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Topological effects manifest in a variety of lattice geometries. While square lattices, due to their simplicity, have been used for models supporting nontrivial topology, several exotic topological phenomena such as Dirac points, Weyl points and Haldane phases are most commonly supported by non-square lattices. Examples of prototypical non-square lattices include the honeycomb lattice of graphene and the Kagome lattice, both of which break fundamental symmetries and can exhibit quantized transport, especially when long-range hoppings and gauge fields are incorporated. The challenge of controllably realizing long-range hoppings and gauge fields has motivated a large body of research focused on harnessing lattices encoded in "synthetic" dimensions. Photons in particular have many internal degrees of freedom and hence show promise for implementing these synthetic dimensions; however, most photonic synthetic dimensions has hitherto created 1D or 2D square lattices. Here we show that non-square lattice Hamiltonians can be implemented using Floquet synthetic dimensions. Our construction uses dynamically modulated ring resonators and provides the capacity for direct $k$-space engineering of lattice Hamiltonians. Such a construction lifts constraints on the orthogonality of lattice vectors that make square geometries simpler to implement, and instead transfers the complexity to the engineering of complex Floquet drive signals. We simulate topological signatures of the Haldane and the brick-wall Haldane model and observe them to be robust in the presence of external optical drive and photon loss, and discuss unique characteristics of their topological transport when implemented on these Floquet lattices. Our proposal demonstrates the potential of driven-dissipative Floquet synthetic dimensions as a new architecture for $k$-space Hamiltonian simulation of high-dimensional lattice geometries.

We consider the problems of testing and learning an unknown $n$-qubit Hamiltonian $H$ from queries to its evolution operator $e^{-iHt}$ under the normalized Frobenius norm. We prove: 1. Local Hamiltonians: We give a tolerant testing protocol to decide if $H$ is $\epsilon_1$-close to $k$-local or $\epsilon_2$-far from $k$-local, with $O(1/(\epsilon_2-\epsilon_1)^{4})$ queries, solving open questions posed in a recent work by Bluhm et al. For learning a $k$-local $H$ up to error $\epsilon$, we give a protocol with query complexity $\exp(O(k^2+k\log(1/\epsilon)))$ independent of $n$, by leveraging the non-commutative Bohnenblust-Hille inequality. 2. Sparse Hamiltonians: We give a protocol to test if $H$ is $\epsilon_1$-close to being $s$-sparse (in the Pauli basis) or $\epsilon_2$-far from being $s$-sparse, with $O(s^{6}/(\epsilon_2^2-\epsilon_1^2)^{6})$ queries. For learning up to error $\epsilon$, we show that $O(s^{4}/\epsilon^{8})$ queries suffice. 3. Learning without memory: The learning results stated above have no dependence on $n$, but require $n$-qubit quantum memory. We give subroutines that allow us to learn without memory; increasing the query complexity by a $(\log n)$-factor in the local case and an $n$-factor in the sparse case. 4. Testing without memory: We give a new subroutine called Pauli hashing, which allows one to tolerantly test $s$-sparse Hamiltonians with $O(s^{14}/(\epsilon_2^2-\epsilon_1^2)^{18})$ queries. A key ingredient is showing that $s$-sparse Pauli channels can be tolerantly tested under the diamond norm with $O(s^2/(\epsilon_2-\epsilon_1)^6)$ queries. Along the way, we prove new structural theorems for local and sparse Hamiltonians. We complement our learning results with polynomially weaker lower bounds. Furthermore, our algorithms use short time evolutions and do not assume prior knowledge of the terms in the support of the Pauli spectrum of $H$., Comment: 45 pages. This work subsumes a prior work by the third author (arXiv:2404.06282)

We show an improved inverse theorem for the Gowers-$3$ norm of $n$-qubit quantum states $|\psi\rangle$ which states that: for every $\gamma\geq 0$, if the $\textsf{Gowers}(|\psi \rangle,3)^8 \geq \gamma$ then the stabilizer fidelity of $|\psi\rangle$ is at least $\gamma^C$ for some constant $C>1$. This implies a constant-sample polynomial-time tolerant testing algorithm for stabilizer states which accepts if an unknown state is $\varepsilon_1$-close to a stabilizer state in fidelity and rejects when $|\psi\rangle$ is $\varepsilon_2 \leq \varepsilon_1^C$-far from all stabilizer states, promised one of them is the case., Comment: 6 pages

