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Rajat Mittal

Publications and source records attributed to Rajat Mittal.

At least 37 records · Page 2Linked to original sources

Approximate Degree Composition for Recursive Functions

Determining the approximate degree composition for Boolean functions remains a significant unsolved problem in Boolean function complexity. In recent decades, researchers have concentrated on proving that approximate degree composes for special types of inner and outer functions. An important and extensively studied class of functions are the recursive functions, i.e.~functions obtained by composing a base function with itself a number of times. Let $h^d$ denote the standard $d$-fold composition of the base function $h$. The main result of this work is to show that the approximate degree composes if either of the following conditions holds: (I) The outer function $f:\{0,1\}^n\to \{0,1\}$ is a recursive function of the form $h^d$, with $h$ being any base function and $d= Ω(\log\log n)$. (II) The inner function is a recursive function of the form $h^d$, with $h$ being any constant arity base function (other than AND and OR) and $d= Ω(\log\log n)$, where $n$ is the arity of the outer function. In terms of proof techniques, we first observe that the lower bound for composition can be obtained by introducing majority in between the inner and the outer functions. We then show that majority can be \emph{efficiently eliminated} if the inner or outer function is a recursive function.

cs.CC↗

Hydrodynamically Beneficial School Configurations in Carangiform Swimmers: Insights from a Flow-Physics Informed Model

Researchers have long debated which spatial arrangements and swimming synchronizations are beneficial for the hydrodynamic performance of fish in schools. In our previous work (Seo and Mittal, Bioinsp. Biomim., Vol. 17, 066020, 2022), we demonstrated using direct numerical simulations that hydrodynamic interactions with the wake of a leading body-caudal fin carangiform swimmer could significantly enhance the swimming performance of a trailing swimmer by augmenting the leading-edge vortex (LEV) on its caudal fin. In this study, we develop a model based on the phenomenology of LEV enhancement, which utilizes wake velocity data from direct numerical simulations of a leading fish to predict the trailing swimmer's hydrodynamic performance without additional simulations. This approach enables a comprehensive analysis of the effects of relative positioning, phase difference, flapping amplitude, Reynolds number, and the number of swimmers in the school on thrust enhancement. The results offer several insights regarding the effect of these parameters that have implications for fish schools as well as for bio-inspired underwater vehicle applications.

physics.flu-dyn↗

Modeling the Effect of Sleeve Gastrectomy on Gastric Digestion in the Stomach: Insights from Multiphase Flow Modeling

The geometry and motility of the stomach play a critical role in the digestion of ingested liquid meals. Sleeve gastrectomy, a common type of bariatric surgery used to reduce the size of the stomach, significantly alters the stomach's anatomy and motility, which impacts gastric emptying and digestion. In this study, we use an imaging data-based computational model, StomachSim, to investigate the consequences of sleeve gastrectomy. The pre-operative stomach anatomy was derived from imaging data and the post-sleeve gastrectomy shapes were generated for different resection volumes. We investigate the effect of sleeve sizes and motility patterns on gastric mixing and emptying. Simulations were conducted using an immersed-boundary flow solver, modeling a liquid meal to analyze changes in gastric content mixing and emptying rates. The results reveal that different degrees of volume reduction and impaired gastric motility have complex effects on stomach's mixing and emptying functions, which are important factors in gastric health of the patient. These findings provide insights into the biomechanical effects of sleeve gastrectomy on gastric digestion and emptying functions, highlighting the potential of computational models to inform surgical planning and post-operative management.

physics.flu-dyn↗

Modeling the effect of hydrodynamic wakes in dynamical models of large-scale fish schools

A novel model of the wake of swimming fish is developed and incorporated into a dynamical model of a fish school to explore the effect of hydrodynamics on the emergent behavior in schooling fish. The model incorporates well-established rules for attraction, alignment, and visual detection via a force-momentum balance in the surge, sway, and yaw directions, thereby allowing us to include the effects of body size, shape, and inertia in to the dynamics of fish motion. The key novelty of the model lies in the modeling of the hydrodynamics, which includes not only the potential flow induced by the body of the fish but also the vortex wakes generated by the fish. These hydrodynamic features, as well as the surge, sway, and yaw force coefficients, are parameterized via three-dimensional high-fidelity direct numerical simulations of a carangiform swimmer, thereby enabling a higher degree of realism in these models. The model is used to examine the effect of wake characteristics on the topology and movement of fish schools. The simulations indicate that these wake vortices lead to improved organization within the schools, especially in situations where the social forces are relatively weak.

