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Graph isomorphism, a classical algorithmic problem, determines whether two input graphs are structurally identical or not. Interestingly, it is one of the few problems that is not yet known to belong to either the P or NP-complete complexity classes. As such, intelligent search-space pruning based strategies were proposed for developing isomorphism testing solvers like nauty and bliss, which are still, unfortunately, exponential in the worst-case scenario. Thus, the polynomial-time Weisfeiler-Lehman (WL) isomorphism testing heuristic, based on colour refinement, has been widely adopted in the literature. However, WL fails for multiple classes of non-isomorphic graph instances such as strongly regular graphs, block structures, and switched edges, among others. In this paper, we propose a novel polynomial-time graph isomorphism testing heuristic, RSVP, and depict its enhanced discriminative power compared to the Weisfeiler-Lehman approach for several challenging classes of graphs. Bounded by a run-time complexity of O(m^2+mn^2+n^3) (where n and m are the number of vertices and edges respectively), we show that RSVP can identify non-isomorphism in several 'hard' graph instance classes including Miyazaki, Paulus, cubic hypohamiltonian, strongly regular, Latin series and Steiner triple system graphs, where the 3-WL test fails. Similar to the WL test, our proposed algorithm is prone to only one-sided errors, where isomorphic graphs will never be determined to be non-isomorphic, although the reverse can happen.

We describe the smallest quantum error correcting (QEC) code to correct for amplitude-damping (AD) noise, namely, a 3-qubit code that corrects up to first order in the damping strength. We generalize this construction to create a family of codes that correct AD noise up to any fixed order. We underpin the fundamental connection between the structure of our codes and the noise structure via a relaxed form of the Knill-Laflamme conditions, that are different from existing formulations of approximate QEC conditions. Although the recovery procedure for this code is non-deterministic, our codes are optimal with respect to overheads and outperform existing codes to tackle AD noise in terms of entanglement fidelity. This alternate formulation of approximate QEC in fact leads us to a new class of quantum codes tailored to AD noise and also gives rise to a noise-adapted quantum Hamming bound for AD noise., Comment: 4 pages + 5 page appendix, 6 figures

In the background of homogeneous and isotropic flat FLRW space-time, both classical and quantum cosmology has been studied for teleparallel dark energy (DE) model. Using Noether symmetry analysis, not only the symmetry vector but also the coupling function in the Lagrangian and the potential of the scalar field has been determined. Also symmetry analysis identifies a cyclic variable in the Lagrangian along the symmetry vector and as a result the Lagrangian simplifies to a great extend so that classical solution is obtained. Subsequently, in quantum cosmology Wheeler-DeWitt(WD) equation has been constructed and the quantum version of the conserved momenta corresponding to Noether symmetry identifies the periodic part of the wave function of the universe and as a result the Wheeler-DeWitt equation becomes solvable. Finally, quantum description shows finite non-zero probability at the classical big-bang singularity., Comment: 16 Pages, 4 figures

The present work deals with Einstein-aether Scalar tensor gravity in the background of homogeneous and isotropic flat FLRW space-time model. The Noether symmetry vector identifies a transformation in the augmented space so that the field equations become solvable. The cosmological solutions are analyzed from the observational point of view. Finally, for quantum cosmology, the Wheeler-DeWitt (WD) has been formulated and solutions have been determined by identifying the periodic nature of the wave function using conserved (Noether) charge., Comment: 15 pages, 4 figures

The present work deals with a multi-field cosmological model in a spatially flat FLRW space-time geometry. The usual Brans-Dicke(BD) field and another scalar field are minimally coupled to gravity while they interact with each other through the Kinetic terms. {The main aim of the present work is to examine whether the model is compatible with cosmic observations. So cosmological solutions are obtained using symmetry analysis only.} By imposing Noether Symmetry to the Lagrangian of the system the potential of the scalar field as well as the coupling function has been determined. The classical solutions are determined after simplifying the Lagrangian using cyclic variables. Finally, Wheeler-DeWitt(WD) equation in quantum cosmology has been formulated and conserved momenta corresponding to Noether symmetry shows the periodic part of the wave function and it helps to have the complete integral for the wave function., Comment: 14 pages, 4 figures