In this work, we present compelling evidence that controlling model capacity during fine-tuning can effectively mitigate memorization in diffusion models. Specifically, we demonstrate that adopting Parameter-Efficient Fine-Tuning (PEFT) within the pre-train fine-tune paradigm significantly reduces memorization compared to traditional full fine-tuning approaches. Our experiments utilize the MIMIC dataset, which comprises image-text pairs of chest X-rays and their corresponding reports. The results, evaluated through a range of memorization and generation quality metrics, indicate that PEFT not only diminishes memorization but also enhances downstream generation quality. Additionally, PEFT methods can be seamlessly combined with existing memorization mitigation techniques for further improvement. The code for our experiments is available at: https://github.com/Raman1121/Diffusion_Memorization_HPO, Comment: Accepted at the GenLaw (Generative AI + Law) workshop at ICML'24

The role of asymmetric nuclear medium on the properties of kaon is investigated at zero and finite temperature employing a hybrid approach integrating the light cone quark model (LCQM) and the chiral SU(3) quark mean field (CQMF) model. The in-medium quark masses are calculated within the CQMF model and are used as inputs to study the medium modifications in the kaon properties. In particular, we have analysed the impact of baryonic density, isospin asymmetry and temperature on the weak decay constant, distribution amplitudes (DAs) and parton quark distributions (PDFs) of valence quark structure of kaons. The effects of isospin asymmetry on the kaon doublet $K =\left(\begin{array}{c} K^{+} \\ K^{0} \end{array} \right)$ and antikaon doublet $\bar{K}$= ($K^-, \bar{K}^0$) are also studied. In order to compare with future experiments, we have also evolved the in-medium DAs and PDFs of kaons to $Q^2=16$ GeV$^2$. As compared to the temperature and isospin asymmetry, change in baryonic density of the nuclear medium makes more significant changes to the DAs and PDFs of kaons., Comment: 33 pages, 16 figures

This work comprises the calculations of magnetic moments of $\frac{1}{2}^-$ baryon resonances in the presence of hot and dense hadronic matter. Using the chiral $SU(3)$ quark mean field model, we have taken into account the impact on in-medium scalar meson fields to investigate the density effects on the in-medium baryon masses and their constituent quarks. In light of chiral constituent quark model $\chi$CQM, we have calculated the magnetic moments of $\frac{1}{2}^-$ baryon resonances and scrutinized the explicit contributions coming from the valence quarks, sea quarks and the orbital angular momentum of sea quarks. The effective baryonic magnetic moments have been investigated for the varying values of isospin asymmetry and strangeness fraction., Comment: 45 pages, 7 tables, 13 figures

Mobile robots rely on maps to navigate through an environment. In the absence of any map, the robots must build the map online from partial observations as they move in the environment. Traditional methods build a map using only direct observations. In contrast, humans identify patterns in the observed environment and make informed guesses about what to expect ahead. Modeling these patterns explicitly is difficult due to the complexity of the environments. However, these complex models can be approximated well using learning-based methods in conjunction with large training data. By extracting patterns, robots can use direct observations and predictions of what lies ahead to better navigate an unknown environment. In this dissertation, we present several learning-based methods to equip mobile robots with prediction capabilities for efficient and safer operation. In the first part of the dissertation, we learn to predict using geometrical and structural patterns in the environment. Partially observed maps provide invaluable cues for accurately predicting the unobserved areas. We first demonstrate the capability of general learning-based approaches to model these patterns for a variety of overhead map modalities. Then we employ task-specific learning for faster navigation in indoor environments by predicting 2D occupancy in the nearby regions. This idea is further extended to 3D point cloud representation for object reconstruction. Predicting the shape of the full object from only partial views, our approach paves the way for efficient next-best-view planning. In the second part of the dissertation, we learn to predict using spatiotemporal patterns in the environment. We focus on dynamic tasks such as target tracking and coverage where we seek decentralized coordination between robots. We first show how graph neural networks can be used for more scalable and faster inference., Comment: 289 pages, 81 figures, 16 tables; Dissertation submitted to UMD to fulfill PhD requirement

