nils-herrmann/clean_scirepeval huggingface dataset:
Robust Watermarking of Neural Network with Exponential Weighting

Deep learning has been achieving top levels of performance in many tasks. However, since it is costly to train a deep learning model, neural network models must be treated as valuable intellectual properties. One concern arising from our current situation is that malicious users might redistribute proprietary models or provide prediction services using such models without permission. One promising solution to this problem is digital watermarking, which works by embedding a mechanism into the model so that the model owners can verify their ownership of the model externally. In this study, we present a novel attack method against such watermarks known as query modification and demonstrate that all currently existing watermarking methods are vulnerable to either query modification or other existing attack methods (such as model modification). To overcome these vulnerabilities, we then present a novel watermarking method that we have named exponential weighting and experimentally show that our watermarking method achieves high watermark verification performance even under malicious invalidation processing attempts by unauthorized service providers (such as model modification and query modification) without sacrificing the predictive performance of the neural network model itself.
Protein Classification using Machine Learning and Statistical Techniques: A Comparative Analysis

In the recent era, the prediction of enzyme class from an unknown protein
is one of the challenging tasks in bioinformatics. Day-to-day, the number of proteins increases
which causes difficulties in clinical verification and classification; as a result, the prediction of enzyme
class gives a new opportunity to bioinformatics scholars. The machine learning classification
technique helps in protein classification and predictions. But it is imperative to know which classification
technique is more suited for protein classification. This study used human proteins data that is
extracted from the UniProtKB databank. A total of 4368 protein data with 45 identified features
were used for experimental analysis.



The prime objective of this article is to find an appropriate classification technique to
classify the reviewed as well as un-reviewed human enzyme class of protein data. Also, find the
significance of different features in protein classification and prediction.



In this article, the ten most significant classification techniques such as CRT, QUEST,
CHAID, C5.0, ANN, SVM, Bayesian, Random Forest, XgBoost, and CatBoost have been used to
classify the data and discover the importance of features. To validate the result of different classification
techniques, accuracy, precision, recall, F-measures, sensitivity, specificity, MCC, ROC, and
AUROC were used. All experiments were done with the help of SPSS Clementine and Python.



Above discussed classification techniques give different results and found that the data are
imbalanced for class C4, C5, and C6. As a result, all of the classification techniques give acceptable
accuracy above 60% for these classes of data, but their precision value is very less or negligible. The
experimental results highlight that the Random forest gives the highest accuracy as well as AUROC
among all, i.e., 96.84% and 0.945, respectively, and also has high precision and recall value.



The experiment conducted and analyzed in this article highlights that the Random Forest
classification technique can be used for protein of human enzyme classification and predictions.
Decay of semilinear damped wave equations: cases without geometric control condition

We consider the semilinear damped wave equation $\partial_{tt}^2 u(x,t)+\gamma(x)\partial_t u(x,t)=\Delta u(x,t)-\alpha u(x,t)-f(x,u(x,t))$. In this article, we obtain the first results concerning the stabilization of this semilinear equation in cases where $\gamma$ does not satisfy the geometric control condition. When some of the geodesic rays are trapped, the stabilization of the linear semigroup is semi-uniform in the sense that $\|e^{At}A^{-1}\|\leq h(t)$ for some function $h$ with $h(t)\rightarrow 0$ when $t\rightarrow +\infty$. We provide general tools to deal with the semilinear stabilization problem in the case where $h(t)$ has a sufficiently fast decay.
TactileGCN: A Graph Convolutional Network for Predicting Grasp Stability with Tactile Sensors

Tactile sensors provide useful contact data during the interaction with an object which can be used to accurately learn to determine the stability of a grasp. Most of the works in the literature represented tactile readings as plain feature vectors or matrix-like tactile images, using them to train machine learning models. In this work, we explore an alternative way of exploiting tactile information to predict grasp stability by leveraging graph-like representations of tactile data, which preserve the actual spatial arrangement of the sensor’s taxels and their locality. In experimentation, we trained a Graph Neural Network to binary classify grasps as stable or slippery ones. To train such network and prove its predictive capabilities for the problem at hand, we captured a novel dataset of ∼ 5000 three-fingered grasps across 41 objects for training and 1000 grasps with 10 unknown objects for testing. Our experiments prove that this novel approach can be effectively used to predict grasp stability.
On-Policy Trust Region Policy Optimisation with Replay Buffers

