nils-herrmann/clean_scirepeval huggingface dataset:
An Optimization-Enhanced MANO for Energy-Efficient 5G Networks

5G network nodes, fronthaul and backhaul alike, will have both forwarding and computational capabilities. This makes energy-efficient network management more challenging, as decisions, such as activating or deactivating a node, impact on both the ability of the network to route traffic and the amount of processing it can perform. To this end, we formulate an optimization problem accounting for the main features of 5G nodes and the traffic they serve, allowing joint decisions about: 1) the nodes to activate; 2) the network functions they run; and 3) the traffic routing. Our optimization module is integrated within the management and orchestration framework of 5G, thus enabling swift and high-quality decisions. We test our scheme with both a real-world testbed based on OpenStack and OpenDaylight, and a large-scale emulated network whose topology and traffic come from a real-world mobile operator, finding it to consistently outperform state-of-the art alternatives and closely match the optimum.
Complete maximum likelihood estimation for SEIR epidemic models: theoretical development

We present a class of SEIR Markov chain models for infectious diseases observed over discrete time in a random human population living in a closed environment. The population changes over time through random births, deaths, and transitions between states of the population. The SEIR models consist of random dynamical equations for each state (S, E, I and R) involving driving events for the process. We characterize some special types of SEIR Markov chain models in the class including: (1) when birth and death are zero or non-zero, and (2) when the incubation and infectious periods are constant or random. A detailed parameter estimation applying the maximum likelihood estimation technique and expectation maximization algorithm are presented for this study. Numerical simulation results are given to validate the epidemic models.
On the Feasibility of Infrastructure Assistance to Autonomous UAV Systems

Infrastructure assistance has been proposed as a viable solution to improve the capabilities of commercial Unmanned Aerial Vehicles (UAV), especially toward fully autonomous operations. The airborne nature of these devices imposes constrains limiting the onboard available energy supply and computing power. The assistance of the surrounding communication and computing infrastructure can mitigate such limitations by extending the communication range and taking over the execution of compute-intense tasks. However, autonomous operations impose specific, and rather extreme in some cases, demands to the infrastructure. Focusing on flight assistance and task offloading to edge servers, this paper presents an in-depth evaluation of the ability of the communication infrastructure to support the necessary flow of information from the UAV to the infrastructure. The study is based on our recently proposed FlyNetSim, an open-source UAV-network simulator accurately modeling both UAV and network operations.
Constant-roll inflation driven by a scalar field with nonminimal derivative coupling

In this work, we study constant-roll inflation driven by a scalar field with nonminimal derivative coupling to gravity, via the Einstein tensor. This model contains a free parameter,  see text], which quantifies the nonminimal derivative coupling and a parameter  see text] which characterizes the constant-roll condition. In this scenario, using the Hamilton–Jacobi-like formalism, an ansatz for the Hubble parameter (as a function of the scalar field) and some restrictions on the model parameters, we found new exact solutions for the inflaton potential which include power-law, de Sitter, quadratic hilltop and natural inflation, among others. Additionally, a phase-space analysis was performed and it is shown that the exact solutions associated to natural inflation and a “cosh-type” potential, are attractors.
Benchmarking TPU, GPU, and CPU Platforms for Deep Learning

Training deep learning models is compute-intensive and there is an industry-wide trend towards hardware specialization to improve performance. To systematically benchmark deep learning platforms, we introduce ParaDnn, a parameterized benchmark suite for deep learning that generates end-to-end models for fully connected (FC), convolutional (CNN), and recurrent (RNN) neural networks. Along with six real-world models, we benchmark Google's Cloud TPU v2/v3, NVIDIA's V100 GPU, and an Intel Skylake CPU platform. We take a deep dive into TPU architecture, reveal its bottlenecks, and highlight valuable lessons learned for future specialized system design. We also provide a thorough comparison of the platforms and find that each has unique strengths for some types of models. Finally, we quantify the rapid performance improvements that specialized software stacks provide for the TPU and GPU platforms.
The haloes and environments of nearby galaxies (HERON) – I. Imaging, sample characteristics, and envelope diameters

