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Federated learning (FL) is vulnerable to backdoor attacks, where adversaries alter model behavior on target classification labels by embedding triggers into data samples. While these attacks have received considerable attention in horizontal FL, they are less understood for vertical FL (VFL), where devices hold different features of the samples, and only the server holds the labels. In this work, we propose a novel backdoor attack on VFL which (i) does not rely on gradient information from the server and (ii) considers potential collusion among multiple adversaries for sample selection and trigger embedding. Our label inference model augments variational autoencoders with metric learning, which adversaries can train locally. A consensus process over the adversary graph topology determines which datapoints to poison. We further propose methods for trigger splitting across the adversaries, with an intensity-based implantation scheme skewing the server towards the trigger. Our convergence analysis reveals the impact of backdoor perturbations on VFL indicated by a stationarity gap for the trained model, which we verify empirically as well. We conduct experiments comparing our attack with recent backdoor VFL approaches, finding that ours obtains significantly higher success rates for the same main task performance despite not using server information. Additionally, our results verify the impact of collusion on attack performance., Comment: This paper is currently under review in the IEEE/ACM Transactions on Networking Special Issue on AI and Networking

Visual anomaly detection targets to detect images that notably differ from normal pattern, and it has found extensive application in identifying defective parts within the manufacturing industry. These anomaly detection paradigms predominantly focus on training detection models using only clean, unlabeled normal samples, assuming an absence of contamination; a condition often unmet in real-world scenarios. The performance of these methods significantly depends on the quality of the data and usually decreases when exposed to noise. We introduce a systematic adaptive method that employs deviation learning to compute anomaly scores end-to-end while addressing data contamination by assigning relative importance to the weights of individual instances. In this approach, the anomaly scores for normal instances are designed to approximate scalar scores obtained from the known prior distribution. Meanwhile, anomaly scores for anomaly examples are adjusted to exhibit statistically significant deviations from these reference scores. Our approach incorporates a constrained optimization problem within the deviation learning framework to update instance weights, resolving this problem for each mini-batch. Comprehensive experiments on the MVTec and VisA benchmark datasets indicate that our proposed method surpasses competing techniques and exhibits both stability and robustness in the presence of data contamination., Comment: Accepted to IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2025)

excel in delivering comprehensive suggestions by deeply analyzing content and user behavior. However, they often inherit biases from skewed training data, favoring mainstream content while underrepresenting diverse or non-traditional options. This study explores the interplay between bias and LLM-based recommendation systems, focusing on music, song, and book recommendations across diverse demographic and cultural groups. This paper analyzes bias in LLM-based recommendation systems across multiple models (GPT, LLaMA, and Gemini), revealing its deep and pervasive impact on outcomes. Intersecting identities and contextual factors, like socioeconomic status, further amplify biases, complicating fair recommendations across diverse groups. Our findings reveal that bias in these systems is deeply ingrained, yet even simple interventions like prompt engineering can significantly reduce it. We further propose a retrieval-augmented generation strategy to mitigate bias more effectively. Numerical experiments validate these strategies, demonstrating both the pervasive nature of bias and the impact of the proposed solutions.

The charge neutrality point of bilayer graphene, denoted as {\nu} = 0 state, manifests competing phases marked by spontaneously broken isospin (spin/valley/layer) symmetries under external magnetic and electric fields. However, due to their electrically insulating nature, identifying these phases through electrical conductance measurements remains challenging. A recent theoretical proposal introduces a novel approach, employing thermal transport measurements to detect these competing phases. Here, we experimentally explore the bulk thermal transport of the {\nu} = 0 state in bilayer graphene to investigate its ground states and collective excitations associated with isospin. While the theory anticipates a finite thermal conductance in the {\nu} = 0 state, our findings unveil an absence of detectable thermal conductance. Through variations in the external electric field and temperature-dependent measurements, our results suggest towards gapped collective excitations at {\nu} = 0 state. Our findings underscore the necessity for further investigations into the nature of {\nu} = 0.