Quantum error correction (QEC) plays a critical role in preventing information loss in quantum systems and provides a framework for reliable quantum computation. Identifying quantum codes with nice code parameters for physically motivated noise models remains an interesting challenge. Going beyond qubit codes, here we propose a class of qudit error correcting codes tailored to protect against amplitude-damping noise. Specifically, we construct a class of four-qudit codes that satisfies the error correction conditions for all single-qudit and a few two-qudit damping errors up to the leading order in the damping parameter $\gamma$. We devise a protocol to extract syndromes that identify this set of errors unambiguously, leading to a noise-adapted recovery scheme that achieves a fidelity loss of $\cO(\gamma^{2})$. For the $d=2$ case, our QEC scheme is identical to the known example of the $4$-qubit code and the associated syndrome-based recovery. We also assess the performance of our class of codes using the Petz recovery map and note some interesting deviations from the qubit case.

Detecting out-of-scope user utterances is essential for task-oriented dialogues and intent classification. Current methodologies face difficulties with the unpredictable distribution of outliers and often rely on assumptions about data distributions. We present the Dual Encoder for Threshold-Based Re-Classification (DETER) to address these challenges. This end-to-end framework efficiently detects out-of-scope intents without requiring assumptions on data distributions or additional post-processing steps. The core of DETER utilizes dual text encoders, the Universal Sentence Encoder (USE) and the Transformer-based Denoising AutoEncoder (TSDAE), to generate user utterance embeddings, which are classified through a branched neural architecture. Further, DETER generates synthetic outliers using self-supervision and incorporates out-of-scope phrases from open-domain datasets. This approach ensures a comprehensive training set for out-of-scope detection. Additionally, a threshold-based re-classification mechanism refines the model's initial predictions. Evaluations on the CLINC-150, Stackoverflow, and Banking77 datasets demonstrate DETER's efficacy. Our model outperforms previous benchmarks, increasing up to 13% and 5% in F1 score for known and unknown intents on CLINC-150 and Stackoverflow, and 16% for known and 24% % for unknown intents on Banking77. The source code has been released at https://github.com/Hossam-Mohammed-tech/Intent_Classification_OOS.

We describe an apparatus where many species of ultracold atoms can be simultaneously trapped and overlapped with many species of ions in a Paul trap. Several design innovations are made to increase the versatility of the apparatus while keeping the size and cost reasonable. We demonstrate the operation of a 3-dimensional (3D) magneto-optical trap (MOT) of $^7$Li using a single external cavity diode laser. The $^7$Li MOT is loaded from an atomic beam, with atoms slowed using a Zeeman slower designed to work simultaneously for Li and Sr. The operation of a 3D MOT of $^{133}$Cs, loaded from a 2D MOT, is demonstrated and provisions for MOTs of Rb and K in the same vacuum manifold exist. We demonstrate the trapping of $^7$Li$^+$ and $^{133}$Cs$^+$ at different settings of the Paul trap and their detection using an integrated time-of-flight mass spectrometer. We present results on low energy neutral-neutral collisions ($^{133}$Cs-$^{133}$Cs, $^7$Li-$^7$Li and $^{133}$Cs-$^7$Li collisions) and charge-neutral collisions ($^{133}$Cs$^+$-$^{133}$Cs and $^7$Li$^+$-$^7$Li collisions). We show evidence of sympathetic cooling of $^7$Li$^+$ ($^{133}$Cs$^+$) due to collisions with the ultracold $^7$Li ($^{133}$Cs)., Comment: 15 pages. The article has been accepted by The Journal of Chemical Physics. After it is published, it will be found at https://pubs.aip.org/aip/jcp

Recent work has found that few-shot sentence classification based on pre-trained Sentence Encoders (SEs) is efficient, robust, and effective. In this work, we investigate strategies for domain-specialization in the context of few-shot sentence classification with SEs. We first establish that unsupervised Domain-Adaptive Pre-Training (DAPT) of a base Pre-trained Language Model (PLM) (i.e., not an SE) substantially improves the accuracy of few-shot sentence classification by up to 8.4 points. However, applying DAPT on SEs, on the one hand, disrupts the effects of their (general-domain) Sentence Embedding Pre-Training (SEPT). On the other hand, applying general-domain SEPT on top of a domain-adapted base PLM (i.e., after DAPT) is effective but inefficient, since the computationally expensive SEPT needs to be executed on top of a DAPT-ed PLM of each domain. As a solution, we propose AdaSent, which decouples SEPT from DAPT by training a SEPT adapter on the base PLM. The adapter can be inserted into DAPT-ed PLMs from any domain. We demonstrate AdaSent's effectiveness in extensive experiments on 17 different few-shot sentence classification datasets. AdaSent matches or surpasses the performance of full SEPT on DAPT-ed PLM, while substantially reducing the training costs. The code for AdaSent is available., Comment: Accepted at EMNLP 2023 Main