Building upon the recent success of deep reinforcement learning methods, we investigate the possibility of on-policy reinforcement learning improvement by reusing the data from several consecutive policies. On-policy methods bring many benefits, such as ability to evaluate each resulting policy. However, they usually discard all the information about the policies which existed before. In this work, we propose adaptation of the replay buffer concept, borrowed from the off-policy learning setting, to create the method, combining advantages of on- and off-policy learning. To achieve this, the proposed algorithm generalises the $Q$-, value and advantage functions for data from multiple policies. The method uses trust region optimisation, while avoiding some of the common problems of the algorithms such as TRPO or ACKTR: it uses hyperparameters to replace the trust region selection heuristics, as well as the trainable covariance matrix instead of the fixed one. In many cases, the method not only improves the results comparing to the state-of-the-art trust region on-policy learning algorithms such as PPO, ACKTR and TRPO, but also with respect to their off-policy counterpart DDPG.
Computing Large Market Equilibria using Abstractions

Computing market equilibria is an important practical problem for market design (e.g. fair division, item allocation). However, computing equilibria requires large amounts of information (e.g. all valuations for all buyers for all items) and compute power. We consider ameliorating these issues by applying a method used for solving complex games: constructing a coarsened abstraction of a given market, solving for the equilibrium in the abstraction, and lifting the prices and allocations back to the original market. We show how to bound important quantities such as regret, envy, Nash social welfare, Pareto optimality, and maximin share when the abstracted prices and allocations are used in place of the real equilibrium. We then study two abstraction methods of interest for practitioners: 1) filling in unknown valuations using techniques from matrix completion, 2) reducing the problem size by aggregating groups of buyers/items into smaller numbers of representative buyers/items and solving for equilibrium in this coarsened market. We find that in real data allocations/prices that are relatively close to equilibria can be computed from even very coarse abstractions.
Data-Driven Air Quality Characterization for Urban Environments: A Case Study

The economic and social impact of poor air quality in towns and cities is increasingly being recognized, together with the need for effective ways of creating awareness of real-time air quality levels and their impact on human health. With local authority maintained monitoring stations being geographically sparse and the resultant datasets also featuring missing labels, computational data-driven mechanisms are needed to address the data sparsity challenge. In this paper, we propose a machine learning-based method to accurately predict the air quality index, using environmental monitoring data together with meteorological measurements. To do so, we develop an air quality estimation framework that implements a neural network that is enhanced with a novel non-linear autoregressive neural network with exogenous input model, especially designed for time series prediction. The framework is applied to a case study featuring different monitoring sites in London, with comparisons against other standard machine-learning-based predictive algorithms showing the feasibility and robust performance of the proposed method for different kinds of areas within an urban region.
Avoiding a Tragedy of the Commons in the Peer Review Process

Peer review is the foundation of scientific publication, and the task of reviewing has long been seen as a cornerstone of professional service. However, the massive growth in the field of machine learning has put this community benefit under stress, threatening both the sustainability of an effective review process and the overall progress of the field. In this position paper, we argue that a tragedy of the commons outcome may be avoided by emphasizing the professional aspects of this service. In particular, we propose a rubric to hold reviewers to an objective standard for review quality. In turn, we also propose that reviewers be given appropriate incentive. As one possible such incentive, we explore the idea of financial compensation on a per-review basis. We suggest reasonable funding models and thoughts on long term effects.
Lie group classification of second-order delay ordinary differential equations

This article is part of a research program the aim of which is to turn Lie group theory into an efficient tool for classifying, transforming and solving a further class of equations, namely Delay Ordinary Differential Systems (DODSs). Such a system consists of two equations involving one independent variable x and one dependent variable y. As opposed to ODEs the variable x figures in more than one point (we consider the case of two points, x and x − ). The dependent variable y and its derivatives figure in both x and x − . This is not a discretization since both x and y remain continuous. Two previous articles were devoted to first-order DODSs, here we concentrate on a large class of second-order ones. We show that within this class the symmetry algebra can be of dimension n with 0 ≤ n ≤ 6 for genuinely nonlinear DODSs and n = ∞ for linear or linearizable ones. The symmetry algebras can be used to perform symmetry reduction and obtain exact particular (invariant) solutions.
FFT and orthogonal discrete transform on weight lattices of semi-simple Lie groups

We give two algebro-geometric inspired approaches to fast algorithms for Fourier transforms in algebraic signal processing theory based on polynomial algebras in several variables. One is based on module induction and one is based on a decomposition property of certain polynomials. The Gauss-Jacobi procedure for the derivation of orthogonal transforms is extended to the multivariate setting. This extension relies on a multivariate Christoffel-Darboux formula for orthogonal polynomials in several variables. As a set of application examples a general scheme for the derivation of fast transforms of weight lattices based on multivariate Chebyshev polynomials is derived. A special case of such transforms is considered, where one can apply the Gauss-Jacobi procedure.
NeuNetS: An Automated Synthesis Engine for Neural Network Design

