Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Robotic Manipulation through Visual Planning and Acting

Planning for robotic manipulation requires reasoning about the changes a robot can affect on objects. When such interactions can be modelled analytically, as in domains with rigid objects, efficient planning algorithms exist. However, in both domestic and industrial domains, the objects of interest can be soft, or deformable, and hard to model analytically. For such cases, we posit that a data-driven modelling approach is more suitable. In recent years, progress in deep generative models has produced methods that learn to `imagine' plausible images from data. Building on the recent Causal InfoGAN generative model, in this work we learn to imagine goal-directed object manipulation directly from raw image data of self-supervised interaction of the robot with the object. After learning, given a goal observation of the system, our model can generate an imagined plan -- a sequence of images that transition the object into the desired goal. To execute the plan, we use it as a reference trajectory to track with a visual servoing controller, which we also learn from the data as an inverse dynamics model. In a simulated manipulation task, we show that separating the problem into visual planning and visual tracking control is more sample efficient and more interpretable than alternative data-driven approaches. We further demonstrate our approach on learning to imagine and execute in 3 environments, the final of which is deformable rope manipulation on a PR2 robot.
Fullerene mediates proliferation and cardiomyogenic differentiation of adipose-derived stem cells via modulation of MAPK pathway and cardiac protein expression

Zero-dimensional fullerenes can modulate the biological behavior of a variety of cell lines. However, the effects and molecular mechanisms of proliferation and cardiomyogenic differentiation in brown adipose-derived stem cells (BADSCs) are still unclear. In this study, we report the initial biological effects of fullerene-C60 on BADSCs at different concentrations. Results suggest that fullerene-C60 has no cytotoxic effects on BADSCs even at a concentration of 100 μg/mL. Fullerene-C60 improves the MAPK expression level and stem cell survival, proliferation, and cardiomyogenesis. Further, we found that the fullerene-C60 modulates cardiomyogenic differentiation. Fullerene-C60 improves the expression of cardiomyocyte-specific proteins (cTnT and α-sarcomeric actinin). At elevated concentration, fullerene-C60 reduces the incidence of diminished spontaneous cardiac differentiation of BADSCs with time. At the genetic level, fullerene-C60 (5 μg/mL) also improves the expression of cTnT. In addition, fullerene-C60 promotes the formation of gap junction among cells. These findings have important implications for clinical application of fullerenes in the treatment of myocardial infarction.
Flat Metric Minimization with Applications in Generative Modeling

We take the novel perspective to view data not as a probability distribution but rather as a current. Primarily studied in the field of geometric measure theory, $k$-currents are continuous linear functionals acting on compactly supported smooth differential forms and can be understood as a generalized notion of oriented $k$-dimensional manifold. By moving from distributions (which are $0$-currents) to $k$-currents, we can explicitly orient the data by attaching a $k$-dimensional tangent plane to each sample point. Based on the flat metric which is a fundamental distance between currents, we derive FlatGAN, a formulation in the spirit of generative adversarial networks but generalized to $k$-currents. In our theoretical contribution we prove that the flat metric between a parametrized current and a reference current is Lipschitz continuous in the parameters. In experiments, we show that the proposed shift to $k>0$ leads to interpretable and disentangled latent representations which behave equivariantly to the specified oriented tangent planes.
Voigt-Profile Analysis of the Lyα Forest in a Cold Dark Matter Universe

We use an automated Voigt-profile fitting procedure to extract statistical properties of the Lyα forest in a numerical simulation of an Ω = 1, cold dark matter (CDM) universe. Our analysis method is similar to that used in most observational studies of the forest, and we compare the simulations to recently published results derived from Keck HIRES spectra. With the Voigt-profile decomposition analysis, the simulation reproduces the large number of weak lines (NH I ≲ 1014 cm-2) found in the HIRES spectra. The column density distribution evolves significantly between z = 3 and z = 2, with the number of lines at fixed column density dropping by a factor ~1.6 in the range where line blending is not severe. At z = 3, the b-parameter distribution has a median of 35 km s-1 and a dispersion of 20 km s-1, in reasonable agreement with the observed values. The comparison between our new analysis and recent data strengthens earlier claims that the Lyα forest arises naturally in hierarchical structure formation as photoionized gas falls into dark matter potential wells. However, there are two statistically signficant discrepancies between the simulated forest and the HIRES results: the model produces too many lines at z = 3 by a factor ~1.5-2, and it produces more narrow lines (b < 20 km s-1) than are seen in the data. The first result is sensitive to our adopted normalization of the mean Lyα optical depth, and the second is sensitive to our assumption that helium reionization has not significantly raised gas temperatures at z = 3. It is therefore too early to say whether these discrepancies indicate a fundamental problem with the high-redshift structure of the Ω = 1 CDM model or reflect errors of detail in our modeling of the gas distribution or the observational procedures.
A Benchmark Study on Machine Learning Methods for Fake News Detection

