Miracle-dz/UniFAR-FTUs huggingface dataset:
Heissophila macrotheleae , a new genus and new species of Plokiophilidae from Thailand ( Hemiptera , Heteroptera ) , with comments on the family diagnosis

Heissophila macrotheleae nov.gen. et nov.sp. , is described from the webs of Macrothele AUSSERER sp. (Araneae: Mygalomorphae: Hexathelidae) in Thailand. Digital macrophotographs, scanning electron micrographs, and line drawings are provided for a range of morphological structures. The taxon is distinguished from previously described arachnophilic Plokiophilidae by the 3-segmented tarsi, found elsewhere only in the New World taxon Lipokophila ŠTYS, and the apparent absence of traumatic insemination as evidenced from the structure of the male genitalia which lack the piercing acus found in all other described plokiophilid taxa.
Comparative Study on the effect of Medications in Acne Vulgaris and Assessment of Medication Adherence and Quality of life of Acne Patients in a Multi Specialty Tertiary Care Centre

Original Research Article Comparative Study on the effect of Medications in Acne Vulgaris and Assessment of Medication Adherence and Quality of life of Acne Patients in a Multi Specialty Tertiary Care Centre Authors Bincy Baby 1 , Sadma Vijayakumar 2 , P. L. Pranavya 3 , C. D. Shaji Selvin 4 * 1,2,3 Sreekrishna College of Pharmacy & Research Centre, Parassala, Thiruvananthapuram, Kerala, India 4 St. John’s College of Pharmaceutical Sciences and Research, Kattappana South, Idukki, Kerala, India *Corresponding Author C. D. Shaji Selvin Email: shajimpharm@gmail.com Abstract Background: Acne vulgaris is the most common inflammatory disorder of pilosebaceous glands and has a substantial impact on patients’ quality of life. Systemic antibiotics are known to be effective in its treatment. Adherence to acne medication is poor and is a major reason why treatment plans are ineffective. This study is undertaken to compare the efficacy of oral Azithromycin and Tetracycline’s and to assess the quality of life and medication adherence among acne vulgaris patients. Methods: A prospective observational study was conducted with 160 acne vulgaris patients for a period of six months in the Dermatology Dept. of a multispecialty tertiary care hospital. The Global Acne Grading System, Cardiff Acne Disability Index, ECOB questionnaire was used to study efficacy, quality of life and medication adherence respectively. Results: It was found that 13-20 years age group, females, students were more affected with acne. Most of the patients have family history of acne, have oily skin and non-vegetarian food habit. Smoking and alcohol habit have a significant impact on acne vulgaris. Regarding with the efficacy, azithromycin has better efficacy than tetracycline. Acne has significant impact on the quality of life of majority of patients’ especially in females before treatment and after the treatment it was improved. The medication adherence assessment reveals Azithromycin group has showed a slightly better medication adherence than Tetracycline group. Conclusion: Results of the present study demonstrated that azithromycin, a long acting macrolide, can be an acceptable alternative for tetracycline in treatment of inflammatory acne and given the long half-life, low complications and higher patient compliance.
Where Does the Robustness Come from?: A Study of the Transformation-based Ensemble Defence

This paper aims to provide a thorough study on the effectiveness of the transformation-based ensemble defence for image classification and its reasons. It has been empirically shown that they can enhance the robustness against evasion attacks, while there is little analysis on the reasons. In particular, it is not clear whether the robustness improvement is a result of transformation or ensemble. In this paper, we design two adaptive attacks to better evaluate the transformation-based ensemble defence. We conduct experiments to show that 1) the transferability of adversarial examples exists among the models trained on data records after different reversible transformations; 2) the robustness gained through transformation-based ensemble is limited; 3) this limited robustness is mainly from the irreversible transformations rather than the ensemble of a number of models; and 4) blindly increasing the number of sub-models in a transformation-based ensemble does not bring extra robustness gain.
Automated Identification of On-hold Self-admitted Technical Debt

