Your Out-of-Distribution Detection Method is Not Robust!

Out-of-distribution (OOD) detection has recently gained substantial attention due to the importance of identifying out-of-domain samples in reliability and safety. Although OOD detection methods have advanced by a great deal, they are still susceptible to adversarial examples, which is a violation of their purpose. To mitigate this issue, several defenses have recently been proposed. Nevertheless, these efforts remained ineffective, as their evaluations are based on either small perturbation sizes, or weak attacks. In this work, we re-examine these defenses against an end-to-end PGD attack on in/out data with larger perturbation sizes, e.g. up to commonly used $\epsilon=8/255$ for the CIFAR-10 dataset. Surprisingly, almost all of these defenses perform worse than a random detection under the adversarial setting. Next, we aim to provide a robust OOD detection method. In an ideal defense, the training should expose the model to almost all possible adversarial perturbations, which can be achieved through adversarial training. That is, such training perturbations should based on both in- and out-of-distribution samples. Therefore, unlike OOD detection in the standard setting, access to OOD, as well as in-distribution, samples sounds necessary in the adversarial training setup. These tips lead us to adopt generative OOD detection methods, such as OpenGAN, as a baseline. We subsequently propose the Adversarially Trained Discriminator (ATD), which utilizes a pre-trained robust model to extract robust features, and a generator model to create OOD samples. Using ATD with CIFAR-10 and CIFAR-100 as the in-distribution data, we could significantly outperform all previous methods in the robust AUROC while maintaining high standard AUROC and classification accuracy. The code repository is available at https://github.com/rohban-lab/ATD .Comment: Accepted to NeurIPS 202

Hydrogen bond-mediated self-assembly of Tin (II) oxide wrapped with Chitosan/[BzPy]Cl network: An effective bionanocomposite for textile wastewater remediation

A novel and efficient bionanocomposite was synthesized by incorporating SnO into chitosan (Ch) and a room-temperature ionic liquid (RTIL). The bionanocomposite was synthesized in benzoyl pyridinium chloride [BzPy]Cl to maintain the unique properties of SnO, chitosan, and the ionic liquid. Adsorption and photodegradation processes were applied to evaluate the bionanocomposite for removing azo and anthraquinone dyes and textile wastewater. SnO/[BzPy]Cl and SnO/[BzPy]Cl/Ch samples were prepared and characterized using various techniques, including FT-IR, SEM, XRD, EDAX, XPS, DSC, TGA, nitrogen adsorption/desorption isotherm, and DRS analysis. SEM analysis revealed a hierarchical roughened rose flower-like morphology for the biocomposite. The band gap energies of SnO/[BzPy]Cl and SnO/[BzPy]Cl/chitosan were found to be 3.9 and 3.3 eV, respectively, indicating a reduction in the band gap energy with the introduction of [BzPy]Cl and chitosan. SnO/[BzPy]Cl/Ch showed high removal rates (92–95 %) for Fast Red, Blue 15, Red 120, Blue 94, Yellow 160, and Acid Orange 7 dyes. The adsorption kinetics followed a pseudo-second-order model.In addition, the effect of different photodegradation parameters such as solution pH, dye concentrations, contact time, and amount of photocatalyst, was studied. Given the optimal results obtained in removing azo and anthraquinone dyes, the SnO/[BzPy]Cl/Ch nanocomposite was used as an efficient nanocomposite for removing dyes from textile wastewater. The highest removal efficiency was found to be 95.8 %, obtained under ultraviolet and visible light. Furthermore, BOD and COD reduction analysis showed significant reductions, indicating the excellent performance of the photocatalyst

A Persian translation of the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition: psychometric properties.

The aim of this study is to assess the reliability and validity of a Persian translation of the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition Axis I Disorders (SCID-I) through a multicenter study in a clinical population in Iran

Structured Clinical Interview for DSM-IV (SCID Persian Translation and Cultural Adaptation):

"nObjective: To translate the Structured Clinical Interview for DSM-IV axisI "ndisorders (SCID-I) into Persian (Farsi) and to adapt this instrument for the "nIranian culture. "nMethod: The SCID was translated into Persian using an elaborate procedure to "nachieve a satisfactory cross-cultural equivalent. This included forward "ntranslation by bilingual (English/Persian) translators, discussion and revision of "nthe translation in an expert panel of bilingual mental health professionals, pilot "nassessment on a small sample of Persian-speaking patients, back-translation "ninto English and comparison with the original SCID. In addition, "nunderstandability and acceptability of the translated items were assessed in 299 "npatients in three psychiatric hospitals in Tehran, Iran. "nResults: Some adaptations were made to bring about cross-cultural "ncomparability, especially with regard to conceptual differences which led to "ndifficulties in transferring some psychiatric concepts from English to Persian. "nThe SCID questions were generally understandable and acceptable for the "nIranian patients. "nConclusion: The SCID was translated into Persian in a multi-stage process to "nensure a satisfactory cross-cultural equivalent