Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyperparameters optimization for Deep Learning based emotion prediction for Human Robot Interaction

To enable humanoid robots to share our social space we need to develop technology for easy interaction with the robots using multiple modes such as speech, gestures and share our emotions with them. We have targeted this research towards addressing the core issue of emotion recognition problem which would require less computation resources and much lesser number of network hyperparameters which will be more adaptive to be computed on low resourced social robots for real time communication. More specifically, here we have proposed an Inception module based Convolutional Neural Network Architecture which has achieved improved accuracy of upto 6% improvement over the existing network architecture for emotion classification when combinedly tested over multiple datasets when tried over humanoid robots in real - time. Our proposed model is reducing the trainable Hyperparameters to an extent of 94% as compared to vanilla CNN model which clearly indicates that it can be used in real time based application such as human robot interaction. Rigorous experiments have been performed to validate our methodology which is sufficiently robust and could achieve high level of accuracy. Finally, the model is implemented in a humanoid robot, NAO in real time and robustness of the model is evaluated.
Joint User Identification, Channel Estimation, and Signal Detection for Grant-Free NOMA

For massive machine-type communications, centralized control may incur a prohibitively high overhead. Grant-free non-orthogonal multiple access (NOMA) provides possible solutions, yet poses new challenges for efficient receiver design. In this paper, we develop a joint user identification, channel estimation, and signal detection (JUICESD) algorithm. We divide the detection scheme into two modules: slot-wise multi-user detection (SMD) and combined signal and channel estimation (CSCE). SMD is designed to decouple the transmissions of different users by leveraging the approximate message passing (AMP) algorithms, and CSCE is designed to deal with the nonlinear coupling of activity state, channel coefficient and transmit signal of each user separately. To address the problem that the exact calculation of the messages exchanged within CSCE and between the two modules is complicated due to phase ambiguity issues, this paper proposes a rotationally invariant Gaussian mixture (RIGM) model, and develops an efficient JUICESD-RIGM algorithm. JUICESD-RIGM achieves a performance close to JUICESD with a much lower complexity. Capitalizing on the feature of RIGM, we further analyze the performance of JUICESD-RIGM with state evolution techniques. Numerical results demonstrate that the proposed algorithms achieve a significant performance improvement over the existing alternatives, and the derived state evolution method predicts the system performance accurately.
Classifying All Interacting Pairs in a Single Shot

In this paper, we introduce a novel human interaction detection approach, based on CALIPSO (Classifying ALl Interacting Pairs in a Single shOt), a classifier of humanobject interactions. This new single-shot interaction classifier estimates interactions simultaneously for all human-object pairs, regardless of their number and class. State-of- the-art approaches adopt a multi-shot strategy based on a pairwise estimate of interactions for a set of human-object candidate pairs, which leads to a complexity depending, at least, on the number of interactions or, at most, on the number of candidate pairs. In contrast, the proposed method estimates the interactions on the whole image. Indeed, it simultaneously estimates all interactions between all human subjects and object targets by performing a single forward pass throughout the image. Consequently, it leads to a constant complexity and computation time independent of the number of subjects, objects or interactions in the image. In detail, interaction classification is achieved on a dense grid of anchors thanks to a joint multi-task network that learns three complementary tasks simultaneously: (i) prediction of the types of interaction, (ii) estimation of the presence of a target and (iii) learning of an embedding which maps interacting subject and target to a same representation, by using a metric learning strategy. In addition, we introduce an object-centric passive-voice verb estimation which significantly improves results. Evaluations on the two well-known Human-Object Interaction image datasets, V- COCO and HICO-DET, demonstrate the competitiveness of the proposed method (2nd place) compared to the state-of- the-art while having constant computation time regardless of the number of objects and interactions in the image.
Upper Bound Scalability on Achievable Rates of Batched Codes for Line Networks

