Reviewerly/exHarmony huggingface dataset:
2-Designs and Redundant Syndrome Extraction for Quantum Error Correction

Imperfect measurement can degrade a quantum error correction scheme. A solution that restores fault tolerance is to add redundancy to the process of syndrome extraction. In this work, we show how to optimize this process for an arbitrary ratio of data qubit error probability to measurement error probability. The key is to design the measurements so that syndromes that correspond to different errors are separated by the maximum distance in the signal space, in close analogy to classical error correction codes. We find that the mathematical theory of 2-designs, appropriately modified, is the right tool for this. Analytical and simulation results for the bit-flip code, the 5-qubit code, and the Steane code are presented. The results show that design-based redundancy protocols show improvement in both cost and performance relative to conventional fault-tolerant error-correction schemes in situations, quite important in practice, where measure errors are common. In the near term, the construction of a fault-tolerant logical qubit with a small number of noisy physical qubits will benefit from targeted redundancy in syndrome extraction.
The Integrated Nested Laplace Approximation for fitting Dirichlet regression models

This paper introduces a Laplace approximation to Bayesian inference in Dirichlet regression models, which can be used to analyze a set of variables on a simplex exhibiting skewness and heteroscedasticity, without having to transform the data. These data, which mainly consist of proportions or percentages of disjoint categories, are widely known as compositional data and are common in areas such as ecology, geology, and psychology. We provide both the theoretical foundations and a description of how Laplace approximation can be implemented in the case of Dirichlet regression. The paper also introduces the package dirinla in the R-language that extends the R-INLA package, which can not deal directly with Dirichlet likelihoods. Simulation studies are presented to validate the good behaviour of the proposed method, while a real data case-study is used to show how this approach can be applied.
Secure IoT access at scale using blockchains and smart contracts

Blockchains and smart contracts are an emerging, promising technology, that has received considerable attention. We use the blockchain technology, and in particular Ethereum, to implement a large-scale event-based Internet of Things (IoT) control system. We argue that the distributed nature of the "ledger," as well as, Ethereum's capability of parallel execution of replicated "smart contracts", provide the sought after automation, generality, flexibility, resilience, and high availability. We design a realistic blockchain-based IoT architecture, using existing technologies while by taking into consideration the characteristics and limitations of IoT devices and applications. Furthermore, we leverage blockchain's immutability and Ethereum's support for custom tokens to build a robust and efficient token-based access control mechanism. Our evaluation shows that our solution is viable and offers significant security and usability advantages.
APML: An Architecture Proof Modeling Language

To address the increasing size and complexity of modern software systems, compositional verification separates the verification of single components from the verification of their composition. In architecture-based verification, the former is done using Model Checking, while this does not seem to be the case in general the latter is done using interactive theorem proving (ITP). As of today, however, architects are usually not trained in using a full-fledged interactive theorem prover. Thus, to bridge the gap between ITP and the architecture domain, we developed APML: an architecture proof modeling language. APML allows one to sketch proofs about component composition at the level of architecture using notations similar to Message Sequence Charts. With this paper, we introduce APML: We describe the language, show its soundness and completeness for the verification of architecture contracts, and provide an algorithm to map an APML proof to a corresponding proof for the interactive theorem prover Isabelle. Moreover, we describe its implementation in terms of an Eclipse/EMF modeling application, demonstrate it by means of a running example, and evaluate it in terms of a larger case study. Although our results are promising, the case study also reveals some limitations, which lead to new directions for future work.
Metamorphic IOTA

IOTA opened recently a new line of research in distributed ledgers area by targeting algorithms that ensure a high throughput for the transactions generated in IoT systems. Transactions are continuously appended to an acyclic structure called tangle and each new transaction selects as parents two existing transactions (called tips) that it approves. G-IOTA, a very recent improvement of IOTA, targets to protect tips left behind offering hence a good confidence level. However, this improvement had a cost: the use of an additional tip selection mechanism which may be critical in IoT systems since it needs additional energy consumption. In this paper we propose a new metamorphic algorithm for tip selection that offers the best guaranties of both IOTA and G-IOTA. Our contribution is two fold. First, we propose a parameterized algorithm, E-IOTA, for tip selection which targets to reduce the number of random walks executed in previous versions (IOTA and G-IOTA) while maintaining the same security guaranties as IOTA and the same confidence level and fairness with respect to tips selection as G-IOTA. Then we propose a formal analysis of the security guaranties offered by E-IOTA against various attacks mentioned in the original IOTA proposal (e.g. large weight attack, parasite chain attack and splitting attack). Interestingly, to the best of our knowledge this is the first formal analysis of the security guaranties of IOTA and its derivatives.
Are deep ResNets provably better than linear predictors?

