arXiv subjects

Karan Singh

Publications and source records attributed to Karan Singh.

72 records · Page 4Linked to original sources

arXiv2019

Predicting Animation Skeletons for 3D Articulated Models via Volumetric Nets

We present a learning method for predicting animation skeletons for input 3D models of articulated characters. In contrast to previous approaches that fit pre-defined skeleton templates or predict fixed sets of joints, our method produces an animation skeleton tailored for the structure and geometry of the input 3D model. Our architecture is based on a stack of hourglass modules trained on a large dataset of 3D rigged characters mined from the web. It operates on the volumetric representation of the input 3D shapes augmented with geometric shape features that provide additional cues for joint and bone locations. Our method also enables intuitive user control of the level-of-detail for the output skeleton. Our evaluation demonstrates that our approach predicts animation skeletons that are much more similar to the ones created by humans compared to several alternatives and baselines.

cs.CV↗

arXiv2019

A system for efficient 3D printed stop-motion face animation

Computer animation in conjunction with 3D printing has the potential to positively impact traditional stop-motion animation. As 3D printing every frame of a computer animation is prohibitively slow and expensive, 3D printed stop-motion can only be viable if animations can be faithfully reproduced using a compact library of 3D printed and efficiently assemblable parts. We thus present the first system for processing computer animation sequences (typically faces) to produce an optimal set of replacement parts for use in 3D printed stop-motion animation. Given an input animation sequence of topology invariant deforming meshes, our problem is to output a library of replacement parts and per-animation-frame assignment of the parts, such that we maximally approximate the input animation, while minimizing the amount of 3D printing and assembly. Inspired by current stop-motion workflows, a user manually indicates which parts of the model are preferred for segmentation; then, we find curves with minimal deformation along which to segment the mesh. We then present a novel algorithm to zero out deformations along the segment boundaries, so that replacement sets for each part can be interchangeably and seamlessly assembled together. The part boundaries are designed to ease 3D printing and instrumentation for assembly. Each part is then independently optimized using a graph-cut technique to find a set of replacements, whose size can be user defined, or automatically computed to adhere to a printing budget or allowed deviation from the original animation. Our evaluation is threefold: we show results on a variety of facial animations, both digital and 3D printed, critiqued by a professional animator; we show the impact of various algorithmic parameters; and compare our results to naive solutions. Our approach can reduce the printing time and cost significantly for stop-motion animated films.

cs.GR↗

arXiv2019

Interplay between disorder driven Non-Fermi-liquid behavior and magnetism in Ce0.24La0.76Ge compound

In this work, we investigate the magnetic, heat capacity and electrical transport properties of Ce0.6La0.4Ge and Ce0.24La0.76Ge compounds. Our results show that two antiferromagnetic transitions (~ at 4.7 and 2.7 K) exhibited by Ce0.6La0.4Ge are suppressed below 1.8 K for Ce0.24La0.76Ge. Interestingly, for Ce0.24La0.76Ge, susceptibility, heat capacity and electrical resistivity vary with temperature as: T0.75, T0.5 and T1.6 respectively. The observation of such anomalous temperature variation suggests to the Non-Fermi-liquid (NFL) behavior due to the presence of disordered 4f spins due to Ce-site dilution. Under the application of magnetic field, it is noted that a crossover from the NFL to a magnetic state occurs around 2 Tesla, where, short-range correlations among the spins is prevalent due to the dominance of coupling between the magnetic moments via conduction electrons. Magnetoresistance scaling indicates that behavior of disorder driven NFL state is described by the dynamical mean field theory of the spin glass quantum critical point.

cond-mat.str-el↗

arXiv2019

Possibility of a new order parameter driven by multipolar moment and Fermi surface evolution in CeGe