Doppler-free two-photon spectroscopy is a standard technique for precision measurement of transition frequencies of dipole-forbidden transitions. The accuracy of such measurements depends critically on fitting of the spectrum to an appropriate line shape model, often a Voigt profile which neglects the effect of quantum interference of optical transitions. Here, we report the observation of quantum interference of optical transition pathways in Doppler-free two-photon spectroscopy of the cesium 6S-7D transitions. The quantum interference manifests itself as asymmetric line shapes of the hyperfine lines of the 7D states, observed through spontaneous emission following excitation by a narrow-linewidth cw laser. The interference persists despite the lines being spectrally well-resolved. Ignoring the effect of quantum interference causes large systematic shifts in the determination of the line-centers, while accounting for it resolves the apparent line shift and enables the precise determination of hyperfine splitting in the 7D states. We calculate the spectral line shape including the effect of quantum interference and show that it agrees with the experimental observations. Our results have implications for precision measurements of hyperfine splittings, isotope shifts and transition frequencies, including those of the hydrogen S-S and S-D transitions., Comment: 15 pages

Fine-tuning pretrained self-supervised language models is widely adopted for transfer learning to downstream tasks. Fine-tuning can be achieved by freezing gradients of the pretrained network and only updating gradients of a newly added classification layer, or by performing gradient updates on all parameters. Gradual unfreezing makes a trade-off between the two by gradually unfreezing gradients of whole layers during training. This has been an effective strategy to trade-off between storage and training speed with generalization performance. However, it is not clear whether gradually unfreezing layers throughout training is optimal, compared to sparse variants of gradual unfreezing which may improve fine-tuning performance. In this paper, we propose to stochastically mask gradients to regularize pretrained language models for improving overall fine-tuned performance. We introduce GradDrop and variants thereof, a class of gradient sparsification methods that mask gradients during the backward pass, acting as gradient noise. GradDrop is sparse and stochastic unlike gradual freezing. Extensive experiments on the multilingual XGLUE benchmark with XLMR-Large show that GradDrop is competitive against methods that use additional translated data for intermediate pretraining and outperforms standard fine-tuning and gradual unfreezing. A post-analysis shows how GradDrop improves performance with languages it was not trained on, such as under-resourced languages., Comment: Accepted to IJCNN 2023

Despite being the current de-facto models in most NLP tasks, transformers are often limited to short sequences due to their quadratic attention complexity on the number of tokens. Several attempts to address this issue were studied, either by reducing the cost of the self-attention computation or by modeling smaller sequences and combining them through a recurrence mechanism or using a new transformer model. In this paper, we suggest to take advantage of pre-trained sentence transformers to start from semantically meaningful embeddings of the individual sentences, and then combine them through a small attention layer that scales linearly with the document length. We report the results obtained by this simple architecture on three standard document classification datasets. When compared with the current state-of-the-art models using standard fine-tuning, the studied method obtains competitive results (even if there is no clear best model in this configuration). We also showcase that the studied architecture obtains better results when freezing the underlying transformers. A configuration that is useful when we need to avoid complete fine-tuning (e.g. when the same frozen transformer is shared by different applications). Finally, two additional experiments are provided to further evaluate the relevancy of the studied architecture over simpler baselines.

Extended Reality (XR) is one of the most important 5G/6G media applications that will fundamentally transform human interactions. However, ensuring low latency, high data rate, and reliability to support XR services poses significant challenges. This letter presents a novel AI-assisted service provisioning scheme that leverages predicted frames for processing rather than relying solely on actual frames. This method virtually increases the network delay budget and consequently improves service provisioning, albeit at the expense of minor prediction errors. The proposed scheme is validated by extensive simulations demonstrating a multi-fold increase in supported XR users and also provides crucial network design insights.

