nils-herrmann/clean_scirepeval huggingface dataset:
Nanoscale NMR Spectroscopy using Self-Calibrating Nanodiamond Quantum Sensors

Conventional nuclear magnetic resonance (NMR) spectroscopy relies on acquiring signal from a macroscopic ensemble of molecules to gain information about molecular structure and dynamics. Transferring this technique to nanoscale sample sizes would enable molecular analysis without the effects of averaging over spatial and temporal inhomogeneities and without the need for macroscopic volumes of analyte, both inherent to large ensemble measurements. Nanoscale NMR based on nitrogen vacancy (NV) centers inside bulk diamond chips achieves single nuclear spin sensitivity and the resolution required to determine chemical structure, but their detection volume is limited to a few nanometers above the diamond surface for the most sensitive devices. This precludes them from use for nuclear spin sensing with nanoscale resolution inside thicker structures, such as cells. Here, we demonstrate the detection of NMR signals from multiple nuclear species in a (19 nm)3 volume using versatile NV-NMR devices inside nanodiamonds that have a typical 30 nm diameter. The devices detect a signal generated by a small number of analyte molecules on the order of 1000. To use these devices in situ, the detected signal must be corrected for the unknown geometry of each nanodiamond device. We show that such a calibration could be performed by exploiting the signal from a thin layer of nuclei on the diamond surface. These results, combined with the low toxicity of nanodiamonds and their amenability to surface functionalization, indicate that nanodiamond NV-NMR devices could become a useful tool for nanoscale NMR-based sensing inside living cells.
How Should You Discount Your Backtest PnL?

In-sample overfitting is a drawback of any backtest-based investment strategy. It is thus of paramount importance to have an understanding of why and how the in-sample overfitting occurs. In this article we propose a simple framework that allows one to model and quantify in-sample PnL overfitting. This allows us to compute the factor appropriate for discounting PnLs of in-sample investment strategies.
An open-source multi-person virtual reality framework for interactive molecular dynamics: from quantum chemistry to drug binding

Conceptually, the mechanics of nanoscale molecular objects arise through electrostatic forces acting on particles in non-uniform fields, and are relatively well characterized owing to decades of study. Nevertheless, because dynamics at this scale differ from the familiar mechanics of everyday objects, they are often non-intuitive, even for highly trained researchers. Moreover, because molecular systems have many of degrees of freedom, their motion involves a complicated, highly correlated, and 3D many-body dynamical choreography with few analogues in day-to-day experience. We recently described how advances in virtual reality (VR) enable researchers to manipulate real-time dynamics simulations of molecular structures in 3D. In this article, we discuss VR's design affordances, outline cognitive and perceptual principles for understanding how people experience VR, and provide an overview of efforts to use immersive technologies for the molecular sciences. We also introduce 'Narupa', a flexible, open-source, multi-person VR software framework designed to enable groups of researchers to simultaneously cohabit real-time simulation environments and interactively inspect, visualize, and manipulate the dynamics of complex molecular structures with atomic-level precision. We highlight the potential of VR to furnish insight into microscopic 3D dynamical concepts. We outline a range of application domains where VR is proving useful in enabling research and communication, including biomolecular conformational sampling, transport dynamics in materials, reaction discovery using 'on-the-fly' quantum chemistry, protein-ligand binding, and machine learning potential energy surfaces. We describe ongoing experiments using sound and proprioception to enable new forms of integrated multisensory molecular perception, and outline future applications for immersive technologies like VR in molecular science.
Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI

This is an integrative review that address the question, "What makes for a good explanation?" with reference to AI systems. Pertinent literatures are vast. Thus, this review is necessarily selective. That said, most of the key concepts and issues are expressed in this Report. The Report encapsulates the history of computer science efforts to create systems that explain and instruct (intelligent tutoring systems and expert systems). The Report expresses the explainability issues and challenges in modern AI, and presents capsule views of the leading psychological theories of explanation. Certain articles stand out by virtue of their particular relevance to XAI, and their methods, results, and key points are highlighted. It is recommended that AI/XAI researchers be encouraged to include in their research reports fuller details on their empirical or experimental methods, in the fashion of experimental psychology research reports: details on Participants, Instructions, Procedures, Tasks, Dependent Variables (operational definitions of the measures and metrics), Independent Variables (conditions), and Control Conditions.
Disguised-Nets: Image Disguising for Privacy-preserving Deep Learning

