pwc-archive/papers-with-abstracts huggingface dataset:
Engineering Effective Hamiltonians for Magnetic Resonance
Authors: Holger Haas
Date: 2019-07-22 00:00:00
Proceeding: University of Waterloo Library 2019 7
Tasks: Band Gap

Effective Hamiltonian engineering is a powerful technique that utilises time-dependent perturbation theory to suppress or enhance certain effects that arise from otherwise weak Hamiltonian terms, when an experimenter attempts to control the evolution of a quantum system. With this thesis we conduct theoretical, numerical and experimental explorations of two somewhat different kinds of effective Hamiltonian engineering in magnetic resonance, aimed at elongating spin coherence times and increasing spin polarization. First, we provide a general framework for computing arbitrary time-dependent perturbation theory terms relevant for effective Hamiltonian engineering, as well as their gradients with respect to control variations, that immediately enables the use of gradient methods for optimizing these terms. We demonstrate the method in various numerical control engineering examples, including decoupling, recoupling, and robustness to control errors and stochastic errors. We then utilize the method in high-resolution nanoscale magnetic resonance experiments that use high-sensitivity silicon nanowire based force detection. With our numerically engineered control sequences, that are optimized to suppress spin decoherence under dipolar and chemical shift Hamiltonians, we achieve an increase by a factor of 500 in the proton spin coherence time for low-temperature, (50-nm)^3 polystyrene sample. We harness the enhanced spin coherence times for magnetic resonance imaging experiments on the nanoscale, and achieve one-dimensional imaging resolution below 2 nm on the same sample. Secondly, we study low-temperature, high magnetic field nuclear hyperpolarization of phosphorus donor nuclei in isotopically enriched 28-silicon crystal irradiated with nonresonant above band gap laser light. We demonstrate hyperpolarization with a time constant of 51.5 s, and introduce a phonon-mediated effective Hamiltonian model detailing the process. We verify our model by designing and conducting a series of magnetic resonance experiments.
General-Purpose Deep Point Cloud Feature Extractor
Authors: Miguel Dominguez, Rohan Dhamdhere, Atir Petkar, Saloni Jain, Shagan Sah, Raymond Ptucha
Date: 2018-03-12 00:00:00
Proceeding: IEEE Winter Conference on Applications of Computer Vision (WACV) 2018 3
Tasks: 3D Object Classification, 3D Point Cloud Classification, Autonomous Driving, Object Tracking, Scene Understanding, Transfer Learning

Depth sensors used in autonomous driving and gaming
systems often report back 3D point clouds. The lack of
structure from these sensors does not allow these systems
to take advantage of recent advances in convolutional neural networks which are dependent upon traditional filtering
and pooling operations. Analogous to image based convolutional architectures, recently introduced graph based architectures afford similar filtering and pooling operations
on arbitrary graphs. We adopt these graph based methods
to 3D point clouds to introduce a generic vector representation of 3D graphs, we call graph 3D (G3D). We believe
we are the first to use large scale transfer learning on 3D
point cloud data and demonstrate the discriminant power
of our salient latent representation of 3D point clouds on
unforeseen test sets. By using our G3D network (G3DNet)
as a feature extractor, and then pairing G3D feature vectors
with a standard classifier, we achieve the best accuracy on
ModelNet10 (93.1%) and ModelNet 40 (91.7%) for a graph
network, and comparable performance on the Sydney Urban Objects dataset to other methods. This general-purpose
feature extractor can be used as an off-the-shelf component
in other 3D scene understanding or object tracking works.
Evolution of Graph Classifiers
Authors: Miguel Dominguez, Rohan Dhamdhere, Naga Durga Harish Kanamarlapudi, Sunand Raghupathi, Raymond Ptucha
Date: 2019-10-04 00:00:00
Proceeding: IEEE Western New York Image and Signal Processing Workshop (WNYISPW) 2019 10
Tasks: Graph Classification

