nils-herrmann/clean_scirepeval huggingface dataset:
The lure of conformal symmetry

The Clifford algebra Cl(4,1) s ℂ [4], generated by the real (Majorana) gamma-matrices and by a hermitian &gamma[_5, gives room to the reductive Lie algebra u(2,2) of the conformal group extended by the u(1) helicity operator. Its unitary positive energy ladder representations, constructed by Gerhard Mack and the author 50 years ago, opened the way to a better understanding of zero-mass particles and fields and their relation to the space of bound states of the hydrogen atom. They became a prototypical example of a minimal representation of a non-compact reductive group introduced during the subsequent decade by Joseph.
Definitively Identifying an Inherent Limitation to Actual Cognition

A century ago, discoveries of a serious kind of logical error made separately by several leading mathematicians led to acceptance of a sharply enhanced standard for rigor within what ultimately became the foundation for Computer Science. By 1931, Godel had obtained a definitive and remarkable result: an inherent limitation to that foundation. The resulting limitation is not applicable to actual human cognition, to even the smallest extent, unless both of these extremely brittle assumptions hold: humans are infallible reasoners and reason solely via formal inference rules. Both assumptions are contradicted by empirical data from well-known Cognitive Science experiments. This article investigates how a novel multi-part methodology recasts computability theory within Computer Science to obtain a definitive limitation whose application to human cognition avoids assumptions contradicting empirical data. The limitation applies to individual humans, to finite sets of humans, and more generally to any real-world entity.
Don't Persist All : Efficient Persistent Data Structures

Data structures used in software development have inbuilt redundancy to improve software reliability and to speed up performance. Examples include a Doubly Linked List which allows a faster deletion due to the presence of the previous pointer. With the introduction of Persistent Memory, storing the redundant data fields into persistent memory adds a significant write overhead, and reduces performance. In this work, we focus on three data structures - Doubly Linked List, B+Tree and Hashmap, and showcase alternate partly persistent implementations where we only store a limited set of data fields to persistent memory. After a crash/restart, we use the persistent data fields to recreate the data structures along with the redundant data fields. We compare our implementation with the base implementation and show that we achieve speedups around 5-20% for some data structures, and up to 165% for a flush-dominated data structure.
Unraveling the origin of black holes from effective spin measurements with LIGO-Virgo

We investigate how to use information on the effective spin parameter of binary black hole mergers from the LIGO-Virgo gravitational wave detections to discriminate the origin of the merging black holes. We calculate the expected probability distribution function for the effective spin parameter for primordial black holes. Using LIGO-Virgo observations, we then calculate odds ratios for different models for the distribution of black holes' spin magnitude and alignment. We evaluate the posterior probability density for a possible mixture of astrophysical and primordial black holes as emerging from current data, and calculate the number of future merger events needed to discriminate different spin and alignment models at a given level of statistical significance.
Memory-efficient and fast implementation of local adaptive binarization methods

Binarization is widely used as an image preprocessing step to separate object especially text from background before recognition. For noisy images with uneven illumination such as degraded documents, threshold values need to be computed pixel by pixel to obtain a good segmentation. Since local threshold values typically depend on moment-based statistics such as mean and variance of gray levels inside rectangular windows, integral images which are memory consuming are commonly used to accelerate the calculation. Observed that moment-based statistics as well as quantiles in a sliding window can be computed recursively, integral images can be avoided without neglecting speed, more binarization methods can be accelerated too. In particular, given a $H\times W$ input image, Sauvola's method and alike can run in $\Theta (HW)$ time independent of window size, while only around $6\min\{H,W\}$ bytes of auxiliary space is needed, which is significantly lower than the $16HW$ bytes occupied by the two integral images. Since the proposed technique enable various well-known local adaptive binarization methods to be applied in real-time use cases on devices with limited resources, it has the potential of wide application.
Shared control schematic for brain controlled vehicle based on fuzzy logic

Brain controlled vehicle refers to the vehicle that obtains control commands by analyzing the driver's EEG through Brain-Computer Interface (BCI). The research of brain controlled vehicles can not only promote the integration of brain machines, but also expand the range of activities and living ability of the disabled or some people with limited physical activity, so the research of brain controlled vehicles is of great significance and has broad application prospects. At present, BCI has some problems such as limited recognition accuracy, long recognition time and limited number of recognition commands in the process of analyzing EEG signals to obtain control commands. If only use the driver's EEG signals to control the vehicle, the control performance is not ideal. Based on the concept of Shared control, this paper uses the fuzzy control (FC) to design an auxiliary controller to realize the cooperative control of automatic control and brain control. Designing a Shared controller which evaluates the current vehicle status and decides the switching mechanism between automatic control and brain control to improve the system control performance. Finally, based on the joint simulation platform of Carsim and MATLAB, with the simulated brain control signals, the designed experiment verifies that the control performance of the brain control vehicle can be improved by adding the auxiliary controller.
A Soft Embedding Lemma for Soft Topological Spaces