Modern software is developed under considerable time pressure, which implies that developers more often than not have to resort to compromises when it comes to code that is well written and code that just does the job. This has led over the past decades to the concept of “technical debt”, a short-term hack that potentially generates long-term maintenance problems. Self-admitted technical debt (SATD) is a particular form of technical debt: developers consciously perform the hack but also document it in the code by adding comments as a reminder (or as an admission of guilt). We focus on a specific type of SATD, namely “On-hold” SATD, in which developers document in their comments the need to halt an implementation task due to conditions outside of their scope of work (e.g., an open issue must be closed before a function can be implemented). We present an approach, based on regular expressions and machine learning, which is able to detect issues referenced in code comments, and to automatically classify the detected instances as either “On-hold” (the issue is referenced to indicate the need to wait for its resolution before completing a task), or as “cross-reference”, (the issue is referenced to document the code, for example to explain the rationale behind an implementation choice). Our approach also mines the issue tracker of the projects to check if the On-hold SATD instances are “superfluous” and can be removed (i.e., the referenced issue has been closed, but the SATD is still in the code). Our evaluation confirms that our approach can indeed identify relevant instances of On-hold SATD. We illustrate its usefulness by identifying superfluous On-hold SATD instances in open source projects as confirmed by the original developers.
APEX-SEPIA660 Early Science: gas at densities above 107 cm−3 towards OMC-1

Context. The star-formation rates and stellar densities found in young massive clusters suggest that these stellar systems originate from gas at densities of n(H2) > 106 cm−3. Until today, however, the physical characterization of this ultra high density material remains largely unconstrained in observations. Aims. We investigate the density properties of the star-forming gas in the OMC-1 region located in the vicinity of the Orion Nebula Cluster (ONC). Methods. We mapped the molecular emission at 652 GHz in OMC-1 as part of the APEX-SEPIA660 Early Science. Results. We detect bright and extended N2H+ (J = 7–6) line emission along the entire OMC-1 region. Comparisons with previous ALMA data of the (J = 1–0) transition and radiative transfer models indicate that the line intensities observed in this N2H+ (7–6) line are produced by large mass reservoirs of gas at densities n(H2) > 107 cm−3. Conclusions. The first detection of this N2H+ (7–6) line at parsec-scales demonstrates the extreme density conditions of the star-forming gas in young massive clusters such as the ONC. Our results highlight the unique combination of sensitivity and mapping capabilities of the new SEPIA660 receiver for the study of the ISM properties at high frequencies.
Bandwidth-Agile Image Transmission With Deep Joint Source-Channel Coding

We propose deep learning based communication methods for adaptive-bandwidth transmission of images over wireless channels. We consider the scenario in which images are transmitted progressively in layers over time or frequency, and such layers can be aggregated by receivers in order to increase the quality of their reconstructions. We investigate two scenarios, one in which the layers are sent sequentially, and incrementally contribute to the refinement of a reconstruction, and another in which the layers are independent and can be retrieved in any order. Those scenarios correspond to the well known problems of <italic>successive refinement</italic> and <italic>multiple descriptions</italic>, respectively, in the context of joint source-channel coding (JSCC). We propose DeepJSCC-<inline-formula> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula>, an innovative solution that uses convolutional autoencoders, and present three architectures with different complexity trade-offs. To the best of our knowledge, this is the first practical multiple-description JSCC scheme developed and tested for practical information sources and channels. Numerical results show that DeepJSCC-<inline-formula> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula> can learn to transmit the source progressively with negligible losses in the end-to-end performance compared with a single transmission. Moreover, DeepJSCC-<inline-formula> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula> has comparable performance with state of the art digital progressive transmission schemes in the challenging low signal-to-noise ratio (SNR) and small bandwidth regimes, with the additional advantage of graceful degradation with channel SNR.
Nonclassical Measurement Error in the Outcome Variable