The proliferation of fake news and its propagation on social media have become a major concern due to its ability to create devastating impacts. Different machine learning approaches have been attempted to detect it. However, most of those focused on a special type of news (such as political) and did not apply many advanced techniques. In this research, we conduct a benchmark study to assess the performance of different applicable approaches on three different datasets where the largest and most diversified one was developed by us. We also implemented some advanced deep learning models that have shown promising results.
Urbanization and fertility rates in Ethiopia

Fertility rates are important determinants of both overall population growth and demographic transitions from high to low age dependency ratios, which in turn have important consequences for economic growth, poverty reduction, and improved health and nutrition outcomes. Ethiopia currently has one of the highest fertility rates in the world, although there are marked differences between rural and urban fertility rates. This paper explores the drivers of rural and urban fertility rates, including systematic tests of differences in key determinants. This further allows us to project fertility rates into the future based on alternative urbanization, economic growth, and education scenarios. Finally, we link these alternative projections with existing estimates of the benefits of fertility reductions on economic growth, nutrition, and poverty reduction
Spectral and optical properties of Ag3Au(Se2,Te2) and dark matter detection

In this work we study the electronic structure of Ag 3 AuSe 2 and Ag 3 AuTe 2 , two chiral insulators whose gap can be tuned through small changes in the lattice parameter by applying hydrostatic pressure or choosing different growth protocols. Based on first principles calculations we compute their band structure for different values of the lattice parameters and show that while Ag 3 AuSe 2 retains its direct narrow gap at the Γ point, Ag 3 AuTe 2 can turn into a metal. Focusing on Ag 3 AuSe 2 we derive a low energy model around Γ using group theory, which we use to calculate the optical conductivity for different values of the lattice constant. We discuss our results in the context of detection of light dark matter particles, which have masses of the order of a keV, and conclude that Ag 3 AuSe 2 satisfies three important requirements for a suitable detector: small Fermi velocities, meV band gap, and low photon screening. Our work motivates the growth of high-quality and large samples of Ag 3 AuSe 2 to be used as target materials in dark matter detectors.
Towards a regularity theory for ReLU networks – chain rule and global error estimates

Although for neural networks with locally Lipschitz continuous activation functions the classical derivative exists almost everywhere, the standard chain rule is in general not applicable. We will consider a way of introducing a derivative for neural networks that admits a chain rule, which is both rigorous and easy to work with. In addition we will present a method of converting approximation results on bounded domains to global (pointwise) estimates. This can be used to extend known neural network approximation theory to include the study of regularity properties. Of particular interest is the application to neural networks with ReLU activation function, where it contributes to the understanding of the success of deep learning methods for high-dimensional partial differential equations.
Black hole discharge: Very-high-energy gamma rays from black hole-neutron star mergers

With mass ratio larger than $\sim 5$ (which depends on the black hole spin and the star radius), star disruption is not expected for a black hole merging with a neutron star during the final plunge phase. In the late inspiral stage, the black hole is likely charged as it cuts through the magnetic field carried by the neutron star, leaving a temporarily charged black hole after merger. The unstable charged state of the remnant black hole rapidly neutralizes by interacting with the surrounding plasma and photons, which we investigate in first principle by numerically solving a coupled set of Boltzmann equations of 1+1 form for non-spinning BH background. The resulting basic picture is as follows. Electrons and positrons are accelerated in the BH electric field, which then lose energy to surrounding soft photons via Compton scattering; more electrons and positrons will be created from pair production as the hard photons colliding with soft photons, or through the Schwinger process in strong electromagnetic fields. The cascade stops when the charged black hole accretes enough opposite charges and becomes neutralized. We find that $\sim 10\%$ (which depends on the soft photon energy and number density) of the total electric energy is carried away to infinity in a time interval $\sim 1$ ms by very-high-energy ($>50$ GeV, the low energy detection threshold of the MAGIC telescope) gamma rays whose spectrum is approximately a power law with spectral index $\sim -2.3$. We expect the discharge picture to be true for spinning charged BHs as well.
Optimal Control of Exhaust Recovery Generator System with Scroll Expander