The capacity of line networks with buffer size constraints is an open, but practically important problem. In this paper, the upper bound on the achievable rate of a class of codes, called batched codes, is studied for line networks where the channels have 0 zero-error capacity. Batched codes enable a range of buffer size constraints, and are general enough to include special coding schemes studied in the literature for line networks. Existing works have characterized the achievable rates of batched codes for several classes of parameter sets, but leave the cut-set bound as the best existing general upper bound. In this paper, we provide upper bounds on the achievable rates of batched codes as functions of line network length for these parameter sets. Our upper bounds in order of the network length match with the existing achievability results.
Computing isogenies from modular equations between Jacobians of genus 2 curves

Let k be a field of large enough characteristic. We present an algorithm solving the following problem: given two genus 2 curves over k with isogenous Jacobians, compute an isogeny between them explicitly. This isogeny can be either an {\ell}-isogeny or, in the real multiplication case, an isogeny with cyclic kernel. The algorithm uses modular equations for these isogeny types.
Phase-field collagen fibrils: Coupling chirality and density modulations

To describe the interaction between longitudinal density modulations along collagen fibrils (the D-band) with the radial twist-field of molecular orientation (double-twist), we couple phase-field-crystal (PFC) with liquid-crystalline free-energies to obtain a hybrid model of equilibrium collagen fibril structure. We numerically compute the resulting axial and radial structure. We find two distinct fibrillar phases, `L' and `C', with a coexistence line that ends in an Ising-like critical point. We propose that coexistence between these phases can explain the bimodal distribution of fibril radii that has been widely reported within tendon tissues. Tensile strain applied to our model fibrils straightens the average fibrillar twist and flattens the D-band modulation. Our PFC approach should apply directly to other longitudinally-modulated chiral filaments, such as fibrin and intermediate filaments.
Estimating the Competitive Storage Model with Stochastic Trends in Commodity Prices

We propose a State-Space Model (SSM) for commodity prices that combines the competitive storage model with a stochastic trend. This approach fits into the economic rationality of storage decisions and adds to previous deterministic trend specifications of the storage model. For a Bayesian posterior analysis of the SSM, which is nonlinear in the latent states, we used a Markov chain Monte Carlo algorithm based on the particle marginal Metropolis–Hastings approach. An empirical application to four commodity markets showed that the stochastic trend SSM is favored over deterministic trend specifications. The stochastic trend SSM identifies structural parameters that differ from those for deterministic trend specifications. In particular, the estimated price elasticities of demand are typically larger under the stochastic trend SSM.
Prediction and evaluation in College Hockey using the Bradley–Terry–Zermelo model

We describe the application of the Bradley-Terry model to NCAA Division I Men's Ice Hockey. A Bayesian construction gives a joint posterior probability distribution for the log-strength parameters, given a set of game results and a choice of prior distribution. For several suitable choices of prior, it is straightforward to find the maximum a posteriori point (MAP) and a Hessian matrix, allowing a Gaussian approximation to be constructed. Posterior predictive probabilities can be estimated by 1) setting the log-strengths to their MAP values, 2) using the Gaussian approximation for analytical or Monte Carlo integration, or 3) applying importance sampling to re-weight the results of a Monte Carlo simulation. We define a method to evaluate any models which generate predicted probabilities for future outcomes, using the Bayes factor given the actual outcomes, and apply it to NCAA tournament results. Finally, we describe an on-line tool which currently estimates probabilities of future results using MAP evaluation and describe how it can be refined using the Gaussian approximation or importance sampling.
Landau equation and QCD sum rules for the tetraquark molecular states