Due to the high training costs of deep learning, model developers often rent cloud GPU servers to achieve better efficiency. However, this practice raises privacy concerns. An adversarial party may be interested in 1) personal identifiable information encoded in the training data and the learned models, 2) misusing the sensitive models for its own benefits, or 3) launching model inversion (MIA) and generative adversarial network (GAN) attacks to reconstruct replicas of training data (e.g., sensitive images). Learning from encrypted data seems impractical due to the large training data and expensive learning algorithms, while differential-privacy based approaches have to make significant trade-offs between privacy and model quality. We investigate the use of image disguising techniques to protect both data and model privacy. Our preliminary results show that with block-wise permutation and transformations, surprisingly, disguised images still give reasonably well performing deep neural networks (DNN). The disguised images are also resilient to the deep-learning enhanced visual discrimination attack and provide an extra layer of protection from MIA and GAN attacks.
Quantum Sparse Support Vector Machines

We present a quantum machine learning algorithm for training Sparse Support Vector Machine, a linear classifier that minimizes the hinge loss and the $L_1$ norm of the feature weights vector. Sparse SVM results in a classifier that uses only a small fraction of the input features in making decisions, and is especially suitable for cases where the total number of features is at the same order, or larger, than the number of training samples. The algorithm utilizes recently proposed quantum solvers for semidefinite programming and linear programming problems. We show that while for an arbitrary binary classification problem no quantum speedup is achieved by using quantum SDP/LP solvers during training, there are realistic scenarios in which using a sparse linear classifier makes sense in terms of the expected accuracy of predictions, and polynomial quantum speedup compared to classical methods can be achieved.
Stability of the Cohomology of the Space of Complex Irreducible Polynomials in Several Variables

We prove that the space of complex irreducible polynomials of degree $d$ in $n$ variables satisfies two forms of homological stability: first, its cohomology stabilizes as $d$ increases, and second, its compactly supported cohomology stabilizes as $n$ increases. Our topological results are inspired by counting results over finite fields due to Carlitz and Hyde.
A Generalized Framework for Population Based Training

Based Training (PBT) is a recent approach that jointly optimizes neural network weights and hyperparameters which periodically copies weights of the best performers and mutates hyperparameters during training. Previous PBT implementations have been synchronized glass-box systems. We propose a general, black-box PBT framework that distributes many asynchronous "trials" (a small number of training steps with warm-starting) across a cluster, coordinated by the PBT controller. The black-box design does not make assumptions on model architectures, loss functions or training procedures. Our system supports dynamic hyperparameter schedules to optimize both differentiable and non-differentiable metrics. We apply our system to train a state-of-the-art WaveNet generative model for human voice synthesis. We show that our PBT system achieves better accuracy and faster convergence compared to existing methods, given the same computational resource.
Optimal Divisible Load Scheduling for Resource-Sharing Network

Scheduling is an important task allowing parallel systems to perform efficiently and reliably. For modern computation systems, divisible load is a special type of data which can be divided into arbitrary sizes and independently processed in parallel. Such loads are commonly encountered in applications which are processing a great amount of similar data units. For a multi-task processor, the processor's speed may be time-varying due to the arrival and departure of other background jobs. This paper studies an optimal divisible loads scheduling problem on a single level tree network, whose processing speeds and channel speeds are time-varying. Two recursive algorithms are provided to solve this problem when the arrival and departure times of the background jobs are known a priori and an iterative algorithm is provided to solve the case where such times are not known. Numerical tests and evaluations are performed for these three algorithms under different numbers of background jobs and processors.
Reinforcement Learning for Optimal Load Distribution Sequencing in Resource-Sharing System

Divisible Load Theory (DLT) is a powerful tool for modeling divisible load problems in data-intensive systems. This paper studied an optimal divisible load distribution sequencing problem using a machine learning framework. The problem is to decide the optimal sequence to distribute divisible load to processors in order to achieve minimum finishing time. The scheduling is performed in a resource-sharing system where each physical processor is virtualized to multiple virtual processors. A reinforcement learning method called Multi-armed bandit (MAB) is used for our problem. We first provide a naive solution using the MAB algorithm and then several optimizations are performed. Various numerical tests are conducted. Our algorithm shows an increasing performance during the training progress and the global optimum will be acheived when the sample size is large enough.
Exponentiated Gradient Meets Gradient Descent