Architecture design and hyperparameter selection for deep neural networks often involves guesswork. The parameter space is too large to try all possibilities, meaning one often settles for a suboptimal solution. Some works have proposed automatic architecture and hyperparameter search, but are constrained to image applications. We propose an evolution framework for graph data which is extensible to generic graphs. Our evolution mutates a population of neural networks to search the architecture and hyperparameter space. At each stage of the neuroevolution process, neural network layers can be added or removed, hyperparameters can be adjusted, or additional epochs of training can be applied. Probabilities of the mutation selection based on recent successes help guide the learning process for efficient and accurate learning. We achieve state-of-the-art on MUTAG protein classification from a small population of 10 networks and gain interesting insight into how to build effective network architectures incrementally.
Learning to Count Grave Sites for Cemetery Observation Models With Satellite Imagery
Authors: Dalton Lunga, Rohan Dhamdhere, Sarah Walters, Lauryn Bragg, Nikhil Makkar, Marie Urban
Date: 2020-09-22 00:00:00
Proceeding: IEEE Geoscience and Remote Sensing Letters 2020 9
Tasks: Object Counting

Understanding how people occupy open spaces is important for research in support of population modeling, policy, national security, emergency response, and sustainability. For the past decade, there has been an increase in research toward capturing and reporting population dynamics and patterns of life at the building level and in some open public spaces such as cemeteries and parks. This is done through observation models developed from local sociocultural information acquired at various spatiotemporal scales to inform night, day, and episodic population occupancy estimates (people/1000 sq ft). Sociocultural information for cemeteries and parks is scarcely available and often collected manually. The process is not only marred by inconsistencies but is laborious and time consuming. In this study, we leverage convolutional neural networks (CNNs) and satellite imagery to derive grave site counts as proxy variables to support scalable and accurate sociocultural data required in a population observation model. Through a hybrid workflow (weak localization plus regression model), we characterize a large scale automation process to counting of grave sites. We evaluate and demonstrate the efficacy of proposed workflow using out-of-data set large satellite imagery and establish its broader impact on cemetery observation models.
Robust Recovery of Subspace Structures by Low-Rank Representation
Authors: Guangcan Liu, Zhouchen Lin, Shuicheng Yan, Ju Sun, Yong Yu, Yi Ma
Date: 2010-10-14 00:00:00

In this work we address the subspace recovery problem. Given a set of data samples (vectors) approximately drawn from a union of multiple subspaces, our goal is to segment the samples into their respective subspaces and correct the possible errors as well. To this end, we propose a novel method termed Low-Rank Representation (LRR), which seeks the lowest-rank representation among all the candidates that can represent the data samples as linear combinations of the bases in a given dictionary. It is shown that LRR well solves the subspace recovery problem: when the data is clean, we prove that LRR exactly captures the true subspace structures; for the data contaminated by outliers, we prove that under certain conditions LRR can exactly recover the row space of the original data and detect the outlier as well; for the data corrupted by arbitrary errors, LRR can also approximately recover the row space with theoretical guarantees. Since the subspace membership is provably determined by the row space, these further imply that LRR can perform robust subspace segmentation and error correction, in an efficient way.
Clustering with Hypergraphs: The Case for Large Hyperedges
Authors: Pulak Purkait, Tat-Jun Chin, Hanno Ackermann, David Suter
Date: 2016-10-04 00:00:00
Proceeding: IEEE Transactions on Pattern Analysis and Machine Intelligence 2016 10
Tasks: Clustering, Face Clustering, Motion Segmentation

The extension of conventional clustering to hypergraph clustering, which involves higher order similarities instead of pairwise similarities, is increasingly gaining attention in computer vision. This is due to the fact that many grouping problems require an affinity measure that must involve a subset of data of size more than two, i.e., a hyperedge. Almost all previous works, however, have considered the smallest possible hyperedge size, due to a lack of study into the potential benefits of large hyperedges and effective algorithms to generate them. In this paper, we show that large hyperedges are better from both theoretical and empirical standpoints. We then propose a novel guided sampling strategy for large hyperedges, based on the concept of random cluster models. Our method can generate pure large hyperedges that significantly improve grouping accuracy without exponential increases in sampling costs. In the important applications of face clustering and motion segmentation, our method demonstrates substantially better accuracy and efficiency.
Experimental Dataset of Electrochemical Efficiency of a Direct Borohydride Fuel Cell (DBFC) with Pd/C, Pt/C and Pd Decorated Ni–Co/rGO Anode Catalysts
Authors: Sarmin Hamidi, Sadra Sabouri, Sepand Haghighi, Kasra Askari
Date: 2020-04-23 00:00:00
Proceeding: ChemRxiv 2020 4