Matrix computations are a fundamental building-block of edge computing systems, with a major recent uptick in demand due to their use in AI/ML training and inference procedures. Existing approaches for distributing matrix computations involve allocating coded combinations of submatrices to worker nodes, to build resilience to slower nodes, called stragglers. In the edge learning context, however, these approaches will compromise sparsity properties that are often present in the original matrices found at the edge server. In this study, we consider the challenge of augmenting such approaches to preserve input sparsity when distributing the task across edge devices, thereby retaining the associated computational efficiency enhancements. First, we find a lower bound on the weight of coding, i.e., the number of submatrices to be combined to obtain coded submatrices, to provide the resilience to the maximum possible number of straggler devices (for given number of devices and their storage constraints). Next we propose distributed matrix computation schemes which meet the exact lower bound on the weight of the coding. Numerical experiments conducted in Amazon Web Services (AWS) validate our assertions regarding straggler mitigation and computation speed for sparse matrices., Comment: arXiv admin note: text overlap with arXiv:2308.04331

Topological quantum numbers are often used to characterise the topological order of phase having protected gapless edge modes when the system is kept in a space with the boundary. The famous examples in this category are the quantized electrical Hall conductance and thermal Hall conductance, which encodes the topological order of integer and fractional quantum Hall states. Here, we review the recent thermal transport study of integer and fractional quantum Hall states realized in graphene-based van der Waals heterostructures., Comment: Invited Review article (Editor's Pick)

Dual-function radar-communication (DFRC) is a key enabler of location-based services for next-generation communication systems. In this paper, we investigate the problem of designing constant modulus waveforms for DFRC systems. For high-precision radar sensing, we consider joint optimization of the correlation properties and spatial beam pattern. For communication, we employ constructive interference-based block-level precoding (CI-BLP) to leverage distortion induced by multiuser multiple-input multiple-output (MU-MIMO) and radar transmission on a block level. We propose two solution algorithms based on the alternating direction method of multipliers (ADMM) and majorization-minimization (MM) principles, which are effective for small and large block sizes, respectively. The proposed ADMM-based solution decomposes the nonconvex formulated problem into multiple tractable subproblems, each of which admits a closed-form solution. To accelerate convergence of the MM-based solution, we propose an improved majorizing function that leverages a novel diagonal matrix structure. After majorization, we decompose the approximated problem into independent subproblems for parallelization, mitigating the complexity that increases with block size. We then evaluate the performance of the proposed algorithms through a series of numerical experiments. Simulation results demonstrate that the proposed methods can substantially enhance spatial/temporal sidelobe suppression through block-level optimization., Comment: arXiv admin note: text overlap with arXiv:2310.10804

Opportunistic spectrum access has the potential to increase the efficiency of spectrum utilization in cognitive radio networks (CRNs). In CRNs, both spectrum sensing and resource allocation (SSRA) are critical to maximizing system throughput while minimizing collisions of secondary users with the primary network. However, many works in dynamic spectrum access do not consider the impact of imperfect sensing information such as mis-detected channels, which the additional information available in joint SSRA can help remediate. In this work, we examine joint SSRA as an optimization which seeks to maximize a CRN's net communication rate subject to constraints on channel sensing, channel access, and transmit power. Given the non-trivial nature of the problem, we leverage multi-agent reinforcement learning to enable a network of secondary users to dynamically access unoccupied spectrum via only local test statistics, formulated under the energy detection paradigm of spectrum sensing. In doing so, we develop a novel multi-agent implementation of hybrid soft actor critic, MHSAC, based on the QMIX mixing scheme. Through experiments, we find that our SSRA algorithm, HySSRA, is successful in maximizing the CRN's utilization of spectrum resources while also limiting its interference with the primary network, and outperforms the current state-of-the-art by a wide margin. We also explore the impact of wireless variations such as coherence time on the efficacy of the system., Comment: Upon further exploration, model is not converging as expected under current formulation. We are working to update the inputs and objective so that it performs in an expected manner

Recently, deep learning approaches have provided solutions to difficult problems in wireless positioning (WP). Although these WP algorithms have attained excellent and consistent performance against complex channel environments, the computational complexity coming from processing high-dimensional features can be prohibitive for mobile applications. In this work, we design a novel positioning neural network (P-NN) that utilizes the minimum description features to substantially reduce the complexity of deep learning-based WP. P-NN's feature selection strategy is based on maximum power measurements and their temporal locations to convey information needed to conduct WP. We improve P-NN's learning ability by intelligently processing two different types of inputs: sparse image and measurement matrices. Specifically, we implement a self-attention layer to reinforce the training ability of our network. We also develop a technique to adapt feature space size, optimizing over the expected information gain and the classification capability quantified with information-theoretic measures on signal bin selection. Numerical results show that P-NN achieves a significant advantage in performance-complexity tradeoff over deep learning baselines that leverage the full power delay profile (PDP). In particular, we find that P-NN achieves a large improvement in performance for low SNR, as unnecessary measurements are discarded in our minimum description features., Comment: This paper has been accepted for the publication in IEEE Journal on Selected Areas in Communications. arXiv admin note: text overlap with arXiv:2402.09580