We use a dedicated 0.7-m telescope to image the haloes of 119 galaxies in the Local Volume to μr ∼ 28–30 mag arcsec−2. The sample is primarily from the Two Micron All Sky Survey Large Galaxy Atlas (Jarrett et al. 2003) and extended to include nearby dwarf galaxies and more distant giant ellipticals, and spans fully the galaxy colour–magnitude diagram including the blue cloud and red sequence. We present an initial overview, including deep images of our galaxies. Our observations reproduce previously reported low surface brightness structures, including extended plumes in M 51, and a newly discovered tidally extended dwarf galaxy in NGC 7331. Low surface brightness structures, or ‘envelopes’, exceeding 50 kpc in diameter are found mostly in galaxies with MV < −20.5, and classic interaction signatures are infrequent. Defining a halo diameter at the surface brightness 28 mag arcsec−2, we find that halo diameter is correlated with total galaxy luminosity. Extended signatures of interaction are found throughout the galaxy colour–magnitude diagram without preference for the red or blue sequences, or the green valley. Large envelopes may be found throughout the colour–magnitude diagram with some preference for the bright end of the red sequence. Spiral and S0 galaxies have broadly similar sizes, but ellipticals extend to notably greater diameters, reaching 150 kpc. We propose that the extended envelopes of disc galaxies are dominated by an extension of the disc population rather than by a classical Population II halo.
Automatic Crack Classification by Exploiting Statistical Event Descriptors for Deep Learning

In modern building infrastructures, the chance to devise adaptive and unsupervised data-driven structural health monitoring (SHM) systems is gaining in popularity. This is due to the large availability of big data from low-cost sensors with communication capabilities and advanced modeling tools such as deep learning. A promising method suitable for smart SHM is the analysis of acoustic emissions (AEs), i.e., ultrasonic waves generated by internal ruptures of the concrete when it is stressed. The advantage in respect to traditional ultrasonic measurement methods is the absence of the emitter and the suitability to implement continuous monitoring. The main purpose of this paper is to combine deep neural networks with bidirectional long short term memory and advanced statistical analysis involving instantaneous frequency and spectral kurtosis to develop an accurate classification tool for tensile, shear and mixed modes originated from AE events (cracks). We investigated effective event descriptors to capture the unique characteristics from the different types of modes. Tests on experimental results confirm that this method achieves promising classification among different crack events and can impact on the design of the future of SHM technologies. This approach is effective to classify incipient damages with 92% of accuracy, which is advantageous to plan maintenance.
Generic Intent Representation in Web Search

This paper presents GEneric iNtent Encoder (GEN Encoder) which learns a distributed representation space for user intent in search. Leveraging large scale user clicks from Bing search logs as weak supervision of user intent, GEN Encoder learns to map queries with shared clicks into similar embeddings end-to-end and then fine-tunes on multiple paraphrase tasks. Experimental results on an intrinsic evaluation task - query intent similarity modeling - demonstrate GEN Encoder's robust and significant advantages over previous representation methods. Ablation studies reveal the crucial role of learning from implicit user feedback in representing user intent and the contributions of multi-task learning in representation generality. We also demonstrate that GEN Encoder alleviates the sparsity of tail search traffic and cuts down half of the unseen queries by using an efficient approximate nearest neighbor search to effectively identify previous queries with the same search intent. Finally, we demonstrate distances between GEN encodings reflect certain information seeking behaviors in search sessions.
A Nonconvex Optimization Approach to IMRT Planning with Dose–Volume Constraints

Fluence map optimization for intensity-modulated radiation therapy planning can be formulated as a large-scale inverse problem with competing objectives and constraints associated with the tumors and organs at risk. Unfortunately, clinically relevant dose–volume constraints are nonconvex, so standard algorithms for convex problems cannot be directly applied. Although prior work focuses on convex approximations for these constraints, we propose a novel relaxation approach to handle nonconvex dose–volume constraints. We develop efficient, provably convergent algorithms based on partial minimization, and show how to adapt them to handle maximum-dose constraints and infeasible problems. We demonstrate our approach using the CORT data set and show that it is easily adaptable to radiation treatment planning with dose–volume constraints for multiple tumors and organs at risk.  This paper proposes a novel approach to deal with dose–volume constraints in radiation treatment planning optimization, which is inherently nonconvex, mixed-integer programming. The authors tackle this NP-hard problem using auxiliary variables and continuous optimization while preserving the problem’s nonconvexity. Algorithms to efficiently solve the nonconvex optimization problem presented in this paper yield computation speeds suitable for a busy clinical setting.
Dynamical Triangulation Induced by Quantum Walk

We present the single-particle sector of a quantum cellular automaton, namely a quantum walk, on a simple dynamical triangulated 2 - manifold. The triangulation is changed through Pachner moves, induced by the walker density itself, allowing the surface to transform into any topologically equivalent one. This model extends the quantum walk over triangular grid, introduced in a previous work, by one of the authors, whose space-time limit recovers the Dirac equation in (2+1)-dimensions. Numerical simulations show that the number of triangles and the local curvature grow as t α e − β t 2 , where α and β parametrize the way geometry changes upon the local density of the walker, and that, in the long run, flatness emerges. Finally, we also prove that the global behavior of the walker, remains the same under spacetime random fluctuations.
Liquid Speed: On-Demand Fast Trading at Distributed Exchanges