Recent results in the literature indicate that a residual network (ResNet) composed of a single residual block outperforms linear predictors, in the sense that all local minima in its optimization landscape are at least as good as the best linear predictor. However, these results are limited to a single residual block (i.e., shallow ResNets), instead of the deep ResNets composed of multiple residual blocks. We take a step towards extending this result to deep ResNets. We start by two motivating examples. First, we show that there exist datasets for which all local minima of a fully-connected ReLU network are no better than the best linear predictor, whereas a ResNet has strictly better local minima. Second, we show that even at the global minimum, the representation obtained from the residual block outputs of a 2-block ResNet do not necessarily improve monotonically over subsequent blocks, which highlights a fundamental difficulty in analyzing deep ResNets. Our main theorem on deep ResNets shows under simple geometric conditions that, any critical point in the optimization landscape is either (i) at least as good as the best linear predictor; or (ii) the Hessian at this critical point has a strictly negative eigenvalue. Notably, our theorem shows that a chain of multiple skip-connections can improve the optimization landscape, whereas existing results study direct skip-connections to the last hidden layer or output layer. Finally, we complement our results by showing benign properties of the "near-identity regions" of deep ResNets, showing depth-independent upper bounds for the risk attained at critical points as well as the Rademacher complexity.
Quantum key distribution based on the quantum eraser

Quantum information and quantum foundations are becoming popular topics for advanced undergraduate courses. Many of the fundamental concepts and applications in these two fields, such as delayed choice experiments and quantum encryption, are comprehensible to undergraduates with basic knowledge of quantum mechanics. In this paper, we show that the quantum eraser, usually used to study the duality between wave and particle properties, can also serve as a generic platform for quantum key distribution. We present a pedagogical example of an algorithm to securely share random keys using the quantum eraser platform and propose its implementation with quantum circuits.
Learning Neural Sequence-to-Sequence Models from Weak Feedback with Bipolar Ramp Loss

In many machine learning scenarios, supervision by gold labels is not available and consequently neural models cannot be trained directly by maximum likelihood estimation (MLE). In a weak supervision scenario, metric-augmented objectives can be employed to assign feedback to model outputs, which can be used to extract a supervision signal for training. We present several objectives for two separate weakly supervised tasks, machine translation and semantic parsing. We show that objectives should actively discourage negative outputs in addition to promoting a surrogate gold structure. This notion of bipolarity is naturally present in ramp loss objectives, which we adapt to neural models. We show that bipolar ramp loss objectives outperform other non-bipolar ramp loss objectives and minimum risk training (MRT) on both weakly supervised tasks, as well as on a supervised machine translation task. Additionally, we introduce a novel token-level ramp loss objective, which is able to outperform even the best sequence-level ramp loss on both weakly supervised tasks.
Diachronic Embedding for Temporal Knowledge Graph Completion

Knowledge graphs (KGs) typically contain temporal facts indicating relationships among entities at different times. Due to their incompleteness, several approaches have been proposed to infer new facts for a KG based on the existing ones-a problem known as KG completion. KG embedding approaches have proved effective for KG completion, however, they have been developed mostly for static KGs. Developing temporal KG embedding models is an increasingly important problem. In this paper, we build novel models for temporal KG completion through equipping static models with a diachronic entity embedding function which provides the characteristics of entities at any point in time. This is in contrast to the existing temporal KG embedding approaches where only static entity features are provided. The proposed embedding function is model-agnostic and can be potentially combined with any static model. We prove that combining it with SimplE, a recent model for static KG embedding, results in a fully expressive model for temporal KG completion. Our experiments indicate the superiority of our proposal compared to existing baselines.
Deep Reinforcement Learning For Modeling Chit-Chat Dialog With Discrete Attributes

Open domain dialog systems face the challenge of being repetitive and producing generic responses. In this paper, we demonstrate that by conditioning the response generation on interpretable discrete dialog attributes and composed attributes, it helps improve the model perplexity and results in diverse and interesting non-redundant responses. We propose to formulate the dialog attribute prediction as a reinforcement learning (RL) problem and use policy gradients methods to optimize utterance generation using long-term rewards. Unlike existing RL approaches which formulate the token prediction as a policy, our method reduces the complexity of the policy optimization by limiting the action space to dialog attributes, thereby making the policy optimization more practical and sample efficient. We demonstrate this with experimental and human evaluations.
A 3D Convolutional Approach to Spectral Object Segmentation in Space and Time