Recent experiments on magic-angle twisted bilayer graphene (MATBLG) indicate an unusual coexistence of heavy and light electrons, a characteristic of heavy-fermion physics. But a global transport measurement showing clear signatures of strong correlations like local moments (LM) arising from the flatbands is missing. Utilizing thermopower ($V_{Th}$) as a sensitive probe for measuring entropy ($S$), we have extensively studied $V_{Th}$ of MATBLG with filling ($\nu$), temperature ($T$) and magnetic field ($B$), and unveiled the presence of LM through their impact on $S$. While at high $T$, the behavior of $V_{Th}$ and resistance ($R$) are related by the Mott formula, it deviates at lower $T$. Below 120K apart from the Dirac point and full band filling, the resistance exhibits prominent peaks at integer fillings. The $V_{Th}$ however, remains featureless and symmetric across the DP with opposite signs. This disparity implies the dominance of different kinds of carriers in $V_{Th}$ and $R$. The $V_{Th}$ has additional sign changes at $\nu_{cross} \sim \pm 1$. The locations of $\nu_{cross}$ do not change with temperature from $5-60K$. For heavy and light band electrons, it can be shown that the doping profile of the $V_{Th}$, and hence $\nu_{cross}$ is highly sensitive to $T$, and therefore solely the contribution from band electrons does not explain the $V_{Th}$ data. Our data is consistent with the dominant contribution arising from the $S$ of the LMs. Additionally, we have investigated the effect of $B$ on the $V_{Th}$, both $B_{\parallel}$ and $B_{\perp}$ showing 30 and 50$\%$ reduction, respectively, and can be attributed to the partial polarization of the LMs (spin/valley), resulting in decreased $S$. Our $V_{Th}$ results highlight the contribution from LMs arising from flatbands, distinct from the band contributions of heavy and light fermions to the resistivity in MATBLG.

Superconductivity (SC) in twisted bilayer graphene (tBLG) has been explored by varying carrier concentrations, twist angles, and screening strength, with the aim of uncovering its origin and possible connections to strong electronic correlations in narrow bands and various resulting broken symmetries. However, the link between the tBLG band structure and the onset of SC and other orders largely remains unclear. In this study, we address this crucial gap by examining in-situ band structure tuning of a near magic-angle ($\theta \approx0.95^\circ$) tBLG device with displacement field ($D$) and reveal remarkable competition between SC and other broken symmetries. At zero $D$, the device exhibits superconducting signatures without the resistance peak at half-filling, a characteristic signature with a strong electronic correlation. As $D$ increases, the SC is suppressed, accompanied by the appearance of a resistance peak at half-filling. Hall density measurements reveal that at zero $D$, SC arises around the van Hove singularity (vHs) from an isospin or spin-valley unpolarized band. At higher $D$, the suppression of SC coincides with broken isospin symmetry near half-filling with lifted degeneracy ($g_d \sim 2$). Additionally, as the SC phase becomes weaker with $D$, vHs shifts to higher fillings, highlighting the modification of the underlying band structure with the applied electric field. These findings, with recent theoretical study on SC in tBLG, highlight the competition, rather being connected concomitantly, between SC and other orders promoted by broken symmetries.

One of the main challenges of decentralized machine learning paradigms such as Federated Learning (FL) is the presence of local non-i.i.d. datasets. Device-to-device transfers (D2D) between distributed devices has been shown to be an effective tool for dealing with this problem and robust to stragglers. In an unsupervised case, however, it is not obvious how data exchanges should take place due to the absence of labels. In this paper, we propose an approach to create an optimal graph for data transfer using Reinforcement Learning. The goal is to form links that will provide the most benefit considering the environment's constraints and improve convergence speed in an unsupervised FL environment. Numerical analysis shows the advantages in terms of convergence speed and straggler resilience of the proposed method to different available FL schemes and benchmark datasets.

A recent line of research has been investigating deep learning approaches to wireless positioning (WP). Although these WP algorithms have demonstrated high accuracy and robust performance against diverse channel conditions, they also have a major drawback: they require processing high-dimensional features, which can be prohibitive for mobile applications. In this work, we design a positioning neural network (P-NN) that substantially reduces the complexity of deep learning-based WP through carefully crafted minimum description features. Our feature selection is based on maximum power measurements and their temporal locations to convey information needed to conduct WP. We also develop a novel methodology for adaptively selecting the size of feature space, which optimizes over balancing the expected amount of useful information and classification capability, quantified using information-theoretic measures on the signal bin selection. Numerical results show that P-NN achieves a significant advantage in performance-complexity tradeoff over deep learning baselines that leverage the full power delay profile (PDP)., Comment: This paper has been accepted in IEEE International Conference on Communications (ICC) 2024