Dataset includes Direct Borohydride Fuel Cell (DBFC) impedance and polarization test in anode with Pd/C, Pt/C and Pd decorated Ni–Co/rGO catalysts. In fact, different concentration of Sodium Borohydride (SBH), applied voltages and various anode catalysts loading with explanation of experimental details of electrochemical analysis are considered in data. Voltage, power density and resistance of DBFC change as a function of weight percent of SBH (%), applied voltage and amount of anode catalyst loading that are evaluated by polarization and impedance curves with using appropriate equivalent circuit of fuel cell. Can be stated that interpretation of electrochemical behavior changes by the data of related cell is inevitable, which can be useful in simulation, power source investigation and depth analysis in DB fuel cell researches.
SDSS J2125-0813: the evidence for the origination of optical FeII emission lines from accretion disk near central black hole
Authors: Zhang Xue-Guang, D. Dultzin-Hacyan, Wang Ting-Gui
Date: 2006-07-25 00:00:00

We report on the radio quiet quasar SDSS J2125-0813 which obviously emits optical FeII emission lines and double-peaked broad Balmer emission lines. Using the accretion disk model for double-peaked broad low-ionization emission lines, we reproduce the composite line spectra at the optical band between 4100$\AA$ and 5600$\AA$. Broad FeII emission lines can be fit simultaneously with the broad H$\beta$ and MgI lines, such that all broad lines have an elliptical disk profile with the same disk parameters. This results indicates that the optical FeII emission lines originate from the accretion disk near the central black hole which produces the double-peaked broad Balmer emission lines. Furthermore, we find that the object has dimensionless accretion rate $\dot{m}\sim0.4-0.6$ which is much larger than accretion rate for ADAF mode, and that the energy budget of the accretion disk is enough to power the double-peaked broad Balmer emission lines.
Do Prompt-Based Models Really Understand the Meaning of their Prompts?
Authors: Albert Webson, Ellie Pavlick
Date: 2021-09-02 00:00:00
Proceeding: NAACL 2022 7
Tasks: Few-Shot Learning, Natural Language Inference

Recently, a boom of papers has shown extraordinary progress in zero-shot and few-shot learning with various prompt-based models. It is commonly argued that prompts help models to learn faster in the same way that humans learn faster when provided with task instructions expressed in natural language. In this study, we experiment with over 30 prompt templates manually written for natural language inference (NLI). We find that models learn just as fast with many prompts that are intentionally irrelevant or even pathologically misleading as they do with instructively "good" prompts. Further, such patterns hold even for models as large as 175 billion parameters (Brown et al., 2020) as well as the recently proposed instruction-tuned models which are trained on hundreds of prompts (Sanh et al., 2022). That is, instruction-tuned models often produce good predictions with irrelevant and misleading prompts even at zero shots. In sum, notwithstanding prompt-based models' impressive improvement, we find evidence of serious limitations that question the degree to which such improvement is derived from models understanding task instructions in ways analogous to humans' use of task instructions.
A Context-Aware Hierarchical BERT Fusion Network for Multi-turn Dialog Act Detection
Authors: Ting-Wei Wu, Ruolin Su, Biing-Hwang Juang
Date: 2021-09-03 00:00:00
Tasks: slot-filling, Slot Filling, Spoken Language Understanding