We formulate object segmentation in video as a graph partitioning problem in space and time, in which nodes are pixels and their relations form local neighborhoods. We claim that the strongest cluster in this pixel-level graph represents the salient object segmentation. We compute the main cluster using a novel and fast 3D filtering technique that finds the spectral clustering solution, namely the principal eigenvector of the graph's adjacency matrix, without building the matrix explicitly - which would be intractable. Our method is based on the power iteration for finding the principal eigenvector of a matrix, which we prove is equivalent to performing a specific set of 3D convolutions in the space-time feature volume. This allows us to avoid creating the matrix and have a fast parallel implementation on GPU. We show that our method is much faster than classical power iteration applied directly on the adjacency matrix. Different from other works, ours is dedicated to preserving object consistency in space and time at the level of pixels. For that, it requires powerful pixel-wise features at the frame level. This makes it perfectly suitable for incorporating the output of a backbone network or other methods and fast-improving over their solution without supervision. In experiments, we obtain consistent improvement, with the same set of hyper-parameters, over the top state of the art methods on DAVIS-2016 dataset, both in unsupervised and semi-supervised tasks. We also achieve top results on the well-known SegTrackv2 dataset.
HashGraph -- Scalable Hash Tables Using A Sparse Graph Data Structure

Hash tables are ubiquitous and used in a wide range of applications for efficient probing of large and unsorted data. If designed properly, hash-tables can enable efficients look ups in a constant number of operations or commonly referred to as O(1) operations. As data sizes continue to grow and data becomes less structured (as is common for big-data applications), the need for efficient and scalable hash table also grows. In this paper we introduce HashGraph, a new scalable approach for building hash tables that uses concepts taken from sparse graph representations--hence the name HashGraph. We show two different variants of HashGraph, a simple algorithm that outlines the method to create the hash-table and an advanced method that creates the hash table in a more efficient manner (with an improved memory access pattern). HashGraph shows a new way to deal with hash-collisions that does not use "open-addressing" or "chaining", yet has all the benefits of both these approaches. HashGraph currently works for static inputs, though recent progress with dynamic graph data structures suggest that HashGraph might be extended to dynamic inputs as well. We show that HashGraph can deal with a large number of hash-values per entry without loss of performance as most open-addressing and chaining approaches have. Further, we show that HashGraph is indifferent to the load-factor. Lastly, we show a new probing algorithm for the second phase of value lookups. Given the above, HashGraph is extremely fast and outperforms several state of the art hash-table implementations. The implementation of HashGraph in this paper is for NVIDIA GPUs, though HashGraph is not architecture dependent. Using a NVIDIA GV100 GPU, HashGraph is anywhere from 2X-8X faster than cuDPP, WarpDrive, and cuDF. HashGraph is able to build a hash-table at a rate of 2.5 billion keys per second and can probe at nearly the same rate.
Solidity 0.5: when typed does not mean type safe

The recent release of Solidity 0.5 introduced a new type to prevent Ether transfers to smart contracts that are not supposed to receive money. Unfortunately, the compiler fails in enforcing the guarantees this type intended to convey, hence the type soundness of Solidity 0.5 is no better than that of Solidity 0.4. In this paper we discuss a paradigmatic example showing that vulnerable Solidity patterns based on potentially unsafe callback expressions are still unchecked. We also point out a solution that strongly relies on formal methods to support a type-safer smart contracts programming discipline, while being retro-compatible with legacy Solidity code.
Global Aggregations of Local Explanations for Black Box models

The decision-making process of many state-of-the-art machine learning models is inherently inscrutable to the extent that it is impossible for a human to interpret the model directly: they are black box models. This has led to a call for research on explaining black box models, for which there are two main approaches. Global explanations that aim to explain a model's decision making process in general, and local explanations that aim to explain a single prediction. Since it remains challenging to establish fidelity to black box models in globally interpretable approximations, much attention is put on local explanations. However, whether local explanations are able to reliably represent the black box model and provide useful insights remains an open question. We present Global Aggregations of Local Explanations (GALE) with the objective to provide insights in a model's global decision making process. Overall, our results reveal that the choice of aggregation matters. We find that the global importance introduced by Local Interpretable Model-agnostic Explanations (LIME) does not reliably represent the model's global behavior. Our proposed aggregations are better able to represent how features affect the model's predictions, and to provide global insights by identifying distinguishing features.
Morphological Word Embeddings

