nils-herrmann/clean_scirepeval huggingface dataset:
The Economics of Enlightenment: Time Value of Knowledge and the Net Present Value (NPV) of Knowledge Machines

We formulate one methodology to put a value or price on knowledge using well accepted techniques from finance. We provide justifications for these finance principles based on the limitations of the physical world we live in. To the best of our knowledge this is the first recorded attempt to put a numerical value on knowledge. The implications of this valuation exercise, which places a high premium on any piece of knowledge, are to ensure that participants in any knowledge system are better trained to notice the knowledge available from any source. Just because someone does not see a connection, does not mean that there is no connection. We need to try harder and be more open to acknowledging the smallest piece of new knowledge that might have been brought to light by anyone from anywhere about anything.
Image-based marker tracking and registration for intraoperative 3D image-guided interventions using augmented reality

Augmented reality (AR) can enable physicians to “see” inside of patients by projecting cross-sectional imaging directly onto the patient during procedures. In order to maintain workflow, imaging must be quickly and accurately registered to the patient. We describe a method for automatically registering a CT image set projected from an augmented reality headset to a set of points in the real world as a first step towards real-time registration of medical images to patients. Sterile, radiopaque fiducial markers with unique optical identifiers were placed on a patient prior to acquiring a CT scan of the abdomen. For testing purposes, the same fiducial markers were then placed on a tabletop as a representation of the patient. Our algorithm then automatically located the fiducial markers in the CT image set, optically identified the fiducial markers on the tabletop, registered the markers in the CT image set with the optically detected markers and finally projected the registered CT image set onto the real-world markers using the augmented reality headset.The registration time for aligning the image set using 3 markers was 0.9 ± 0.2 seconds with an accuracy of 5 ± 2 mm. These findings demonstrate the feasibility of fast and accurate registration using unique radiopaque markers for aligning patient imaging onto patients for procedural planning and guidance.
Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics

Efficient network slicing is vital to deal with the highly variable and dynamic characteristics of traffic in 5G networks. Network slicing addresses a challenging dynamic network resource allocation problem where a single network infrastructure is divided into (virtual) multiple slices to meet the demands of different users with varying requirements, the main challenges being — the traffic arrival characteristics and the job resource requirements (e.g., compute, memory and bandwidth resources) for each slice can be highly dynamic. Traditional model-based optimization or queueing theoretic modeling becomes intractable with the high reliability, and stringent bandwidth and latency requirements imposed by 5G. We propose a deep reinforcement learning approach to address this dynamic coupled resource allocation problem. Model evaluation using synthetic and real workload data demonstrates that our deep reinforcement learning solution improves overall resource utilization, latency performance, and demands satisfied as compared to a baseline equal slicing strategy.
Random Sum-Product Forests with Residual Links

Tractable yet expressive density estimators are a key building block of probabilistic machine learning. While sum-product networks (SPNs) offer attractive inference capabilities, obtaining structures large enough to fit complex, high-dimensional data has proven challenging. In this paper, we present random sum-product forests (RSPFs), an ensemble approach for mixing multiple randomly generated SPNs. We also introduce residual links, which reference specialized substructures of other component SPNs in order to leverage the context-specific knowledge encoded within them. Our empirical evidence demonstrates that RSPFs provide better performance than their individual components. Adding residual links improves the models further, allowing the resulting ResSPNs to be competitive with commonly used structure learning methods.
Finite type $\xi$-asymptotic lines of plane fields in $\mathbb{R}^3$

We prove that a finite type curve is an $\xi$-asymptotic line (without parabolic points) of a suitable plane field. It is also given an explicit example of a hyperbolic closed finite type $\xi$-asymptotic line. These results obtained here are generalizations, for plane fields, of the results of V. Arnold .
De-anonymization Attacks on Neuroimaging Datasets

Advances in imaging technologies, combined with inexpensive storage, have led to an explosion in the volume of publicly available neuroimaging datasets. Effective analyses of these images hold the potential for uncovering mechanisms that govern functioning of the human brain, and understanding various neurological diseases and disorders. The potential significance of these studies notwithstanding, a growing concern relates to the protection of privacy and confidentiality of subjects who participate in these studies. In this paper, we present a de-anonymization attack rooted in the innate uniqueness of the structure and function of the human brain. We show that the attack reveals not only the identity of an individual, but also the efficacy with which they performing cognitive tasks. Our attack relies on novel matrix analyses techniques that are used to extract discriminating features in neuroimages. These features correspond to individual-specific signatures that can be matched across datasets for highly accurate identification. We present data preprocessing, signature extraction, and matching techniques that are computationally inexpensive, and can scale to large datasets. We characterize the efficacy of our de-anonymization attacks on publicly available databases. Finally, we discuss implications of the attack and challenges associated with defending against such attacks.
Trajectory-wise Control Variates for Variance Reduction in Policy Gradient Methods

