Miracle-dz/UniFAR-FTUs huggingface dataset:
Demystifying Uneven Vulnerability of Link Stealing Attacks against Graph Neural Networks

While graph neural networks (GNNs) dominate the state-of-the-art for exploring graphs in real-world applications, they have been shown to be vulnerable to a growing number of privacy attacks. For instance, link stealing is a well-known membership inference attack (MIA) on edges that infers the presence of an edge in a GNN’s training graph. Recent studies on independent and identically distributed data (e.g., images) have empirically demonstrated that individuals from different groups suffer from different levels of privacy risks to MIAs, i.e., uneven vulnerability. However, theoretical evidence for such uneven vulnerability is missing. In this paper, we first present theoretical evidence of the uneven vulnerability of GNNs to link stealing attacks, which lays the foundation for demystifying such uneven risks among different groups of edges. We further demonstrate a group-based attack paradigm to expose the practical privacy harm to GNN users derived from the uneven vulnerability of edges. Finally, we empirically validate the existence of obvious uneven vulnerability on ten real-world datasets (e.g., about 25% AUC difference between different groups in the Credit graph). Compared with existing methods, the outperformance of our group-based attack paradigm confirms that cus-tomising different strategies for different groups results in more effective privacy attacks.
Smart Initial Basis Selection for Linear Programs

The simplex method, introduced by Dantzig more than half a century ago, is still to date one of the most efficient methods for solving large-scale linear programming (LP) problems. While the simplex method is known to have the finite termination property under mild assumptions, the number of iterations until optimality largely depends on the choice of initial basis. Existing strategies for selecting an advanced initial basis are mostly rule-based. These rules usually require extensive expert knowledge and empirical study to develop. Yet, many of them fail to exhibit consistent improvement, even for LP problems that arise in a single application scenario. In this paper, we pro-pose a learning-based approach for initial basis selection. We employ graph neural networks as a building block and develop a model that attempts to capture the relationship between LP problems and their optimal bases. In addition, during the inference phase, we supplement the learning-based prediction with linear algebra tricks to ensure the validity of the generated initial basis. We validate the effectiveness of our proposed strategy by extensively testing it with state-of-the-art simplex solvers, including the open-source solver HiGHS and the commercial solver OptVerse. Through these rigorous experiments, we demonstrate that our strategy achieves substantial speedup and consistently outperforms existing rule-based methods. Furthermore, we extend the proposed approach to generating restricted master problems for column generation methods and present encouraging numerical results.
Comparison of Machine Learning Detection of Low Left Ventricular Ejection Fraction Using Individual ECG Leads

The 12-lead electrocardiogram (ECG) is the most common front-line diagnosis tool for assessing cardiovascular health, yet traditional ECG analysis cannot detect many diseases. Machine learning (ML) techniques have emerged as a powerful set of techniques for producing automated and robust ECG analysis tools that can often predict diseases and conditions not detectable by traditional ECG analysis. Many contemporary ECG-ML studies have focused on utilizing the full 12-lead ECG; however, with the increased availability of single-lead ECG data from wearable devices, there is a clear motivation to explore the development of single-lead ECG-ML techniques. In this study we developed and applied a deep learning architecture for the detection of low left ventricular ejection fraction (LVEF), and compared the performance of this ar-chitecture when it was trained with individual leads of the 12-lead ECG to the performance when trained using the entire 12-lead ECG. We observed that single-lead-trained networks performed similarly to the full 12 -lead-trained network. We also noted patterns of agreement and disagreement between network low LVEF predictions across the different lead-trained networks.
FLASH: Automating federated learning using CASH