This letter presents a non-local study on the electric field tunable edge transport in an hBN-encapsulated dual-gated Bernal stacked (ABA) trilayer graphene across various displacement fields ($D$) and temperatures ($T$). Our measurements revealed that the non-local resistance ($R_{NL}$) surpassed the expected classical ohmic contribution by a factor of at least two orders of magnitude. Through scaling analysis, we found that the non-local resistance scales linearly with the local resistance ($R_{L}$) only when the $D$ exceeds a critical value of $\sim0.2$ V/nm. Additionally, we observed that the scaling exponent remains constant at unity for temperatures below the bulk-band gap energy threshold ($T<25$ K). Further, the value of $R_{NL}$ decreases in a linear fashion as the channel length ($L$) increases. These experimental findings provide evidence for edge-mediated charge transport in ABA trilayer graphene under the influence of a finite displacement field. Furthermore, our theoretical calculations support these results by demonstrating the emergence of dispersive edge modes within the bulk-band gap energy range when a sufficient displacement field is applied., Comment: Accepted in Physical Review Letters

Although user cooperation cannot improve the capacity of Gaussian two-way channels (GTWCs) with independent noises, it can improve communication reliability. In this work, we aim to enhance and balance the communication reliability in GTWCs by minimizing the sum of error probabilities via joint design of encoders and decoders at the users. We first formulate general encoding/decoding functions, where the user cooperation is captured by the coupling of user encoding processes. The coupling effect renders the encoder/decoder design non-trivial, requiring effective decoding to capture this effect, as well as efficient power management at the encoders within power constraints. To address these challenges, we propose two different two-way coding strategies: linear coding and learning-based coding. For linear coding, we propose optimal linear decoding and discuss new insights on encoding regarding user cooperation to balance reliability. We then propose an efficient algorithm for joint encoder/decoder design. For learning-based coding, we introduce a novel recurrent neural network (RNN)-based coding architecture, where we propose interactive RNNs and a power control layer for encoding, and we incorporate bi-directional RNNs with an attention mechanism for decoding. Through simulations, we show that our two-way coding methodologies outperform conventional channel coding schemes (that do not utilize user cooperation) significantly in sum-error performance. We also demonstrate that our linear coding excels at high signal-to-noise ratios (SNRs), while our RNN-based coding performs best at low SNRs. We further investigate our two-way coding strategies in terms of power distribution, two-way coding benefit, different coding rates, and block-length gain., Comment: This work has been submitted to the IEEE Transactions on Information Theory

Dual-functional radar-communication (DFRC) is a promising technology where radar and communication functions operate on the same spectrum and hardware. In this paper, we propose an algorithm for designing constant modulus waveforms for DFRC systems. Particularly, we jointly optimize the correlation properties and the spatial beam pattern. For communication, we employ constructive interference-based block-level precoding (CI-BLP) to exploit distortion due to multi-user and radar transmission. We propose a majorization-minimization (MM)-based solution to the formulated problem. To accelerate convergence, we propose an improved majorizing function that leverages a novel diagonal matrix structure. We then evaluate the performance of the proposed algorithm through rigorous simulations. Simulation results demonstrate the effectiveness of the proposed approach and the proposed majorizer., Comment: Accepted to IEEE International Conference on Communications (ICC) 2024

Most current methods for detecting anomalies in text concentrate on constructing models solely relying on unlabeled data. These models operate on the presumption that no labeled anomalous examples are available, which prevents them from utilizing prior knowledge of anomalies that are typically present in small numbers in many real-world applications. Furthermore, these models prioritize learning feature embeddings rather than optimizing anomaly scores directly, which could lead to suboptimal anomaly scoring and inefficient use of data during the learning process. In this paper, we introduce FATE, a deep few-shot learning-based framework that leverages limited anomaly examples and learns anomaly scores explicitly in an end-to-end method using deviation learning. In this approach, the anomaly scores of normal examples are adjusted to closely resemble reference scores obtained from a prior distribution. Conversely, anomaly samples are forced to have anomalous scores that considerably deviate from the reference score in the upper tail of the prior. Additionally, our model is optimized to learn the distinct behavior of anomalies by utilizing a multi-head self-attention layer and multiple instance learning approaches. Comprehensive experiments on several benchmark datasets demonstrate that our proposed approach attains a new level of state-of-the-art performance., Comment: Accepted in ICONIP 2023

Existing approaches to distributed matrix computations involve allocating coded combinations of submatrices to worker nodes, to build resilience to stragglers and/or enhance privacy. In this study, we consider the challenge of preserving input sparsity in such approaches to retain the associated computational efficiency enhancements. First, we find a lower bound on the weight of coding, i.e., the number of submatrices to be combined to obtain coded submatrices to provide the resilience to the maximum possible number of stragglers (for given number of nodes and their storage constraints). Next we propose a distributed matrix computation scheme which meets this exact lower bound on the weight of the coding. Further, we develop controllable trade-off between worker computation time and the privacy constraint for sparse input matrices in settings where the worker nodes are honest but curious. Numerical experiments conducted in Amazon Web Services (AWS) validate our assertions regarding straggler mitigation and computation speed for sparse matrices.