The quarks and gluons are confined objects; they cannot be put on the mass shell. It is questionable to apply the Landau equation to study the Feynman diagrams in the QCD sum rules. Furthermore, we carry out the operator product expansion in the deep Euclidean region p2→-∞, where the Landau singularities cannot exist. The Landau equation servers as a kinematical equation in the momentum space and is independent on the factorizable and nonfactorizable properties of the Feynman diagrams in the color space. The meson-meson scattering state and tetraquark molecular state both have four valence quarks, which form two color-neutral clusters; we cannot distinguish the contributions based on the two color-neutral clusters in the factorizable Feynman diagrams. Lucha et al. assert that the contributions at the order O(αsk) with k≤1 in the operator product expansion, which are factorizable in the color space, are exactly canceled out by the meson-meson scattering states at the hadron side, and the tetraquark molecular states begin to receive contributions at the order O(αs2). Such an assertion is questionable, we refute the assertion in details, and choose an axial-vector current and a tensor current to examine the outcome of the assertion. After a detailed analysis, we observe that the meson-meson scattering states cannot saturate the QCD sum rules, while the tetraquark molecular states can saturate the QCD sum rules. The Landau equation is of no use to study the Feynman diagrams in the QCD sum rules for the tetraquark molecular states; the tetraquark molecular states begin to receive contributions at the order O(αs0/αs1) rather than at the order O(αs2).
Extraction of Relevant Images for Boilerplate Removal in Web Browsers

Boilerplate refers to unwanted and repeated parts of a webpage (such as ads or table of contents) that distracts the user from reading the core content of the webpage, such as a news article. Accurate detection and removal of boilerplate content from a webpage can enable the users to have a clutter free view of the webpage or news article. This can be useful in features like reader mode in web browsers. Current implementations of reader mode in web browsers such as Firefox, Chrome and Edge perform reasonably well for textual content in webpages. However, they are mostly heuristic based and not flexible when the webpage content is dynamic. Also they often do not perform well for removing boilerplate content in the form of images and multimedia in webpages. For detection of boilerplate images, one needs to have knowledge of the actual layout of the images in the webpage, which is only possible when the webpage is rendered. In this paper we discuss some of the issues in relevant image extraction. We also present the design of a testing framework to measure accuracy and a classifier to extract relevant images by leveraging a headless browser solution that gives the rendering information for images.
Maximal and Frattini L-subgroups of an L-group

In this paper, the concept of a maximal L-subgroup of an L-group has been defined in the spirit of classical group theory. Then, a level subset characterization has been established for the same. Then, this notion of maximal L-subgroups has been used to define Frattini L-subgroups. Further, the concept of non-generators of an L-group has been developed and its relation with the Frattini L-subgroup of an L-group has been established like their classical counterparts. Moreover, several properties pertaining to the concepts of maximal L-subgroups and Frattini L-subgroup have also been investigated. These two notions have been illustrated through several examples.
Unsupervised Any-to-Many Audiovisual Synthesis via Exemplar Autoencoders

We present an unsupervised approach that enables us to convert the speech input of any one individual to an output set of potentially-infinitely many speakers. One can stand in front of a mic and be able to make their favorite celebrity say the same words. Our approach builds on simple autoencoders that project out-of-sample data to the distribution of the training set (motivated by PCA/linear autoencoders). We use an exemplar autoencoder to learn the voice and specific style (emotions and ambiance) of a target speaker. In contrast to existing methods, the proposed approach can be easily extended to an arbitrarily large number of speakers in a very little time using only two-three minutes of audio data from a speaker. We also exhibit the usefulness of our approach for generating video from audio signals and vice-versa. We suggest the reader to check out our project webpage for various synthesized examples: this https URL
The Two-Pass Softmax Algorithm

