Miracle-dz/UniFAR-FTUs huggingface dataset:
Info-Evo: Using Information Geometry to Guide Evolutionary Program Learning

A novel optimization strategy, Info-Evo, is described, in which natural gradient search using nonparametric Fisher information is used to provide ongoing guidance to an evolutionary learning algorithm, so that the evolutionary process preferentially moves in the directions identified as"shortest paths"according to the natural gradient. Some specifics regarding the application of this approach to automated program learning are reviewed, including a strategy for integrating Info-Evo into the MOSES program learning framework.
Repurposing GANs for One-Shot Semantic Part Segmentation

While GANs have shown success in realistic image generation, the idea of using GANs for other tasks unrelated to synthesis is underexplored. Do GANs learn meaningful structural parts of objects during their attempt to reproduce those objects? And can image synthesis serve as an “upstream” representation learning task? In this work, we test these hypotheses and propose a simple and effective approach based on GANs for fundamental vision tasks: semantic part segmentation and landmark detection. With our approach, these tasks only require as few as one labeled example along with an unlabeled dataset, rather than thousands of examples. Our key idea is to leverage a trained GAN to extract a pixel-wise representation from the input image and use it as feature vectors for a segmentation network. Our experiments demonstrate that this GAN-derived representation is “readily discriminative” and produces surprisingly good results that are comparable to those from supervised baselines trained with significantly more labels. We believe this novel repurposing of GANs underlies a new class of unsupervised representation learning, which can generalize to many other tasks. More results are available at https://RepurposeGANs.github. io/.
Transport in Stochastic Goupillaud Media

The paper addresses one-dimensional transport in a Goupillaud medium (a layered medium in which the layer thickness is proportional to the propagation speed), as a prototypical case of wave propagation in random media. Suitable stochastic assumptions and limiting procedures lead to characteristic curves that are Lévy processes. Solutions corresponding to the discretely layered medium are shown to converge to limits as the thickness of the layers goes to zero. The probability distribution of the limiting characteristic curves is explicitly computed and exemplified when the underlying Lévy process is an inverse Gaussian process.
Agricultural Production Monitoring in the Sahel Using Remote Sensing: Present Possibilities and Research Needs

Abstract The main objective is to review the current status of research related to the monitoring of agricultural production in the Sahel (west Africa). The Sahel suffers from frequent shortages of food. It is therefore important to have a tool to monitor environmental variables, and thus crop production, during the agricultural season. Satellite remote sensing can contribute significantly to such a system by collecting information on crops and on environmental variables at a sub-continental geographical scale and with a high temporal frequency. One part of the problem is to estimate crop acreage. The technique of area-sampling frame has to adapted to the Sahelian landscape, which is dominated by traditional farming systems. The second part is to estimate crop yields. Three main approaches exist: statistical, semi-deterministic or deterministic. The use of vegetation indices is discussed as well as techniques to derive biophysical variables from remotely-sensed data. Finally, the integration of these remote-sensing techniques with crop-growth models is discussed and some research needs are identified. It is argued that the quantitative assessment of agricultural production in the Sahel should be based on the integration of remotely-sensed data with semi-deterministic agrometeorological models. This approach will allow a regionalization of the production estimates.
Driving forces of soil organic matter change in Jiangsu Province of China

Soil organic matter (SOM) is a key property determining soil functions and a major form of carbon stored in soil. Understanding the spatial and temporal variability of SOM and the driving forces responsible for spatial and temporal changes is important to assess regional soil quality and carbon sequestration potential and, particularly, to establish better practices for land use and management. We evaluated the spatio‐temporal change in SOM content from 1979–1982 to 2006 and its driving forces in Jiangsu Province, East China, using geostatistics. The results showed that mean SOM content increased from 16.60 ± 8.50 to 18.31 ± 8.32 g/kg over a 26‐yr period. The maps of SOM generated by ordinary kriging represented the increasing trend from north to south across the province in the two periods. The level of SOM in 1979–1982 affected the pattern of change: the SOM increasing in areas initially with a small content while decreasing in areas having a large content. The map of SOM change showed that the rate of increase decreased from north to south within the province. Increased fertilizer application promoted crop production with more residual biomass being retained in the soil, which resulted in increased SOM content. Land use changes to paddy, upland or forest improved SOM content, whereas abandoning land reduced SOM content.
Bridging the Distribution Gap of Visible-Infrared Person Re-identification with Modality Batch Normalization