Augmenting federated learning (FL) with direct device-to-device (D2D) communications can help improve convergence speed and reduce model bias through rapid local information exchange. However, data privacy concerns, device trust issues, and unreliable wireless channels each pose challenges to determining an effective yet resource efficient D2D structure. In this paper, we develop a decentralized reinforcement learning (RL) methodology for D2D graph discovery that promotes communication of non-sensitive yet impactful data-points over trusted yet reliable links. Each device functions as an RL agent, training a policy to predict the impact of incoming links. Local (device-level) and global rewards are coupled through message passing within and between device clusters. Numerical experiments confirm the advantages offered by our method in terms of convergence speed and straggler resilience across several datasets and FL schemes.

Federated learning (FL) is a popular technique for training a global model on data distributed across client devices. Like other distributed training techniques, FL is susceptible to straggler (slower or failed) clients. Recent work has proposed to address this through device-to-device (D2D) offloading, which introduces privacy concerns. In this paper, we propose a novel straggler-optimal approach for coded matrix computations which can significantly reduce the communication delay and privacy issues introduced from D2D data transmissions in FL. Moreover, our proposed approach leads to a considerable improvement of the local computation speed when the generated data matrix is sparse. Numerical evaluations confirm the superiority of our proposed method over baseline approaches., Comment: arXiv admin note: text overlap with arXiv:2301.12685

Straggler nodes are well-known bottlenecks of distributed matrix computations which induce reductions in computation/communication speeds. A common strategy for mitigating such stragglers is to incorporate Reed-Solomon based MDS (maximum distance separable) codes into the framework; this can achieve resilience against an optimal number of stragglers. However, these codes assign dense linear combinations of submatrices to the worker nodes. When the input matrices are sparse, these approaches increase the number of non-zero entries in the encoded matrices, which in turn adversely affects the worker computation time. In this work, we develop a distributed matrix computation approach where the assigned encoded submatrices are random linear combinations of a small number of submatrices. In addition to being well suited for sparse input matrices, our approach continues have the optimal straggler resilience in a certain range of problem parameters. Moreover, compared to recent sparse matrix computation approaches, the search for a "good" set of random coefficients to promote numerical stability in our method is much more computationally efficient. We show that our approach can efficiently utilize partial computations done by slower worker nodes in a heterogeneous system which can enhance the overall computation speed. Numerical experiments conducted through Amazon Web Services (AWS) demonstrate up to 30% reduction in per worker node computation time and 100x faster encoding compared to the available methods.

Radio access networks (RANs) in monolithic architectures have limited adaptability to supporting different network scenarios. Recently, open-RAN (O-RAN) techniques have begun adding enormous flexibility to RAN implementations. O-RAN is a natural architectural fit for cell-free massive multiple-input multiple-output (CFmMIMO) systems, where many geographically-distributed access points (APs) are employed to achieve ubiquitous coverage and enhanced user performance. In this paper, we address the decentralized pilot assignment (PA) problem for scalable O-RAN-based CFmMIMO systems. We propose a low-complexity PA scheme using a multi-agent deep reinforcement learning (MA-DRL) framework in which multiple learning agents perform distributed learning over the O-RAN communication architecture to suppress pilot contamination. Our approach does not require prior channel knowledge but instead relies on real-time interactions made with the environment during the learning procedure. In addition, we design a codebook search (CS) scheme that exploits the decentralization of our O-RAN CFmMIMO architecture, where different codebook sets can be utilized to further improve PA performance without any significant additional complexities. Numerical evaluations verify that our proposed scheme provides substantial computational scalability advantages and improvements in channel estimation performance compared to the state-of-the-art., Comment: The journal version of this paper is published in IEEE Journal on Selected Areas in Communications

Recently, magic-angle twisted bilayer graphene (MATBLG) has shown the emergence of various interaction-driven novel quantum phases at the commensurate fillings of the moir'e superlattice, while the charge neutrality point (CNP) remains mostly a vanilla insulator. Here, we show an emerging phase of nearly compensated semimetallicity at the CNP of twisted double bilayer graphene (TDBLG), a close cousin of MATBLG, with signatures of electronic correlation. Using electrical and thermal transport, we find almost two orders of magnitude enhancement of the thermopower in magnetic fields much smaller than the extreme quantum limit, accompanied by a large magnetoresistance($\sim 2500\%$) at CNP. This provides indisputable experimental evidence that TDBLG near CNP is a compensated semimetal. Moreover, at low temperatures, we observe an unusual sublinear temperature dependence of resistance. A recent theory predicts the formation of an excitonic metal near CNP, where small electron and hole pockets coexist. We understand the sublinear temperature dependence in terms of critical fluctuations in this theory.