Application of neural networks to a vast variety of practical applications is transforming the way AI is applied in practice. Pre-trained neural network models available through APIs or capability to custom train pre-built neural network architectures with customer data has made the consumption of AI by developers much simpler and resulted in broad adoption of these complex AI models. While prebuilt network models exist for certain scenarios, to try and meet the constraints that are unique to each application, AI teams need to think about developing custom neural network architectures that can meet the tradeoff between accuracy and memory footprint to achieve the tight constraints of their unique use-cases. However, only a small proportion of data science teams have the skills and experience needed to create a neural network from scratch, and the demand far exceeds the supply. In this paper, we present NeuNetS : An automated Neural Network Synthesis engine for custom neural network design that is available as part of IBM's AI OpenScale's product. NeuNetS is available for both Text and Image domains and can build neural networks for specific tasks in a fraction of the time it takes today with human effort, and with accuracy similar to that of human-designed AI models.
Comparing two deep learning sequence-based models for protein-protein interaction prediction

Biological data are extremely diverse, complex but also quite sparse. The recent developments in deep learning methods are offering new possibilities for the analysis of complex data. However, it is easy to be get a deep learning model that seems to have good results but is in fact either overfitting the training data or the validation data. In particular, the fact to overfit the validation data, called "information leak", is almost never treated in papers proposing deep learning models to predict protein-protein interactions (PPI). In this work, we compare two carefully designed deep learning models and show pitfalls to avoid while predicting PPIs through machine learning methods. Our best model predicts accurately more than 78% of human PPI, in very strict conditions both for training and testing. The methodology we propose here allow us to have strong confidences about the ability of a model to scale up on larger datasets. This would allow sharper models when larger datasets would be available, rather than current models prone to information leaks. Our solid methodological foundations shall be applicable to more organisms and whole proteome networks predictions.
Improving Sequence-to-Sequence Learning via Optimal Transport

Sequence-to-sequence models are commonly trained via maximum likelihood estimation (MLE). However, standard MLE training considers a word-level objective, predicting the next word given the previous ground-truth partial sentence. This procedure focuses on modeling local syntactic patterns, and may fail to capture long-range semantic structure. We present a novel solution to alleviate these issues. Our approach imposes global sequence-level guidance via new supervision based on optimal transport, enabling the overall characterization and preservation of semantic features. We further show that this method can be understood as a Wasserstein gradient flow trying to match our model to the ground truth sequence distribution. Extensive experiments are conducted to validate the utility of the proposed approach, showing consistent improvements over a wide variety of NLP tasks, including machine translation, abstractive text summarization, and image captioning.
Estimating the effective dimension of large biological datasets using Fisher separability analysis

Modern large-scale datasets are frequently said to be high-dimensional. However, their data point clouds frequently possess structures, significantly decreasing their intrinsic dimensionality (ID) due to the presence of clusters, points being located close to low-dimensional varieties or fine-grained lumping. We introduce and test a dimensionality estimator, based on analysing the separability properties of data points, on several benchmarks and real biological datasets. We show that the introduced measure of ID has performance competitive with state-of-the-art measures, being efficient across a wide range of dimensions and performing better in the case of noisy samples. Moreover, it allows estimating the intrinsic dimension in situations where the intrinsic manifold assumption is not valid.
Embedded CNN based vehicle classification and counting in non-laned road traffic

Classifying and counting vehicles in road traffic has numerous applications in the transportation engineering domain. However, the wide variety of vehicles (two-wheelers, three-wheelers, cars, buses, trucks etc.) plying on roads of developing regions without any lane discipline, makes vehicle classification and counting a hard problem to automate. In this paper, we use state of the art Convolutional Neural Network (CNN) based object detection models and train them for multiple vehicle classes using data from Delhi roads. We get upto 75% MAP on an 80-20 train-test split using 5562 video frames from four different locations. As robust network connectivity is scarce in developing regions for continuous video transmissions from the road to cloud servers, we also evaluate the latency, energy and hardware cost of embedded implementations of our CNN model based inferences.
Uniqueness of asymptotically conical tangent flows

Singularities of the mean curvature flow of an embedded surface in R^3 are expected to be modelled on self-shrinkers that are compact, cylindrical, or asymptotically conical. In order to understand the flow before and after the singular time, it is crucial to know the uniqueness of tangent flows at the singularity. 
In all dimensions, assuming the singularity is multiplicity one, uniqueness in the compact case has been established by the second-named author, and in the cylindrical case by Colding-Minicozzi. We show here the uniqueness of multiplicity-one asymptotically conical tangent flows for mean curvature flow of hypersurfaces. 
In particular, this implies that when a mean curvature flow has a multiplicity-one conical singularity model, the evolving surface at the singular time has an (isolated) regular conical singularity at the singular point. This should lead to a complete understanding of how to "flow through" such a singularity.
Great Circle Fibrations and Contact Structures on Odd-Dimensional Spheres