Polycrystalline CeGe is investigated by means of DC and AC susceptibility, non-linear DC susceptibility, electrical transport and heat capacity measurements in the low temperature regime. This compound shows two peaks at low magnetic field approximately around 10.7 and 7.3 K due to antiferromagnetic ordering and subsequent spin rearrangement respectively. Investigation of non-linear DC susceptibility reveals a presence of higher order magnetization which results in the development of a new order parameter around 10.7 K. This leads to a lowering of symmetry of the magnetic state. The order parameter increases with decreasing temperature and stabilizes around 7.3 K. Consequently, the symmetry of the magnetic state is preserved below this transition. Heat capacity and resistivity results indicate the presence of a gap opening around 10.7 K on portion of Fermi surface, due to evolution of the Fermi surface. Magnetoresistance behavior and violation of Kohlers rule suggest that the evolution of Fermi surface changes the symmetry of magnetic state. The observation of new order parameter (which is of second order) is also confirmed from the Landau free energy theory.

cond-mat.str-el↗

arXiv2019

Online Control with Adversarial Disturbances

We study the control of a linear dynamical system with adversarial disturbances (as opposed to statistical noise). The objective we consider is one of regret: we desire an online control procedure that can do nearly as well as that of a procedure that has full knowledge of the disturbances in hindsight. Our main result is an efficient algorithm that provides nearly tight regret bounds for this problem. From a technical standpoint, this work generalizes upon previous work in two main aspects: our model allows for adversarial noise in the dynamics, and allows for general convex costs.

cs.LG↗

arXiv2019

Provably Efficient Maximum Entropy Exploration

Suppose an agent is in a (possibly unknown) Markov Decision Process in the absence of a reward signal, what might we hope that an agent can efficiently learn to do? This work studies a broad class of objectives that are defined solely as functions of the state-visitation frequencies that are induced by how the agent behaves. For example, one natural, intrinsically defined, objective problem is for the agent to learn a policy which induces a distribution over state space that is as uniform as possible, which can be measured in an entropic sense. We provide an efficient algorithm to optimize such such intrinsically defined objectives, when given access to a black box planning oracle (which is robust to function approximation). Furthermore, when restricted to the tabular setting where we have sample based access to the MDP, our proposed algorithm is provably efficient, both in terms of its sample and computational complexities. Key to our algorithmic methodology is utilizing the conditional gradient method (a.k.a. the Frank-Wolfe algorithm) which utilizes an approximate MDP solver.

cs.LG↗

arXiv2018

Observation of field induced anomalous quantum criticality in Ce0.6Y0.4NiGe2 compound

We report the results of our investigation of magnetization and heat capacity on a series of compounds Ce1-xYxNiGe2 (x = 0.1, 0.2 and 0.4) under the influence of external magnetic field. Our studies of the thermodynamic quantity -dM/dT on these compounds indicate that magnetic frustration persists in Ce0.9Y0.1NiGe2, as also reported for the parent compound CeNiGe2. The weak signature of this frustration is also noted in Ce0.8Y0.2NiGe2, whereas, it is suppressed in Ce0.6Y0.4NiGe2. Heat capacity studies on Ce0.9Y0.1NiGe2 and Ce0.8Y0.2NiGe2 indicate the presence of a new magnetic anomaly at high field which indicates that quantum criticality is absent in these compounds. However, for Ce0.6Y0.4NiGe2 such an anomaly is not noted. For this later compound, the magnetic field (H) and temperature (T) dependence of heat capacity and magnetization obey H/T scaling above critical fields. However, the obtained scaling critical parameter (δ) is 1.6, which is away from mean field value of 3. This deviation suggests the presence of unusual fluctuations and anomalous quantum criticality in these compounds. This unusual fluctuation may arise from disorderness induced by Y-substitution.

cond-mat.str-el↗

arXiv2018

Evidence of partial gap opening and Ce-site dilution effects in a heavy fermion compound CeNiGe2