Twisted double bilayer graphene is a compensated semi-metal near the charge neutrality point with the presence of small electron and hole pockets in its band structure. We show that strong Coulomb attraction between the electrons and holes can lead to the formation of indirect excitons. Condensation of these excitons at low temperature creates an excitonic metal with charge density wave order for an appropriate range of interaction strength. This has interesting implications for low-temperature transport in the system as a function of carrier density and temperature. The reorganization of the single particle excitations and their density of states in the excitonic metal can lead to peaks in resistivity as a function of carrier density, recently seen in experiments at low temperatures. The fluctuations of the Landau damped order parameter in the quantum critical metal lead to non-Fermi liquid behaviour, which can explain the sublinear $T^{2/3}$ dependence of the resistance near the charge neutrality point., Comment: Changed the reference of an experimental paper with its new Arxiv id

The ground state of a graphene sheet at charge neutrality in a perpendicular magnetic field remains enigmatic, with various experiments supporting canted antiferromagnetic, bond ordered, and even charge density wave phases. A promising avenue to elucidating the nature of this state is to sandwich it between regions of different filling factors, and study spin-dependent tunneling across the edge modes at the interfaces. Here we report on tunnel transport through a $\nu=0$ region in a graphite-gated, hexagonal boron nitride ($hBN$) encapsulated monolayer graphene device, with the $\nu=0$ strip sandwiched by spin-polarized $\nu=\pm1$ quantum Hall states. We observe finite tunneling ($t \sim 0.3-0.6$) between the $\nu=\pm1$ edges at not too small magnetic fields ($B>3T$) and low tunnel bias voltage ($<30-60\mu V$), which is surprising because electrons at the edge states nominally have opposite spins. Hartree-Fock calculations elucidate these phenomena as being driven by the formation of a CAF order parameter in the $\nu=0$ region at zero bias (for wide enough junctions) leading to non-orthogonal spins at the edges. Remarkably, this tunneling can be controllably switched off by increasing bias; bias voltage leads to a pileup of charge at the junction, leading to a collapse of the CAF order and a suppression of the tunneling.

To determine the topological quantum numbers of fractional quantum Hall (FQH) states hosting counter-propagating (CP) downstream ($N_d$) and upstream ($N_u$) edge modes, it is pivotal to study quantized transport both in the presence and absence of edge mode equilibration. While reaching the non-equilibrated regime is challenging for charge transport, we target here the thermal Hall conductance $G_{Q}$, which is purely governed by edge quantum numbers $N_d$ and $N_u$. Our experimental setup is realized with a hBN encapsulated graphite gated monolayer graphene device. For temperatures up to $35mK$, our measured $G_{Q}$ at $\nu = $ 2/3 and 3/5 (with CP modes) match the quantized values of non-equilibrated regime $(N_d + N_u)\kappa_{0}T$, where $\kappa_{0}T$ is a quanta of $G_{Q}$. With increasing temperature, $G_{Q}$ decreases and eventually takes the value of equilibrated regime $|N_d - N_u|\kappa_{0}T$. By contrast, at $\nu = $1/3 and 2/5 (without CP modes), $G_Q$ remains robustly quantized at $N_d\kappa_{0}T$ independent of the temperature. Thus, measuring the quantized values of $G_{Q}$ at two regimes, we determine the edge quantum numbers, which opens a new route for finding the topological order of exotic non-Abelian FQH states.

A comprehensive understanding of vision and language and their interrelation are crucial to realize the underlying similarities and differences between these modalities and to learn more generalized, meaningful representations. In recent years, most of the works related to Text-to-Image synthesis and Image-to-Text generation, focused on supervised generative deep architectures to solve the problems, where very little interest was placed on learning the similarities between the embedding spaces across modalities. In this paper, we propose a novel self-supervised deep learning based approach towards learning the cross-modal embedding spaces; for both image to text and text to image generations. In our approach, we first obtain dense vector representations of images using StackGAN-based autoencoder model and also dense vector representations on sentence-level utilizing LSTM based text-autoencoder; then we study the mapping from embedding space of one modality to embedding space of the other modality utilizing GAN and maximum mean discrepancy based generative networks. We, also demonstrate that our model learns to generate textual description from image data as well as images from textual data both qualitatively and quantitatively., Comment: ICONIP 2021 : The 28th International Conference on Neural Information Processing