Visible-infrared cross-modality person re-identification (VI-ReID), whose aim is to match person images between visible and infrared modality, is a challenging cross-modality image retrieval task. Most existing works integrate batch normalization layers into their neural network, but we found out that batch normalization layers would lead to two types of distribution gap: 1) inter-mini-batch distribution gap— the distribution gap of the same modality between each mini-batch; 2) intra-mini-batch modality distribution gap—the distribution gap of different modality within the same mini-batch. To address these problems, we propose a new batch normalization layer called Modality Batch Normalization (MBN), which normalizes each modality sub-mini-batch respectively instead of the whole mini-batch, and can reduce these distribution gap significantly. Extensive experiments show that our MBN is able to boost the performance of VI-ReID models, even with different datasets, backbones and losses.
Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions

Unobserved confounding is one of the main challenges when estimating causal effects. We propose a novel causal reduction method that replaces an arbitrary number of possibly high-dimensional latent confounders with a single latent confounder that lives in the same space as the treatment variable without changing the observational and interventional distributions entailed by the causal model. After the reduction, we parameterize the reduced causal model using a flexible class of transformations, so-called normalizing flows. We propose a learning algorithm to estimate the parameterized reduced model jointly from observational and interventional data. This allows us to estimate the causal effect in a principled way from combined data. We perform a series of experiments on data simulated using nonlinear causal mechanisms and find that we can often substantially reduce the number of interventional samples when adding observational training samples without sacrificing accuracy. Thus, adding observational data may help to more accurately estimate causal effects even in the presence of unobserved confounders.
Onset of Instability Patterns between Miscible Fluids in Porous Media

A theoretical treatment of the stability of isoconcentration surfaces in a porous sample saturated with a single‐phase liquid of variable density and viscosity is presented. In a rectangular system with horizontal displacement front the early growth or decay of Fourier perturbation components is exponential with time. Dependence of growth exponent on wavenumber is presented, including effect of dispersion. Regions of stability with respect to nondimensionalized flow rate, viscosity gradient, and density gradient are shown.
Prosumer Behavior: Decision Making with Bounded Horizon

Most studies of prosumer decision making in the smart grid have focused on single, temporally discrete decisions within the framework of expected utility theory (EUT) and behavioral theories such as prospect theory. In this work, we study prosumer decision making in a more natural, ongoing market situation in which a prosumer has to decide every day whether to sell any surplus energy units generated by the solar panels on her roof or hold (store) the energy units in anticipation of a future sale at a better price. Within this context, we propose a new behavioral model that extends EUT to take into account the notion of a bounded temporal horizon over which various decision parameters are considered. Specifically, we introduce the notion of a bounded time window (the number of upcoming days over which a prosumer evaluates the probability that each possible price will be the highest) that prosumers implicitly impose on their decision making in arriving at hold or sell decisions. The new behavioral model assumes that humans make decisions that will affect their lives within a bounded time window regardless of how far into the future their units may be sold. Modeling the utility of the prosumer using parameters such as the offered price on a day, the number of energy units the prosumer has available for sale on a day, and the probabilities of the forecast prices, we fit both traditional EUT and the proposed behavioral model with bounded time windows to data collected from 57 homeowners over 68 days in a simulated energy market. Each prosumer generated surplus units of solar power and had the opportunity to sell those units to the local utility at the price set that day by the utility or hold the units for sale in the future. For most participants, a bounded horizon in the range of 4-5 days provided a much better fit to their responses than was found for the traditional (unbounded) EUT model
Ab Initio Frequency Calculations of Pyridine Adsorbed on an Adatom Model of a SERS Active Site of a Silver Surface

Ab initio and Urey−Bradley calculations of normal modes were carried out to analyze the frequency shifts observed on the SERS spectrum of pyridine obtained on a Ag surface. A molecular model with a Ag atom attached to the pyridine nitrogen was used in the Urey−Bradley calculation. The frequency shifts in the SERS spectrum were calculated by adding K(Ag−N) and F(Ag· · ·Cα) force constants and by adjusting the ring stretches according to how their bond lengths changed in an ab initio calculation with a (Ag−Py)+ model. The ab initio calculations were done with the HF, MP2, and B3LYP methods using the 3-21G, 6-31G, 6-31G*, 6-31G**, and LanL2DZ basis sets. The surface active site was modeled in the calculation by means of Ag0, Ag+ atoms, and a Ag4+ pyramidal cluster structure. The calculations were evaluated on how well they matched the observed frequencies, the ν(Ag−N) stretch, and the frequency shifts for the SERS spectrum in an electrochemical environment. The calculation with the Ag4+ model yielded the bes.
When Being Soft Makes You Tough: A Collision-Resilient Quadcopter Inspired by Arthropods' Exoskeletons

