nils-herrmann/clean_scirepeval huggingface dataset:
Deep Learned Path Planning via Randomized Reward-Linked-Goals and Potential Space Applications

Space exploration missions have seen use of increasingly sophisticated robotic systems with ever more autonomy. Deep learning promises to take this even a step further, and has applications for high-level tasks, like path planning, as well as low-level tasks, like motion control, which are critical components for mission efficiency and success. Using deep reinforcement end-to-end learning with randomized reward function parameters during training, we teach a simulated 8 degree-of-freedom quadruped ant-like robot to travel anywhere within a perimeter, conducting path plan and motion control on a single neural network, without any system model or prior knowledge of the terrain or environment. Our approach also allows for user specified waypoints, which could translate well to either fully autonomous or semi-autonomous/teleoperated space applications that encounter delay times. We trained the agent using randomly generated waypoints linked to the reward function and passed waypoint coordinates as inputs to the neural network. Such applications show promise on a variety of space exploration robots, including high speed rovers for fast locomotion and legged cave robots for rough terrain.
DARTS+: Improved Differentiable Architecture Search with Early Stopping

Recently, there has been a growing interest in automating the process of neural architecture design, and the Differentiable Architecture Search (DARTS) method makes the process available within a few GPU days. However, the performance of DARTS is often observed to collapse when the number of search epochs becomes large. Meanwhile, lots of "{\em skip-connect}s" are found in the selected architectures. In this paper, we claim that the cause of the collapse is that there exists overfitting in the optimization of DARTS. Therefore, we propose a simple and effective algorithm, named "DARTS+", to avoid the collapse and improve the original DARTS, by "early stopping" the search procedure when meeting a certain criterion. We also conduct comprehensive experiments on benchmark datasets and different search spaces and show the effectiveness of our DARTS+ algorithm, and DARTS+ achieves $2.32\%$ test error on CIFAR10, $14.87\%$ on CIFAR100, and $23.7\%$ on ImageNet. We further remark that the idea of "early stopping" is implicitly included in some existing DARTS variants by manually setting a small number of search epochs, while we give an {\em explicit} criterion for "early stopping".
Generalized Duality for Model-Free Superhedging given Marginals.

In a discrete-time financial market, a generalized duality is established for model-free superhedging, given marginal distributions of the underlying asset. Contrary to prior studies, we do not require contingent claims to be upper semicontinuous, allowing for upper semi-analytic ones. The generalized duality stipulates an extended version of risk-neutral pricing. To compute the model-free superhedging price, one needs to find the supremum of expected values of a contingent claim, evaluated not directly under martingale (risk-neutral) measures, but along sequences of measures that converge, in an appropriate sense, to martingale ones. To derive the main result, we first establish a portfolio-constrained duality for upper semi-analytic contingent claims, relying on Choquet's capacitability theorem. As we gradually fade out the portfolio constraint, the generalized duality emerges through delicate probabilistic estimations.
d-blink: Distributed End-to-End Bayesian Entity Resolution

Entity resolution (ER; also known as record linkage or de-duplication) is the process of merging noisy databases, often in the absence of unique identifiers. A major advancement in ER methodology has been the application of Bayesian generative models, which provide a natural framework for inferring latent entities with rigorous quantification of uncertainty. Despite these advantages, existing models are severely limited in practice, as standard inference algorithms scale quadratically in the number of records. While scaling can be managed by fitting the model on separate blocks of the data, such a naïve approach may induce significant error in the posterior. In this article, we propose a principled model for scalable Bayesian ER, called “distributed Bayesian linkage” or d-blink, which jointly performs blocking and ER without compromising posterior correctness. Our approach relies on several key ideas, including: (i) an auxiliary variable representation that induces a partition of the entities and records into blocks; (ii) a method for constructing well-balanced blocks based on k-d trees; (iii) a distributed partially collapsed Gibbs sampler with improved mixing; and (iv) fast algorithms for performing Gibbs updates. Empirical studies on six datasets—including a case study on the 2010 Decennial Census—demonstrate the scalability and effectiveness of our approach.
DL2: A Deep Learning-Driven Scheduler for Deep Learning Clusters