Low frequency 1/f noise is investigated in graphene, encapsulated between hexagonal boron nitride (hBN) substrate in dual gated geometry. The overall noise magnitude is smaller as compared to graphene on Si/SiO2 substrate. The noise amplitude in the hole doped region is independent of carrier density while in the electron doped region, a pronounced peak is observed, at Fermi energy, EF ~ 90 meV. The physical mechanism of the anomalous noise peak in the electron doped region is attributed to the impurity states originating from the Carbon atom replacing the Nitrogen site in hBN crystal. Furthermore, the noise study near Dirac point shows characteristic "M-shape", which is found to be strongly correlated with the charge inhomogeneity region near Dirac point., Comment: Accepted in Applied Physics Letters

The overall execution time of distributed matrix computations is often dominated by slow worker nodes (stragglers) within the clusters. Recently, different coding techniques have been utilized to mitigate the effect of stragglers where worker nodes are assigned the job of processing encoded submatrices of the original matrices. In many machine learning or optimization problems the relevant matrices are often sparse. Several prior coded computation methods operate with dense linear combinations of the original submatrices; this can significantly increase the worker node computation times and consequently the overall job execution time. Moreover, several existing techniques treat the stragglers as failures (erasures) and discard their computations. In this work, we present a coding approach which operates with limited encoding of the original submatrices and utilizes the partial computations done by the slower workers. While our scheme can continue to have the optimal threshold of prior work, it also allows us to trade off the straggler resilience with the worker computation speed for sparse input matrices. Extensive numerical experiments done in AWS (Amazon Web Services) cluster confirm that the proposed approach enhances the speed of the worker computations (and thus the whole process) significantly.

Bulk 1T$^\prime$-MoTe$_2$ shows a structural phase transition from 1T$^\prime$ to Weyl semimetallic (WSM) $ T_{d} $ phase at $\sim$ 240 K. This phase transition and transport properties in the two phases have not been investigated on ultra-thin crystals. Here we report electrical transport, $1/f$ noise and Raman studies in ultra-thin 1T$^\prime$-MoTe$_2$ ($\sim$ 5 to 16 nm thick) field-effect transistors (FETs) devices as a function of temperature. The electrical resistivities for thickness 16 nm and 11 nm show maxima at temperatures 208 K and 178 K, respectively, making a transition from semiconducting to semi-metallic phase, hitherto not observed in bulk samples. Raman frequencies and linewidths for 11nm thick crystal show change around 178 K, attributed to additional contribution to the phonon self-energy due to enhanced electron-phonon interaction in the WSM phase. Further, the resistivity at low-temperature shows an upturn below 20 K along with the maximum in the power spectral density of the low frequency $1/f$ noise. The latter rules out the metal-insulator transition (MIT) being responsible for the upturn of resistivity below 20 K. The low temperature resistivity follows $\rho \propto 1/T$, changing to $\rho \propto T$ with increasing temperature supports electron-electron interaction physics at electron-hole symmetric Weyl nodes below 20 K. These observations will pave the way to unravel the properties of WSM state in layered ultra-thin van der Waals materials.

Magic-angle twisted bilayer graphene (MtBLG) has proven to be an extremely promising new platform to realize and study a host of emergent quantum phases arising from the strong correlations in its narrow bandwidth flat band. In this regard, thermal transport phenomena like thermopower, in addition to being coveted technologically, is also sensitive to the particle-hole (PH) asymmetry, making it a crucial tool to probe the underlying electronic structure of this material. We have carried out thermopower measurements of MtBLG as a function of carrier density, temperature and magnetic field, and report the observation of an unusually large thermopower reaching up to a value as high as $\sim \bf{100\mu V/K}$ at a low temperature of 1K. Surprisingly, our observed thermopower exhibiting peak-like features in close correspondence to the resistance peaks around the integer Moire fillings, including the Dirac Point, violating the Mott formula. %Surprisingly, our observed thermopower exhibits peak-like features in close correspondence to the resistance peaks around the integer Moire fillings, including the Dirac Point, which completely violates the Mott formula. We show that the large thermopower peaks and their %non-monotonic dependence with temperature and magnetic field associated behaviour arise from the emergent highly PH asymmetric electronic structure due to the cascade of Dirac revivals. Furthermore, the thermopower shows an anomalous peak around the superconducting transition on the hole side and points towards the possible role of enhanced superconducting fluctuations in MtBLG.