Flying robots are usually rather delicate and require protective enclosures when facing the risk of collision, while high complexity and reduced payload are recurrent problems with collision-resilient flying robots. Inspired by arthropods' exoskeletons, we design a simple, open source, easily manufactured, semi-rigid structure with soft joints that can withstand high-velocity impacts. With an exoskeleton, the protective shell becomes part of the main robot structure, thereby minimizing its loss in payload capacity. Our design is simple to build and customize using cheap components (e.g. bamboo skewers) and consumer-grade 3D printers. The result is CogniFly, a sub-250 g autonomous quadcopter that survives multiple collisions at speeds up to 7 m s−1. In addition to its collision-resilience, CogniFly carries sensors that allow it to fly for approx. 17 min without the need of GPS or an external motion capture system, and it has enough computing power to run deep neural network models on-board. This structure becomes an ideal platform for high-risk activities, such as flying in a cluttered environment or reinforcement learning training, by dramatically reducing the risks of damaging its own hardware or the environment. Source code, 3D files, instructions and videos are available (open source license) through the project's website: https://thecognifly.github. io.
Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration

Aligning humans’ assessment of what a robot can do with its true capability is crucial for establishing a common ground between human and robot partners when they collaborate on a joint task. In this work, we propose an approach to calibrate humans’ estimate of a robot’s reachable workspace through a small number of demonstrations before collaboration. We develop a novel motion planning method, reachability-expressive motion planning (REMP), which jointly optimizes the physical cost and the expressiveness of robot motion to reveal the robot’s reachability to a human observer. Our experiments with human participants demonstrate that a short calibration using REMP can effectively align a non-expert user’s estimation of the robot’s reachability with its true capacity. We show that this calibration procedure not only results in better user perception, but also promotes more efficient human-robot collaborations in a subsequent joint task.1
Black Hole Leftovers: The Remnant Population from Binary Black Hole Mergers

The inspiral and merger of two black holes produces a remnant black hole with mass and spin determined by the properties of its parent black holes. Using the inferred population properties of component black holes from the first two and a half observing runs of Advanced LIGO and Virgo, we calculate the population properties of the leftover remnant black holes. By integrating their rate of formation over the age of the universe, we estimate the number density of remnant black holes today. Using simple prescriptions for the cosmic star formation rate and black hole inspiral delay times, we determine the number density of this leftover black hole population to be , corresponding to ∼60,000 black hole remnants per Milky Way–equivalent galaxy. The mass spectrum of these remnants starts at ∼10 M ⊙ and can be approximated by a decreasing exponential with characteristic length ∼15 M ⊙, the final spin distribution is sharply peaked at χ f ∼ 0.7, and the kick velocities range from tens to thousands of kilometers per second. These kick velocities suggest that globular clusters and nuclear star clusters may retain up to and of their remnant black holes, respectively, while young star clusters would only retain a few tenths of a percent. The estimates in this work assume that none of the remnants participate in subsequent hierarchical mergers. If hierarchical mergers occur, the overall number density would drop accordingly and the remnant mass distribution shape would evolve over time. This population of leftover black holes is an inescapable result from gravitational-wave observations of binary black hole mergers.
Digital Beamforming Robust to Time-Varying Carrier Frequency Offset

Adaptive interference cancellation is rapidly becoming a necessity for our modern wireless communication systems, due to the proliferation of wireless devices that interfere with each other. To cancel interference, digital beamforming algorithms adaptively adjust the weight vector of the antenna array, and in turn its radiation pattern, to minimize interference while maximizing the desired signal power. While these algorithms are effective in ideal scenarios, they are sensitive to signal corruptions. In this work, we consider the case when the transmitter and receiver in a communication system cannot be synchronized, resulting in a carrier frequency offset that corrupts the signal. We present novel beamforming algorithms that are robust to signal corruptions arising from this time-variant carrier frequency offset. In particular, we bring in the Discrete Prolate Spheroidal Sequences (DPSS's) and propose two atomic-norm-minimization (ANM)-based methods in both 1D and 2D frameworks to design a weight vector that can be used to cancel interference when there exist unknown time-varying frequency drift in the pilot and interferer signals. Both algorithms do not assume a pilot signal is known. Noting that solving ANM optimization problems via semi-definite programs can be a computational burden, we also present a novel fast algorithm to approximately solve our 1D ANM optimization problem. Finally, we confirm the benefits of our proposed algorithms and show the advantages over existing approaches with a series of experiments.
Adaptive Lidar Scan Frame Integration: Tracking Known MAVs in 3D Point Clouds