In this paper, we present FLASH, a framework which addresses for the ﬁrst time the central Au-toML problem of Combined Algorithm Selection and HyperParameter (HP) Optimization (CASH) in the context of Federated Learning (FL). To limit training cost, FLASH incrementally adapts the set of algorithms to train based on their projected loss rates, while supporting decentralized (federated) implementation of the embedded hyper-parameter optimization (HPO), model selection and loss calculation problems. We provide a theoretical analysis of the training and validation loss under FLASH, and their tradeoff with the training cost measured as the data wasted in training sub-optimal algorithms. The bounds depend on the degree of dissimilarity between the datasets of the clients, a result of FL restriction that client datasets remain private. Through extensive experimental investigation on several datasets, we evaluate three variants of FLASH, and show that FLASH performs close to centralized CASH methods.
A chain-model genetic algorithm for Bayesian network structure learning

Bayesian Networks are today used in various fields and domains due to their inherent ability to deal with uncertainty. Learning Bayesian Networks, however is an NP-Hard task [7]. The super exponential growth of the number of possible networks given the number of factors in the studied problem domain has meant that more often, approximate and heuristic rather than exact methods are used. In this paper, a novel genetic algorithm approach for reducing the complexity of Bayesian network structure discovery is presented. We propose a method that uses chain structures as a model for Bayesian networks that can be constructed from given node orderings. The chain model is used to evolve a small number of orderings which are then injected into a greedy search phase which searches for an optimal structure. We present a series of experiments that show a significant reduction can be made in computational cost although with some penalty in success rate.
Noradrenergic locus coeruleus ensembles evoke different states in rat prefrontal cortex

13 Identifying the neurons that control global brain state has been a fundamental topic of research 14 that has largely focused on diffusely-projecting neuromodulatory centers, such as the locus 15 coeruleus (LC). This noradrenergic brain stem nucleus, which projects throughout the forebrain, 16 is thought to act as an “undifferentiated state controller” across all forebrain targets because LC 17 neurons spike synchronously. However, recent work demonstrated ensembles in the LC and 18 therefore made targeted neuromodulation a possibility. In order to demonstrate that LC 19 ensembles cause targeted neuromodulation, it is necessary to resolve LC ensemble dynamics 20 over time in relation to ongoing cortical states. Here, we used non-negative matrix factorization 21 on LC single unit recordings to investigate the spatial and temporal properties of ensemble 22 activation patterns. We assessed the potential for targeted neuromodulation of the prefrontal 23 cortex (PFC) using LC ensemble activity-triggered local field potential (LFP) power spectrograms. 24 We analyzed 285 single units recorded from 15 urethane-anesthetized rats (range of 5 to 34 25 simultaneously recorded units). LC ensembles became active at different times. Analysis of auto- the of
Rewarding Persistence: Effects of a Performance-Based Scholarship Program for Low-Income Parents

This report describes the impacts of a performance-based scholarship program with a counseling component on academic success and persistence among low-income parents. Students who participated in the program, which was operated at two New Orleans-area colleges as part of MDRC's multi site Opening Doors demonstration, were more likely to stay in school, get higher grades, and earn more credits.
Harmonious Teacher

Mean Teacher: The mean teacher (MT) framework was originally proposed by [7] for semi-supervised learning. Many cross-domain object detection (CDOD) works [2, 5] adopted the MT framework for its simplicity and effectiveness. The student model is updated by the SGD algorithm and supervised by the source labeled samples and pseudolabeled target samples. The parameters of the teacher model are updated by the exponential moving average (EMA) of the student model as follows:
Computational Studies in Design of Anti-Tuberculosis Drugs by Homology Modeling and Docking Studies of MycP 1 of Mycobacterium tuberculosis H37Rv

: Driven by chemistry but increasingly guided by pharmacology and the clinical sciences, drug research has contributed more to the progress of medicine during the past century than any other scientific factor. The advent of molecular biology and, in particular, of genomic sciences is having a deep impact on drug discovery. Recombinant proteins and monoclonal antibodies have greatly enriched our therapeutic armamentarium. Genome sciences, combined with bioinformatics tools, allow us to dissect the genetic basis of multifactorial diseases and to determine the most suitable points of attack for future medicines, thereby increasing the number of treatment options. Virtual screening, lead optimization, predictions of bioavailability and bioactivity of optimized lead molecules designed using Bioinformatics and Chemiinformatics tools can help to guide experimental research. Docking performed with substrates of MycP1 with enzyme MycP1 and it should be useful for further drug design.
Overcoming language barriers: An exploration of the police perceptions of the digitisation of witness statements in South Africa

