Turkish Adaptation and Psychometric Evaluation of the Colorado Learning Attitudes About Science Survey (CLASS) in Physics

The Colorado Learning Attitudes about Science Survey (CLASS) is an instrument to measure student beliefs about physics and learning physics. In this research, Turkish adaptation and psychometric evaluation of the CLASS is discussed. In the first stage, the translation process, which included examination of six experts (four experts in physics education and two experts in English and Turkish languages) for content validity and 13 student interviews for face validity, was described. In the second stage, exploratory (EFA) and confirmatory (CFA) factor analysis results obtained from 1391 freshman students were discussed for construct validty. The EFA yielded three factors that consisted of 20 items, which explained 39.61 % of the total variance. These factors were named as: Problem Solving Effort, Conceptual Understanding, and Personal Interest and Real-World Connection. Based on the CFA results, the three-factor 20-item instrument showed acceptable fit statistics. Compared to the original CLASS, the proposed version with 20-item model was shorter, easier to administer and easier to score, valid and reliable survey, so feasible to use. The overlapping of the constructs (i.e., violation of the unidimensionality) in the original CLASS was a problem, while in the proposed model none of the items were included in more than one construct.

Hierarchical Self-supervised Augmented Knowledge Distillation

Knowledge distillation often involves how to define and transfer knowledge from teacher to student effectively. Although recent self-supervised contrastive knowledge achieves the best performance, forcing the network to learn such knowledge may damage the representation learning of the original class recognition task. We therefore adopt an alternative self-supervised augmented task to guide the network to learn the joint distribution of the original recognition task and self-supervised auxiliary task. It is demonstrated as a richer knowledge to improve the representation power without losing the normal classification capability. Moreover, it is incomplete that previous methods only transfer the probabilistic knowledge between the final layers. We propose to append several auxiliary classifiers to hierarchical intermediate feature maps to generate diverse self-supervised knowledge and perform the one-to-one transfer to teach the student network thoroughly. Our method significantly surpasses the previous SOTA SSKD with an average improvement of 2.56% on CIFAR-100 and an improvement of 0.77% on ImageNet across widely used network pairs. Codes are available at https://github.com/winycg/HSAKD.

Class Retrieval of Detected Adversarial Attacks

Adversarial attack is a genuine threat compromising the safety of many intelligent systems curbing the standardization of using neural networks in security-critical applications. Since the emergence of adversarial attacks, the research community has worked relentlessly to avert the malicious damage of these attacks. Here, we present a new, additional and required element to ameliorate adversarial attacks: the recovery of the original class after a detected attack. Recovering the original class of an adversarial sample without taking any precautions is an uncharted concept which we would like to introduce with our novel class retrieval algorithm. As case studies, we demonstrate the validity of our approach on MNIST, CIFAR10 and ImageNet datasets where recovery rates were 72%, 65% and 65%, respectively.

Explaining the Black-box Smoothly- A Counterfactual Approach

<p>We propose a BlackBox <i>Counterfactual Explainer</i> that is explicitly developed for medical imaging applications. Classical approaches (<i>e.g.,</i> saliency maps) assessing feature importance do not explain <i>how</i> and <i>why</i> variations in a particular anatomical region are relevant to the outcome, which is crucial for transparent decision making in healthcare application. Our framework explains the outcome by gradually <i>exaggerating</i> the semantic effect of the given outcome label. Given a query input to a classifier, Generative Adversarial Networks produce a progressive set of perturbations to the query image that gradually changes the posterior probability from its original class to its negation. We design the loss function to ensure that essential and potentially relevant details, such as support devices, are preserved in the counterfactually generated images. We provide an extensive evaluation of different classification tasks on the chest X-Ray images. Our experiments show that a counterfactually generated visual explanation is consistent with the disease's clinical relevant measurements, both quantitatively and qualitatively.</p>

