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Organic water-gated thin-film transistors (WG-TFTs) are of great interest in developing low-cost and high-performance biosensors. The device's sensitivity to changes in measurement conditions can impair long-term operation, and care must be taken to ensure that the WG-TFT sensor response is due to an actual biorecognition event occurring on the sensing electrode. This work aims to clarify the long-term stability of a poly-3-hexylthiophene (P3HT) WG-TFT operated intermittently over two months during 5750 measurement cycles. We have evaluated the device figures of merit (FOM), such as threshold voltage, mobility, and trap density, during the whole measurement period. Short-term changes in the FOM are mainly attributed to work function changes on the gate electrode, whereas long-term changes are consistent with an increase in the semiconductor trap density. The shift in threshold voltage and decrease in mobility are found to be linear as a function of measurement cycles and caused by electrical stress, with time immersed in water having a negligible effect on the device. The trap density-of-states estimated using the subthreshold slope is similar to earlier reported values for P3HT OFETs and exhibits a gradual increase during device use and a partial recovery after rest, indicating the formation of shorter- and longer-lived traps., Comment: Eight pages and five figures

We establish Guay-Paquet's unpublished linear relation between certain chromatic symmetric functions by relating his algebra on paths to the $q$-Klyachko algebra. The coefficients in this relations are $q$-hit polynomials, and they come up naturally in our setup as connected remixed Eulerian numbers, in contrast to the computational approach of Colmenarejo-Morales-Panova. As Guay-Paquet's algebra is a down-up algebra, we are able to harness algebraic results in the context of the latter and establish results of a combinatorial flavour. In particular we resolve a conjecture of Colmenarejo-Morales-Panova on chromatic symmetric functions. This concerns the abelian case of the Stanley-Stembridge conjecture, which we briefly survey., Comment: 18 pages, 7 figures

Remixed Eulerian numbers are a polynomial $q$-deformation of Postnikov's mixed Eulerian numbers. They arose naturally in previous work by the authors concerning the permutahedral variety and subsume well-known families of polynomials such as $q$-binomial coefficients and Garsia--Remmel's $q$-hit numbers. We study their combinatorics in more depth. As polynomials in $q$, they are shown to be symmetric and unimodal. By interpreting them as computing success probabilities in a simple probabilistic process we arrive at a combinatorial interpretation involving weighted trees. By decomposing the permutahedron into certain combinatorial cubes, we obtain a second combinatorial interpretation. At $q=1$, the former recovers Postnikov's interpretation whereas the latter recovers Liu's interpretation, both of which were obtained via methods different from ours., Comment: 26 pages, 8 figures. v2: minor corrections

Given a set of images of a scene, the re-rendering of this scene from novel views and lighting conditions is an important and challenging problem in Computer Vision and Graphics. On the one hand, most existing works in Computer Vision usually impose many assumptions regarding the image formation process, e.g. direct illumination and predefined materials, to make scene parameter estimation tractable. On the other hand, mature Computer Graphics tools allow modeling of complex photo-realistic light transport given all the scene parameters. Combining these approaches, we propose a method for scene relighting under novel views by learning a neural precomputed radiance transfer function, which implicitly handles global illumination effects using novel environment maps. Our method can be solely supervised on a set of real images of the scene under a single unknown lighting condition. To disambiguate the task during training, we tightly integrate a differentiable path tracer in the training process and propose a combination of a synthesized OLAT and a real image loss. Results show that the recovered disentanglement of scene parameters improves significantly over the current state of the art and, thus, also our re-rendering results are more realistic and accurate.

We apply the method of orbit harmonics to the set of break divisors and orientable divisors on graphs to obtain the central and external zonotopal algebras respectively. We then relate a construction of Efimov in the context of cohomological Hall algebras to the central zonotopal algebra of a graph $G_{Q,\gamma}$ constructed from a symmetric quiver $Q$ with enough loops and a dimension vector $\gamma$. This provides a concrete combinatorial perspective on the former work, allowing us to identify the quantum Donaldson-Thomas invariants as the Hilbert series of the space of $S_{\gamma}$-invariants of the Postnikov-Shapiro slim subgraph space attached to $G_{Q,\gamma}$. The connection with orbit harmonics in turn allows us to give a manifestly nonnegative combinatorial interpretation to numerical Donaldson-Thomas invariants as the number of $S_{\gamma}$-orbits under the permutation action on the set of break divisors on $G_{Q,\gamma}$. We conclude with several representation-theoretic consequences, whose combinatorial ramifications may be of independent interest., Comment: minor typos corrected