We report the results of magnetization, non-linear dc susceptibility, electrical transport, and heat capacity measurements on Y-substituted heavy fermion CeNiGe2. Investigations are carried out on the compounds CeNiGe2, Ce0.9Y0.1NiGe2, Ce0.8Y0.2NiGe2 and Ce0.6Y0.4NiGe2. It is observed that with the increase in Y-concentration, the magnetic ordering temperature decreases. For CeNiGe2, below ordering temperature Arrott plots suggest the presence of spin density wave (SDW). Third and fifth order dc susceptibility indicates magnetic instability which possibly leads to partial gap opening resulting in the observation of SDW. These observations are further investigated through resistivity and heat capacity measurements which also point toward partial gap opening in CeNiGe2. Interestingly, with the increase in Y-substitution, it is noted that the gap opening is suppressed and also shifted towards lower temperature. Moreover, our investigations reveal absence of non-Fermi liquid behavior or zero field quantum critical point even after 40% dilution of Ce-site.

cond-mat.str-el↗

arXiv2018

Designing Volumetric Truss Structures

We present the first algorithm for designing volumetric Michell Trusses. Our method uses a parametrization approach to generate trusses made of structural elements aligned with the primary direction of an object's stress field. Such trusses exhibit high strength-to-weight ratios. We demonstrate the structural robustness of our designs via a posteriori physical simulation. We believe our algorithm serves as an important complement to existing structural optimization tools and as a novel standalone design tool itself.

cs.GR↗

arXiv2018

Color Sails: Discrete-Continuous Palettes for Deep Color Exploration

We present color sails, a discrete-continuous color gamut representation that extends the color gradient analogy to three dimensions and allows interactive control of the color blending behavior. Our representation models a wide variety of color distributions in a compact manner, and lends itself to applications such as color exploration for graphic design, illustration and similar fields. We propose a Neural Network that can fit a color sail to any image. Then, the user can adjust color sail parameters to change the base colors, their blending behavior and the number of colors, exploring a wide range of options for the original design. In addition, we propose a Deep Learning model that learns to automatically segment an image into color-compatible alpha masks, each equipped with its own color sail. This allows targeted color exploration by either editing their corresponding color sails or using standard software packages. Our model is trained on a custom diverse dataset of art and design. We provide both quantitative evaluations, and a user study, demonstrating the effectiveness of color sail interaction. Interactive demos are available at www.colorsails.com.

cs.GR↗

arXiv2018

VisemeNet: Audio-Driven Animator-Centric Speech Animation

We present a novel deep-learning based approach to producing animator-centric speech motion curves that drive a JALI or standard FACS-based production face-rig, directly from input audio. Our three-stage Long Short-Term Memory (LSTM) network architecture is motivated by psycho-linguistic insights: segmenting speech audio into a stream of phonetic-groups is sufficient for viseme construction; speech styles like mumbling or shouting are strongly co-related to the motion of facial landmarks; and animator style is encoded in viseme motion curve profiles. Our contribution is an automatic real-time lip-synchronization from audio solution that integrates seamlessly into existing animation pipelines. We evaluate our results by: cross-validation to ground-truth data; animator critique and edits; visual comparison to recent deep-learning lip-synchronization solutions; and showing our approach to be resilient to diversity in speaker and language.

cs.GR↗

arXiv2018

Spectral Filtering for General Linear Dynamical Systems

We give a polynomial-time algorithm for learning latent-state linear dynamical systems without system identification, and without assumptions on the spectral radius of the system's transition matrix. The algorithm extends the recently introduced technique of spectral filtering, previously applied only to systems with a symmetric transition matrix, using a novel convex relaxation to allow for the efficient identification of phases.

cs.LG↗

arXiv2017

Signature of partially frustrated moments and a new magnetic phase in CeNiGe2

We report the magnetic, thermodynamic, and transport properties of a heavy fermion compound CeNiGe2. This compound undergoes two antiferromagnetic transitions around 4.1 and 3 K. It is observed in heat capacity that as magnetic field is increased to ~ 1 T, the two peak merge into a single peak around 3 K. However this peak is not suppressed under the application of magnetic field. Instead a new feature develops at 3.6 K above 1 T. The magnetic field induced new feature is investigated through entropy evolution, magnetic Gruneisen parameter and resistivity studies. These studies emphasis the fact that partial magnetic frustration due to field induced spin fluctuation is responsible for this observed feature. This partially frustrated regime develops a new antiferromagnetically ordered phase at high fields. In this compound magnetic field induced QCP is absent implying that the behavior of CeNiGe2 is not in accordance to Doniach model proposed for heavy fermions compounds.