The (stochastic) gradient descent and the multiplicative update method are probably the most popular algorithms in machine learning. We introduce and study a new regularization which provides a unification of the additive and multiplicative updates. This regularization is derived from an hyperbolic analogue of the entropy function, which we call hypentropy. It is motivated by a natural extension of the multiplicative update to negative numbers. The hypentropy has a natural spectral counterpart which we use to derive a family of matrix-based updates that bridge gradient methods and the multiplicative method for matrices. While the latter is only applicable to positive semi-definite matrices, the spectral hypentropy method can naturally be used with general rectangular matrices. We analyze the new family of updates by deriving tight regret bounds. We study empirically the applicability of the new update for settings such as multiclass learning, in which the parameters constitute a general rectangular matrix.
Adaptive Stress Testing for Autonomous Vehicles

This paper presents a method for testing the decision making systems of autonomous vehicles. Our approach involves perturbing stochastic elements in the vehicle's environment until the vehicle is involved in a collision. Instead of applying direct Monte Carlo sampling to find collision scenarios, we formulate the problem as a Markov decision process and use reinforcement learning algorithms to find the most likely failure scenarios. This paper presents Monte Carlo Tree Search (MCTS) and Deep Reinforcement Learning (DRL) solutions that can scale to large environments. We show that DRL can find more likely failure scenarios than MCTS with fewer calls to the simulator. A simulation scenario involving a vehicle approaching a crosswalk is used to validate the framework. Our proposed approach is very general and can be easily applied to other scenarios given the appropriate models of the vehicle and the environment.
CMS Sematrix: A Tool to Aid the Development of Clinical Quality Measures (CQMs)

As part of the effort to improve quality and to reduce national healthcare costs, the Centers for Medicare and Medicaid Services (CMS) are responsible for creating and maintaining an array of clinical quality measures (CQMs) for assessing healthcare structure, process, outcome, and patient experience across various conditions, clinical specialties, and settings. The development and maintenance of CQMs involves substantial and ongoing evaluation of the evidence on the measure's properties: importance, reliability, validity, feasibility, and usability. As such, CMS conducts monthly environmental scans of the published clinical and health service literature. Conducting time consuming, exhaustive evaluations of the ever-changing healthcare literature presents one of the largest challenges to an evidence-based approach to healthcare quality improvement. Thus, it is imperative to leverage automated techniques to aid CMS in the identification of clinical and health services literature relevant to CQMs. Additionally, the estimated labor hours and related cost savings of using CMS Sematrix compared to a traditional literature review are roughly 818 hours and 122,000 dollars for a single monthly environmental scan.
Active Learning for High-Dimensional Binary Features

Erbium-doped fiber amplifier (EDFA) is an optical amplifier/repeater device used to boost the intensity of optical signals being carried through fiber optic communication networks. A highly accurate EDFA model – to predict the signal gain for each channel – is required because of its crucial role in optical network management and optimization. EDFA channel inputs (i.e. features) either carry signal or are idle, therefore they can be treated as binary features. However, channel outputs (and the corresponding signal gains) are continuous values. Labeled training data is very expensive to collect for EDFA devices, therefore we devise an active learning strategy suitable for binary features to overcome this issue. We propose to take advantage of sparse linear models to simplify the predictive model. This approach improves signal gain prediction and accelerates active learning query generation. We show the performance of our proposed active learning strategies on simulated data and real EDFA data.
Combining Physically Based Modeling and Deep Learning for Fusing GRACE Satellite Data: Can We Learn From Mismatch?

Global hydrological and land surface models are increasingly used for tracking terrestrial total water storage (TWS) dynamics, but the utility of existing models is hampered by conceptual and/or data uncertainties related to various underrepresented and unrepresented processes, such as groundwater storage. The gravity recovery and climate experiment (GRACE) satellite mission provided a valuable independent data source for tracking TWS at regional and continental scales. Strong interests exist in fusing GRACE data into global hydrological models to improve their predictive performance. Here we develop and apply deep convolutional neural network (CNN) models to learn the spatiotemporal patterns of mismatch between TWS anomalies (TWSA) derived from GRACE and those simulated by NOAH, a widely used land surface model. Once trained, our CNN models can be used to correct the NOAH‐simulated TWSA without requiring GRACE data, potentially filling the data gap between GRACE and its follow‐on mission, GRACE‐FO. Our methodology is demonstrated over India, which has experienced significant groundwater depletion in recent decades that is nevertheless not being captured by the NOAH model. Results show that the CNN models significantly improve the match with GRACE TWSA, achieving a country‐average correlation coefficient of 0.94 and Nash‐Sutcliff efficient of 0.87, or 14% and 52% improvement, respectively, over the original NOAH TWSA. At the local scale, the learned mismatch pattern correlates well with the observed in situ groundwater storage anomaly data for most parts of India, suggesting that deep learning models effectively compensate for the missing groundwater component in NOAH for this study region.
Market Manipulation of Bitcoin: Evidence from Mining the Mt. Gox Transaction Network