We present a method to map 2D image observations of a scene to a persistent 3D scene representation, enabling novel view synthesis and disentangled representation of the movable and immovable components of the scene. Motivated by the bird's-eye-view (BEV) representation commonly used in vision and robotics, we propose conditional neural groundplans, ground-aligned 2D feature grids, as persistent and memory-efficient scene representations. Our method is trained self-supervised from unlabeled multi-view observations using differentiable rendering, and learns to complete geometry and appearance of occluded regions. In addition, we show that we can leverage multi-view videos at training time to learn to separately reconstruct static and movable components of the scene from a single image at test time. The ability to separately reconstruct movable objects enables a variety of downstream tasks using simple heuristics, such as extraction of object-centric 3D representations, novel view synthesis, instance-level segmentation, 3D bounding box prediction, and scene editing. This highlights the value of neural groundplans as a backbone for efficient 3D scene understanding models., Comment: Project page: https://prafullsharma.net/neural_groundplans/

Under mechanical abuse conditions, the failure of lithium-ion batteries occurs in various stages characterized by different force, temperature and voltage response which require it's in-situ measurements for analysis. Firstly, four sizes of commercially available lithium-iron phosphate batteries (LFPB) viz. 18650, 22650, 26650, and 32650 are subjected to quasi static lateral, longitudinal compression, and nail penetration tests. The failure, characterized by the voltage drop and temperature rise, at the onset of the first internal short-circuit (ISC), is identified by Aurdino-based voltage sensor module and temperature measurement module, respectively. The battery failure load and peak temperature at the onset of ISC are found to rely on the battery size strongly. The failure is observed to be delayed for small-sized 18650 batteries during lateral compression, unlike longitudinal compression and nail penetration test. At the onset of the short circuit, the temperature rise above the ambient value is different for different LFPBs. It is found to be maximum (64.4 degree C ) for LFPB 32650 under longitudinal compression and minimum (29.5 degree C) under lateral compression tests amongst the considered geometries. Further, LFPB 26650 exhibited a balanced thermal behavior during the considered abused condition. Such data can be sensed timely for effective thermal management and improved safety of lithium-ion batteries., Comment: 15 pages, 6 Figures, 2 table

2D images are observations of the 3D physical world depicted with the geometry, material, and illumination components. Recovering these underlying intrinsic components from 2D images, also known as inverse rendering, usually requires a supervised setting with paired images collected from multiple viewpoints and lighting conditions, which is resource-demanding. In this work, we present GAN2X, a new method for unsupervised inverse rendering that only uses unpaired images for training. Unlike previous Shape-from-GAN approaches that mainly focus on 3D shapes, we take the first attempt to also recover non-Lambertian material properties by exploiting the pseudo paired data generated by a GAN. To achieve precise inverse rendering, we devise a specularity-aware neural surface representation that continuously models the geometry and material properties. A shading-based refinement technique is adopted to further distill information in the target image and recover more fine details. Experiments demonstrate that GAN2X can accurately decompose 2D images to 3D shape, albedo, and specular properties for different object categories, and achieves the state-of-the-art performance for unsupervised single-view 3D face reconstruction. We also show its applications in downstream tasks including real image editing and lifting 2D GANs to decomposed 3D GANs., Comment: Accepted to 3DV 2022. The video demo is available at the project page: https://vcai.mpi-inf.mpg.de/projects/GAN2X/

Boosting is a fundamental approach in machine learning that enjoys both strong theoretical and practical guarantees. At a high-level, boosting algorithms cleverly aggregate weak learners to generate predictions with arbitrarily high accuracy. In this way, boosting algorithms convert weak learners into strong ones. Recently, Brukhim et al. extended boosting to the online agnostic binary classification setting. A key ingredient in their approach is a clean and simple reduction to online convex optimization, one that efficiently converts an arbitrary online convex optimizer to an agnostic online booster. In this work, we extend this reduction to multiclass problems and give the first boosting algorithm for online agnostic mutliclass classification. Our reduction also enables the construction of algorithms for statistical agnostic, online realizable, and statistical realizable multiclass boosting., Comment: Camera-Ready Version