Coherent states, being the closest analog to classical states of wave systems, are well known to possess special properties that set them apart from most other quantum optical states. For example, they are robust against photon loss and do not easily get entangled upon interaction with a beamsplitter, and hence are called ``pointer states'', which is often attributed to them being eigenstates of the annihilation operator. Here we provide insights into a topological argument for their robustness using two separate but exact mappings of a prototypical quantum optics model - the driven Jaynes-Cummings model. The first mapping is based on bosonization and refermionization of the Jaynes-Cummings model into the fermionic Su-Schrieffer-Heeger model hosting zero-energy topologically protected edge states. The second mapping is based on the algebra of deformed f-oscillators. We choose these mappings to explicitly preserve the translational symmetry of the model along a Fock-state ladder basis, which is important for maintaining the symmetry-protected topology of such 1D lattices. In addition, we show that the edge state form is preserved even when certain chiral symmetry is broken, corresponding to a single-photon drive for the quantum optics model that preserves the coherent state; however, the addition of two-photon drive immediately disturbs the edge state form, as confirmed by numerical simulations of the mapped SSH model; this is expected since two-photon drive strongly perturbs the coherent state into a squeezed state. Our theory sheds light on a fundamental reason for the robustness of coherent states, both in existence and entanglement -- an underlying connection to topology.

In the present work, we have studied the masses and magnetic moments of spin$-{\frac{1}{2}}^+$ and spin$-{\frac{3}{2}}^+$ singly and doubly charmed baryons in the strange hadronic medium at finite temperature using the chiral SU(3) quark mean field model. The properties of baryons within the framework of chiral SU(3) mean field model are defined in terms of constituent quark masses and energies, which are modified through the exchange of scalar fields $\sigma$, $\zeta$ and $\delta$ and the vector fields $\omega$, $\rho$ and $\phi$. The scalar-isovector field, $\delta$ and the vector-isovector field, $\rho$ contribute when medium has finite isospin asymmetry. We have calculated the effective masses of constituent quarks and charmed baryons in the nuclear and strange matter within the chiral SU(3) quark mean field model and have used these as the input in SU(4) constituent chiral quark model to compute the effective magnetic moments of these baryons. Considering the configuration mixing, the contributions of valence quarks, quark sea and orbital angular momentum of quark sea have been considered explicitly to calculate the in-medium magnetic moments., Comment: 55 pages, 18 figures, 16 tables

The adverse effects of loneliness on both physical and mental well-being are profound. Although previous research has utilized mobile sensing techniques to detect mental health issues, few studies have utilized state-of-the-art wearable devices to forecast loneliness and estimate the physiological manifestations of loneliness and its predictive nature. The primary objective of this study is to examine the feasibility of forecasting loneliness by employing wearable devices, such as smart rings and watches, to monitor early physiological indicators of loneliness. Furthermore, smartphones are employed to capture initial behavioral signs of loneliness. To accomplish this, we employed personalized machine learning techniques, leveraging a comprehensive dataset comprising physiological and behavioral information obtained during our study involving the monitoring of college students. Through the development of personalized models, we achieved a notable accuracy of 0.82 and an F-1 score of 0.82 in forecasting loneliness levels seven days in advance. Additionally, the application of Shapley values facilitated model explainability. The wealth of data provided by this study, coupled with the forecasting methodology employed, possesses the potential to augment interventions and facilitate the early identification of loneliness within populations at risk.