The research identified instances were the English version of sworn statements was an inaccurate translation of the indigenous statement provided by a witness or complainant. The inaccurate translation of indigenous witness statements is a global phenomenon that manifests against the backdrop of the fourth industrial revolution (4IR). This study was conducted to explore police experiences of taking indigenous witness statements, and to establish their perceptions of the digitisation of witness statements. Purposive sampling was used for sampling. Interview schedules were administered to eighteen (18) South African Police Service (SAPS) members from nine Community Service Centres (CSC) in Soweto, a township in the Gauteng province. Ethical consideration, measures of trustworthiness and the Theory of Performance (ToP) were applied. The ToP provides that a person’s level of performance is dependent on the interaction of various contextual factors. Thematic content analysis was conducted using Atlas ti resulting in five dominant themes or impact factors namely “skills”, “collaboration”, “resources”, “duration” and “language”. Results showed that a majority of the SAPS members perceived that witness statements should be digitised to address language challenges experienced and improve their level of performance. Furthermore, they perceived the usefulness of technology in modernising outdated resources in the SAPS.
A Unifying Framework to the Analysis of Interaction Methods using Synergy Functions

Deep learning has revolutionized many areas of machine learning, from computer vision to natural language processing, but these high-performance models are generally “black box. ” Explaining such models would improve transparency and trust in AI-powered decision making and is necessary for understanding other practical needs such as robustness and fairness. A popular means of enhancing model transparency is to quantify how individual inputs contribute to model outputs (called attributions) and the magnitude of interactions be-tween groups of inputs. A growing number of these methods import concepts and results from game theory to produce attributions and interactions. This work presents a unifying framework for game-theory-inspired attribution and k th -order interaction methods. We show that, given modest assumptions, a unique full account of interactions between features, called synergies, is possible in the continuous input setting. We identify how various methods are characterized by their policy of distributing synergies. We establish that gradient-based methods are characterized by their actions on monomials, a type of synergy function, and introduce unique gradient-based methods. We show that the combination of various criteria uniquely defines the attribution/interaction methods. Thus, the community needs to identify goals and contexts when developing and employing attribution and interaction methods.
IMAGE TO LATEX VIA NEURAL NETWORKS

Many research papers in mathematics, computer science, and physics are written in LaTeX. Technical papers and articles in these areas often involve mathematical equations. Writing such equations in LaTeX takes longer than handwriting the same equations on paper. In this report, we want to show that the time-consuming process of typesetting LaTeX equations from images of these equations can be automated and optimized. Neural networks are good at solving related problems such as handwritten digit recognition, so we adapted these well-studied approaches to the LaTeX problem. Neural network model training involves large amounts of good quality data. So, for our project, we propose a convolutional neural network architecture to recognize LaTeX equations along with a way to generate labeled datasets of mathematical equation images and their corresponding LaTeX expressions. Our neural network model predicts from mathematical equations involving numbers, letters, mathematical symbols, and matrix images, the corresponding LaTeX for these equations. We have achieved an accuracy of more than 90% in predicting LaTeX for these complex equations involving up to 35 characters.
Inferring Relational Potentials in Interacting Systems