The cryptocurrency market is a very huge market without effective supervision. It is of great importance for investors and regulators to recognize whether there are market manipulation and its manipulation patterns. This paper proposes an approach to mine the transaction networks of exchanges for answering this question. By taking the leaked transaction history of Mt. Gox Bitcoin exchange as a sample, we first divide the accounts into three categories according to its characteristic and then construct the transaction history into three graphs. Many observations and findings are obtained via analyzing the constructed graphs. To evaluate the influence of the accounts’ transaction behavior on the Bitcoin exchange price, the graphs are reconstructed into series and reshaped as matrices. By using singular value decomposition (SVD) on the matrices, we identify many base networks which have a great correlation with the price fluctuation. When further analyzing the most important accounts in the base networks, plenty of market manipulation patterns are found. According to these findings, we conclude that there was serious market manipulation in Mt. Gox exchange and the cryptocurrency market must strengthen the supervision.
Thirty Years of Machine Learning: The Road to Pareto-Optimal Next-Generation Wireless Networks

Next-generation wireless networks (NGWN) have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of machine learning by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning, respectively. Furthermore, we investigate their employment in the compelling applications of NGWNs, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various machine learning algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.
A General Theory for Structured Prediction with Smooth Convex Surrogates

In this work we provide a theoretical framework for structured prediction that generalizes the existing theory of surrogate methods for binary and multiclass classification based on estimating conditional probabilities with smooth convex surrogates (e.g. logistic regression). The theory relies on a natural characterization of structural properties of the task loss and allows to derive statistical guarantees for many widely used methods in the context of multilabeling, ranking, ordinal regression and graph matching. In particular, we characterize the smooth convex surrogates compatible with a given task loss in terms of a suitable Bregman divergence composed with a link function. This allows to derive tight bounds for the calibration function and to obtain novel results on existing surrogate frameworks for structured prediction such as conditional random fields and quadratic surrogates.
Permutation Invariant Likelihoods and Equivariant Transformations

In this work, we fill a substantial void in machine learning and statistical methodology by developing extensive generative density estimation techniques for exchangeable non-iid data. We do so through the use of permutation invariant likelihoods and permutation equivariant transformations of variables. These methods exploit the intradependencies within sets in ways that are independent of ordering (for likelihoods) or order preserving (for transformations). The proposed techniques are able to directly model exchangeable data (such as sets) without the need to account for permutations or assume independence of elements. We consider applications to point clouds and provide several interesting experiments on both synthetic and real-world datasets.
Polyphonic Music Composition with LSTM Neural Networks and Reinforcement Learning

In the domain of algorithmic music composition, machine learning-driven systems eliminate the need for carefully hand-crafting rules for composition. In particular, the capability of recurrent neural networks to learn complex temporal patterns lends itself well to the musical domain. Promising results have been observed across a number of recent attempts at music composition using deep RNNs. These approaches generally aim at first training neural networks to reproduce subsequences drawn from existing songs. Subsequently, they are used to compose music either at the audio sample-level or at the note-level. We designed a representation that divides polyphonic music into a small number of monophonic streams. This representation greatly reduces the complexity of the problem and eliminates an exponential number of probably poor compositions. On top of our LSTM neural network that learnt musical sequences in this representation, we built an RL agent that learnt to find combinations of songs whose joint dominance produced pleasant compositions. We present Amadeus, an algorithmic music composition system that composes music that consists of intricate melodies, basic chords, and even occasional contrapuntal sequences.
Reconstruction of top quark pair dilepton decays in electron-positron collisions

A new algorithm is presented to perform the full kinematic reconstruction of top quark pair events produced at future electron-positron colliders in the case of dilepton decays of the $W$ bosons to electrons or muons. The momentum components of the undetected neutrino and anti-neutrino in the event are reconstructed by employing several kinematic conditions comprising a non-linear system of six equations. This system is solved numerically using two independent methods and the selection of the best candidate real solution for each event is determined by a likelihood discriminant. Results are presented for several reconstructed kinematic properties of the $W^\pm$ bosons, top (and anti-top) quarks using generator level information produced at leading order.