It is known that for every smooth great circle fibration of the 3-sphere, the distribution of tangent 2-planes orthogonal to the fibres is a contact structure, in fact a tight one, but we show here that, beginning with the 5-sphere, there exist smooth great circle fibrations of all odd-dimensional spheres for which the hyperplane distribution orthogonal to the fibres is not a contact structure.
Tangled Worldview Model of Opinion Dynamics

We study the joint evolution of worldviews by proposing a model of opinion dynamics, which is inspired in notions from evolutionary ecology. Agents update their opinion on a specific issue based on their propensity to change -- asserted by the social neighbours -- weighted by their mutual similarity on other issues. Agents are, therefore, more influenced by neighbours with similar worldviews (set of opinions on various issues), resulting in a complex co-evolution of each opinion. Simulations show that the worldview evolution exhibits events of intermittent polarization when the social network is scale-free. This, in turn, triggers extreme crashes and surges in the popularity of various opinions. Using the proposed model, we highlight the role of network structure, bounded rationality of agents, and the role of key influential agents in causing polarization and intermittent reformation of worldviews on scale-free networks.
Coded Caching based on Combinatorial Designs

We consider the standard broadcast setup with a single server broadcasting information to a number of clients, each of which contains local storage (called cache) of some size, which can store some parts of the available files at the server. The centralized coded caching framework, consists of a caching phase and a delivery phase, both of which are carefully designed in order to use the cache and the channel together optimally. In prior literature, various combinatorial structures have been used to construct coded caching schemes. In this work, we propose a binary matrix model to construct the coded caching scheme. The ones in such a caching matrix indicate uncached subfiles at the users. Identity submatrices of the caching matrix represent transmissions in the delivery phase. Using this model, we then propose several novel constructions for coded caching based on the various types of combinatorial designs. While most of the schemes constructed in this work (based on existing designs) have a high cache requirement (uncached fraction being $\Theta \left( {\frac{1}{{\sqrt K }}} \right)$, K being the number of users), they provide a rate R that is upper bounded by a constant (R≤ 1) with increasing K, and moreover require extremely small levels of subpacketization (being O(K)), which is an extremely important parameter in practical applications of coded caching.
Clocking the assembly of double-barred galaxies with the MUSE TIMER project

The formation of two stellar bars within a galaxy has proved challenging for numerical studies. It is not yet clear whether the inner bar is born via a star formation process promoted by gas inflow along the outer bar or whether it is dynamically assembled from instabilities in a small-scale stellar disc. Observational constraints to these scenarios are scarce. We present a thorough study of the stellar content of two double-barred galaxies observed by the MUSE TIMER project, NGC 1291 and NGC 5850, combined with a two-dimensional multicomponent photometric decomposition performed on the 3.6 μμm images from S^4G. Our analysis confirms the presence of σ-hollows appearing in the stellar velocity dispersion distribution at the ends of the inner bars. Both galaxies host inner discs matching in size with the inner bars, suggestive of a dynamical formation for the inner bars from small-scale discs. The analysis of the star formation histories for the structural components shaping the galaxies provides constraints on the epoch of dynamical assembly of the inner bars, which took place >6.5 Gyr ago for NGC 1291 and >4.5 Gyr ago for NGC 5850. This implies that inner bars are long-lived structures.
Multisource Region Attention Network for Fine-Grained Object Recognition in Remote Sensing Imagery

Fine-grained object recognition concerns the identification of the type of an object among a large number of closely related subcategories. Multisource data analysis that aims to leverage the complementary spectral, spatial, and structural information embedded in different sources is a promising direction toward solving the fine-grained recognition problem that involves low between-class variance, small training set sizes for rare classes, and class imbalance. However, the common assumption of coregistered sources may not hold at the pixel level for small objects of interest. We present a novel methodology that aims to simultaneously learn the alignment of multisource data and the classification model in a unified framework. The proposed method involves a multisource region attention network that computes per-source feature representations, assigns attention scores to candidate regions sampled around the expected object locations by using these representations, and classifies the objects by using an attention-driven multisource representation that combines the feature representations and the attention scores from all sources. All components of the model are realized using deep neural networks and are learned in an end-to-end fashion. Experiments using RGB, multispectral, and LiDAR elevation data for classification of street trees showed that our approach achieved 64.2% and 47.3% accuracies for the 18-class and 40-class settings, respectively, which correspond to 13% and 14.3% improvement relative to the commonly used feature concatenation approach from multiple sources.