Systems consisting of interacting agents are prevalent in the world, ranging from dynamical systems in physics to complex biological networks. To build systems which can interact robustly in the real world, it is thus important to be able to infer the precise interactions governing such systems. Existing approaches typically discover such interactions by explicitly modeling the feed-forward dynamics of the trajectories. In this work, we propose Neural Interaction Inference with Potentials (NIIP) as an alternative approach to discover such interactions that enables greater flexibility in trajectory modeling: it discovers a set of relational potentials, represented as energy functions, which when minimized reconstruct the original trajectory. NIIP assigns low energy to the subset of trajectories which respect the relational constraints observed. We illustrate that with these representations NIIP displays unique capabilities in test-time. First, it allows trajectory manipulation, such as interchanging interaction types across separately trained models, as well as trajectory forecasting. Additionally, it allows adding external hand-crafted potentials at test-time. Finally, NIIP enables the detection of out-of-distribution samples and anomalies without explicit training. Website: https://energy-based-model.github. io/interaction-potentials.
Radiological regulatory improvements related to the remediation of the nuclear legacy sites in Northwest Russia

This report describes work carried in 2006 under the NRPA – Federal Medical-Biological Agency regulatory support program. It focuses on development of improved regulatory documents and supervision of remediation activities due to be carried out at Andreeva Bay and Gremikha in Northwest Russia. The work program for 2007 is also introduced.
Identification of Substitutable Context-Free Languages over Infinite Alphabets from Positive Data

This paper is concerned with the identification in the limit from positive data of sub-stitutable context-free languages cfl s) over infinite alphabets. Clark and Eyraud (2007) showed that substitutable cfl s over finite alphabets are learnable in this learning paradigm. We show that substitutable cfl s generated by grammars whose production rules may have predicates that represent sets of potentially infinitely many terminal symbols in a compact manner are learnable if the terminal symbol sets represented by those predicates are learnable, under a certain condition. This can be seen as a result parallel to Argyros and D’Antoni’s work (2018) that amplifies the query learnability of predicate classes to that of symbolic automata classes. Our result is the first that shows such amplification is possible for identifying some cfl s in the limit from positive data.
Developing And Assessing A Case Study For Teaching Engineering Entrepreneurship At San Jose State University

In this paper the authors share their experience of developing a case study for teaching engineering entrepreneurship and subsequently developing assessment and instructional material for the case. We also present the lessons learned from that experience. The authors developed the case study in question on the basis of a survey of engineering students’ perceptions of entrepreneurship 6 . Our survey showed that many students were interested in entrepreneurship, but were hesitant about starting their own business. We proposed two complementary approaches to learning, namely, case studies and active learning, to enhance the engineering students’ understanding of the entrepreneurial process and dispel some of their commonly held fears and myths about entrepreneurship. The authors wanted to develop a case study on a local, Silicon Valley-based company so as to make it more relevant to the students. Since a large proportion of SJSU engineering students are foreign-born, we wanted the case to focus on an ethnic minority immigrant entrepreneur. We used a stage-wise process in developing a case study on the founding of Serus, a global provider of technology and managed services for companies with distributed or outsourced manufacturing operations, based in a Silicon Valley, California. The stage-wise process included the following steps:
Appendix for Introducing Competition to Boost the Transferability of Targeted Adversarial Examples through Clean Feature Mixup

The weights for the following nine models are downloaded from Pytorch Image Models (timm) library [20]: Xception (Xcep) [1], EfficientNet-B0 (EF-B0) [18], Inception ResNet-v2 (IR-v2) [16], Inception-v4 (Inc-v4) [16], Vision Transformer (ViT) [3], LeViT [5], ConViT [4], Twins [2], and Pooling-based Vision Transformer (PiT) [7]. The pre-trained weights for the adversarially trained RN50 (adv-RN-50) [21] is provided by the official repository of [12].
A transboundary transport episode of nitrogen dioxide as observed from GOME and its impact in the Alpine region