Efficient resource scheduling is essential for maximal utilization of expensive deep learning (DL) clusters. Existing cluster schedulers either are agnostic to machine learning (ML) workload characteristics, or use scheduling heuristics based on operators’ understanding of particular ML framework and workload, which are less efficient or not general enough. In this article, we show that DL techniques can be adopted to design a generic and efficient scheduler. Specifically, we propose DL<inline-formula><tex-math notation="LaTeX">$^2$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="peng-ieq2-3052895.gif"/></alternatives></inline-formula>, a DL-driven scheduler for DL clusters, targeting global training job expedition by dynamically resizing resources allocated to jobs. DL<inline-formula><tex-math notation="LaTeX">$^2$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="peng-ieq3-3052895.gif"/></alternatives></inline-formula> advocates a joint supervised learning and reinforcement learning approach: a neural network is warmed up via offline supervised learning based on job traces produced by the existing cluster scheduler; then the neural network is plugged into the live DL cluster, fine-tuned by reinforcement learning carried out throughout the training progress of the DL jobs, and used for deciding job resource allocation in an online fashion. We implement DL<inline-formula><tex-math notation="LaTeX">$^2$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="peng-ieq4-3052895.gif"/></alternatives></inline-formula> on Kubernetes and enable dynamic resource scaling in DL jobs on MXNet. Extensive evaluation shows that DL<inline-formula><tex-math notation="LaTeX">$^2$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="peng-ieq5-3052895.gif"/></alternatives></inline-formula> outperforms fairness scheduler (i.e., DRF) by 44.1 percent and expert heuristic scheduler (i.e., Optimus) by 17.5 percent in terms of average job completion time.
End-to-End Learnable Geometric Vision by Backpropagating PnP Optimization

Deep networks excel in learning patterns from large amounts of data. On the other hand, many geometric vision tasks are specified as optimization problems. To seamlessly combine deep learning and geometric vision, it is vital to perform learning and geometric optimization end-to-end. Towards this aim, we present BPnP, a novel network module that backpropagates gradients through a Perspective-n-Points (PnP) solver to guide parameter updates of a neural network. Based on implicit differentiation, we show that the gradients of a ``self-contained" PnP solver can be derived accurately and efficiently, as if the optimizer block were a differentiable function. We validate BPnP by incorporating it in a deep model that can learn camera intrinsics, camera extrinsics (poses) and 3D structure from training datasets. Further, we develop an end-to-end trainable pipeline for object pose estimation, which achieves greater accuracy by combining feature-based heatmap losses with 2D-3D reprojection errors. Since our approach can be extended to other optimization problems, our work helps to pave the way to perform learnable geometric vision in a principled manner. Our PyTorch implementation of BPnP is available on
Say What I Want: Towards the Dark Side of Neural Dialogue Models

Neural dialogue models have been widely adopted in various chatbot applications because of their good performance in simulating and generalizing human conversations. However, there exists a dark side of these models -- due to the vulnerability of neural networks, a neural dialogue model can be manipulated by users to say what they want, which brings in concerns about the security of practical chatbot services. In this work, we investigate whether we can craft inputs that lead a well-trained black-box neural dialogue model to generate targeted outputs. We formulate this as a reinforcement learning (RL) problem and train a Reverse Dialogue Generator which efficiently finds such inputs for targeted outputs. Experiments conducted on a representative neural dialogue model show that our proposed model is able to discover such desired inputs in a considerable portion of cases. Overall, our work reveals this weakness of neural dialogue models and may prompt further researches of developing corresponding solutions to avoid it.
A Collaborative Approach using Ridge-Valley Minutiae for More Accurate Contactless Fingerprint Identification

Contactless fingerprint identification has emerged as an reliable and user friendly alternative for the personal identification in a range of e-business and law-enforcement applications. It is however quite known from the literature that the contactless fingerprint images deliver remarkably low matching accuracies as compared with those obtained from the contact-based fingerprint sensors. This paper develops a new approach to significantly improve contactless fingerprint matching capabilities available today. We systematically analyze the extent of complimentary ridge-valley information and introduce new approaches to achieve significantly higher matching accuracy over state-of-art fingerprint matchers commonly employed today. We also investigate least explored options for the fingerprint color-space conversions, which can play a key-role for more accurate contactless fingerprint matching. This paper presents experimental results from different publicly available contactless fingerprint databases using NBIS, MCC and COTS matchers. Our consistently outperforming results validate the effectiveness of the proposed approach for more accurate contactless fingerprint identification.
Improved Upper Limit on the Neutrino Mass from a Direct Kinematic Method by KATRIN.

We report on the neutrino mass measurement result from the first four-week science run of the Karlsruhe Tritium Neutrino experiment KATRIN in spring 2019. Beta-decay electrons from a high-purity gaseous molecular tritium source are energy analyzed by a high-resolution MAC-E filter. A fit of the integrated electron spectrum over a narrow interval around the kinematic end point at 18.57 keV gives an effective neutrino mass square value of (-1.0_{-1.1}^{+0.9})  eV^{2}. From this, we derive an upper limit of 1.1 eV (90% confidence level) on the absolute mass scale of neutrinos. This value coincides with the KATRIN sensitivity. It improves upon previous mass limits from kinematic measurements by almost a factor of 2 and provides model-independent input to cosmological studies of structure formation.
The Rising Sun Envelope Method: An Automatic and Accurate Peak Location Technique for XANES Measurements.