cond-mat.str-el↗

arXiv2017

Learning Linear Dynamical Systems via Spectral Filtering

We present an efficient and practical algorithm for the online prediction of discrete-time linear dynamical systems with a symmetric transition matrix. We circumvent the non-convex optimization problem using improper learning: carefully overparameterize the class of LDSs by a polylogarithmic factor, in exchange for convexity of the loss functions. From this arises a polynomial-time algorithm with a near-optimal regret guarantee, with an analogous sample complexity bound for agnostic learning. Our algorithm is based on a novel filtering technique, which may be of independent interest: we convolve the time series with the eigenvectors of a certain Hankel matrix.

cs.LG↗

arXiv2017

Efficient Regret Minimization in Non-Convex Games

We consider regret minimization in repeated games with non-convex loss functions. Minimizing the standard notion of regret is computationally intractable. Thus, we define a natural notion of regret which permits efficient optimization and generalizes offline guarantees for convergence to an approximate local optimum. We give gradient-based methods that achieve optimal regret, which in turn guarantee convergence to equilibrium in this framework.

cs.LG↗

arXiv2017

The Price of Differential Privacy For Online Learning

We design differentially private algorithms for the problem of online linear optimization in the full information and bandit settings with optimal $\tilde{O}(\sqrt{T})$ regret bounds. In the full-information setting, our results demonstrate that $ε$-differential privacy may be ensured for free -- in particular, the regret bounds scale as $O(\sqrt{T})+\tilde{O}\left(\frac{1}ε\right)$. For bandit linear optimization, and as a special case, for non-stochastic multi-armed bandits, the proposed algorithm achieves a regret of $\tilde{O}\left(\frac{1}ε\sqrt{T}\right)$, while the previously known best regret bound was $\tilde{O}\left(\frac{1}εT^{\frac{2}{3}}\right)$.

cs.LG↗

arXiv2017

Dynamic Task Allocation for Crowdsourcing Settings

We consider the problem of optimal budget allocation for crowdsourcing problems, allocating users to tasks to maximize our final confidence in the crowdsourced answers. Such an optimized worker assignment method allows us to boost the efficacy of any popular crowdsourcing estimation algorithm. We consider a mutual information interpretation of the crowdsourcing problem, which leads to a stochastic subset selection problem with a submodular objective function. We present experimental simulation results which demonstrate the effectiveness of our dynamic task allocation method for achieving higher accuracy, possibly requiring fewer labels, as well as improving upon a previous method which is sensitive to the proportion of users to questions.

cs.LG↗

arXiv2016

Exchange bias in a mixed metal oxide based magnetocaloric compound YFe0.5Cr0.5O3

We report a detailed investigation of magnetization, magnetocaloric effect and exchange bias studies on a mixed metal oxide YFe0.5Cr0.5O3 belonging to perovskite family. Our results reveal that the compound is in canted magnetic state (CMS) where ferromagnetic correlations are present in an antiferromagnetic state. Magnetic entropy change of this compound follows a power law (deltaS_M ~ H^m) dependence of magnetic field. In this compound, inverse magnetocaloric effect (IMCE) is observed below 260 K while conventional magnetocaloric effect (CMCE) above it. The exponent m is found to be independent of temperature and field only in the IMCE region. Investigation of temperature and magnetic field dependence studies of exchange bias, reveal a competition between effective Zeeman energy of the ferromagnetic regions and anisotropic exchange energy at the interface between ferromagnetic and antiferromagnetic regions. Variation of exchange bias due to temperature and field cycling is also investigated.

cond-mat.str-el↗