Abstract. High tropospheric NO2 amounts are occasionally detected by space-borne spectrometers above cloudy scenes. For monitoring of near-ground air pollution such data are not directly applicable because clouds shield the highly polluted planetary boundary layer (PBL). We present a method based on trajectories which implicitly estimates the additional sub-cloud NO2 distribution in order to model concentrations at ground stations. The method is applied to a transboundary pollution transport episode which led to high NO2 vertical tropospheric column densities (VTCs) over middle Europe observed by the Global Ozone Monitoring Experiment (GOME) instrument above clouds on 17 February 2001. The case study shows that pollution originally residing near the ground in central Germany, the Ruhr area and adjacent parts of the Netherlands and Belgium has been advected to higher tropospheric levels by a passing weather front. Combining the above-cloud NO2 VTCs with trajectory information covering the GOME columns and including their sub-cloud part yields an estimate of the total NO2 distribution within the tropospheric columns. The highly polluted air masses are then traced by forward trajectories starting from the GOME columns to move further to the Alpine region and their impact there is assessed. Considering ground-based in-situ measurements in the Alpine region, we conclude that for this episode, at least 50% of the NO2 concentration recorded at the sites can be attributed to transboundary transport during the frontal passage. This study demonstrates the potential of using NO2 VTCs from GOME detected above clouds when combined with transport modelling.
DESIGN AND PERFORMANCE ANALYSIS OF SHOCK ABSORBERS IN TWO WHEELERS

: Automobile suspension arrangement plays a significant role for the comfort and stability of the vehicle. A suspension system or shock absorber is a mechanical device which is designed to smooth out impulse and dissipate kinetic energy. Shock absorber is a critical part of suspension system, which is used to connect the vehicle to the wheels. It contributes to the vehicles control providing safety and ease by absorbing the energy through bumps and pot holes on the road while providing smooth ride. The main purpose is to design and to do performance analysis of different shock absorber profiles using structural analysis. Modelling is done on CATIA and performance analysis is done for the different profiles for the shock absorbers using ANSYS software. The results are compared for different profiles of shock absorbers by applying different loads and stress, strain, deformation in static structural analysis is calculated.
Supplementary Material for “Prototype-based Embedding Network for Scene Graph Generation” A. Calculation of IV and IIVR

In this part, we construct additional ablation studies from another perspective to explore the effect of cosine similarity (CS) and Euclidean distance (ED) metrology in Prototypeguided Learning (PL) and Prototype Regularization (PR), respectively. The results are summarized in Tab. 2. Exp 1, PE-Net is the baseline model, which is trained without PL and PR, and only uses a linear classifier to classify the relation representation. Exp 2, PE-Net is trained with only cosine similarity metrology in PL and PR, i.e., training with Le sim and Lr sim. Exp 3, PE-Net is trained with both cosine similarity and Euclidean distance metrology in PL and PR. When trained with only cosine similarity metrology in Exp 2, the model significantly outperforms the baseline (i.e., Exp 1), e.g., 70.2% vs. 68.2% at R@100, and 26.2% vs. 20.0% on PredCls tasks. This verifies that explicitly establishing entity-predicate matching empowers the SGG model with more powerful relation recognition ability than learning decision boundaries with trainable classifiers. In Exp 3, we further integrate the Euclidean distance metrology into PL and PR, and our model obtains significant gains γ1 γ2 PredCls R@50 R@100 mR@50 mR@100 M@50 M@100 1.0 1.0 68.1 70.1 24.7 26.9 46.4 48.5 1.0 3.0 67.6 69.6 27.1 29.3 47.4 49.5 1.0 5.0 66.6 68.6 29.3 31.6 48.0 50.1 1.0 7.0 64.9 67.2 31.5 33.8 48.2 50.5 1.0 9.0 63.3 65.6 32.1 34.3 47.7 50.0 1.0 7.0 64.9 67.2 31.5 33.8 48.2 50.5 3.0 7.0 67.3 69.3 27.5 29.7 47.4 49.5 5.0 7.0 67.6 69.5 26.0 28.2 46.8 48.9 7.0 7.0 67.7 69.6 24.7 27.0 46.2 48.3 9.0 7.0 67.8 69.8 24.2 26.5 46.0 48.2