The lack of theoretical understanding of X-Ray Absorption Near Edge Structure (XANES) spectroscopy makes the development of analysis tools for its study a necessity. Here, an algorithm for judicious choice of local minima and maxima points of XANES spectrum (experimental or simulated) is proposed, without any loss of information on peaks location nor on peak strength. The method is named Rising Sun Envelope Method, and is based on successive regularizations of the spectral measurement that, according to parameter choices that are intrinsic to the measurements, keep peaks location and strength as invariants. This is the first method that finds peaks in XANES automatically, without depending on first derivative information. Nevertheless, a direct computation of Absorption-Edge is provided, overcoming the low quality and poor information given by inflection point computations based on the XANES second derivative: computations of inflection points from a higher quality cubic spline approximation are used instead. To illustrate its use, the algorithm is applied to XANES, Electron Energy Loss Spectroscopy (EELS), and Raman spectra.
Towards Open-Domain Named Entity Recognition via Neural Correction Models

Named Entity Recognition (NER) plays an important role in a wide range of natural language processing tasks, such as relation extraction, question answering, etc. However, previous studies on NER are limited to a particular genre, using small manually-annotated or large but low-quality datasets. In this work, we propose a semi-supervised annotation framework to make full use of abstracts from Wikipedia and obtain a large and high-quality dataset called AnchorNER. We assume anchored strings in abstracts are named entities and annotate them with entity types mentioned in DBpedia. To improve the coverage, we design a neural correction model trained with a human-annotated NER dataset, DocRED, to correct the false-negative entity labels, and then train a BERT model with the corrected dataset. We evaluate our trained model on six NER datasets and our experimental results show that we have obtained state-of-the-art open-domain performances --- on top of the strong baselines BERT-base and BERT-large, we achieve relative improvements of 4.66% and 3.07% respectively.
Identifying Kinematic Structures in Simulated Galaxies Using Unsupervised Machine Learning

Galaxies host a wide array of internal stellar components, which need to be decomposed accurately in order to understand their formation and evolution. While significant progress has been made with recent integral-field spectroscopic surveys of nearby galaxies, much can be learned from analyzing the large sets of realistic galaxies now available through state-of-the-art hydrodynamical cosmological simulations. We present an unsupervised machine-learning algorithm, named auto-GMM, based on Gaussian mixture models, to isolate intrinsic structures in simulated galaxies based on their kinematic phase space. For each galaxy, the number of Gaussian components allowed by the data is determined through a modified Bayesian information criterion. We test our method by applying it to prototype galaxies selected from the cosmological simulation IllustrisTNG. Our method can effectively decompose most galactic structures. The intrinsic structures of simulated galaxies can be inferred statistically by non-human supervised identification of galaxy structures. We successfully identify four kinds of intrinsic structures: cold disks, warm disks, bulges, and halos. Our method fails for barred galaxies because of the complex kinematics of particles moving on bar orbits.
Balance in Signed Bipartite Networks

A large portion of today's big data can be represented as networks. However, not all networks are the same, and in fact, for many that have additional complexities to their structure, traditional general network analysis methods are no longer applicable. For example, signed networks contain both positive and negative links, and thus dedicated theories and algorithms have been developed. However, previous work mainly focuses on the unipartite setting where signed links connect any pair of nodes. Signed bipartite networks on the one hand, are commonly found, but have primarily been overlooked. Their complexities of having two node types where signed links can only form across the two sets introduce challenges that prevent most existing literature on unipartite signed and unsigned bipartite networks from being applied. On the other hand, balance theory, a key signed social theory, has been generally defined for cycles of any length and is being used in the form of triangles for numerous unipartite signed network tasks. However, in bipartite networks there are no triangles and furthermore there exist two types of nodes. Therefore, in this work, we conduct the first comprehensive analysis and validation of balance theory using the smallest cycle in signed bipartite networks - signed butterflies (i.e., cycles of length 4 containing the two node types). Then, to investigate the applicability of balance theory aiding signed bipartite network tasks, we develop multiple sign prediction methods that utilize balance theory in the form of signed butterflies. Our sign prediction experiment on three real-world signed bipartite networks demonstrates the effectiveness of using these signed butterflies for not only sign prediction, but paves the way for improvements in other signed bipartite network analysis tasks.
Enabling Humans to Plan Inspection Paths Using a Virtual Reality Interface