In 1999, Molodtsov initiated the theory of soft sets as a new mathematical tool for dealing with uncertainties in many fields of applied sciences. In 2011, Shabir and Naz introduced and studied the notion of soft topological spaces, also defining and investigating many new soft properties as generalization of the classical ones. In this paper, we introduce the notions of soft separation between soft points and soft closed sets in order to obtain a generalization of the well-known Embedding Lemma to the class of soft topological spaces.
Unpredictability of AI

The young field of AI Safety is still in the process of identifying its challenges and limitations. In this paper, we formally describe one such impossibility result, namely Unpredictability of AI. We prove that it is impossible to precisely and consistently predict what specific actions a smarter-than-human intelligent system will take to achieve its objectives, even if we know terminal goals of the system. In conclusion, impact of Unpredictability on AI Safety is discussed.
MoonLight: Effective Fuzzing with Near-Optimal Corpus Distillation

Mutation-based fuzzing typically uses an initial set of valid seed inputs from which to generate new inputs by random mutation. A corpus of potential seeds will often contain thousands of similar inputs. This lack of diversity can lead to wasted effort, as fuzzing will exhaustively explore mutation from all seeds. To address this, fuzzers such as AFL come with distillation tools (eg, cmin) that select seeds as the smallest subset of a given corpus that triggers the same range of instrumentation data points as the full corpus. Experience suggests that minimizing both number and cumulative size of seeds may improve fuzzing efficiency. 
We present a theoretical framework for understanding the value of distillation techniques and a new algorithm for minimization based on this theory called ML. The theory characterizes the performance of ML as near-optimal, outperforming existing greedy methods to deliver smaller seed sets. We then compare the effectiveness of ML-distilled seed selection in a long campaign, comparing against cmin, with ML configured to give weight to different characteristics of the seeds (ie, unweighted, file size, or execution time), as well as against each target's full corpus and a singleton set containing only an "empty" valid input seed. 
Our results demonstrate that seeds selected by ML outperform the existing greedy cmin, and that weighting by file size is usually the best option. We target six common open-source programs, covering seven different file formats, and show that ML outperforms cmin in terms of the number of (unique) crashes generated over multiple sustained campaigns. Moreover, ML not only generates at least as many crashes as cmin, but also finds bugs that cmin does not. Our crash analysis for one target reveals four previously unreported bugs, all of which are security-relevant and have received CVEs. Three of these four were found only with ML.
The Bloom Clock

The bloom clock is a space-efficient, probabilistic data structure designed to determine the partial order of events in highly distributed systems. The bloom clock, like the vector clock, can autonomously detect causality violations by comparing its logical timestamps. Unlike the vector clock, the space complexity of the bloom clock does not depend on the number of nodes in a system. Instead it depends on a set of chosen parameters that determine its confidence interval, i.e. false positive rate. To reduce the space complexity from which the vector clock suffers, the bloom clock uses a 'moving window' in which the partial order of events can be inferred with high confidence. If two clocks are not comparable, the bloom clock can always deduce it, i.e. false negatives are not possible. If two clocks are comparable, the bloom clock can calculate the confidence of that statement, i.e. it can compute the false positive rate between comparable pairs of clocks. By choosing an acceptable threshold for the false positive rate, the bloom clock can properly compare the order of its timestamps, with that of other nodes in a highly accurate and space efficient way.
A Novel Time-Based Modulation Scheme in Time-Asynchronous Channels for Molecular Communications

In this paper, a novel time-based modulation scheme is proposed in the time-asynchronous channel for diffusion-based molecular communication systems with drift. Based on this modulation scheme, we demonstrate that the sample variance of information molecules’ arrival time approximately follows a noncentral chi-squared distribution. According to its conditional probability density function (PDF), the asynchronous receiver designs are deduced based on the maximum likelihood (ML) detection, with or without background noise in the channel environment. Since the proposed schemes can be applied to the case of transmitting multiple information molecules, simulation results reveal that the bit error ratio (BER) performance improves with the increase of the number of released information molecules per bit. Furthermore, when the background noise is not negligible, our proposed asynchronous scheme outperforms the asynchronous modulation techniques based on encoding information on the time between two consecutive release of information molecules.
Customer mobility and congestion in supermarkets