physics.flu-dyn↗

Relations between monotone complexity measures based on decision tree complexity

In a recent result, Knop, Lovett, McGuire and Yuan (STOC 2021) proved the log-rank conjecture for communication complexity, up to log n factor, for any Boolean function composed with AND function as the inner gadget. One of the main tools in this result was the relationship between monotone analogues of well-studied Boolean complexity measures like block sensitivity and certificate complexity. The relationship between the standard measures has been a long line of research, with a landmark result by Huang (Annals of Mathematics 2019), finally showing that sensitivity is polynomially related to all other standard measures. In this article, we study the monotone analogues of standard measures like block sensitivity (mbs(f)), certificate complexity (MCC(f)) and fractional block sensitivity (fmbs(f)); and study the relationship between these measures given their connection with AND-decision tree and sparsity of a Boolean function. We show the following results: 1) Given a Boolean function $f : \{0, 1\}^{n} \rightarrow \{0, 1\}$, the ratio $fmbs(f^l )/mbs(f^l )$ is bounded by a function of n (and not l). A similar result was known for the corresponding standard measures (Tal, ITCS 2013). This result allows us to extend any upper bound by a well behaved measure on monotone block sensitivity to monotone fractional block sensitivity. 2) The question of the best possible upper bound on monotone block sensitivity by the logarithm of sparsity is equivalent to the natural question of best upper bound by degree on sensitivity. One side of this relationship was used in the proof by Knop, Lovett, McGuire and Yuan (STOC 2021). 3) For two natural classes of functions, symmetric and monotone, hitting set complexity (MCC) is equal to monotone sensitivity.

cs.CC↗

On query complexity measures and their relations for symmetric functions

The main reason for query model's prominence in complexity theory and quantum computing is the presence of concrete lower bounding techniques: polynomial and adversary method. There have been considerable efforts to give lower bounds using these methods, and to compare/relate them with other measures based on the decision tree. We explore the value of these lower bounds on quantum query complexity and their relation with other decision tree based complexity measures for the class of symmetric functions, arguably one of the most natural and basic sets of Boolean functions. We show an explicit construction for the dual of the positive adversary method and also of the square root of private coin certificate game complexity for any total symmetric function. This shows that the two values can't be distinguished for any symmetric function. Additionally, we show that the recently introduced measure of spectral sensitivity gives the same value as both positive adversary and approximate degree for every total symmetric Boolean function. Further, we look at the quantum query complexity of Gap Majority, a partial symmetric function. It has gained importance recently in regard to understanding the composition of randomized query complexity. We characterize the quantum query complexity of Gap Majority and show a lower bound on noisy randomized query complexity (Ben-David and Blais, FOCS 2020) in terms of quantum query complexity. Finally, we study how large certificate complexity and block sensitivity can be as compared to sensitivity for symmetric functions (even up to constant factors). We show tight separations, i.e., give upper bounds on possible separations and construct functions achieving the same.

quant-ph↗

3D audio-visual recordings of mosquito wings for aeroacoustic simulation

Mosquito acoustic communication is studied for its singular and poorly-known in-flight hearing mechanism, for its efficiency in mechanical-to-acoustical power transduction, as well as for being the deadliest disease vector. A combined computational and experimental methods to predict and extract the wing-tone sound from individual tethered or free-flying mosquitoes was developed. This paper describes the experimental methods and gives some preliminary results of the simulations. Simultaneous slow-motion images (20k fps) and 3D-sound of Culex quinquefasciatus mosquitoes were recorded. The sound map around the mosquitoes was recorded in one or two planes with a rotating array of 12-microphones. Backilluminated mosquito-wings allowed to extract 11 veincrossing locations on each high-speed camera image over 3-4 wingbeat periods to generate 3D deformations of the wing. Simultaneous 3D sound data recorded by microphone arrays were post-processed by using the physicsbased independent component analysis to filter out the noise and generate a 3D sound map. The simulated wingtone sound pattern generated from the aeroacoustic simulation agrees well with the original recording in the experiment using the microphone array. The methods we developed will allow us to investigate the wing-tone soundscape of individual mosquitoes during the courtship and mate-chasing.