Policy gradient methods have demonstrated success in reinforcement learning tasks that have high-dimensional continuous state and action spaces. However, policy gradient methods are also notoriously sample inefficient. This can be attributed, at least in part, to the high variance in estimating the gradient of the task objective with Monte Carlo methods. Previous research has endeavored to contend with this problem by studying control variates (CVs) that can reduce the variance of estimates without introducing bias, including the early use of baselines, state dependent CVs, and the more recent state-action dependent CVs. In this work, we analyze the properties and drawbacks of previous CV techniques and, surprisingly, we find that these works have overlooked an important fact that Monte Carlo gradient estimates are generated by trajectories of states and actions. We show that ignoring the correlation across the trajectories can result in suboptimal variance reduction, and we propose a simple fix: a class of "trajectory-wise" CVs, that can further drive down the variance. We show that constructing trajectory-wise CVs can be done recursively and requires only learning state-action value functions like the previous CVs for policy gradient. We further prove that the proposed trajectory-wise CVs are optimal for variance reduction under reasonable assumptions.
On the Variance of the Adaptive Learning Rate and Beyond

The learning rate warmup heuristic achieves remarkable success in stabilizing training, accelerating convergence and improving generalization for adaptive stochastic optimization algorithms like RMSprop and Adam. Here, we study its mechanism in details. Pursuing the theory behind warmup, we identify a problem of the adaptive learning rate (i.e., it has problematically large variance in the early stage), suggest warmup works as a variance reduction technique, and provide both empirical and theoretical evidence to verify our hypothesis. We further propose RAdam, a new variant of Adam, by introducing a term to rectify the variance of the adaptive learning rate. Extensive experimental results on image classification, language modeling, and neural machine translation verify our intuition and demonstrate the effectiveness and robustness of our proposed method. All implementations are available at: this https URL.
Efficient Inference of CNNs via Channel Pruning

The deployment of Convolutional Neural Networks (CNNs) on resource constrained platforms such as mobile devices and embedded systems has been greatly hindered by their high implementation cost, and thus motivated a lot research interest in compressing and accelerating trained CNN models. Among various techniques proposed in literature, structured pruning, especially channel pruning, has gain a lot focus due to 1) its superior performance in memory, computation, and energy reduction; and 2) it is friendly to existing hardware and software libraries. In this paper, we investigate the intermediate results of convolutional layers and present a novel pivoted QR factorization based channel pruning technique that can prune any specified number of input channels of any layer. We also explore more pruning opportunities in ResNet-like architectures by applying two tweaks to our technique. Experiment results on VGG-16 and ResNet-50 models with ImageNet ILSVRC 2012 dataset are very impressive with 4.29X and 2.84X computation reduction while only sacrificing about 1.40\% top-5 accuracy. Compared to many prior works, the pruned models produced by our technique require up to 47.7\% less computation while still achieve higher accuracies.
Representing Polish groupoids via metric structures.

We prove that every open $\sigma$-locally Polish groupoid $G$ is Borel equivalent to the groupoid of models on the Urysohn sphere $\mathbb{U}$ of an $\mathcal{L}_{\omega_1\omega}$-sentence in continuous logic. In particular, the orbit equivalence relations of such groupoids are up to Borel bireducibility precisely those of Polish group actions, answering a question of Lupini. Analogously, every non-Archimedean (i.e., every unit morphism has a neighborhood basis of open subgroupoids) open quasi-Polish groupoid is Borel equivalent to the groupoid of models on $\mathbb{N}$ of an $\mathcal{L}_{\omega_1\omega}$-sentence in discrete logic. 
The proof in fact gives a topological representation of $G$ as the groupoid of isomorphisms between a "continuously varying" family of structures over the space of objects of $G$, constructed via a topological Yoneda-like lemma of Moerdijk for localic groupoids and its metric analog. Other ingredients in our proof include the Lopez-Escobar theorem for continuous logic, a uniformization result for full Borel functors between open quasi-Polish groupoids, a uniform Borel version of Katětov's construction of $\mathbb{U}$, groupoid versions of the Pettis and Birkhoff--Kakutani theorems, and a development of the theory of non-Hausdorff topometric spaces and their quotients.
Uncheatable Machine Learning Inference