We investigate the effects of post-training quantization and quantization-aware training on the generalization of Transformer language models. We present a new method called self-distilled quantization (SDQ) that minimizes accumulative quantization errors and outperforms baselines. We apply SDQ to multilingual models XLM-R-Base and InfoXLM-Base and demonstrate that both models can be reduced from 32-bit floating point weights to 8-bit integer weights while maintaining a high level of performance on the XGLUE benchmark. Our results also highlight the challenges of quantizing multilingual models, which must generalize to languages they were not fine-tuned on.

Gamma-ray bursts (GRBs) can be classified with their linearly dependent parameters alongside the standard $T_{90}$ distribution. The Generalized linear mixture model(GLM) identifies the number of linear dependencies in a two-parameter space. Classically, GRBs are classified into two classes by the presence of bimodality in the histogram of T$_{90}$. However, additional classes and sub-classes of GRBs are fascinating topics to explore. In this work, we investigate the GRBs classes in the $ T_{90} {-}T_{50}$ plane using the Generalized Linear Models(GLM) for Fermi GBM and BATSE catalogs. This study shows five linear features for the Fermi GBM catalog and four linear features for the BATSE catalog, directing towards the possibility of more than two GRB classes., Comment: 8 figures, 1 table

In the framework of $f(T)$-gravity theory, classical and quantum cosmology has been studied in the present work for FLRW space-time model. The Noether symmetry, a point-like symmetry of the Lagrangian is used to the physical system and a specific functional form of $f(T)$ is determined. A point transformation in the 2D augmented space restricts one of the variable to be cyclic so that the Lagrangian as well as the field equations are simplified so that they are solvable. Lastly for quantum cosmology, the WD equation is constructed and possible solution has been evaluated., Comment: 15 pages, 2 figures

We report the design and performance of a Littrow-type 671 nm External Cavity Diode Laser (ECDL) that delivers output power greater than 150 mW and features enhanced passive stability. The main body of the ECDL is constructed using titanium to minimize temperature related frequency drifts. The laser diode is mounted in a cylindrical mount that allows vertical adjustments while maintaining thermal contact with the temperature stabilized base plate. The wavelength tuning is achieved by horizontal displacement of the diffraction grating about an optimal pivot point. The compact design increases the robustness and passive stability of the ECDL and the stiff but light-weight diffraction grating-arm reduces the susceptibility to low-frequency mechanical vibrations. The linewidth of the ECDL is ~360 kHz. We use the 671 nm ECDL, without any additional power amplification, for laser cooling and trapping of lithium atoms in a magneto-optical trap. This simple, low-cost ECDL design using off-the-shelf laser diodes without anti-reflection coating can also be adapted to other wavelengths., Comment: 4 pages, 5 figures

This work deals with chameleon field cosmology (a scalar field nonminimally coupled to cold dark matter) in the background of flat Friedmann-Lemaitre-Robertson-Walker (FLRW) space-time. Both classical and quantum cosmology have been investigated using Noether symmetry analysis of the underlying physical system. The Wheeler-DeWitt (WD) equation has been constructed on the minisuperspace and solutions have been obtained using conserved charge., Comment: 19 pages, 4 figures

In this work, a cosmological model is considered having two scalar fields minimally coupled to gravity with a mixed kinetic term. The model is characterized by the coupling function and the potential function which are assumed to depend on one of the scalar fields. Instead of choosing these functions phenomenologically here, they are evaluated assuming the existence of Noether symmetry. By appropriate choice of a point transformation in the augmented space, one of the variables in the Lagrangian becomes cyclic and the evolution equations become much simpler to have solutions. Finally, the solutions are analyzed from cosmological view point., Comment: 12 pages, 3 figures

Aspect Based Sentiment Analysis is a dominant research area with potential applications in social media analytics, business, finance, and health. Prior works in this area are primarily based on supervised methods, with a few techniques using weak supervision limited to predicting a single aspect category per review sentence. In this paper, we present an extremely weakly supervised multi-label Aspect Category Sentiment Analysis framework which does not use any labelled data. We only rely on a single word per class as an initial indicative information. We further propose an automatic word selection technique to choose these seed categories and sentiment words. We explore unsupervised language model post-training to improve the overall performance, and propose a multi-label generator model to generate multiple aspect category-sentiment pairs per review sentence. Experiments conducted on four benchmark datasets showcase our method to outperform other weakly supervised baselines by a significant margin., Comment: to be published in EMNLP 2022