Linguistic similarity is multi-faceted. For instance, two words may be similar with respect to semantics, syntax, or morphology inter alia. Continuous word-embeddings have been shown to capture most of these shades of similarity to some degree. This work considers guiding word-embeddings with morphologically annotated data, a form of semi-supervised learning, encouraging the vectors to encode a word's morphology, i.e., words close in the embedded space share morphological features. We extend the log-bilinear model to this end and show that indeed our learned embeddings achieve this, using German as a case study.
The Alternating BWT: an algorithmic perspective

The Burrows-Wheeler Transform (BWT) is a word transformation introduced in 1994 for Data Compression. It has become a fundamental tool for designing self-indexing data structures, with important applications in several area in science and engineering. The Alternating Burrows-Wheeler Transform (ABWT) is another transformation recently introduced in [Gessel et al. 2012] and studied in the field of Combinatorics on Words. It is analogous to the BWT, except that it uses an alternating lexicographical order instead of the usual one. Building on results in [Giancarlo et al. 2018], where we have shown that BWT and ABWT are part of a larger class of reversible transformations, here we provide a combinatorial and algorithmic study of the novel transform ABWT. We establish a deep analogy between BWT and ABWT by proving they are the only ones in the above mentioned class to be rank-invertible, a novel notion guaranteeing efficient invertibility. In addition, we show that the backward-search procedure can be efficiently generalized to the ABWT; this result implies that also the ABWT can be used as a basis for efficient compressed full text indices. Finally, we prove that the ABWT can be efficiently computed by using a combination of the Difference Cover suffix sorting algorithm [K\"{a}rkk\"{a}inen et al., 2006] with a linear time algorithm for finding the minimal cyclic rotation of a word with respect to the alternating lexicographical order.
Analyzing the Cross-Sensor Portability of Neural Network Architectures for LiDAR-based Semantic Labeling

State-of-the-art approaches for the semantic labeling of LiDAR point clouds heavily rely on the use of deep Convolutional Neural Networks (CNNs). However, transferring network architectures across different LiDAR sensor types represents a significant challenge, especially due to sensor specific design choices with regard to network architecture as well as data representation. In this paper we propose a new CNN architecture for the point-wise semantic labeling of LiDAR data which achieves state-of-the-art results while increasing portability across sensor types. This represents a significant advantage given the fast-paced development of LiDAR hardware technology. We perform a thorough quantitative cross-sensor analysis of semantic labeling performance in comparison to a state-of-the-art reference method. Our evaluation shows that the proposed architecture is indeed highly portable, yielding an improvement of 10 percentage points in the Intersection-over-Union (IoU) score when compared to the reference approach. Further, the results indicate that the proposed network architecture can provide an efficient way for the automated generation of large-scale training data for novel LiDAR sensor types without the need for extensive manual annotation or multi-modal label transfer.
Multi-Task Networks With Universe, Group, and Task Feature Learning

We present methods for multi-task learning that take advantage of natural groupings of related tasks. Task groups may be defined along known properties of the tasks, such as task domain or language. Such task groups represent supervised information at the inter-task level and can be encoded into the model. We investigate two variants of neural network architectures that accomplish this, learning different feature spaces at the levels of individual tasks, task groups, as well as the universe of all tasks: (1) parallel architectures encode each input simultaneously into feature spaces at different levels; (2) serial architectures encode each input successively into feature spaces at different levels in the task hierarchy. We demonstrate the methods on natural language understanding (NLU) tasks, where a grouping of tasks into different task domains leads to improved performance on ATIS, Snips, and a large inhouse dataset.
An Econometric Perspective on Algorithmic Subsampling

Datasets that are terabytes in size are increasingly common, but computer bottlenecks often frustrate a complete analysis of the data. While more data are better than less, diminishing returns suggest that we may not need terabytes of data to estimate a parameter or test a hypothesis. But which rows of data should we analyze, and might an arbitrary subset of rows preserve the features of the original data? This paper reviews a line of work that is grounded in theoretical computer science and numerical linear algebra, and which finds that an algorithmically desirable sketch, which is a randomly chosen subset of the data, must preserve the eigenstructure of the data, a property known as a subspace embedding. Building on this work, we study how prediction and inference can be affected by data sketching within a linear regression setup. We show that the sketching error is small compared to the sample size effect which a researcher can control. As a sketch size that is algorithmically optimal may not be suitable for prediction and inference, we use statistical arguments to provide 'inference conscious' guides to the sketch size. When appropriately implemented, an estimator that pools over different sketches can be nearly as efficient as the infeasible one using the full sample.