physics.bio-ph↗

Force moment partitioning and scaling analysis of vortices shed by a 2D pitching wing in quiescent fluid

We experimentally study the dynamics and strength of vortices shed from a NACA 0012 wing undergoing sinusoidal pitching in quiescent water. We characterize the temporal evolution of the vortex trajectory and circulation over a range of pitching frequencies, amplitudes and pivot locations. By employing a physics-based force and moment partitioning method (FMPM), we estimate the vortex-induced aerodynamic moment from the velocity fields measured using particle image velocimetry. The vortex circulation, formation time and vorticity-induced moment are shown to follow scaling laws based on the feeding shear-layer velocity. The vortex dynamics, together with the spatial distribution of the vorticity-induced moment, provide quantitative explanations for the nonlinear behaviors observed in the fluid damping (Zhu et al., J. Fluid Mech., vol. 923, 2021, R2). The FMPM-estimated moment and damping are shown to match well in trend with direct force measurements, despite a discrepancy in magnitude. Our results demonstrate the powerful capability of the FMPM in dissecting experimental flow field data and providing valuable insights into the underlying flow physics.

physics.flu-dyn↗

On the Composition of Randomized Query Complexity and Approximate Degree

For any Boolean functions $f$ and $g$, the question whether $R(f\circ g) = \tildeΘ(R(f)R(g))$, is known as the composition question for the randomized query complexity. Similarly, the composition question for the approximate degree asks whether $\widetilde{deg}(f\circ g) = \tildeΘ(\widetilde{deg}(f)\cdot\widetilde{deg}(g))$. These questions are two of the most important and well-studied problems, and yet we are far from answering them satisfactorily. It is known that the measures compose if one assumes various properties of the outer function $f$ (or inner function $g$). This paper extends the class of outer functions for which $\text{R}$ and $\widetilde{\text{deg}}$ compose. A recent landmark result (Ben-David and Blais, 2020) showed that $R(f \circ g) = Ω(noisyR(f)\cdot R(g))$. This implies that composition holds whenever $noisyR(f) = \TildeΘ(R(f))$. We show two results: (1)When $R(f) = Θ(n)$, then $noisyR(f) = Θ(R(f))$. (2) If $\text{R}$ composes with respect to an outer function, then $\text{noisyR}$ also composes with respect to the same outer function. On the other hand, no result of the type $\widetilde{deg}(f \circ g) = Ω(M(f) \cdot \widetilde{deg}(g))$ (for some non-trivial complexity measure $M(\cdot)$) was known to the best of our knowledge. We prove that $\widetilde{deg}(f\circ g) = \widetildeΩ(\sqrt{bs(f)} \cdot \widetilde{deg}(g)),$ where $bs(f)$ is the block sensitivity of $f$. This implies that $\widetilde{\text{deg}}$ composes when $\widetilde{\text{deg}}(f)$ is asymptotically equal to $\sqrt{\text{bs}(f)}$. It is already known that both $\text{R}$ and $\widetilde{\text{deg}}$ compose when the outer function is symmetric. We also extend these results to weaker notions of symmetry with respect to the outer function.

cs.CC↗

Improving Zero-Shot Detection of Low Prevalence Chest Pathologies using Domain Pre-trained Language Models

Recent advances in zero-shot learning have enabled the use of paired image-text data to replace structured labels, replacing the need for expert annotated datasets. Models such as CLIP-based CheXzero utilize these advancements in the domain of chest X-ray interpretation. We hypothesize that domain pre-trained models such as CXR-BERT, BlueBERT, and ClinicalBERT offer the potential to improve the performance of CLIP-like models with specific domain knowledge by replacing BERT weights at the cost of breaking the original model's alignment. We evaluate the performance of zero-shot classification models with domain-specific pre-training for detecting low-prevalence pathologies. Even though replacing the weights of the original CLIP-BERT degrades model performance on commonly found pathologies, we show that pre-trained text towers perform exceptionally better on low-prevalence diseases. This motivates future ensemble models with a combination of differently trained language models for maximal performance.

physics.med-ph↗

Contribution of spanwise and cross-span vortices to the lift generation of low-aspect-ratio wings: Insights from force partitioning