We report the measurement of hyperfine splitting in the $7d$ $^{2}D_{3/2}$ state of $^{133}$Cs using high resolution Doppler-free two-photon spectroscopy in a Cs vapor cell. We determine the hyperfine coupling constants $A = 7.3509(9)$ MHz and $B = -0.041(8)$ MHz, which represent an order of magnitude improvement in the precision. We also obtain bounds on the magnitude of the nuclear magnetic octupole coupling constant $C$. Additionally, we measure the ac Stark shift of the $6s$ $^{2}S_{1/2} \rightarrow 7d$ $^{2}D_{3/2}$ transition at 767.8 nm to be $-49 \pm 5$ Hz/(W/cm$^2$), in agreement with theoretical calculations. We further report the measurement of collisional shift [$-32.6 \pm 2.0$ kHz/mTorr] and pressure broadening for the individual hyperfine levels of the $6s$ $^{2}S_{1/2} \rightarrow 7d$ $^{2}D_{3/2}$ transition. These measurements provide valuable inputs for analysis of systematic effects in optical frequency standards based on the cesium $6s$ $^{2}S_{1/2} \rightarrow 7d$ $^{2}D_{3/2}$ two-photon transition, Comment: 14 pages, 7 figures, to appear in Physical Review A

We report the measurement of the hyperfine splitting in the $7D_{5/2}$ state of $^{133}$Cs using high resolution Doppler-free two-photon spectroscopy enabled by precise frequency scans using an acousto-optic modulator. All the six hyperfine levels are resolved in our spectra. We determine the hyperfine coupling constants A = -1.70867(62) MHz and B = 0.050(14) MHz which represent over 20-times improvement in the precision of both A and B. Moreover, our measurement is sufficiently precise to put bounds on the value of the magnetic octupole coupling constant C = 0.4(1.4) kHz for the $7D_{5/2}$ state. We additionally report the measurement of ac Stark shift [-46 $\pm$ 4 Hz/(W/cm$^2$)], collisional shift and pressure broadening which are important for optical frequency standards based on the $6S_{1/2} \rightarrow 7D_{5/2}$ two-photon transition., Comment: 4 pages of main text + 4 pages of supplementary information

This paper describes a new approach for approximating the inverse kinematics of a manipulator using an Ant Colony Optimization (ACO) based RBFN (Radial Basis Function Network). In this paper, a training solution using the ACO and the LMS (Least Mean Square) algorithm is presented in a two-phase training procedure. To settle the problem that the cluster results of k-mean clustering Radial Basis Function (RBF) are easy to be influenced by the selection of initial characters and converge to a local minimum, Ant Colony Optimization (ACO) for the RBF neural networks which will optimize the center of RBF neural networks and reduce the number of the hidden layer neurons nodes is presented. The result demonstrates that the accuracy of Ant Colony Optimization for the Radial Basis Function (RBF) neural networks is higher, and the extent of fitting has been improved.

While various avenues of research have been explored for iterative pruning, little is known what effect pruning has on zero-shot test performance and its potential implications on the choice of pruning criteria. This pruning setup is particularly important for cross-lingual models that implicitly learn alignment between language representations during pretraining, which if distorted via pruning, not only leads to poorer performance on language data used for retraining but also on zero-shot languages that are evaluated. In this work, we show that there is a clear performance discrepancy in magnitude-based pruning when comparing standard supervised learning to the zero-shot setting. From this finding, we propose two weight regularizers that aim to maximize the alignment between units of pruned and unpruned networks to mitigate alignment distortion in pruned cross-lingual models and perform well for both non zero-shot and zero-shot settings. We provide experimental results on cross-lingual tasks for the zero-shot setting using XLM-RoBERTa$_{\mathrm{Base}}$, where we also find that pruning has varying degrees of representational degradation depending on the language corresponding to the zero-shot test set. This is also the first study that focuses on cross-lingual language model compression., Comment: Accepted to ACL Findings 2022

The paper deals with static traversable wormhole having shape function a polynomial of the radial coordinate of degree 2. Embedding of the wormholes in space-time is examined both analytically and graphically. Also both timelike and null geodesics are studied in this wormhole geometry.