The success of interactive dialog systems is usually associated with the quality of the spoken language understanding (SLU) task, which mainly identifies the corresponding dialog acts and slot values in each turn. By treating utterances in isolation, most SLU systems often overlook the semantic context in which a dialog act is expected. The act dependency between turns is non-trivial and yet critical to the identification of the correct semantic representations. Previous works with limited context awareness have exposed the inadequacy of dealing with complexity in multiproned user intents, which are subject to spontaneous change during turn transitions. In this work, we propose to enhance SLU in multi-turn dialogs, employing a context-aware hierarchical BERT fusion Network (CaBERT-SLU) to not only discern context information within a dialog but also jointly identify multiple dialog acts and slots in each utterance. Experimental results show that our approach reaches new state-of-the-art (SOTA) performances in two complicated multi-turn dialogue datasets with considerable improvements compared with previous methods, which only consider single utterances for multiple intents and slot filling.
CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge
Authors: Yasumasa Onoe, Michael J. Q. Zhang, Eunsol Choi, Greg Durrett
Date: 2021-09-03 00:00:00
Tasks: Fact Checking, Fact Verification, Natural Language Understanding

Most benchmark datasets targeting commonsense reasoning focus on everyday scenarios: physical knowledge like knowing that you could fill a cup under a waterfall [Talmor et al., 2019], social knowledge like bumping into someone is awkward [Sap et al., 2019], and other generic situations. However, there is a rich space of commonsense inferences anchored to knowledge about specific entities: for example, deciding the truthfulness of a claim "Harry Potter can teach classes on how to fly on a broomstick." Can models learn to combine entity knowledge with commonsense reasoning in this fashion? We introduce CREAK, a testbed for commonsense reasoning about entity knowledge, bridging fact-checking about entities (Harry Potter is a wizard and is skilled at riding a broomstick) with commonsense inferences (if you're good at a skill you can teach others how to do it). Our dataset consists of 13k human-authored English claims about entities that are either true or false, in addition to a small contrast set. Crowdworkers can easily come up with these statements and human performance on the dataset is high (high 90s); we argue that models should be able to blend entity knowledge and commonsense reasoning to do well here. In our experiments, we focus on the closed-book setting and observe that a baseline model finetuned on existing fact verification benchmark struggles on CREAK. Training a model on CREAK improves accuracy by a substantial margin, but still falls short of human performance. Our benchmark provides a unique probe into natural language understanding models, testing both its ability to retrieve facts (e.g., who teaches at the University of Chicago?) and unstated commonsense knowledge (e.g., butlers do not yell at guests).
AI Descartes: Combining Data and Theory for Derivable Scientific Discovery
Authors: Cristina Cornelio, Sanjeeb Dash, Vernon Austel, Tyler Josephson, Joao Goncalves, Kenneth Clarkson, Nimrod Megiddo, Bachir El Khadir, Lior Horesh
Date: 2021-09-03 00:00:00
Tasks: Automated Theorem Proving, BIG-bench Machine Learning, Equation Discovery, Form, Logical Reasoning, scientific discovery, Symbolic Regression

Scientists have long aimed to discover meaningful formulae which accurately describe experimental data. A common approach is to manually create mathematical models of natural phenomena using domain knowledge, and then fit these models to data. In contrast, machine-learning algorithms automate the construction of accurate data-driven models while consuming large amounts of data. The problem of incorporating prior knowledge in the form of constraints on the functional form of a learned model (e.g., nonnegativity) has been explored in the literature. However, finding models that are consistent with prior knowledge expressed in the form of general logical axioms (e.g., conservation of energy) is an open problem. We develop a method to enable principled derivations of models of natural phenomena from axiomatic knowledge and experimental data by combining logical reasoning with symbolic regression. We demonstrate these concepts for Kepler's third law of planetary motion, Einstein's relativistic time-dilation law, and Langmuir's theory of adsorption, automatically connecting experimental data with background theory in each case. We show that laws can be discovered from few data points when using formal logical reasoning to distinguish the correct formula from a set of plausible formulas that have similar error on the data. The combination of reasoning with machine learning provides generalizeable insights into key aspects of natural phenomena. We envision that this combination will enable derivable discovery of fundamental laws of science and believe that our work is an important step towards automating the scientific method.
Stochastic Physics-Informed Neural Ordinary Differential Equations
Authors: Jared O'Leary, Joel A. Paulson, Ali Mesbah
Date: 2021-09-03 00:00:00