In this work, we investigate whether humans can manually generate high-quality robot paths for optical inspections. Typically, automated algorithms are used to solve the inspection planning problem. The use of automated algorithms implies that specialized knowledge from users is needed to set up the algorithm. We aim to replace this need for specialized experience, by entrusting a non-expert human user with the planning task. We augment this user with intuitive visualizations and interactions in virtual reality. To investigate if humans can generate high-quality inspection paths, we perform a user study in which users from different experience categories, generate inspection paths with the proposed virtual reality interface. From our study, it can be concluded that users without experience can generate high-quality inspection paths: The median inspection quality of user generated paths ranged between 66-81\% of the quality of a state-of-the-art automated algorithm on various inspection planning scenarios. We noticed however, a sizable variation in the performance of users, which is a result of some typical user behaviors. These behaviors are discussed, and possible solutions are provided.
Human Following for Wheeled Robot with Monocular Pan-tilt Camera

Human following on mobile robots has witnessed significant advances due to its potentials for real-world applications. Currently most human following systems are equipped with depth sensors to obtain distance information between human and robot, which suffer from the perception requirements and noises. In this paper, we design a wheeled mobile robot system with monocular pan-tilt camera to follow human, which can stay the target in the field of view and keep following simultaneously. The system consists of fast human detector, real-time and accurate visual tracker, and unified controller for mobile robot and pan-tilt camera. In visual tracking algorithm, both Siamese networks and optical flow information are exploited to locate and regress human simultaneously. In order in perform following with a monocular camera, the constraint of human height is introduced to design the controller. In experiments, human following are conducted and analysed in simulations and a real robot platform, which demonstrate the effectiveness and robustness of the overall system.
NIKA2 observations around LBV stars Emission from stars and circumstellar material

Luminous Blue Variable (LBV) stars are evolved massive objects, previous to core-collapse supernova. LBVs are characterized by photometric and spectroscopic variability, produced by strong and dense winds, mass-loss events and very intense UV radiation. LBVs strongly disturb their surroundings by heating and shocking, and produce important amounts of dust. The study of the circumstellar material is therefore crucial to understand how these massive stars evolve, and also to characterize their effects onto the interstellar medium. The versatility of NIKA2 is a key in providing simultaneous observations of both the stellar continuum and the extended, circumstellar contribution. The NIKA2 frequencies (150 and 260 GHz) are in the range where thermal dust and free-free emission compete, and hence NIKA2 has the capacity to provide key information about the spatial distribution of circumstellar ionized gas, warm dust and nearby dark clouds; non-thermal emission is also possible even at these high frequencies. We show the results of the first NIKA2 survey towards five LBVs. We detected emission from four stars, three of them immersed in tenuous circumstellar material. The spectral indices show a complex distribution and allowed us to separate and characterize different components. We also found nearby dark clouds, with spectral indices typical of thermal emission from dust. Spectral indices of the detected stars are negative and hard to be explained only by free-free processes. In one of the sources, G79.29+0.46, we also found a strong correlation of the 1mm and 2mm continuum emission with respect to nested molecular shells at ≈1 pc from the LBV. The spectral index in this region clearly separates four components: the LBV star, a bubble characterized by free-free emission, and a shell interacting with a nearby infrared dark cloud.
Neural Machine Translation with 4-Bit Precision and Beyond

Neural Machine Translation (NMT) is resource intensive. We design a quantization procedure to compress NMT models better for devices with limited hardware capability. Because most neural network parameters are near zero, we employ logarithmic quantization in lieu of fixed-point quantization. However, we find bias terms are less amenable to log quantization but note they comprise a tiny fraction of the model, so we leave them uncompressed. We also propose to use an error-feedback mechanism during retraining, to preserve the compressed model as a stale gradient. We empirically show that NMT models based on Transformer or RNN architecture can be compressed up to 4-bit precision without any noticeable quality degradation. Models can be compressed up to binary precision, albeit with lower quality. The RNN architecture seems to be more robust to quantization, compared to the Transformer.
Weakly-Supervised 3D Pose Estimation from a Single Image using Multi-View Consistency

We present a novel data-driven regularizer for weakly-supervised learning of 3D human pose estimation that eliminates the drift problem that affects existing approaches. We do this by moving the stereo reconstruction problem into the loss of the network itself. This avoids the need to reconstruct 3D data prior to training and unlike previous semi-supervised approaches, avoids the need for a warm-up period of supervised training. The conceptual and implementational simplicity of our approach is fundamental to its appeal. Not only is it straightforward to augment many weakly-supervised approaches with our additional re-projection based loss, but it is obvious how it shapes reconstructions and prevents drift. As such we believe it will be a valuable tool for any researcher working in weakly-supervised 3D reconstruction. Evaluating on Panoptic, the largest multi-camera and markerless dataset available, we obtain an accuracy that is essentially indistinguishable from a strongly-supervised approach making full use of 3D groundtruth in training.