and homologs throughout all involved pathways to create knockouts for further investigation. Universal sgRNA were designed for highly homologous sequences using MultiTargeter and visualised using Sequencher, creating unique sgRNA avoiding SNP and shared nucleotide locations targeting optimal edit sites. Conclusions Using this framework, the approach has wider applications to all plant species regardless of ploidy number or highly homologous gene sequences. Significance statement Using this framework, a best-practice approach to genome editing is possible in all plant species, including cannabis, delivering a comprehensive in silico evaluation of the cannabinoid pathway diversity from a large set of whole genome sequences. Identification of SNP variants across all genes could improve genome editing potentially leading to novel applications across multiple disciplines, including agriculture and medicine.