In this paper, we study a sequential decision-making problem, called Adaptive Sampling for Discovery (ASD). Starting with a large unlabeled dataset, algorithms for ASD adaptively label the points with the goal to maximize the sum of responses. This problem has wide applications to real-world discovery problems, for example drug discovery with the help of machine learning models. ASD algorithms face the well-known exploration-exploitation dilemma. The algorithm needs to choose points that yield information to improve model estimates but it also needs to exploit the model. We rigorously formulate the problem and propose a general information-directed sampling (IDS) algorithm. We provide theoretical guarantees for the performance of IDS in linear, graph and low-rank models. The benefits of IDS are shown in both simulation experiments and real-data experiments for discovering chemical reaction conditions.

We propose an algorithm for non-stationary kernel bandits that does not require prior knowledge of the degree of non-stationarity. The algorithm follows randomized strategies obtained by solving optimization problems that balance exploration and exploitation. It adapts to non-stationarity by restarting when a change in the reward function is detected. Our algorithm enjoys a tighter dynamic regret bound than previous work on the non-stationary kernel bandit setting. Moreover, when applied to the non-stationary linear bandit setting by using a linear kernel, our algorithm is nearly minimax optimal, solving an open problem in the non-stationary linear bandit literature. We extend our algorithm to use a neural network for dynamically adapting the feature mapping to observed data. We prove a dynamic regret bound of the extension using the neural tangent kernel theory. We demonstrate empirically that our algorithm and the extension can adapt to varying degrees of non-stationarity.

Low-temperature scanning tunneling microscopy is used here to study dynamic bonding of gold atoms on surfaces under low coordination conditions. In the experiments, using an atomically-sharp gold tip, a gold adatom is deposited onto a gold surface with atomic precision either on the first hollow site near a step edge, or far away from it. Classical molecular dynamics simulations at 4.2 K and density functional theory calculations serve to elucidate the difference in the bonding behavior between these two different placements, while also providing information on the crystalline classification of the STM tips based on their experimental performance.

Identifying and mitigating safety risks is paramount in a number of industries. In addition to guidelines and best practices, many industries already have safety management systems (SMSs) designed to monitor and reinforce good safety behaviors. The analytic capabilities to analyze the data acquired through such systems, however, are still lacking in terms of their ability to robustly quantify risks posed by various occupational hazards. Moreover, best practices and modern SMSs are unable to account for dynamically evolving environments/behavioral characteristics commonly found in many industrial settings. This article proposes a method to address these issues by enabling continuous and quantitative assessment of safety risks in a data-driven manner. The backbone of our method is an intuitive hierarchical probabilistic model that explains sparse and noisy safety data collected by a typical SMS. A fully Bayesian approach is developed to calibrate this model from safety data in an online fashion. Thereafter, the calibrated model holds necessary information that serves to characterize risk posed by different safety hazards. Additionally, the proposed model can be leveraged for automated decision making, for instance solving resource allocation problems -- targeted towards risk mitigation -- that are often encountered in resource-constrained industrial environments. The methodology is rigorously validated on a simulated test-bed and its scalability is demonstrated on real data from large maintenance projects at a petrochemical plant.

Sampling biases in training data are a major source of algorithmic biases in machine learning systems. Although there are many methods that attempt to mitigate such algorithmic biases during training, the most direct and obvious way is simply collecting more representative training data. In this paper, we consider the task of assembling a training dataset in which minority groups are adequately represented from a given set of data sources. In essence, this is an adaptive sampling problem to determine if a given point lies in the convex hull of the means from a set of unknown distributions. We present adaptive sampling methods to determine, with high confidence, whether it is possible to assemble a representative dataset from the given data sources. We also demonstrate the efficacy of our policies in simulations in the Bernoulli and a multinomial setting.