This study reports on the vortex-induced lift production mechanisms in low Reynolds number flows over low aspect-ratio rectangular wings. We use a rigorous force partitioning method which allows for the estimation of the pressure-induced aerodynamic loads due to distinct flow features or vortex structures in the flow around the wing. The specific focus of this work is on distinguishing the effect of spanwise and cross-span oriented vortex structures on pressure-induced lift production. We quantify the lift induced on the wing by these different vortices, and also estimate their influence within different regions of the flow-field around the wing and in the wake. By varying the aspect-ratio and angle-of-attack of the wing, we show that for most cases, the spanwise oriented vorticity contributes less to the total lift than cross-span oriented vortices. Furthermore, the spanwise vorticity in the near wake is capable of producing net negative lift on the wing and this is explained by separating and quantifying the influence of vortex cores and regions of strain in the wake. The results demonstrate the utility of the force partitioning method for dissecting the flow physics of vortex dominated flows.

physics.flu-dyn↗

Effect of Antral Motility on Food Hydrolysis and Gastric Emptying from the Stomach: Insights from Computational Models

The peristaltic motion of the stomach walls combines with the secretion of enzymes to initiate the process that breaks down food. Computational modelling of this phenomenon can help reveal the details that would be hard to capture via in-vivo or in-vitro means. In this study, the digestion of a liquid meal containing protein is simulated in a human-stomach model based on imaging data. Pepsin, the gastric enzyme for protein hydrolysis, is secreted from the proximal region of the stomach walls and allowed to react with the contents of the stomach. The jet velocities, the emptying rate, and the extent of hydrolysis are quantified for a control case, and also for three other cases of reduced motility with varying peristaltic amplitudes. The findings quantify the effect of motility on the rate of food breakdown and emptying, and correlate the observations with the mixing in the stomach induced by the antral contraction waves.

physics.flu-dyn↗

A Joint Learning Approach for Semi-supervised Neural Topic Modeling

Topic models are some of the most popular ways to represent textual data in an interpret-able manner. Recently, advances in deep generative models, specifically auto-encoding variational Bayes (AEVB), have led to the introduction of unsupervised neural topic models, which leverage deep generative models as opposed to traditional statistics-based topic models. We extend upon these neural topic models by introducing the Label-Indexed Neural Topic Model (LI-NTM), which is, to the extent of our knowledge, the first effective upstream semi-supervised neural topic model. We find that LI-NTM outperforms existing neural topic models in document reconstruction benchmarks, with the most notable results in low labeled data regimes and for data-sets with informative labels; furthermore, our jointly learned classifier outperforms baseline classifiers in ablation studies.

cs.IR↗

Computational model of drug dissolution in the stomach: effects of posture and gastroparesis on drug bioavailability

The oral route is the most common choice for drug administration because of convenience, low cost, and high patient compliance, but is also a complex route. The rate of dissolution and gastric emptying of the dissolved active pharmaceutical ingredient (API) into the duodenum is modulated by factors such as gastric motility, but current in-vitro procedures for assessing drug dissolution are limited in their ability to recapitulate this process. This is particularly relevant for disease conditions, such as gastroparesis, that alter the anatomy and/or physiology of the stomach. In this study we employ a biomimetic in-silico simulator based on the realistic anatomy and morphology of the stomach, to investigate the effect of body posture and stomach motility on drug bioavailability. The simulations show that changes in posture can have a significant (up to 83%) effect on the emptying rate of the API into the duodenum. Similarly, reduction in antral contractility associated with gastroparesis can also significantly reduce the dissolution of the pill as well as emptying of the API into the duodenum. The simulations show that for an equivalent motility index, reduction in gastric emptying due to neuropathic gastroparesis is larger by a factor of about five compared to myopathic gastroparesis.