We study a semi-/nonparametric regression model with a general form of nonclassical measurement error in the outcome variable. We show equivalence of this model to a generalized regression model. Our main identifying assumptions are a special regressor type restriction and monotonicity in the nonlinear relationship between the observed and unobserved true outcome. Nonparametric identification is then obtained under a normalization of the unknown link function, which is a natural extension of the classical measurement error case. We propose a novel sieve rank estimator for the regression function. We establish a rate of convergence of the estimator which depends on the strength of identification. In Monte Carlo simulations, we find that our estimator corrects for biases induced by measurement errors and provides numerically stable results. We apply our method to analyze belief formation of stock market expectations with survey data from the German Socio-Economic Panel (SOEP) and find evidence for non-classical measurement error in subjective belief data.
Few-Shot Object Detection With Self-Adaptive Attention Network for Remote Sensing Images

In remote sensing field, there are many applications of object detection in recent years, which demands a great number of labeled data. However, we may be faced with some cases where only limited data are available. In this article, we proposed a few-shot object detector which is designed for detecting novel objects provided with only a few examples. Particularly, in order to fit the object detection settings, our proposed few-shot detector concentrates on the relations that lie in the level of objects instead of the full image with the assistance of self-adaptive attention network (SAAN). The SAAN can fully leverage the object-level relations through a relation gate recurrent unit and simultaneously attach attention on object features in a self-adaptive way according to the object-level relations to avoid some situations where the additional attention is useless or even detrimental. Eventually, the detection results are produced from the features that are added with attention and thus are able to be detected simply. The experiments demonstrate the effectiveness of the proposed method in few-shot scenes.
Hamilton-Jacobi-Bellman Equations for Maximum Entropy Optimal Control

Maximum entropy reinforcement learning (RL) methods have been successfully applied to a range of challenging sequential decision-making and control tasks. However, most of existing techniques are designed for discrete-time systems. As a first step toward their extension to continuous-time systems, this paper considers continuous-time deterministic optimal control problems with entropy regularization. Applying the dynamic programming principle, we derive a novel class of Hamilton-Jacobi-Bellman (HJB) equations and prove that the optimal value function of the maximum entropy control problem corresponds to the unique viscosity solution of the HJB equation. Our maximum entropy formulation is shown to enhance the regularity of the viscosity solution and to be asymptotically consistent as the effect of entropy regularization diminishes. A salient feature of the HJB equations is computational tractability. Generalized Hopf-Lax formulas can be used to solve the HJB equations in a tractable grid-free manner without the need for numerically optimizing the Hamiltonian. We further show that the optimal control is uniquely characterized as Gaussian in the case of control affine systems and that, for linear-quadratic problems, the HJB equation is reduced to a Riccati equation, which can be used to obtain an explicit expression of the optimal control. Lastly, we discuss how to extend our results to continuous-time model-free RL by taking an adaptive dynamic programming approach. To our knowledge, the resulting algorithms are the first data-driven control methods that use an information theoretic exploration mechanism in continuous time.
Deformable and Robust Core–Shell Protein Microcapsules Templated by Liquid–Liquid Phase‐Separated Microdroplets

Microcapsules are a key class of microscale materials with applications in areas ranging from personal care to biomedicine, and with increasing potential to act as extracellular matrix (ECM) models of hollow organs, tissues, or biomolecular condensates. Such capsules are conventionally generated from non‐ECM materials including synthetic polymers. Here, robust microcapsules with controllable shell thickness from physically‐ and enzymatically‐crosslinked gelatin are fabricated, and a core–shell architecture is achieved by exploiting a liquid–liquid phase‐separated aqueous system in a one‐step microfluidic process. Microfluidic mechanical testing reveals that the mechanical robustness of thicker‐shell capsules could be controlled through modulation of the shell thickness. Furthermore, the microcapsules demonstrate environmentally‐responsive deformation, including buckling driven by osmosis and external mechanical forces. A sequential release of cargo species is obtained through the degradation of the capsules. Stability measurements show the capsules are stable at 37 °C for more than 2 weeks. Finally, through gel–sol transition, microgels function as precursors for the formation of all‐aqueous liquid–liquid phase‐separated systems that are two‐phase or multiphase. These smart capsules that can undergo phase transition are promising models of hollow biostructures, microscale drug carriers, and building blocks or compartments for active soft materials and robots.
A Diagnostic Study of Explainability Techniques for Text Classification