Classification-as-a-Service (CaaS) is widely deployed today in machine intelligence stacks for a vastly diverse set of applications including anything from medical prognosis to computer vision tasks to natural language processing to identity fraud detection. The computing power required for training complex models on large datasets to perform inference to solve these problems can be very resource-intensive. A CaaS provider may cheat a customer by fraudulently bypassing expensive training procedures in favor of weaker, less computationally-intensive algorithms which yield results of reduced quality. Given a classification service supplier $S$, intermediary CaaS provider $P$ claiming to use $S$ as a classification backend, and customer $C$, our work addresses the following questions: (i) how can $P$'s claim to be using $S$ be verified by $C$? (ii) how might $S$ make performance guarantees that may be verified by $C$? and (iii) how might one design a decentralized system that incentivizes service proofing and accountability? To this end, we propose a variety of methods for $C$ to evaluate the service claims made by $P$ using probabilistic performance metrics, instance seeding, and steganography. We also propose a method of measuring the robustness of a model using a blackbox adversarial procedure, which may then be used as a benchmark or comparison to a claim made by $S$. Finally, we propose the design of a smart contract-based decentralized system that incentivizes service accountability to serve as a trusted Quality of Service (QoS) auditor.
Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization

In this paper we propose a novel semantic localization algorithm that exploits multiple sensors and has precision on the order of a few centimeters. Our approach does not require detailed knowledge about the appearance of the world, and our maps require orders of magnitude less storage than maps utilized by traditional geometry- and LiDAR intensity-based localizers. This is important as self-driving cars need to operate in large environments. Towards this goal, we formulate the problem in a Bayesian filtering framework, and exploit lanes, traffic signs, as well as vehicle dynamics to localize robustly with respect to a sparse semantic map. We validate the effectiveness of our method on a new highway dataset consisting of 312km of roads. Our experiments show that the proposed approach is able to achieve 0.05m lateral accuracy and 1.12m longitudinal accuracy on average while taking up only 0.3% of the storage required by previous LiDAR intensity-based approaches.
Latency and Liquidity Risk

Latency (i.e., time delay) in electronic markets affects the efficacy of liquidity taking strategies. During the time liquidity takers process information and send marketable limit orders (MLOs) to the exchange, the limit order book (LOB) might undergo updates, so there is no guarantee that MLOs are filled. We develop a latency-optimal trading strategy that improves the marksmanship of liquidity takers. The interaction between the LOB and MLOs is modelled as a marked point process. Each MLO specifies a price limit so the order can receive worse prices and quantities than those the liquidity taker targets if the updates in the LOB are against the interest of the trader. In our model, the liquidity taker balances the tradeoff between missing trades and the costs of walking the book. We employ techniques of variational analysis to obtain the optimal price limit of each MLO the agent sends. The price limit of a MLO is characterized as the solution to a new class of forward-backward stochastic differential equations (FBSDEs) driven by random measures. We prove the existence and uniqueness of the solution to the FBSDE and numerically solve it to illustrate the performance of the latency-optimal strategies.
Ordinal Tax To Sustain a Digital Economy

Recently, the French Senate approved a law that imposes a 3% tax on revenue generated from digital services by companies above a certain size. While there is a lot of political debate about economic consequences of this action, it is actually interesting to reverse the question: We consider the long-term implications of an economy with no such digital tax. More generally, we can think of digital services as a special case of products with low or zero cost of transportation. With basic economic models we show that a market with no transportation costs is prone to monopolization as minuscule, random differences in quality are rewarded disproportionally. We then propose a distance-based tax to counter-balance the tendencies of random centralisation. Unlike a tax that scales with physical (cardinal) distance, a ranked (ordinal) distance tax leverages the benefits of digitalization while maintaining a stable economy.
Question-Agnostic Attention for Visual Question Answering

Visual Question Answering (VQA) models employ attention mechanisms to discover image locations that are most relevant for answering a specific question. For this purpose, several multimodal fusion strategies have been proposed, ranging from relatively simple operations (e.g. linear sum) to more complex ones (e.g. Block . The resulting multimodal representations define an intermediate feature space for capturing the interplay between visual and semantic features, that is helpful in selectively focusing on image content. In this paper, we propose a question-agnostic attention mechanism that is complementary to the existing question-dependent attention mechanisms. Our proposed model parses object instances to obtain an ‘object map’ and applies this map on the visual features to generate Question-Agnostic Attention (QAA) features. In contrast to question-dependent attention approaches that are learned end-to-end, the proposed QAA does not involve question-specific training, and can be easily included in almost any existing VQA model as a generic light-weight pre-processing step, thereby adding minimal computation overhead for training. Further, when used in complement with the question-dependent attention, the QAA allows the model to focus on the regions containing objects that might have been overlooked by the learned attention representation. Through extensive evaluation on VQAv1, VQAv2 and TDIUC datasets, we show that incorporating complementary QAA allows state-of-the-art VQA models to perform better, and provides significant boost to simplistic VQA models, enabling them to performance on par with highly sophisticated fusion strategies,
PosNeg-Balanced Anchors with Aligned Features for Single-Shot Object Detection