physics.bio-ph↗

Tight Chang's-lemma-type bounds for Boolean functions

Chang's lemma (Duke Mathematical Journal, 2002) is a classical result with applications across several areas in mathematics and computer science. For a Boolean function $f$ that takes values in {-1,1} let $r(f)$ denote its Fourier rank. For each positive threshold $t$, Chang's lemma provides a lower bound on $wt(f):=\Pr[f(x)=-1]$ in terms of the dimension of the span of its characters with Fourier coefficients of magnitude at least $1/t$. We examine the tightness of Chang's lemma w.r.t. the following three natural settings of the threshold: - the Fourier sparsity of $f$, denoted $k(f)$, - the Fourier max-supp-entropy of $f$, denoted $k'(f)$, defined to be $\max \{1/|\hat{f}(S)| : \hat{f}(S) \neq 0\}$, - the Fourier max-rank-entropy of $f$, denoted $k''(f)$, defined to be the minimum $t$ such that characters whose Fourier coefficients are at least $1/t$ in absolute value span a space of dimension $r(f)$. We prove new lower bounds on $wt(f)$ in terms of these measures. One of our lower bounds subsumes and refines the previously best known upper bound on $r(f)$ in terms of $k(f)$ by Sanyal (ToC, 2019). Another lower bound is based on our improvement of a bound by Chattopadhyay, Hatami, Lovett and Tal (ITCS, 2019) on the sum of the absolute values of the level-$1$ Fourier coefficients. We also show that Chang's lemma for the these choices of the threshold is asymptotically outperformed by our bounds for most settings of the parameters involved. Next, we show that our bounds are tight for a wide range of the parameters involved, by constructing functions (which are modifications of the Addressing function) witnessing their tightness. Finally we construct Boolean functions $f$ for which - our lower bounds asymptotically match $wt(f)$, and - for any choice of the threshold $t$, the lower bound obtained from Chang's lemma is asymptotically smaller than $wt(f)$.

cs.CC↗

Significance of the strain-dominated region around a vortex on induced aerodynamic loads

The ability of vortices to induce aerodynamic loads on proximal surfaces plays a significant role in a wide variety of flows. However, most studies of vortex-induced effects primarily focus on analyzing the influence of the rotation-dominated cores of vortices. In this work, we show that not only are vortices in viscous flows surrounded by strain-dominated regions, but that these regions are dynamically important and can sometimes even dictate the induced aerodynamic loads. We demonstrate this for a pitching airfoil, which exhibits dynamic stall and generates several force-inducing vortices. Using a data-driven force partitioning method, we quantify the influence of vortices as well as vortex-associated strain to show that our current understanding of vortex-dominated phenomena, such as dynamic stall, is incomplete without considering the substantial effect of strain-dominated regions that are associated with vortices.

physics.flu-dyn↗

On algorithms to find p-ordering

The concept of p-ordering for a prime p was introduced by Manjul Bhargava (in his PhD thesis) to develop a generalized factorial function over an arbitrary subset of integers. This notion of p-ordering provides a representation of polynomials modulo prime powers, and has been used to prove properties of roots sets modulo prime powers. We focus on the complexity of finding a p-ordering given a prime p, an exponent k and a subset of integers modulo p^k. Our first algorithm gives a p-ordering for set of size n in time O(nk\log p), where set is considered modulo p^k. The subsets modulo p^k can be represented succinctly using the notion of representative roots (Panayi, PhD Thesis, 1995; Dwivedi et.al, ISSAC, 2019); a natural question would be, can we find a p-ordering more efficiently given this succinct representation. Our second algorithm achieves precisely that, we give a p-ordering in time O(d^2k\log p + nk \log p + nd), where d is the size of the succinct representation and n is the required length of the p-ordering. Another contribution that we make is to compute the structure of roots sets for prime powers p^k, when k is small. The number of root sets have been given in the previous work (Dearden and Metzger, Eur. J. Comb., 1997; Maulick, J. Comb. Theory, Ser. A, 2001), we explicitly describe all the root sets for p^2, p^3 and p^4.

math.NT↗

Quantitative analysis of the kinematics and induced aerodynamic loading of individual vortices in vortex-dominated flows: a computation and data-driven approach

A physics-based data-driven computational framework for the quantitative analysis of vortex kinematics and vortex-induced loads in vortex-dominated problems is presented. Such flows are characterized by the dominant influence of a small number of vortex structures, but the complexity of these flows makes it difficult to conduct a quantitative analysis of this influence at the level of individual vortices. The method presented here combines machine learning-inspired clustering methods with a rigorous mathematical partitioning of aerodynamic loads to enable detailed quantitative analysis of vortex kinematics and vortex-induced aerodynamic loads. We demonstrate the utility of this approach by applying it to an ensemble of 165 distinct Navier-Stokes simulations of flow past a sinusoidally pitching airfoil. Insights enabled by the current methodology include the identification of a period-doubling route to chaos in this flow, and the precise quantification of the role that leading-edge vortices play in driving aeroelastic pitch oscillations.

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