Among the popular TE materials, selenides and tellurides are the benchmarks of high-efficiency systems. However, for the high-temperature application (>700 K), it is required to rely on the silicides and the oxides due to their exceptional thermal stability. ZnO is among the first few oxides in the field of thermoelectricity. Al-doped ZnO is a proven material for its high-temperature thermoelectric applications. However, the high grain boundary resistance limits further improvement of the efficiency of this oxide. Band-engineering, band-modification is a successful approach in lowering the grain boundary resistance. The addition of graphite and graphite-based materials at the grain boundaries is shown to serve this purpose. In this work, graphite powder is added in varying proportions to Al-doped ZnO triangular microcrystals. Thus, prepared materials are characterized to confirm the formation and investigate the nature of interface, morphology, etc. TE parameters such as electrical conductivity, Seebeck coefficient, and thermal conductivity of those materials also have been measured. The theoretical calculation of TE efficiency zT often differs from the actual experimental results due to the wide range of preparation methods, leading to changes in porosity, the nature and density defects, and several other factors. In this paper, an effort has been made to estimate the maximum achievable power factor (PFmax) from the measured TE parameters of this set of samples by the Jonker and Ioffe analysis. Based on the predicted PFmax, an appropriate material composition has been identified to achieve that same. Subsequently, including the measured parameters the TE efficiency (zT) is calculated. Further, a sudden dip observed in the thermal conductivity at the high-temperature range (625 K - 1000 K) of the prepared undoped ZnO graphite composite is investigated in this paper.

We calculate the valence quark transverse momentum-dependent parton distributions (TMDs) of the lightest pseudoscalar meson, pions, in isospin asymmetric nuclear matter at zero temperature by employing a light-cone quark model. The medium modifications in the pion unpolarized TMDs are induced through the effective quark masses computed using the chiral SU($3$) quark mean field model. The spin densities at different momentum fraction ($x$) have also been calculated at different baryonic densities., Comment: 7 pages and 4 figures

We have calculated the valence quark distributions of the lightest pseudoscalar meson, pions, in the isospin asymmetric nuclear matter at zero and finite temperature employing a light-cone quark model. The medium modifications in the pion properties have been stimulated through the effective quark masses computed using the chiral SU($3$) quark mean field model. We have primarily focused on the impact of isospin asymmetric medium on the distribution amplitudes (DAs) and parton distribution functions (PDFs) of a valence quark for different baryon density and temperature values. Also, the DAs and PDFs have been evolved to $Q^2=10$ GeV$^2$ and $Q^2=16$ GeV$^2$ for different densities of nuclear medium and results have been compared with the available experimental data. The DAs and PDFs are found to modify substantially as a function of baryon density as compared to temperature and isospin asymmetry of the medium., Comment: 26 pages and 10 figures

Developing models with robust group fairness properties is paramount, particularly in ethically sensitive domains such as medical diagnosis. Recent approaches to achieving fairness in machine learning require a substantial amount of training data and depend on model retraining, which may not be practical in real-world scenarios. To mitigate these challenges, we propose Bias-based Weight Masking Fine-Tuning (BMFT), a novel post-processing method that enhances the fairness of a trained model in significantly fewer epochs without requiring access to the original training data. BMFT produces a mask over model parameters, which efficiently identifies the weights contributing the most towards biased predictions. Furthermore, we propose a two-step debiasing strategy, wherein the feature extractor undergoes initial fine-tuning on the identified bias-influenced weights, succeeded by a fine-tuning phase on a reinitialised classification layer to uphold discriminative performance. Extensive experiments across four dermatological datasets and two sensitive attributes demonstrate that BMFT outperforms existing state-of-the-art (SOTA) techniques in both diagnostic accuracy and fairness metrics. Our findings underscore the efficacy and robustness of BMFT in advancing fairness across various out-of-distribution (OOD) settings. Our code is available at: https://github.com/vios-s/BMFT, Comment: Accepted by MICCAI 2024 FAIMI Workshop Oral