The structure of edge modes at the boundary of quantum Hall (QH) phases forms the basis for understanding low energy transport properties. In particular, the presence of ``upstream'' modes, moving against the direction of charge current flow, is critical for the emergence of renormalized modes with exotic quantum statistics. Detection of excess noise at the edge is a smoking gun for the presence of upstream modes. Here we report on noise measurements at the edges of fractional QH (FQH) phases realized in dual graphite-gated bilayer graphene devices. A noiseless dc current is injected at one of the edge contacts, and the noise generated at contacts at $L= 4\,\mu$m or $10\,\mu$m away along the upstream direction is studied. For integer and particle-like FQH states, no detectable noise is measured. By contrast, for ``hole-conjugate'' FQH states, we detect a strong noise proportional to the injected current, unambiguously proving the existence of upstream modes. The noise magnitude remaining independent of length together with a remarkable agreement with our theoretical analysis demonstrates the ballistic nature of upstream energy transport, quite distinct from the diffusive propagation reported earlier in GaAs-based systems. Our investigation opens the door to the study of upstream transport in more complex geometries and in edges of non-Abelian phases in graphene.

The relative twist angle in heterostructures of two-dimensional (2D) materials with similar lattice constants result in a dramatic alteration of the electronic properties. Here, we investigate the electrical and magnetotransport properties in bilayer graphene (BLG) encapsulated between two hexagonal boron nitride (hBN) crystals, where the top and bottom hBN are rotationally aligned with bilayer graphene with a twist angle $\theta_t\sim 0^{\circ} \text{and}~ \theta_b < 1^{\circ}$, respectively. This results in the formation of two moir\'e superlattices, with the appearance of satellite resistivity peaks at carrier densities $n_{s1}$ and $n_{s2}$, in both hole and electron doped regions, together with the resistivity peak at zero carrier density. Furthermore, we measure the temperature(T) dependence of the resistivity ($\rho$). The resistivity shows a linear increment with temperature within the range 10K to 50K for the density regime $n_{s1}

Electrical conductance measurements have limited scope in identifying Andreev edge states (AESs), which form the basis for realizing various topological excitations in quantum Hall (QH) - superconductor (SC) junctions. To unambiguously detect AESs, we measure shot noise along with electrical conductance in a graphene based QH-SC junction at integer filling nu=2. Remarkably, we find that the Fano factor of shot noise approaches half when the bias energy is less than the superconducting gap, whereas it is close to zero above the superconducting gap. This is striking, given that, at the same time, the electrical conductance remains quantized at 2e^2/h within and above the superconducting gap. A quantized conductance is expected to produce zero shot noise due to its dissipationless flow. However, at a QH-SC interface, AESs carry the current in the zero-bias limit and an equal mixing of electron and hole like states produces half of the Poissonian shot noise with quantized conductance. The observed results are in accord with our detailed theoretical calculations of electrical conductance and shot noise based on non-equilibrium Green's function method in the presence of disorder. Our results pave the way in using shot noise as a detection tool in the search of exotic topological excitations in QH-SC hybrids.

Atomically thin monolayers of graphene show excellent electronic properties which have led to a great deal of research on their use in nanoscale devices. However, heat management of such nanoscale devices is essential in order to improve their performance. Graphene supported on hexagonal boron nitride (h-BN) substrate has been reported to show enhanced (opto)electronic and thermal properties as compared to extensively used SiO2/Si supported graphene. Motivated by this, we have performed temperature- and power-dependent Raman Spectroscopic measurements on four different types of (hetero)structures: (a) h-BN (BN), (b) graphene (Gr), (c) h-BN on graphene (BG), and (d) graphene encapsulated by h-BN layers from both top and bottom (BGB), all supported on SiO2/Si substrate. We have estimated the values of thermal conductivity (\k{appa}) and interfacial thermal conductance per unit area (g) of these four (hetero)structures to demonstrate the structure-activity (thermal) relationship. We report here the values of \k{appa} and g for h-BN supported on SiO2/Si as 280.0 +-58.0 Wm-1K-1 and 25.6+-0.4 MWm-2K-1, respectively. More importantly, we have observed an improvement in both thermal conductivity and interfacial thermal conductance per unit area in the heterostructures which ensures a better heat dissipation in devices. The \k{appa} and g of h-BN encapsulated graphene on SiO2/Si (BGB) sample was observed to be 850.0+-81.0 Wm-1K-1 and 105+-1 MWm-2K-1, respectively, as opposed to 600.0+-93.0 Wm-1K-1 and 1.15+-0.40 MWm-2K-1, respectively, for graphene on SiO2/Si substrate. Therefore, we propose that for graphene-based nanoscale devices, encapsulation with h-BN is a better alternative to address heat management issues.