A scroll expander exhaust recovery system with the battery is proposed to improve exhaust recovery efficiency. Based on the exhaust flow, storage pressure and the battery current, expander supply pressure was optimized with the loss model based on the expansion and generator loss. And then the supply pressure controller was designed to track the optimized supply pressure reference using the input-output linearization method. DC bus voltage was efficiently regulated by the PI voltage controller, the duty feedforward compensation and the operation mode of battery storage system. Experimental results proved the control strategy validity, realizing the efficiency maximum of the recovery system and exhaust discharging system.
Efficient Algorithms for Optimal Perimeter Guarding

We investigate the problem of optimally assigning a large number of robots (or other types of autonomous agents) to guard the perimeters of closed 2D regions, where the perimeter of each region to be guarded may contain multiple disjoint polygonal chains. Each robot is responsible for guarding a subset of a perimeter and any point on a perimeter must be guarded by some robot. In allocating the robots, the main objective is to minimize the maximum 1D distance to be covered by any robot along the boundary of the regions. For this optimization problem which we call optimal perimeter guarding (OPG), thorough structural analysis is performed, which is then exploited to develop fast exact algorithms that run in guaranteed low polynomial time. In addition to formal analysis and proofs, experimental evaluations and simulations are performed that further validate the correctness and effectiveness of our algorithmic results.
Modelling failures times with dependent renewal type models via exchangeability

ABSTRACT Failure times of a machinery cannot always be assumed independent and identically distributed, eg, if after reparations the machinery is not restored to a same-as-new condition. Framed within the renewal processes approach, a generalization that considers exchangeable inter-arrival times is presented. The resulting model provides a more realistic approach to capture the dependence among events occurring at random times, while retaining much of the tractability of the classical renewal process. Extensions of some classical results and special cases of renewal functions are analysed, in particular, the one corresponding to an exchangeable sequence driven by a Dirichlet process. The proposal is tested through an estimation procedure using simulated data sets and with an application to the reliability of hydraulic subsystems in load-haul-dump machines.
A Linear-algebraic Proof of Hilbert’s Ternary Quartic Theorem

Abstract Hilbert’s ternary quartic theorem states that every nonnegative degree 4 homogeneous polynomial in three variables can be written as a sum of three squares of homogeneous quadratic polynomials. We give a linear-algebraic approach to Hilbert’s theorem by showing that a structured cone of positive semidefinite matrices is generated by rank 1 elements.
INVITED: Enabling Practical Processing in and near Memory for Data-Intensive Computing

Modern computing systems suffer from the dichotomy between computation on one side, which is performed only in the processor (and accelerators), and data storage/movement on the other, which all other parts of the system are dedicated to. Due to this dichotomy, data moves a lot in order for the system to perform computation on it. Unfortunately, data movement is extremely expensive in terms of energy and latency, much more so than computation. As a result, a large fraction of system energy is spent and performance is lost solely on moving data in a modern computing system.In this work, we re-examine the idea of reducing data movement by performing Processing in Memory (PIM). PIM places computation mechanisms in or near where the data is stored (i.e., inside the memory chips, in the logic layer of 3D-stacked logic and DRAM, or in the memory controllers), so that data movement between the computation units and memory is reduced or eliminated. While the idea of PIM is not new, we examine two new approaches to enabling PIM: 1) exploiting analog properties of DRAM to perform massively-parallel operations in memory, and 2) exploiting 3Dstacked memory technology design to provide high bandwidth to in-memory logic. We conclude by discussing work on solving key challenges to the practical adoption of PIM.
Nonvolatile Memory Cell Based on Memristor Emulator

Memristor, one of the fundamental circuit elements, has promising applications in non-volatile memory and storage technology as it can theoretically achieve infinite states. Information can be stored independently in these states and retrieved whenever required. In this paper, we have proposed a non volatile memory cell based on memristor emulator. The circuit is able to perform read and write operations. In this memristor based memroy cell, unipolar pulse is used for writing and bipolar pulse is used for reading. Unlike other earlier designs, the circuit does not need external read/write enable switches to switch between read and write operations; the switching is achieved by the zero average bipolar read pulse given after the completion of write cycle. In our proposed memristor based memory cell, single bit can be read and any voltages from 0 to 5 volts can be written. Mathematical analysis and the simulation results of memristor emulator based read write circuit have been presented to confirm its operation.
Robust inference on the average treatment effect using the outcome highly adaptive lasso