We consider the following task: suppose an algorithm is given copies of an unknown $n$-qubit quantum state $|\psi\rangle$ promised $(i)$ $|\psi\rangle$ is $\varepsilon_1$-close to a stabilizer state in fidelity or $(ii)$ $|\psi\rangle$ is $\varepsilon_2$-far from all stabilizer states, decide which is the case. We show two results: (i) Assuming $|\psi\rangle$ is a phase state, i.e., $|\psi\rangle=\frac{1}{\sqrt{2^n}}\sum \limits_{x \in \{0,1\}^n} {f(x)}|x\rangle$ where $f:\{0,1\}^n\rightarrow \{-1,1\}$, then we give a $\textsf{poly}(1/\varepsilon_1)$ sample and $n\cdot \textsf{poly}(1/\varepsilon_1)$ time algorithm for every $\varepsilon_1 > 0$ and $\varepsilon_2 \leq \textsf{poly}(\varepsilon_1)$, for tolerant testing stabilizer states. (ii) For arbitrary quantum states $|\psi\rangle$, assuming a conjecture in additive combinatorics, we give a $\textsf{poly}(1/\varepsilon_1)$-sample and $n\cdot \textsf{poly}(1/\varepsilon_1)$-time algorithm for this task for every $\varepsilon_1>0$ and $\varepsilon_2\leq 2^{-\textsf{poly}(1/\varepsilon_1)}$ Our proof includes a new definition of Gowers norm for quantum states, an inverse theorem for the Gowers-$3$ norm of states and new bounds on stabilizer covering for structured subsets of Paulis using results in additive combinatorics., Comment: 38 pages, 2 figures; v1 required an unproven result in combinatorics which is rephrased as a conjecture here

We propose a new method for improving zero-shot ObjectNav that aims to utilize potentially available environmental percepts for navigational assistance. Our approach takes into account that the ground agent may have limited and sometimes obstructed view. Our formulation encourages Generative Communication (GC) between an assistive overhead agent with a global view containing the target object and the ground agent with an obfuscated view; both equipped with Vision-Language Models (VLMs) for vision-to-language translation. In this assisted setup, the embodied agents communicate environmental information before the ground agent executes actions towards a target. Despite the overhead agent having a global view with the target, we note a drop in performance (-13% in OSR and -13% in SPL) of a fully cooperative assistance scheme over an unassisted baseline. In contrast, a selective assistance scheme where the ground agent retains its independent exploratory behaviour shows a 10% OSR and 7.65% SPL improvement. To explain navigation performance, we analyze the GC for unique traits, quantifying the presence of hallucination and cooperation. Specifically, we identify the novel linguistic trait of preemptive hallucination in our embodied setting, where the overhead agent assumes that the ground agent has executed an action in the dialogue when it is yet to move, and note its strong correlation with navigation performance. We conduct real-world experiments and present some qualitative examples where we mitigate hallucinations via prompt finetuning to improve ObjectNav performance.

The task of searching through patent documents is crucial for chemical patent recommendation and retrieval. This can be enhanced by creating a patent knowledge base (ChemPatKB) to aid in prior art searches and to provide a platform for domain experts to explore new innovations in chemical compound synthesis and use-cases. An essential foundational component of this KB is the extraction of important reaction snippets from long patents documents which facilitates multiple downstream tasks such as reaction co-reference resolution and chemical entity role identification. In this work, we explore the problem of extracting reactions spans from chemical patents in order to create a reactions resource database. We formulate this task as a paragraph-level sequence tagging problem, where the system is required to return a sequence of paragraphs that contain a description of a reaction. We propose several approaches and modifications of the baseline models and study how different methods generalize across different domains of chemical patents., Comment: Work completed in 2022 at Carnegie Mellon University

We introduce Temporal Residual Jacobians as a novel representation to enable data-driven motion transfer. Our approach does not assume access to any rigging or intermediate shape keyframes, produces geometrically and temporally consistent motions, and can be used to transfer long motion sequences. Central to our approach are two coupled neural networks that individually predict local geometric and temporal changes that are subsequently integrated, spatially and temporally, to produce the final animated meshes. The two networks are jointly trained, complement each other in producing spatial and temporal signals, and are supervised directly with 3D positional information. During inference, in the absence of keyframes, our method essentially solves a motion extrapolation problem. We test our setup on diverse meshes (synthetic and scanned shapes) to demonstrate its superiority in generating realistic and natural-looking animations on unseen body shapes against SoTA alternatives. Supplemental video and code are available at https://temporaljacobians.github.io/ ., Comment: 15 pages, 6 figures