Neural 3D implicit representations learn priors that are useful for diverse applications, such as single- or multiple-view 3D reconstruction. A major downside of existing approaches while rendering an image is that they require evaluating the network multiple times per camera ray so that the high computational time forms a bottleneck for downstream applications. We address this problem by introducing a novel neural scene representation that we call the directional distance function (DDF). To this end, we learn a signed distance function (SDF) along with our DDF model to represent a class of shapes. Specifically, our DDF is defined on the unit sphere and predicts the distance to the surface along any given direction. Therefore, our DDF allows rendering images with just a single network evaluation per camera ray. Based on our DDF, we present a novel fast algorithm (FIRe) to reconstruct 3D shapes given a posed depth map. We evaluate our proposed method on 3D reconstruction from single-view depth images, where we empirically show that our algorithm reconstructs 3D shapes more accurately and it is more than 15 times faster (per iteration) than competing methods., Comment: News: Accepted to WACV'24. Project page: https://vision.in.tum.de/research/geometry/fire

Learning 3D generative models from a dataset of monocular images enables self-supervised 3D reasoning and controllable synthesis. State-of-the-art 3D generative models are GANs which use neural 3D volumetric representations for synthesis. Images are synthesized by rendering the volumes from a given camera. These models can disentangle the 3D scene from the camera viewpoint in any generated image. However, most models do not disentangle other factors of image formation, such as geometry and appearance. In this paper, we design a 3D GAN which can learn a disentangled model of objects, just from monocular observations. Our model can disentangle the geometry and appearance variations in the scene, i.e., we can independently sample from the geometry and appearance spaces of the generative model. This is achieved using a novel non-rigid deformable scene formulation. A 3D volume which represents an object instance is computed as a non-rigidly deformed canonical 3D volume. Our method learns the canonical volume, as well as its deformations, jointly during training. This formulation also helps us improve the disentanglement between the 3D scene and the camera viewpoints using a novel pose regularization loss defined on the 3D deformation field. In addition, we further model the inverse deformations, enabling the computation of dense correspondences between images generated by our model. Finally, we design an approach to embed real images into the latent space of our disentangled generative model, enabling editing of real images., Comment: CVPR 2022

There has been a significant increase in the development of data-driven safety analytics approaches in recent years. In light of these advances it has become imperative to evaluate such approaches in a principled way to determine their merits and limitations. To that end, we propose an evaluation methodology underpinned by a simulated environment that allows for a comprehensive assessment of safety analytics approaches. While assessing those approaches with historical field data is undoubtedly important, such an assessment has limited statistical power because it corresponds to only one realization. The proposed methodology enables validation over a large number of realizations, thereby circumventing the statistical limitations of evaluation on historical data. Moreover, by using a simulated environment one is able to clearly distinguish between the variability in the observed data and differences in performance between safety approaches. A simulated environment does this by comparing the approaches under controlled circumstances, resulting in a fair and systematic evaluation of the potential long-term benefits. We demonstrate the utility of the proposed methodology via a case study that compares a few candidate safety analytics approaches. These approaches differ in how they assimilate field safety data to assess safety risk and suggest mitigative actions. We show that the proposed methodology indeed reveals useful insights and quantifies the relative merits and drawbacks of the different approaches, which would be otherwise difficult to objectively determine in a real-world scenario., Comment: Accepted to Safety Science

We provide new forbidden criterion for realizability of smooth tropical plane curves. This in turn provides us a complete classification of smooth tropical plane curves up to genus six., Comment: Comments are welcome

In view of searching the signature of the celebrated chiral anomaly (CA) in Weyl semimetals (WSMs) in ongoing experiments, quantum oscillation in linear response regime has been considered as an important signature in the magneto transports in WSMs, due to its unique relation to CA. Investigating the nonlinear planar effects (NPEs) starting from the semiclassical regime to the ultra-quantum limit within the framework of Boltzmann transport theory incorporating Landau quantization, we here propose the quantum oscillations in NPEs can serve as a robust signature of CA in WSMs. By obtaining analytical expressions, we show that the quantum oscillations of the nonlinear effects exhibit two different period scales in 1/B (B is the magnetic field) compared to the linear responses where only one period scale exists. We find that these quantum oscillations in NPEs are attributed to the deviation of chiral chemical potential (CCP), which is proportional to the finite band tilt as well as transverse electric field and therefore, directly linked to CA in WSMs. In addition, we also show that the CA-induced nonlinear magneto conductivity is linear and independent in the magnetic field in the semiclassical and ultraquantum regimes, respectively. We conclude that the proposed behaviors of NPEs in different regimes uniquely signify the existence of CA and therefore, can serve as a probe of identifying CA in WSMs in experiments., Comment: Published version in PRB as a Letter