Many estimators of the average effect of a treatment on an outcome require estimation of the propensity score, the outcome regression, or both. It is often beneficial to utilize flexible techniques, such as semiparametric regression or machine learning, to estimate these quantities. However, optimal estimation of these regressions does not necessarily lead to optimal estimation of the average treatment effect, particularly in settings with strong instrumental variables. A recent proposal addressed these issues via the outcome‐adaptive lasso, a penalized regression technique for estimating the propensity score that seeks to minimize the impact of instrumental variables on treatment effect estimators. However, a notable limitation of this approach is that its application is restricted to parametric models. We propose a more flexible alternative that we call the outcome highly adaptive lasso. We discuss the large sample theory for this estimator and propose closed‐form confidence intervals based on the proposed estimator. We show via simulation that our method offers benefits over several popular approaches.
On Graph Classification Networks, Datasets and Baselines

Graph classification receives a great deal of attention from the non-Euclidean machine learning community. Recent advances in graph coarsening have enabled the training of deeper networks and produced new state-of-the-art results in many benchmark tasks. We examine how these architectures train and find that performance is highly-sensitive to initialisation and depends strongly on jumping-knowledge structures. We then show that, despite the great complexity of these models, competitive performance is achieved by the simplest of models -- structure-blind MLP, single-layer GCN and fixed-weight GCN -- and propose these be included as baselines in future.
Constraining Collapsar r-process Models through Stellar Abundances

We use observations of heavy elements in very metal-poor stars ([Fe/H] < −2.5) in order to place constraints on the viability of collapsar models as a significant source of the r-process. We combine bipolar explosion nucleosynthesis calculations with recent disk calculations to make predictions of the observational imprints that these explosions would leave on very metal-poor stars. We find that a source of low (≈0.1–0.5 ) Fe mass, which also yields a relatively high (>0.08 ) r-process mass, would, after subsequently mixing and forming new stars, result in [r/Fe] abundances up to three orders of magnitude higher than those seen in stars. In order to match inferred abundances, 10–103 of Fe would need be efficiently incorporated into the r-process ejecta. We show that Fe enhancement, and hence [r/Fe] dilution from other nearby supernovae, is not able to explain the observations unless significant inflow of pristine gas occurs before the ejecta are able to form new stars. Finally, we show that the inferred [Eu/Fe] abundances require levels of gas mixing that are in conflict with other properties of r-process enhanced metal-poor stars. Our results suggest that early r-process production is likely to be spatially uncorrelated with Fe production, a condition that can be satisfied by neutron star mergers due to their large kick velocities and purely r-process yields.
Style transfer based data augmentation in material microscopic image processing

Recently progress in material microscopic image semantic segmentation has been driven by high-capacity models trained on large datasets. However, collecting microscopic images with pixel-level labels has been extremely costly due to the amount of human effort required. In this paper, we present an approach to rapidly creating microscopic images with pixel-level labels from material 3d simulated models. Usually images extracted directly from those 3d simulated models are not realistic enough. It is easy to get semantic labels, though. We introduce style transfer technique to make simulated image data more similar to real microscopic data. We validate the presented approach by using real image data from experiment and simulated image data from Monte Carlo Potts Models, which simulate the growth of polycrystal. Experiments show that using the acquired simulated image data and style transfer technique to supplement real images of polycrystalline iron significantly improves the mean precision of image processing. Besides, models trained with simulated image data and just 1/3 of the real data outperform models trained on the complete real image data. In the study of such polycrystalline materials, this approach can reduce pressure of getting and labeling images from microscopes. Also, it can be applied to numbers of other material images.
Terahertz-Band Ultra-Massive Spatial Modulation MIMO

The prospect of ultra-massive multiple-input multiple-output (UM-MIMO) technology to combat the distance problem at the Terahertz (THz) band is considered. It is well-known that the very large available bandwidths at THz frequencies come at the cost of severe propagation losses and power limitations, which result in very short communication distances. Recently, graphene-based plasmonic nano-antenna arrays that can accommodate hundreds of antenna elements in a few millimeters have been proposed. While such arrays enable efficient beamforming that can increase the communication range, they fail to provide sufficient spatial degrees of freedom for spatial multiplexing. In this paper, we examine spatial modulation (SM) techniques that can leverage the properties of densely packed configurable arrays of subarrays of nano-antennas, to increase capacity and spectral efficiency, while maintaining acceptable beamforming performance. Depending on the communication distance and the frequency of operation, a specific SM configuration that ensures good channel conditions is recommended. We analyze the performance of the proposed schemes theoretically and numerically in terms of symbol and bit error rates, where significant gains are observed compared to conventional SM. We demonstrate that SM at very high frequencies is a feasible paradigm, and we motivate several extensions that can make THz-band SM a future research trend.