The estimation of low energies of many-body systems is a cornerstone of computational quantum sciences. Variational quantum algorithms can be used to prepare ground states on pre-fault-tolerant quantum processors, but their lack of convergence guarantees and impractical number of cost function estimations prevent systematic scaling of experiments to large systems. Alternatives to variational approaches are needed for large-scale experiments on pre-fault-tolerant devices. Here, we use a superconducting quantum processor to compute eigenenergies of quantum many-body systems on two-dimensional lattices of up to 56 sites, using the Krylov quantum diagonalization algorithm, an analog of the well-known classical diagonalization technique. We construct subspaces of the many-body Hilbert space using Trotterized unitary evolutions executed on the quantum processor, and classically diagonalize many-body interacting Hamiltonians within those subspaces. These experiments show that quantum diagonalization algorithms are poised to complement their classical counterpart at the foundation of computational methods for quantum systems., Comment: 25 pages, 13 figures

In today's fast-paced world, accurately monitoring stress levels is crucial. Sensor-based stress monitoring systems often need large datasets for training effective models. However, individual-specific models are necessary for personalized and interactive scenarios. Traditional methods like Ecological Momentary Assessments (EMAs) assess stress but struggle with efficient data collection without burdening users. The challenge is to timely send EMAs, especially during stress, balancing monitoring efficiency and user convenience. This paper introduces a novel context-aware active reinforcement learning (RL) algorithm for enhanced stress detection using Photoplethysmography (PPG) data from smartwatches and contextual data from smartphones. Our approach dynamically selects optimal times for deploying EMAs, utilizing the user's immediate context to maximize label accuracy and minimize intrusiveness. Initially, the study was executed in an offline environment to refine the label collection process, aiming to increase accuracy while reducing user burden. Later, we integrated a real-time label collection mechanism, transitioning to an online methodology. This shift resulted in an 11% improvement in stress detection efficiency. Incorporating contextual data improved model accuracy by 4%. Personalization studies indicated a 10% enhancement in AUC-ROC scores, demonstrating better stress level differentiation. This research marks a significant move towards personalized, context-driven real-time stress monitoring methods.

In this paper, a unique form of generators of a constacyclic code of arbitrary length over a non-chain ring of the type $\mathtt{R_{_{\theta}}}=Z_{4}+\nu Z_{4}, \nu^{2}=\theta \in Z_{4}+\nu Z_{4}$ has been obtained. Further, rank and cardinality of a constacyclic code of arbitrary length over a non-chain ring of the type $\mathtt{R_{_{\theta}}}$ have been obtained by determining a minimal spanning set of the code. Also, necessary and sufficient conditions for a constacyclic code of arbitrary length over a non-chain ring of the type $\mathtt{R_{_{\theta}}}$ to be reversible have been determined. Examples have also been presented in support of our results., Comment: 19pages

In the present work, we investigate the impact of finite volume on the in-medium properties of kaons ($K^+$, $K^0$) and antikaons ($K^-$, $\bar{K^0}$), and $\phi$ mesons in the isospin asymmetric strange hadronic medium at finite density and temperature. We use the chiral SU(3) hadronic mean-field model, which accounts for the interactions between baryons through the exchange of scalar ($\sigma, \zeta, \delta $) and vector ($\omega$, $\rho$, $\phi$) fields. To investigate the effects of finite volume, we apply the multiple reflection expansion (MRE) technique for calculations of the density of states. The non-strange scalar field $\sigma$ shows significant variation in an asymmetric medium, while the strange scalar field $\zeta$ shows good dependency in the strange medium. We use the medium-modified masses of kaons and antikaons calculated using the chiral SU(3) model to obtain the masses and decay width of $\phi$ mesons in finite volume hadronic medium. To obtain the masses and decay widths of $\phi$ mesons, an effective Lagrangian approach with $\phi$$K$$\bar{K}$ interactions at one-loop level is used in the present work. We obtain the effective masses and decay widths in the finite volume matter, for the spherical geometry of the medium with Neumann and Dirichlet boundary conditions as well as for the cubic geometry. The finite volume effects are found to be appreciable at high baryon densities., Comment: 40 pages and 13 figures