The analysis and characterization of human mobility using population-level mobility models is important for numerous applications, ranging from the estimation of commuter flows in cities to modeling trade flows between countries. However, almost all of these applications have focused on large spatial scales, which typically range between intracity scales and intercountry scales. In this paper, we investigate population-level human mobility models on a much smaller spatial scale by using them to estimate customer mobility flow between supermarket zones. We use anonymized, ordered customer-basket data to infer empirical mobility flow in supermarkets, and we apply variants of the gravity and intervening-opportunities models to fit this mobility flow and estimate the flow on unseen data. We find that a doubly-constrained gravity model and an extended radiation model (which is a type of intervening-opportunities model) can successfully estimate 65%-70% of the flow inside supermarkets. Using a gravity model as a case study, we then investigate how to reduce congestion in supermarkets using mobility models. We model each supermarket zone as a queue, and we use a gravity model to identify store layouts with low congestion, which we measure either by the maximum number of visits to a zone or by the total mean queue size. We then use a simulated-annealing algorithm to find store layouts with lower congestion than a supermarket's original layout. In these optimized store layouts, we find that popular zones are often in the perimeter of a store. Our research gives insight both into how customers move in supermarkets and into how retailers can arrange stores to reduce congestion. It also provides a case study of human mobility on small spatial scales.
Rigid fibers of spinning tops

(Non-)displaceability of fibers of integrable systems has been an important problem in symplectic geometry. In this paper, for a large class of classical Liouville integrable systems containing the Lagrangian top, the Kovalevskaya top and the C. Neumann problem, we find a non-displaceable fiber for each of them. Moreover, we show that the non-displaceable fiber which we detect is the unique fiber which is non-displaceable from the zero-section. As a special case of this result, we also show that a singular level set of a convex Hamiltonian is non-displaceable from the zero-section. To prove these results, we use the notion of superheaviness introduced by Entov and Polterovich.
Standing on the Shoulders of Giants: AI-Driven Calibration of Localisation Technologies

High accuracy localisation technologies exist but are prohibitively expensive to deploy for large indoor spaces such as warehouses, factories, and supermarkets to track assets and people. However, these technologies can be used to lend their highly accurate localisation capabilities to low-cost, commodity, and less-accurate technologies. In this paper, we bridge this link by proposing a technology-agnostic calibration framework based on artificial intelligence to assist such low-cost technologies through highly accurate localisation systems. A single-layer neural network is used to calibrate a less accurate technology using a more accurate one such as Bluetooth Low Energy (BLE) using Ultra-Wideband (UWB) and UWB using a professional motion tracking system. On a real indoor testbed, we demonstrate an improvement in accuracy of approximately 70% for BLE and 50% for UWB. Not only does the proposed approach require a very short measurement campaign, the low complexity of a single-layer neural network makes it ideal for deployment on constrained devices typically used for localisation purposes.
On Stochastic Gradient Langevin Dynamics with Dependent Data Streams: The Fully Nonconvex Case

We consider the problem of sampling from a target distribution, which is \emph {not necessarily logconcave}, in the context of empirical risk minimization and stochastic optimization as presented in Raginsky et al. (2017). Non-asymptotic analysis results are established in the $L^1$-Wasserstein distance for the behaviour of Stochastic Gradient Langevin Dynamics (SGLD) algorithms. We allow the estimation of gradients to be performed even in the presence of \emph{dependent} data streams. Our convergence estimates are sharper and \emph{uniform} in the number of iterations, in contrast to those in previous studies.
Anomaly Detection in Images

Visual defect assessment is a form of anomaly detection. This is very relevant in finding faults such as cracks and markings in various surface inspection tasks like pavement and automotive parts. The task involves detection of deviation/divergence of anomalous samples from the normal ones. Two of the major challenges in supervised anomaly detection are the lack of labelled training data and the low availability of anomaly instances. Semi-supervised methods which learn the underlying distribution of the normal samples and then measure the deviation/divergence from the estimated model as the anomaly score have limitations in their overall ability to detect anomalies. This paper proposes the application of network-based deep transfer learning using convolutional neural networks (CNNs) for the task of anomaly detection. Single class SVMs have been used in the past with some success, however we hypothesize that deeper networks for single class classification should perform better. Results obtained on established anomaly detection benchmarks as well as on a real-world dataset, show that the proposed method clearly outperforms the existing state-of-the-art methods, by achieving a staggering average area under the receiver operating characteristic curve value of 0.99 for the tested data-sets which is an average improvement of 41% on the CIFAR10, 20% on MNIST and 16% on Cement Crack data-sets.
Moving Target Defense for Deep Visual Sensing against Adversarial Examples