Explaining the Black-box Smoothly- A Counterfactual Approach

<p>We propose a BlackBox <i>Counterfactual Explainer</i> that is explicitly developed for medical imaging applications. Classical approaches (<i>e.g.,</i> saliency maps) assessing feature importance do not explain <i>how</i> and <i>why</i> variations in a particular anatomical region are relevant to the outcome, which is crucial for transparent decision making in healthcare application. Our framework explains the outcome by gradually <i>exaggerating</i> the semantic effect of the given outcome label. Given a query input to a classifier, Generative Adversarial Networks produce a progressive set of perturbations to the query image that gradually changes the posterior probability from its original class to its negation. We design the loss function to ensure that essential and potentially relevant details, such as support devices, are preserved in the counterfactually generated images. We provide an extensive evaluation of different classification tasks on the chest X-Ray images. Our experiments show that a counterfactually generated visual explanation is consistent with the disease's clinical relevant measurements, both quantitatively and qualitatively.</p>

New machine learning and physics-based scoring functions for drug discovery

Scoring functions are essential for modern in silico drug discovery. However, the accurate prediction of binding affinity by scoring functions remains a challenging task. The performance of scoring functions is very heterogeneous across different target classes. Scoring functions based on precise physics-based descriptors better representing protein–ligand recognition process are strongly needed. We developed a set of new empirical scoring functions, named DockTScore, by explicitly accounting for physics-based terms combined with machine learning. Target-specific scoring functions were developed for two important drug targets, proteases and protein–protein interactions, representing an original class of molecules for drug discovery. Multiple linear regression (MLR), support vector machine and random forest algorithms were employed to derive general and target-specific scoring functions involving optimized MMFF94S force-field terms, solvation and lipophilic interactions terms, and an improved term accounting for ligand torsional entropy contribution to ligand binding. DockTScore scoring functions demonstrated to be competitive with the current best-evaluated scoring functions in terms of binding energy prediction and ranking on four DUD-E datasets and will be useful for in silico drug design for diverse proteins as well as for specific targets such as proteases and protein–protein interactions. Currently, the MLR DockTScore is available at www.dockthor.lncc.br.

The evolution of consonant mutation and noun class marking in Wolof

This paper analyzes the origins and evolution of the Wolof (Atlantic: Senegal) consonant mutation and noun class marking systems. I attribute Wolof mutation to the earlier presence of CV(C)- class prefixes on nouns, the (usually final) consonants of which fused with the following root-initial consonant to yield the modern mutation alternations. I reconstruct these original class prefixes using newly-proposed internal and comparative evidence, drawing on early documentary sources dating from the late 17th century. An understanding of the history of Wolof mutation allows for a better account of the synchronic system, in which mutation is triggered by specific noun classes rather than sporadically marking deverbal derivation. This study contributes to the broader understanding of how consonant mutation systems emerge and evolve, and of phonological considerations in noun class assignment.

DISCOVERY OF RUSSIAN ORIGINAL CLASS III ANTIARRHYTHMIC DRUGS: FROM ACTIVE INGREDIENT TO PHARMACEUTICAL FORM

This paper summarizes data on research and development of Russian original class III anti-arrhythmic drugs.

Semi-Supervised Cross-Modal Retrieval Based on Discriminative Comapping

Most cross-modal retrieval methods based on subspace learning just focus on learning the projection matrices that map different modalities to a common subspace and pay less attention to the retrieval task specificity and class information. To address the two limitations and make full use of unlabelled data, we propose a novel semi-supervised method for cross-modal retrieval named modal-related retrieval based on discriminative comapping (MRRDC). The projection matrices are obtained to map multimodal data into a common subspace for different tasks. In the process of projection matrix learning, a linear discriminant constraint is introduced to preserve the original class information in different modal spaces. An iterative optimization algorithm based on label propagation is presented to solve the proposed joint learning formulations. The experimental results on several datasets demonstrate the superiority of our method compared with state-of-the-art subspace methods.