Recent developments in machine learning have introduced models that approach human performance at the cost of increased architectural complexity. Efforts to make the rationales behind the models' predictions transparent have inspired an abundance of new explainability techniques. Provided with an already trained model, they compute saliency scores for the words of an input instance. However, there exists no definitive guide on (i) how to choose such a technique given a particular application task and model architecture, and (ii) the benefits and drawbacks of using each such technique. In this paper, we develop a comprehensive list of diagnostic properties for evaluating existing explainability techniques. We then employ the proposed list to compare a set of diverse explainability techniques on downstream text classification tasks and neural network architectures. We also compare the saliency scores assigned by the explainability techniques with human annotations of salient input regions to find relations between a model's performance and the agreement of its rationales with human ones. Overall, we find that the gradient-based explanations perform best across tasks and model architectures, and we present further insights into the properties of the reviewed explainability techniques.
The GOGREEN and GCLASS surveys: first data release

We present the first public data release of the GOGREEN and GCLASS surveys of galaxies in dense environments, spanning a redshift range $0.8<z<1.5$. The surveys consist of deep, multiwavelength photometry and extensive Gemini GMOS spectroscopy of galaxies in 26 overdense systems ranging in halo mass from small groups to the most massive clusters. The objective of both projects was primarily to understand how the evolution of galaxies is affected by their environment, and to determine the physical processes that lead to the quenching of star formation. There was an emphasis on obtaining unbiased spectroscopy over a wide stellar mass range ($M\gtrsim 2\times 10^{10}~\mathrm{M}_\odot$), throughout and beyond the cluster virialized regions. The final spectroscopic sample includes 2771 unique objects, of which 2257 have reliable spectroscopic redshifts. Of these, 1704 have redshifts in the range $0.8<z<1.5$, and nearly 800 are confirmed cluster members. Imaging spans the full optical and near-infrared wavelength range, at depths comparable to the UltraVISTA survey, and includes \textit{HST}/WFC3 F160W (GOGREEN) and F140W (GCLASS). This data release includes fully reduced images and spectra, with catalogues of advanced data products including redshifts, line strengths, star formation rates, stellar masses and rest-frame colours. Here we present an overview of the data, including an analysis of the spectroscopic completeness and redshift quality.
Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon

Episodic reinforcement learning and contextual bandits are two widely studied sequential decision-making problems. Episodic reinforcement learning generalizes contextual bandits and is often perceived to be more difficult due to long planning horizon and unknown state-dependent transitions. The current paper shows that the long planning horizon and the unknown state-dependent transitions (at most) pose little additional difficulty on sample complexity. We consider the episodic reinforcement learning with $S$ states, $A$ actions, planning horizon $H$, total reward bounded by $1$, and the agent plays for $K$ episodes. We propose a new algorithm, \textbf{M}onotonic \textbf{V}alue \textbf{P}ropagation (MVP), which relies on a new Bernstein-type bonus. The new bonus only requires tweaking the \emph{constants} to ensure optimism and thus is significantly simpler than existing bonus constructions. We show MVP enjoys an $O\left(\left(\sqrt{SAK} + S^2A\right) \text{poly}\log \left(SAHK\right)\right)$ regret, approaching the $\Omega\left(\sqrt{SAK}\right)$ lower bound of \emph{contextual bandits}. Notably, this result 1) \emph{exponentially} improves the state-of-the-art polynomial-time algorithms by Dann et al. [2019], Zanette et al. [2019] and Zhang et al. [2020] in terms of the dependency on $H$, and 2) \emph{exponentially} improves the running time in [Wang et al. 2020] and significantly improves the dependency on $S$, $A$ and $K$ in sample complexity.
Exciton-Trion-Polaritons in Two-Dimensional Materials