A series of recent papers have claimed that intranode scattering, alone, can contribute to positive longitudinal magnetoconductance (LMC) due to chiral anomaly (CA) in Weyl semimetals (WSMs). We revisit the problem of CA induced LMC in WSMs, and show that intranode scattering, by itself, does not result in enhancement of LMC. In the limit of zero internode scattering, chiral charge must remain conserved, which is shown to actually decrease LMC. Only in the presence of a non-zero internode scattering (however weak), one obtains a positive LMC due to non-conservation of chiral charge. Even weak internode scattering suffices in generating positive LMC, since it redistributes charges across both the nodes, although on a time scale larger than that of the intranode scattering. Furthermore, our calculations reveal that, in contrast to recent works, in inhomogeneous WSMs strain induced axial magnetic field $B_5$, by itself, leads to negative longitudinal magnetoconductance and a negative planar Hall conductance., Comment: 5 pages, 6 figures

We study the problem of exact support recovery for high-dimensional sparse linear regression under independent Gaussian design when the signals are weak, rare, and possibly heterogeneous. Under a suitable scaling of the sample size and signal sparsity, we fix the minimum signal magnitude at the information-theoretic optimal rate and investigate the asymptotic selection accuracy of best subset selection (BSS) and marginal screening (MS) procedures. We show that despite the ideal setup, somewhat surprisingly, marginal screening can fail to achieve exact recovery with probability converging to one in the presence of heterogeneous signals, whereas BSS enjoys model consistency whenever the minimum signal strength is above the information-theoretic threshold. To mitigate the computational intractability of BSS, we also propose an efficient two-stage algorithmic framework called ETS (Estimate Then Screen) comprised of an estimation step and gradient coordinate screening step, and under the same scaling assumption on sample size and sparsity, we show that ETS achieves model consistency under the same information-theoretic optimal requirement on the minimum signal strength as BSS. Finally, we present a simulation study comparing ETS with LASSO and marginal screening. The numerical results agree with our asymptotic theory even for realistic values of the sample size, dimension and sparsity., Comment: 46 pages, 3 figures

We have performed magneto-transport and high-resolution angle-resolved photoelectron spectroscopy (ARPES) measurements on palladium (Pd) doped topological insulator Pd$_{x}$Bi$_{2}$Te$_{3}$ (0 $\leq$ x $\leq$ 0.20) single crystals. We have observed unusually high values of magnetoresistance ($\sim$ 1500%) and mobility ($\sim$ 93000 cm$^{2}$V$^{-1}$s$^{-1}$) at low temperatures for pristine Bi2Te3 that decrease on Pd doping. The Shubnikov-de Haas (SdH) oscillations have been detected for x = 0.05, 0.10, confirming the presence of 2D topological surface states (TSSs) for these samples. The Hall measurement shows the crossover from n-type charge carriers in pristine Bi$_{2}$Te$_{3}$ to p-type charge carriers upon Pd doping. The ARPES measurements show that the conduction band crosses the Fermi level for pristine Bi$_{2}$Te$_{3}$, and the Dirac point of the TSSs and bulk-derived valence bands indicated shift to lower binding energy upon Pd doping in a rigid-band-like way up to x $\sim$0.10. Based on the comparison of the parameters obtained from the SdH and ARPES measurements, the reduction in the kF value in the magneto-transport measurements likely due to the band bending induced by the Schottky barrier., Comment: 18 pages, 16 figures

Linear time-invariant systems are very popular models in system theory and applications. A fundamental problem in system identification that remains rather unaddressed in extant literature is to leverage commonalities amongst related linear systems to estimate their transition matrices more accurately. To address this problem, the current paper investigates methods for jointly estimating the transition matrices of multiple systems. It is assumed that the transition matrices are unknown linear functions of some unknown shared basis matrices. We establish finite-time estimation error rates that fully reflect the roles of trajectory lengths, dimension, and number of systems under consideration. The presented results are fairly general and show the significant gains that can be achieved by pooling data across systems in comparison to learning each system individually. Further, they are shown to be robust against model misspecifications. To obtain the results, we develop novel techniques that are of interest for addressing similar joint-learning problems. They include tightly bounding estimation errors in terms of the eigen-structures of transition matrices, establishing sharp high probability bounds for singular values of dependent random matrices, and capturing effects of misspecified transition matrices as the systems evolve over time.