Pruning aims to reduce the number of parameters while maintaining performance close to the original network. This work proposes a novel \emph{self-distillation} based pruning strategy, whereby the representational similarity between the pruned and unpruned versions of the same network is maximized. Unlike previous approaches that treat distillation and pruning separately, we use distillation to inform the pruning criteria, without requiring a separate student network as in knowledge distillation. We show that the proposed {\em cross-correlation objective for self-distilled pruning} implicitly encourages sparse solutions, naturally complementing magnitude-based pruning criteria. Experiments on the GLUE and XGLUE benchmarks show that self-distilled pruning increases mono- and cross-lingual language model performance. Self-distilled pruned models also outperform smaller Transformers with an equal number of parameters and are competitive against (6 times) larger distilled networks. We also observe that self-distillation (1) maximizes class separability, (2) increases the signal-to-noise ratio, and (3) converges faster after pruning steps, providing further insights into why self-distilled pruning improves generalization.

We describe the EdinSaar submission to the shared task of Multilingual Low-Resource Translation for North Germanic Languages at the Sixth Conference on Machine Translation (WMT2021). We submit multilingual translation models for translations to/from Icelandic (is), Norwegian-Bokmal (nb), and Swedish (sv). We employ various experimental approaches, including multilingual pre-training, back-translation, fine-tuning, and ensembling. In most translation directions, our models outperform other submitted systems., Comment: To be published WMT2021

Frequently-Asked-Question (FAQ) retrieval provides an effective procedure for responding to user's natural language based queries. Such platforms are becoming common in enterprise chatbots, product question answering, and preliminary technical support for customers. However, the challenge in such scenarios lies in bridging the lexical and semantic gap between varied query formulations and the corresponding answers, both of which typically have a very short span. This paper proposes TI-S2S, a novel learning framework combining TF-IDF based keyword extraction and Word2Vec embeddings for training a Sequence-to-Sequence (Seq2Seq) architecture. It achieves high precision for FAQ retrieval by better understanding the underlying intent of a user question captured via the representative keywords. We further propose a variant with an additional neural network module for guiding retrieval via relevant candidate identification based on similarity features. Experiments on publicly available dataset depict our approaches to provide around 92% precision-at-rank-5, exhibiting nearly 13% improvement over existing approaches., Comment: 6 pages

Approximate subgraph matching, which is an important primitive for many applications like question answering, community detection, and motif discovery, often involves large labeled graphs such as knowledge graphs, social networks, and protein sequences. Effective methods for extracting matching subgraphs, in terms of label and structural similarities to a query, should depict accuracy, computational efficiency, and robustness to noise. In this paper, we propose VerSaChI for finding the top-k most similar subgraphs based on 2-hop label and structural overlap similarity with the query. The similarity is characterized using Chebyshev's inequality to compute the chi-square statistical significance for measuring the degree of matching of the subgraphs. Experiments on real-life graph datasets showcase significant improvements in terms of accuracy compared to state-of-the-art methods, as well as robustness to noise.

Model reduction for fluid flow simulation continues to be of great interest across a number of scientific and engineering fields. In a previous work [arXiv:2104.13962], we explored the use of Neural Ordinary Differential Equations (NODE) as a non-intrusive method for propagating the latent-space dynamics in reduced order models. Here, we investigate employing deep autoencoders for discovering the reduced basis representation, the dynamics of which are then approximated by NODE. The ability of deep autoencoders to represent the latent-space is compared to the traditional proper orthogonal decomposition (POD) approach, again in conjunction with NODE for capturing the dynamics. Additionally, we compare their behavior with two classical non-intrusive methods based on POD and radial basis function interpolation as well as dynamic mode decomposition. The test problems we consider include incompressible flow around a cylinder as well as a real-world application of shallow water hydrodynamics in an estuarine system. Our findings indicate that deep autoencoders can leverage nonlinear manifold learning to achieve a highly efficient compression of spatial information and define a latent-space that appears to be more suitable for capturing the temporal dynamics through the NODE framework., Comment: 16 pages, 7 figures, To Appear in the proceedings of the IXth International Conference on Computational Methods for Coupled Problems in Science and Engineering (COUPLED PROBLEMS 2021), 14-16 June, 2021. arXiv admin note: substantial text overlap with arXiv:2104.13962

Silicon ferroelectric field-effect transistors (FeFETs) with low-k interfacial layer (IL) between ferroelectric gate stack and silicon channel suffers from high write voltage, limited write endurance and large read-after-write latency due to early IL breakdown and charge trapping and detrapping at the interface. We demonstrate low voltage, high speed memory operation with high write endurance using an IL-free back-end-of-line (BEOL) compatible FeFET. We fabricate IL-free FeFETs with 28nm channel length and 126nm width under a thermal budget <400C by integrating 5nm thick Hf0.5Zr0.5O2 gate stack with amorphous Indium Tungsten Oxide (IWO) semiconductor channel. We report 1.2V memory window and read current window of 10^5 for program and erase, write latency of 20ns with +/-2V write pulses, read-after-write latency <200ns, write endurance cycles exceeding 5x10^10 and 2-bit/cell programming capability. Array-level analysis establishes IL-free BEOL FeFET as a promising candidate for logic-compatible high-performance on-chip buffer memory and multi-bit weight cell for compute-in-memory accelerators.