The softmax (also called softargmax) function is widely used in machine learning models to normalize real-valued scores into a probability distribution. To avoid floating-point overflow, the softmax function is conventionally implemented in three passes: the first pass to compute the normalization constant, and two other passes to compute outputs from normalized inputs. We analyze two variants of the Three-Pass algorithm and demonstrate that in a well-optimized implementation on HPC-class processors performance of all three passes is limited by memory bandwidth. We then present a novel algorithm for softmax computation in just two passes. The proposed Two-Pass algorithm avoids both numerical overflow and the extra normalization pass by employing an exotic representation for intermediate values, where each value is represented as a pair of floating-point numbers: one representing the "mantissa" and another representing the "exponent". Performance evaluation demonstrates that on out-of-cache inputs on an Intel Skylake-X processor the new Two-Pass algorithm outperforms the traditional Three-Pass algorithm by up to 28% in AVX512 implementation, and by up to 18% in AVX2 implementation. The proposed Two-Pass algorithm also outperforms the traditional Three-Pass algorithm on Intel Broadwell and AMD Zen 2 processors. To foster reproducibility, we released an open-source implementation of the new Two-Pass Softmax algorithm and other experiments in this paper as a part of XNNPACK library at GitHub.com/google/XNNPACK.
Nested Polyhedra and Indices of Orbits of Coxeter Groups of Non-Crystallographic Type

The invariants of finite-dimensional representations of simple Lie algebras, such as even-degree indices and anomaly numbers, are considered in the context of the non-crystallographic finite reflection groups H2, H3 and H4. Using a representation-orbit replacement, the definitions and properties of the indices are formulated for individual orbits of the examined groups. The indices of orders two and four of the tensor product of k orbits are determined. Using the branching rules for the non-crystallographic Coxeter groups, the embedding index is defined similarly to the Dynkin index of a representation. Moreover, since the definition of the indices can be applied to any orbit of non-crystallographic type, the algorithm allowing to search for the orbits of smaller radii contained within any considered one is presented for the Coxeter groups H2 and H3. The geometrical structures of nested polytopes are exemplified.
The LOFAR Tied-Array all-sky survey: Timing of 21 pulsars including the first binary pulsar discovered with LOFAR

We report on the multifrequency timing observations of 21 pulsars discovered in the LOFAR Tied-Array All-Sky Survey (LOTAAS). The timing data were taken at central frequencies of 149 MHz (LOFAR) as well as 334 and 1532 MHz (Lovell Telescope). The sample of pulsars includes 20 isolated pulsars and the first binary pulsar discovered by the survey, PSR J1658+3630. We modelled the timing properties of the pulsars, which showed that they have, on average, larger characteristic ages. We present the pulse profiles of the pulsars across the three observing bands, where PSR J1643+1338 showed profile evolution that appears not to be well-described by the radius-to-frequency-mapping model. Furthermore, we modelled the spectra of the pulsars across the same observing bands, using a simple power law, and found an average spectral index of −1.9 ± 0.5. Amongst the pulsars studied here, PSR J1657+3304 showed large flux density variations of a factor of 10 over 300 d, as well as mode changing and nulling on time-scales of a few minutes. We modelled the rotational and orbital properties of PSR J1658+3630, which has a spin period of 33 ms in a binary orbit of 3.0 d with a companion of minimum mass of 0.87 M⊙, likely a carbon–oxygen or oxygen–neon–magnesium type white dwarf. PSR J1658+3630 has a dispersion measure of 3.0 pc cm−3, making it possibly one of the closest binary pulsars known.
Communications-Caching-Computing Tradeoff Analysis for Bidirectional Data Computation in Mobile Edge Networks

With the advent of the modern mobile traffic, e.g., online gaming, augmented reality delivery and etc., a novel bidirectional computation task model where the input data of each task consists of two parts, one generated at the mobile device in real-time and the other originated from the Internet proactively, is emerging as an important use case of 5G. In this paper, for ease of analytical analysis, we consider the homogeneous bidirectional computation task model in a mobile edge network which consists of one mobile edge computing (MEC) server and one mobile device, both enabled with computing and caching capabilities. Each task can be served via three mechanisms, i.e., local computing with local caching, local computing without local caching and computing at the MEC server. To minimize the average bandwidth, we formulate the joint caching and computing optimization problem under the latency, cache size and average power constraints. We derive the closed-form expressions for the optimal policy and the minimum bandwidth. The tradeoff among communications, computing and caching is illustrated both analytically and numerically, which provides insightful guideline for the network designers.
On the synthesis of control policies from noisy example datasets: a probabilistic approach