Micro-aerial vehicles (MAVs) are becoming ubiquitous across multiple industries and application domains. Lightweight MAVs with only an onboard flight controller and a minimal sensor suite (e.g., IMU, vision, and vertical ranging sensors) have potential as mobile and easily deployable sensing platforms. When deployed from a ground robot, a key parameter is a relative localization between the ground robot and the MAV. This paper proposes a novel method for tracking MAVs in lidar point clouds. In lidar point clouds, we consider the speed and distance of the MAV to actively adapt the lidar's frame integration time and, in essence, the density and size of the point cloud to be processed. We show that this method enables more persistent and robust tracking when the speed of the MAV or its distance to the tracking sensor changes. In addition, we propose a multi-modal tracking method that relies on high-frequency scans for accurate state estimation, lower-frequency scans for robust and persistent tracking, and sub-Hz processing for trajectory and object identification. These three integration and processing modalities allow for an overall accurate and robust MAV tracking while ensuring the object being tracked meets shape and size constraints.
Improving Zero-Shot Entity Retrieval through Effective Dense Representations

Entity Linking (EL) aligns entity mentions in text to entries in a knowledge base. It usually comprises of two phases: candidate generation and candidate ranking. While most methods focus on the latter phase, it is candidate generation that sets the upper bound for both time and accuracy of an EL system. We propose a simple approach for improving candidate generation by efficiently embedding mention-entity pairs in dense space through a BERT-based bi-encoder. Specifically, we introduce a new pooling function and incorporate entity type side-information. We achieve a new state-of-the-art 84.28% recall of the gold entity in the Zero-shot EL dataset with just 50 candidates, compared to the previous 82.06% with 64 candidates. We report the results from extensive experimentation using our proposed model on both seen and unseen entity datasets. Our results suggest that our approach could be a useful complement to existing EL methods.
ChartStory: Automated Partitioning, Layout, and Captioning of Charts into Comic-Style Narratives

Visual data storytelling is gaining importance as a means of presenting data-driven information or analysis results, especially to the general public. This has resulted in design principles being proposed for data-driven storytelling, and new authoring tools being created to aid such storytelling. However, data analysts typically lack sufficient background in design and storytelling to make effective use of these principles and authoring tools. To assist this process, we present ChartStory for crafting data stories from a collection of user-created charts, using a style akin to comic panels to imply the underlying sequence and logic of data-driven narratives. Our approach is to operationalize established design principles into an advanced pipeline that characterizes charts by their properties and similarities to each other, and recommends ways to partition, layout, and caption story pieces to serve a narrative. ChartStory also augments this pipeline with intuitive user interactions for visual refinement of generated data comics. We extensively and holistically evaluate ChartStory via a trio of studies. We first assess how the tool supports data comic creation in comparison to a manual baseline tool. Data comics from this study are subsequently compared and evaluated to ChartStory’s automated recommendations by a team of narrative visualization practitioners. This is followed by a pair of interview studies with data scientists using their own datasets and charts who provide an additional assessment of the system. We find that ChartStory provides cogent recommendations for narrative generation, resulting in data comics that compare favorably to manually-created ones.
Thermodynamics of 
z=4
 Hořava-Lifshitz black holes

Thermodynamics of z = 4 Hořava-Lifshitz black holes in 3+1 dimensions is studied in extended phase space. By using the scaling argument we find the Smarr relation and the first law for the black hole solutions of z = 4 Hořava-Lifshitz gravity. We find that it is necessary to take into account the variation of dimensionful parameters of the theory in the first law. We find that the reverse isoperimetric inequality can be violated for spherical, flat, and hyperbolic horizons and in all such cases we have black holes for which the specific heat at constant pressure and volume are positive. This provides a counterexample to a recent conjecture stating that black holes violating the reverse isoperimetric inequality are thermodynamically unstable. We find for z = 4 Hořava-Lifshitz black holes with hyperbolic horizons that there are two critical points: one showing Van der Waals behavior, the other reverse Van der Waals behavior. ar X iv :2 10 3. 04 36 5v 1 [ he pth ] 7 M ar 2 02 1
Routing Towards Discriminative Power of Class Capsules

Capsule networks are recently proposed as an alternative to modern neural network architectures. Neurons are replaced with capsule units that represent specific features or entities with normalized vectors or matrices. The activation of lower layer capsules affects the behavior of the following capsules via routing links that are constructed during training via certain routing algorithms. We discuss the routing-by-agreement scheme in dynamic routing algorithm which, in certain cases, leads the networks away from optimality. To obtain better and faster convergence, we propose a routing algorithm that incorporates a regularized quadratic programming problem which can be solved efficiently. Particularly, the proposed routing algorithm targets directly on the discriminative power of class capsules making the correct decision on input instances. We conduct experiments on MNIST, MNIST-Fashion, and CIFAR-10 and show competitive classification results compared to existing capsule networks.