Exchanges acquire excess processing capacity to accommodate trading activity surges associated with zero-sum high-frequency trader (HFT) "duels." The idle capacity's opportunity cost is an externality of low-latency trading. We build a model of decentralized exchanges (DEX) with flexible capacity. On DEX, HFTs acquire speed in real-time from peer-to-peer networks. The price of speed surges during activity bursts, as HFTs simultaneously race to market. Relative to centralized exchanges, HFTs acquire more speed on DEX, but for shorter timespans. Low-latency "sprints" speed up price discovery without harming liquidity. Overall, speed rents decrease and fewer resources are locked-in to support zero-sum HFT trades.
Relativistic redshift of the star S0-2 orbiting the Galactic Center supermassive black hole

Gravitational redshift in the Galactic Center General relativity predicts that light emitted by an object in a strong gravitational field—for example, close to a black hole—should be shifted to longer wavelengths. This gravitational redshift does not exist in the Newtonian theory of gravity. Do et al. monitored the position and spectrum of the star S0-2 as it passed Sagittarius A*, the supermassive black hole at the center of the Milky Way. Around the closest part of S0-2's 16-year orbit, they detected the effect of gravitational redshift on its spectrum. These results are more consistent with general relativity than Newtonian gravity at the 5σ level. Science, this issue p. 664 A star orbiting the supermassive black hole Sagittarius A* exhibits gravitational redshift, as predicted by general relativity. The general theory of relativity predicts that a star passing close to a supermassive black hole should exhibit a relativistic redshift. In this study, we used observations of the Galactic Center star S0-2 to test this prediction. We combined existing spectroscopic and astrometric measurements from 1995–2017, which cover S0-2’s 16-year orbit, with measurements from March to September 2018, which cover three events during S0-2’s closest approach to the black hole. We detected a combination of special relativistic and gravitational redshift, quantified using the redshift parameter ϒ. Our result, ϒ = 0.88 ± 0.17, is consistent with general relativity (ϒ = 1) and excludes a Newtonian model (ϒ = 0) with a statistical significance of 5σ.
Hessian based analysis of SGD for Deep Nets: Dynamics and Generalization

While stochastic gradient descent (SGD) and variants have been surprisingly successful for training deep nets, several aspects of the optimization dynamics and generalization are still not well understood. In this paper, we present new empirical observations and theoretical results on both the optimization dynamics and generalization behavior of SGD for deep nets based on the Hessian of the training loss and associated quantities. We consider three specific research questions: (1) what is the relationship between the Hessian of the loss and the second moment of stochastic gradients (SGs)? (2) how can we characterize the stochastic optimization dynamics of SGD with fixed and adaptive step sizes and diagonal pre-conditioning based on the first and second moments of SGs? and (3) how can we characterize a scale-invariant generalization bound of deep nets based on the Hessian of the loss, which by itself is not scale invariant? We shed light on these three questions using theoretical results supported by extensive empirical observations, with experiments on synthetic data, MNIST, and CIFAR-10, with different batch sizes, and with different difficulty levels by synthetically adding random labels.
Joint Adversarial Training: Incorporating both Spatial and Pixel Attacks

Conventional adversarial training methods using attacks that manipulate the pixel value directly and individually, leading to models that are less robust in face of spatial transformation-based attacks. In this paper, we propose a joint adversarial training method that incorporates both spatial transformation-based and pixel-value based attacks for improving model robustness. We introduce a spatial transformation-based attack with an explicit notion of budget and develop an algorithm for spatial attack generation. We further integrate both pixel and spatial attacks into one generation model and show how to leverage the complementary strengths of each other in training for improving the overall model robustness. Extensive experimental results on different benchmark datasets compared with state-of-the-art methods verified the effectiveness of the proposed method.
Effective spacetime geometry of graviton condensates in 
f(R)
 gravity

Working in static spherically symmetric setup, recent study has demonstrated that the effective spacetime geometry of a Bose-Einstein condensate of weakly interacting gravitons is analogous to a gravastar, hence providing a bridge between these two attempts in describing the black hole interior. In this paper we make three generalizations: introducing a composite of two graviton condensates so that the exterior spacetime is not necessarily asymptotically flat, working in $f({\mathcal R})$ gravity, and extending the calculations to higher dimensions. We find that the effective spacetime geometry is again analogous to a gravastar, but the interior can be de Sitter or Anti-de Sitter and the exterior can be Schwarzschild, Schwarzschild-de Sitter, or Schwarzschild-Anti de Sitter, where the cosmological constant for the exterior must be smaller than the one for the interior. These geometries are determined by the modified gravity function $f({\mathcal R})$, in contrast to previous works where they were selected by hand. We also presented a new possible value for the size of the interior condensate provided a certain restriction is satisfied, which would not be met if we are still working in ordinary gravity with four spacetime dimensions. This restriction can be seen manifested in the behavior of the interior graviton wavelength as a function of spacetime dimension.
Cell-to-cell variability in organelle abundance reveals mechanisms of organelle biogenesis.