Multiple Signal Classification (MUSIC) is a widely used Direction of Arrival (DoA)/Angle of Arrival (AoA) estimation algorithm applied to various application domains such as autonomous driving, medical imaging, and astronomy. However, MUSIC is computationally expensive and challenging to implement in low-power hardware, requiring exploration of trade-offs between accuracy, cost, and power. We present MUSIC-lite, which exploits approximate computing to generate a design space exploring accuracy-area-power trade-offs. This is specifically applied to the computationally intensive singular value decomposition (SVD) component of the MUSIC algorithm in an orthogonal frequency-division multiplexing (OFDM) radar use case. MUSIC-lite incorporates approximate adders into the iterative CORDIC algorithm that is used for hardware implementation of MUSIC, generating interesting accuracy-area-power trade-offs. Our experiments demonstrate MUSIC-lite's ability to save an average of 17.25% on-chip area and 19.4% power with a minimal 0.14% error for efficient MUSIC implementations., Comment: Paper accepted at ESWEEK-CASES 2024 as a Late Breaking (LB) Result paper. The definitive version of the work will appear in IEEE Embedded Systems Letters

We present a generalizable classification approach that leverages Large Language Models (LLMs) to facilitate the detection of implicitly encoded social meaning in conversations. We design a multi-faceted prompt to extract a textual explanation of the reasoning that connects visible cues to underlying social meanings. These extracted explanations or rationales serve as augmentations to the conversational text to facilitate dialogue understanding and transfer. Our empirical results over 2,340 experimental settings demonstrate the significant positive impact of adding these rationales. Our findings hold true for in-domain classification, zero-shot, and few-shot domain transfer for two different social meaning detection tasks, each spanning two different corpora., Comment: To appear at The Proceedings of the Association for Computational Linguistics, 2024

We study the problem of visual surface inspection of infrastructure for defects using an Unmanned Aerial Vehicle (UAV). We do not assume that the geometric model of the infrastructure is known beforehand. Our planner, termed GATSBI, plans a path in a receding horizon fashion to inspect all points on the surface of the infrastructure. The input to GATSBI consists of a 3D occupancy map created online with 3D pointclouds. Occupied voxels corresponding to the infrastructure in this map are semantically segmented and used to create an infrastructure-only occupancy map. Inspecting an infrastructure voxel requires the UAV to take images from a desired viewing angle and distance. We then create a Generalized Traveling Salesperson Problem (GTSP) instance to cluster candidate viewpoints for inspecting the infrastructure voxels and use an off-the-shelf GTSP solver to find the optimal path for the given instance. As the algorithm sees more parts of the environment over time, it replans the path to inspect uninspected parts of the infrastructure while avoiding obstacles. We evaluate the performance of our algorithm through high-fidelity simulations conducted in AirSim and real-world experiments. We compare the performance of GATSBI with a baseline inspection algorithm where the map is known a priori. Our evaluation reveals that targeting the inspection to only the segmented infrastructure voxels and planning carefully using a GTSP solver leads to a more efficient and thorough inspection than the baseline inspection algorithm., Comment: 10 pages, 12 figures, 2 tables. Submitted to IEEE TAES. arXiv admin note: text overlap with arXiv:2012.04803

Diffusion models excel in generating images that closely resemble their training data but are also susceptible to data memorization, raising privacy, ethical, and legal concerns, particularly in sensitive domains such as medical imaging. We hypothesize that this memorization stems from the overparameterization of deep models and propose that regularizing model capacity during fine-tuning can mitigate this issue. Firstly, we empirically show that regulating the model capacity via Parameter-efficient fine-tuning (PEFT) mitigates memorization to some extent, however, it further requires the identification of the exact parameter subsets to be fine-tuned for high-quality generation. To identify these subsets, we introduce a bi-level optimization framework, MemControl, that automates parameter selection using memorization and generation quality metrics as rewards during fine-tuning. The parameter subsets discovered through MemControl achieve a superior tradeoff between generation quality and memorization. For the task of medical image generation, our approach outperforms existing state-of-the-art memorization mitigation strategies by fine-tuning as few as 0.019% of model parameters. Moreover, we demonstrate that the discovered parameter subsets are transferable to non-medical domains. Our framework is scalable to large datasets, agnostic to reward functions, and can be integrated with existing approaches for further memorization mitigation. To the best of our knowledge, this is the first study to empirically evaluate memorization in medical images and propose a targeted yet universal mitigation strategy. The code is available at https://github.com/Raman1121/Diffusion_Memorization_HPO, Comment: Accepted at WACV'25 (Applications Track)