Deep learning based visual sensing has achieved attractive accuracy but is shown vulnerable to adversarial example attacks. Specifically, once the attackers obtain the deep model, they can construct adversarial examples to mislead the model to yield wrong classification results. Deployable adversarial examples such as small stickers pasted on the road signs and lanes have been shown effective in misleading advanced driver-assistance systems. Many existing countermeasures against adversarial examples build their security on the attackers' ignorance of the defense mechanisms. Thus, they fall short of following Kerckhoffs's principle and can be subverted once the attackers know the details of the defense. This paper applies the strategy of moving target defense (MTD) to generate multiple new deep models after system deployment, that will collaboratively detect and thwart adversarial examples. Our MTD design is based on the adversarial examples' minor transferability to models differing from the one (e.g., the factory-designed model) used for attack construction. The post-deployment quasi-secret deep models significantly increase the bar for the attackers to construct effective adversarial examples. We also apply the technique of serial data fusion with early stopping to reduce the inference time by a factor of up to 5 while maintaining the sensing and defense performance. Extensive evaluation based on three datasets including a road sign image database and a GPU-equipped Jetson embedded computing board shows the effectiveness of our approach.
The Art of Food: Meal Image Synthesis from Ingredients

In this work we propose a new computational framework, based on generative deep models, for synthesis of photo-realistic food meal images from textual descriptions of its ingredients. Previous works on synthesis of images from text typically rely on pre-trained text models to extract text features, followed by a generative neural networks (GANs) aimed to generate realistic images conditioned on the text features. These works mainly focus on generating spatially compact and well-defined categories of objects, such as birds or flowers. In contrast, meal images are significantly more complex, consisting of multiple ingredients whose appearance and spatial qualities are further modified by cooking methods. We propose a method that first builds an attention-based ingredients-image association model, which is then used to condition a generative neural network tasked with synthesizing meal images. Furthermore, a cycle-consistent constraint is added to further improve image quality and control appearance. Extensive experiments show our model is able to generate meal image corresponding to the ingredients, which could be used to augment existing dataset for solving other computational food analysis problems.
Hierarchical Transformers for Multi-Document Summarization

In this paper, we develop a neural summarization model which can effectively process multiple input documents and distill Transformer architecture with the ability to encode documents in a hierarchical manner. We represent cross-document relationships via an attention mechanism which allows to share information as opposed to simply concatenating text spans and processing them as a flat sequence. Our model learns latent dependencies among textual units, but can also take advantage of explicit graph representations focusing on similarity or discourse relations. Empirical results on the WikiSum dataset demonstrate that the proposed architecture brings substantial improvements over several strong baselines.
Distinguishing states of conscious arousal using statistical complexity

We apply techniques from the field of computational mechanics to evaluate the statistical complexity of neural recording data in fruit flies. We connect statistical complexity to the flies' level of conscious arousal, which is manipulated by general anaesthesia (isoflurane). We show that the complexity of even single channel time series data decreases under anaesthesia. The observed difference complexity between the two conscious arousal states increases as higher orders of temporal correlations are taken into account. In contrast to prior work, our results show that complexity differences can emerge at very short time scales and across broad regions of the fly brain, thus heralding the macroscopic state of anaesthesia in a previously unforeseen manner.
Cross-section measurements of radiative proton-capture reactions in 
Cd112
 at energies of astrophysical interest

Reactions involving the group of nuclei commonly known as p nuclei are part of the nucleosynthetic mechanisms at astrophysical sites. The $^{113}$In nucleus is such a case with several open questions regarding its origin at extreme stellar environments. In this work, the experimental study of the cross sections of the radiative proton-capture reaction $^{112}$Cd(p,$\gamma$)$^{113}$In is attempted for the first time at energies lying inside the Gamow window with an isotopically enriched $^{112}$Cd target. Two different techniques, the in-beam $\gamma$-ray spectroscopy and the activation method, have been applied. The latter method is required to account for the presence of a low-lying $^{113}$In isomer at 392 keV having a halflife of $\approx 100$ min. From the cross sections, the astrophysical S factors and the isomeric ratios have been additionally deduced. The experimental results are compared to detailed Hauser-Feshbach theoretical calculations using TALYS, and discussed in terms of their significance to the optical model potential involved.
Primordial black hole formation with non-Gaussian curvature perturbations

In the context of transient constant-roll inflation near a local maximum, we derive the non-perturbative field redefinition that relates a Gaussian random field with the true non-Gaussian curvature perturbation. Our analysis shows the emergence of a new critical amplitude ζ*, corresponding to perturbations that prevent the inflaton from overshooting the local maximum, thus becoming trapped in the false minimum of the potential. For potentials with a mild curvature at the local maximum (and thus small non-Gaussianity), we recover the known perturbative field redefinition. We apply these results to the formation of primordial black holes, and discuss the cases for which ζ* is smaller or of the same order than the critical value for collapse of spherically symmetric overdensities. In the latter case, we present a simple potential for which the power spectrum needs an amplitude 10 times smaller that in the Gaussian case for producing a sizeable amount of primordial black holes.