In this note we consider the problem of synthesizing optimal control policies for a system from noisy datasets. We present a novel algorithm that takes as input the available dataset and, based on these inputs, computes an optimal policy for possibly stochastic and nonlinear systems that also satisfies actuation constraints. The algorithm relies on solid theoretical foundations, which have their key roots into a probabilistic interpretation of dynamical systems. The effectiveness of our approach is illustrated by considering an autonomous car use case. For such use case, we make use of our algorithm to synthesize a control policy from noisy data allowing the car to merge onto an intersection, while satisfying additional constraints on the variance of the car speed
Experimental Determination of an Extended DC Servo-Motor State Space Model: An Undergraduate Experiment

State space systems and experimental system identification are essential components of control education. Early introduction of these tools to the curriculum of a control laboratory in an interconnected and accessible manner that does not over-dilute the concepts is important in two ways. First, it facilitates a student’s transition to more senior and graduate level control concepts. It also provides an effective link to industrial applications. This paper provides two novel experimental procedures for directly identifying the state space model of a DC motor in an undergraduate control laboratory. The procedures do not require any specialized hardware and can be performed using standard laboratory equipment. They also do not place any simplifying assumptions on the motor’s model. The first procedure is based on direct pseudo inversion of the state space model. It does not require advanced knowledge of the state space approach or signal filtering. It is easy to understand and suits a first control laboratory. The second procedure is more efficient. It is based on the Markov approach commonly used for realizing, indirectly, a state space model from an estimated transfer function. While the procedure is designed for use by undergraduate students, it requires relatively advanced knowledge in state space that makes it suitable for a second undergraduate control laboratory.
Emission tomography with a multi-bang assumption on attenuation

We consider the problem of joint reconstruction of both attenuation $a$ and source density $f$ in emission tomography in two dimensions. This is sometimes called the Single Photon Emission Computed Tomography (SPECT) identification problem, or referred to as attenuation correction in SPECT. Assuming that $a$ takes only finitely many values and $f \in C_c^1(\mathbb{R}^2)$ we are able to characterise singularities appearing in the Attenuated Radon Transform $R_a f$, which models emission tomography data. Using this characterisation we prove that both $a$ and $f$ can be determined in some circumstances. We also propose a numerical algorithm to jointly compute $a$ and $f$ from $R_af$ based on a weakly convex regularizer when $a$ only takes values from a known finite list, and show that this algorithm performs well on some synthetic examples.
Generalized Linear Models for Longitudinal Data with Biased Sampling Designs: A Sequential Offsetted Regressions Approach

Biased sampling designs can be highly efficient when studying rare (binary) or low variability (continuous) endpoints. We consider longitudinal data settings in which the probability of being sampled depends on a repeatedly measured response through an outcome-related, auxiliary variable. Such auxiliary variable- or outcome-dependent sampling improves observed response and possibly exposure variability over random sampling, {even though} the auxiliary variable is not of scientific interest. {For analysis,} we propose a generalized linear model based approach using a sequence of two offsetted regressions. The first estimates the relationship of the auxiliary variable to response and covariate data using an offsetted logistic regression model. The offset hinges on the (assumed) known ratio of sampling probabilities for different values of the auxiliary variable. Results from the auxiliary model are used to estimate observation-specific probabilities of being sampled conditional on the response and covariates, and these probabilities are then used to account for bias in the second, target population model. We provide asymptotic standard errors accounting for uncertainty in the estimation of the auxiliary model, and perform simulation studies demonstrating substantial bias reduction, correct coverage probability, and improved design efficiency over simple random sampling designs. We illustrate the approaches with two examples.