We introduce a novel single-shot object detector to ease the imbalance of foreground-background class by suppressing the easy negatives while increasing the positives. To achieve this, we propose an Anchor Promotion Module (APM) which predicts the probability of each anchor as positive and adjusts their initial locations and shapes to promote both the quality and quantity of positive anchors. In addition, we design an efficient Feature Alignment Module (FAM) to extract aligned features for fitting the promoted anchors with the help of both the location and shape transformation information from the APM. We assemble the two proposed modules to the backbone of VGG-16 and ResNet-101 network with an encoder-decoder architecture. Extensive experiments on MS COCO well demonstrate our model performs competitively with alternative methods (40.0\% mAP on \textit{test-dev} set) and runs faster (28.6 \textit{fps}).
On the Fundamental Limits of MIMO Massive Access Communication

The multiple access channel (MAC) with many-user is a general model for massive machine type communications. In this paradigm, the number of users may be comparable or even larger than the coding blocklength $n$. In contrast, classical MAC often assumes fixed and small number of the users. In this paper, we consider the massive access channel with multiple antennas system, where the base station (BS) with multiple receiving antennas serves the users in a single cell. The magnitude of users is assumed to grow unbounded with $n$. We investigate the achievable region of MIMO massive access channel, where among the total users, an unknown subset referred to active users may transmit data periodically. The asymptotic active user identification cost is also quantified. With the theoretical analysis, it was shown that given finite number of the receiving antennas, the individual rate for each user can be formulated as the sum rate multiplied by some specific factors, which correspond to the allocation of sum capacity. The successive decoding does not apply due to the interferences from growing unbounded users. Theoretical analysis shows that successive decoding works only when the number of receiving antennas goes to infinity with the increasing codelength.
ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection

This paper introduces a new dataset called "ToyADMOS" designed for anomaly detection in machine operating sounds (ADMOS). To the best our knowledge, no large-scale datasets are available for ADMOS, although large-scale datasets have contributed to recent advancements in acoustic signal processing. This is because anomalous sound data are difficult to collect. To build a large-scale dataset for ADMOS, we collected anomalous operating sounds of miniature machines (toys) by deliberately damaging them. The released dataset consists of three sub-datasets for machine-condition inspection, fault diagnosis of machines with geometrically fixed tasks, and fault diagnosis of machines with moving tasks. Each sub-dataset includes over 180 hours of normal machine-operating sounds and over 4,000 samples of anomalous sounds collected with four microphones at a 48-kHz sampling rate. The dataset is freely available for download at https://github.com/YumaKoizumi/ToyADMOS-dataset.
Measurement of $q_T$-weighted transverse-spin-dependent azimuthal asymmetries at COMPASS

COMPASS is a fixed-target experiment in operation at the CERN North Area (SPS, M2 beam-line) since 2002. An important part of the broad physics programme of the experiment is dedicated to the exploration of the transverse spin-structure of the nucleon studying target transverse spin dependent azimuthal asymmetries (TSAs) arising in the Semi-Inclusive DIS (SIDIS) and Drell-Yan (DY) cross-sections. In addition to those measurements, COMPASS has recently studied also the TSAs weighted by powers of the hadron transverse momentum (in SIDIS) and virtual photon transverse momentum, $q_T$ (in DY). In the transverse momentum dependent (TMD) QCD approach, the conventional DY TSAs are interpreted as convolutions of the beam pion and of the transversely polarized target proton TMD parton distribution functions (PDFs), while the $q_T$-weighted TSAs can be interpreted as simple products of transverse moments of the TMD PDFs. 
In 2015 and 2018 COMPASS performed two years of Drell-Yan data taking with a 190 GeV/$c$ $\pi^-$ beam impinging on a transversely polarized NH$_3$ target. The analysis of the $q_T$-weighted TSAs performed on these two data sets is presented in this paper. The results for DY Sivers $q_T$ weighted TSA are compared with the expectations based on the studies of the weighted Sivers asymmetry measured in the SIDIS process. Combining the information from SIDIS and DY measurements, the pion Boer-Mulders TMD PDF is also studied.
Using Semantic Role Knowledge for Relevance Ranking of Key Phrases in Documents: An Unsupervised Approach

The overwhelming growth of scientific and technical documents over the years calls for smart, efficient and automatic methods to facilitate the process of key phrase extraction and ranking. Most ranking models use features like TF-IDF, topic proportions, and phrase positions to score and rank the key phrases. However, integrating semantic roles of key phrases to compute their relevance ranking scores has not yet been investigated. In this paper, we investigate the integration of sentence position and the semantic role of words in a PageRank method to build a key phrase ranking method. We present the evaluation results of our approach on three scientific datasets. We show that semantic role information, when integrated with a PageRank method, can become a new lexical feature. Our approach had an overall improvement on all the data sets over the state-of-art baseline approaches.