Stochastic differential equations (SDEs) are used to describe a wide variety of complex stochastic dynamical systems. Learning the hidden physics within SDEs is crucial for unraveling fundamental understanding of these systems' stochastic and nonlinear behavior. We propose a flexible and scalable framework for training artificial neural networks to learn constitutive equations that represent hidden physics within SDEs. The proposed stochastic physics-informed neural ordinary differential equation framework (SPINODE) propagates stochasticity through the known structure of the SDE (i.e., the known physics) to yield a set of deterministic ODEs that describe the time evolution of statistical moments of the stochastic states. SPINODE then uses ODE solvers to predict moment trajectories. SPINODE learns neural network representations of the hidden physics by matching the predicted moments to those estimated from data. Recent advances in automatic differentiation and mini-batch gradient descent with adjoint sensitivity are leveraged to establish the unknown parameters of the neural networks. We demonstrate SPINODE on three benchmark in-silico case studies and analyze the framework's numerical robustness and stability. SPINODE provides a promising new direction for systematically unraveling the hidden physics of multivariate stochastic dynamical systems with multiplicative noise.
Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding
Authors: YingMei Guo, Linjun Shou, Jian Pei, Ming Gong, Mingxing Xu, Zhiyong Wu, Daxin Jiang
Date: 2021-09-03 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Data Augmentation, Denoising, Spoken Language Understanding

Lack of training data presents a grand challenge to scaling out spoken language understanding (SLU) to low-resource languages. Although various data augmentation approaches have been proposed to synthesize training data in low-resource target languages, the augmented data sets are often noisy, and thus impede the performance of SLU models. In this paper we focus on mitigating noise in augmented data. We develop a denoising training approach. Multiple models are trained with data produced by various augmented methods. Those models provide supervision signals to each other. The experimental results show that our method outperforms the existing state of the art by 3.05 and 4.24 percentage points on two benchmark datasets, respectively. The code will be made open sourced on github.
Continuous-Time Behavior Trees as Discontinuous Dynamical Systems
Authors: Christopher Iliffe Sprague, Petter Ögren
Date: 2021-09-03 00:00:00

Behavior trees represent a hierarchical and modular way of combining several low-level control policies into a high-level task-switching policy. Hybrid dynamical systems can also be seen in terms of task switching between different policies, and therefore several comparisons between behavior trees and hybrid dynamical systems have been made, but only informally, and only in discrete time. A formal continuous-time formulation of behavior trees has been lacking. Additionally, convergence analyses of specific classes of behavior tree designs have been made, but not for general designs. In this letter, we provide the first continuous-time formulation of behavior trees, show that they can be seen as discontinuous dynamical systems (a subclass of hybrid dynamical systems), which enables the application of existence and uniqueness results to behavior trees, and finally, provide sufficient conditions under which such systems will converge to a desired region of the state space for general designs. With these results, a large body of results on continuous-time dynamical systems can be brought to use when designing behavior tree controllers.
Situated Conditional Reasoning
Authors: Giovanni Casini, Thomas Meyer, Ivan Varzinczak
Date: 2021-09-03 00:00:00

Conditionals are useful for modelling, but are not always sufficiently expressive for capturing information accurately. In this paper we make the case for a form of conditional that is situation-based. These conditionals are more expressive than classical conditionals, are general enough to be used in several application domains, and are able to distinguish, for example, between expectations and counterfactuals. Formally, they are shown to generalise the conditional setting in the style of Kraus, Lehmann, and Magidor. We show that situation-based conditionals can be described in terms of a set of rationality postulates. We then propose an intuitive semantics for these conditionals, and present a representation result which shows that our semantic construction corresponds exactly to the description in terms of postulates. With the semantics in place, we proceed to define a form of entailment for situated conditional knowledge bases, which we refer to as minimal closure. It is reminiscent of and, indeed, inspired by, the version of entailment for propositional conditional knowledge bases known as rational closure. Finally, we proceed to show that it is possible to reduce the computation of minimal closure to a series of propositional entailment and satisfiability checks. While this is also the case for rational closure, it is somewhat surprising that the result carries over to minimal closure.