Model reduction for fluid flow simulation continues to be of great interest across a number of scientific and engineering fields. Here, we explore the use of Neural Ordinary Differential Equations, a recently introduced family of continuous-depth, differentiable networks (Chen et al 2018), as a way to propagate latent-space dynamics in reduced order models. We compare their behavior with two classical non-intrusive methods based on proper orthogonal decomposition and radial basis function interpolation as well as dynamic mode decomposition. The test problems we consider include incompressible flow around a cylinder as well as real-world applications of shallow water hydrodynamics in riverine and estuarine systems. Our findings indicate that Neural ODEs provide an elegant framework for stable and accurate evolution of latent-space dynamics with a promising potential of extrapolatory predictions. However, in order to facilitate their widespread adoption for large-scale systems, significant effort needs to be directed at accelerating their training times. This will enable a more comprehensive exploration of the hyperparameter space for building generalizable Neural ODE approximations over a wide range of system dynamics., Comment: 10 pages, 6 figures, To Appear in Proceedings of AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physics Sciences, March 22-24, 2021

Quantum cosmology with quintom dark energy model has been investigated in the present work using symmetry analysis of the underlying physical system. In the background of the flat FLRW model quintom cosmological model has been studied using Noether symmetry and appropriate conserved charge is obtained. The Wheeler-DeWitt (WD) equation is constructed on the minisuperspace and solutions are obtained using conserved charge., Comment: 15 pages

Many real-world mission-critical applications require continual online learning from noisy data and real-time decision making with a defined confidence level. Probabilistic models and stochastic neural networks can explicitly handle uncertainty in data and allow adaptive learning-on-the-fly, but their implementation in a low-power substrate remains a challenge. Here, we introduce a novel hardware fabric that implements a new class of stochastic NN called Neural-Sampling-Machine that exploits stochasticity in synaptic connections for approximate Bayesian inference. Harnessing the inherent non-linearities and stochasticity occurring at the atomic level in emerging materials and devices allows us to capture the synaptic stochasticity occurring at the molecular level in biological synapses. We experimentally demonstrate in-silico hybrid stochastic synapse by pairing a ferroelectric field-effect transistor -based analog weight cell with a two-terminal stochastic selector element. Such a stochastic synapse can be integrated within the well-established crossbar array architecture for compute-in-memory. We experimentally show that the inherent stochastic switching of the selector element between the insulator and metallic state introduces a multiplicative stochastic noise within the synapses of NSM that samples the conductance states of the FeFET, both during learning and inference. We perform network-level simulations to highlight the salient automatic weight normalization feature introduced by the stochastic synapses of the NSM that paves the way for continual online learning without any offline Batch Normalization. We also showcase the Bayesian inferencing capability introduced by the stochastic synapse during inference mode, thus accounting for uncertainty in data. We report 98.25%accuracy on standard image classification task as well as estimation of data uncertainty in rotated samples.

Cross-lingual alignment of word embeddings play an important role in knowledge transfer across languages, for improving machine translation and other multi-lingual applications. Current unsupervised approaches rely on similarities in geometric structure of word embedding spaces across languages, to learn structure-preserving linear transformations using adversarial networks and refinement strategies. However, such techniques, in practice, tend to suffer from instability and convergence issues, requiring tedious fine-tuning for precise parameter setting. This paper proposes BioSpere, a novel framework for unsupervised mapping of bi-lingual word embeddings onto a shared vector space, by combining adversarial initialization and refinement procedure with point set registration algorithm used in image processing. We show that our framework alleviates the shortcomings of existing methodologies, and is relatively invariant to variable adversarial learning performance, depicting robustness in terms of parameter choices and training losses. Experimental evaluation on parallel dictionary induction task demonstrates state-of-the-art results for our framework on diverse language pairs.