How cells regulate the number of organelles is a fundamental question in cell biology. While decades of experimental work have uncovered four fundamental processes that regulate organelle biogenesis, namely, de novo synthesis, fission, fusion, and decay, a comprehensive understanding of how these processes together control organelle abundance remains elusive. Recent fluorescence microscopy experiments allow for the counting of organelles at the single-cell level. These measurements provide information about the cell-to-cell variability in organelle abundance in addition to the mean level. Motivated by such measurements, we build upon a recent study and analyze a general stochastic model of organelle biogenesis. We compute the exact analytical expressions for the probability distribution of organelle numbers, their mean, and variance across a population of single cells. It is shown that different mechanisms of organelle biogenesis lead to distinct signatures in the distribution of organelle numbers which allow us to discriminate between these various mechanisms. By comparing our theory against published data for peroxisome abundance measurements in yeast, we show that a widely believed model of peroxisome biogenesis that involves de novo synthesis, fission, and decay is inadequate in explaining the data. Also, our theory predicts bimodality in certain limits of the model. Overall, the framework developed here can be harnessed to gain mechanistic insights into the process of organelle biogenesis.
Analytical and experimental guide for axionlike particle search using biphotons.

This paper presents a cutting-edge optical system for detecting axion-like particles (ALPs) that employs the quantum entanglement of photon pairs created via a spontaneous parametric down-conversion (SPDC) and Primakoff process that converts photons into ALPs in the presence of a magnetic field. The laser beam from the source is first projected through a birefringent crystal where SPDC occurs, converting a single high-energy photon into a pair of low-energy photons. The photons in each pair then flow through a region with an applied magnetic field and eventually arrive at separate detectors known as photon counters. By examining the biphoton correlation using the probability distribution of the photons at the detectors, which varies according to the displacement of the main pump photon stream and the change in the number of photons due to the photon-to-axion conversion, the presence of ALPs can be determined. This optical setup enables detection of ALPs with low noise as well as remarkably high precision and sensitivity using quantum entanglement. The proposed technique provides an analytical model and system design for detecting ALPs.
Bilingual Lexicon Induction through Unsupervised Machine Translation

A recent research line has obtained strong results on bilingual lexicon induction by aligning independently trained word embeddings in two languages and using the resulting cross-lingual embeddings to induce word translation pairs through nearest neighbor or related retrieval methods. In this paper, we propose an alternative approach to this problem that builds on the recent work on unsupervised machine translation. This way, instead of directly inducing a bilingual lexicon from cross-lingual embeddings, we use them to build a phrase-table, combine it with a language model, and use the resulting machine translation system to generate a synthetic parallel corpus, from which we extract the bilingual lexicon using statistical word alignment techniques. As such, our method can work with any word embedding and cross-lingual mapping technique, and it does not require any additional resource besides the monolingual corpus used to train the embeddings. When evaluated on the exact same cross-lingual embeddings, our proposed method obtains an average improvement of 6 accuracy points over nearest neighbor and 4 points over CSLS retrieval, establishing a new state-of-the-art in the standard MUSE dataset.
Towards AutoML in the presence of Drift: first results

Research progress in AutoML has lead to state of the art solutions that can cope quite wellwith supervised learning task, e.g., classification with AutoSklearn. However, so far thesesystems do not take into account the changing nature of evolving data over time (i.e., theystill assume i.i.d. data); even when this sort of domains are increasingly available in realapplications (e.g., spam filtering, user preferences, etc.). We describe a first attempt to de-velop an AutoML solution for scenarios in which data distribution changes relatively slowlyover time and in which the problem is approached in a lifelong learning setting. We extendAuto-Sklearn with sound and intuitive mechanisms that allow it to cope with this sort ofproblems. The extended Auto-Sklearn is combined with concept drift detection techniquesthat allow it to automatically determine when the initial models have to be adapted. Wereport experimental results in benchmark data from AutoML competitions that adhere tothis scenario. Results demonstrate the effectiveness of the proposed methodology.
INS: An Interactive Chinese News Synthesis System

Nowadays, we are surrounded by more and more online news articles. Tens or hundreds of news articles need to be read if we wish to explore a hot news event or topic. So it is of vital importance to automatically synthesize a batch of news articles related to the event or topic into a new synthesis article (or overview article) for reader’s convenience. It is so challenging to make news synthesis fully automatic that there is no successful solution by now. In this paper, we put forward a novel Interactive News Synthesis system (i.e. INS), which can help generate news overview articles automatically or by interacting with users. More importantly, INS can serve as a tool for editors to help them finish their jobs. In our experiments, INS performs well on both topic representation and synthesis article generation. A user study also demonstrates the usefulness and users’ satisfaction with the INS tool. A demo video is available at https://youtu.be/7ItteKW3GEk.