We present a many-body theory for exciton-trion-polaritons in doped two-dimensional materials. Exciton-trion-polaritons are robust coherent hybrid excitations involving excitons, trions, and photons. Signatures of these polaritons have been recently seen in experiments. In these polaritons, the 2-body exciton states are coupled to the material ground state via exciton-photon interaction and the 4-body trion states are coupled to the exciton states via Coulomb interaction. The trion states are not directly optically coupled to the material ground state. The energy-momentum dispersion of these polaritons exhibit three bands. We calculate the energy band dispersions and the compositions of polaritons at different doping densities using Green's functions. The energy splittings between the polariton bands, as well as the spectral weights of the polariton bands, depend on the strength of the Coulomb coupling between the excitons and the trions and which in turn depends on the doping density. The excitons are Coulomb coupled to both bound and unbound trion states. The latter are exciton-electron scattering states and their inclusion is necessary to capture the spectral weight transfer among the polariton bands as a function of the doping density.
On the sojourn time of a batch in the M[X]/M/1 Processor Sharing Queue

In this paper, we analyze the sojourn of an entire batch in a processor sharing $M^{[X]}/M/1$ processor queue, where geometrically distributed batches arrive according to a Poisson process and jobs require exponential service times. By conditioning on the number of jobs in the systems and the number of jobs in a tagged batch, we establish recurrence relations between conditional sojourn times, which subsequently allow us to derive a partial differential equation for an associated bivariate generating function. This equation involves an unknown generating function, whose coefficients can be computed by solving an infinite lower triangular linear system. Once this unknown function is determined, we compute the Laplace transform and the mean value of the sojourn time of a batch in the system.
Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization

Recently, we have witnessed great progress in the field of medical imaging classification by adopting deep neural networks. However, the recent advanced models still require accessing sufficiently large and representative datasets for training, which is often unfeasible in clinically realistic environments. When trained on limited datasets, the deep neural network is lack of generalization capability, as the trained deep neural network on data within a certain distribution (e.g. the data captured by a certain device vendor or patient population) may not be able to generalize to the data with another distribution. In this paper, we introduce a simple but effective approach to improve the generalization capability of deep neural networks in the field of medical imaging classification. Motivated by the observation that the domain variability of the medical images is to some extent compact, we propose to learn a representative feature space through variational encoding with a novel linear-dependency regularization term to capture the shareable information among medical data collected from different domains. As a result, the trained neural network is expected to equip with better generalization capability to the "unseen" medical data. Experimental results on two challenging medical imaging classification tasks indicate that our method can achieve better cross-domain generalization capability compared with state-of-the-art baselines.
The model reduction of the Vlasov-Poisson-Fokker-Planck system to the Poisson-Nernst-Planck system via the Deep Neural Network Approach

The model reduction of a mesoscopic kinetic dynamics to a macroscopic continuum dynamics has been one of the fundamental questions in mathematical physics since Hilbert's time. In this paper, we consider a diagram of the diffusion limit from the Vlasov-Poisson-Fokker-Planck (VPFP) system on a bounded interval with the specular reflection boundary condition to the Poisson-Nernst-Planck (PNP) system with the no-flux boundary condition. We provide a Deep Learning algorithm to simulate the VPFP system and the PNP system by computing the time-asymptotic behaviors of the solution and the physical quantities. We analyze the convergence of the neural network solution of the VPFP system to that of the PNP system via the Asymptotic-Preserving (AP) scheme. Also, we provide several theoretical evidence that the Deep Neural Network (DNN) solutions to the VPFP and the PNP systems converge to the a priori classical solutions of each system if the total loss function vanishes.