OpenRaiser/Intern-Atlas huggingface dataset:
Generic Tracking and Probabilistic Prediction Framework and Its Application in Autonomous Driving
Venue: arXiv cs.RO 2019
Year: 2019

Accurately tracking and predicting behaviors of surrounding objects are key prerequisites for intelligent systems such as autonomous vehicles to achieve safe and high-quality decision making and motion planning. However, there still remain challenges for multi-target tracking due to object number fluctuation and occlusion. To overcome these challenges, we propose a constrained mixture sequential Monte Carlo (CMSMC) method in which a mixture representation is incorporated in the estimated posterior distribution to maintain multi-modality. Multiple targets can be tracked simultaneously within a unified framework without explicit data association between observations and tracking targets. The framework can incorporate an arbitrary prediction model as the implicit proposal distribution of the CMSMC method. An example in this paper is a learning-based model for hierarchical time-series prediction, which consists of a behavior recognition module and a state evolution module. Both modules in the proposed model are generic and flexible so as to be applied to a class of time-series prediction problems where behaviors can be separated into different levels. Finally, the proposed framework is applied to a numerical case study as well as a task of on-road vehicle tracking, behavior recognition, and prediction in highway scenarios. Instead of only focusing on forecasting trajectory of a single entity, we jointly predict continuous motions for interactive entities simultaneously. The proposed approaches are evaluated from multiple aspects, which demonstrate great potential for intelligent vehicular systems and traffic surveillance systems.
Parameter Modeling for Small-Scale Mobility in Indoor THz Communication
Venue: arXiv cs.NI 2019
Year: 2019

Despite such challenges as high path loss and equipment cost, THz communication is becoming one of the potentially viable means through which ultra-high data rate can be achieved. To compensate for the high path loss, we present parameter modeling for indoor THz communication. To maximize efficient and opportunistic use of resources, we analyze the potential workarounds for a single access point to satisfy most of the mobile terminals by varying such parameters as humidity, distance, frequency windows, beamwidths, antenna placement, and user mobility type. One promising parameter is antenna beamwidth, where narrower beams results in higher antenna gain. However, this can lead to "\textit{beamwidth dilemma}" scenario, where narrower beamwidth can result in significant outages due to device mobility and orientation. In this paper, we address this challenge by presenting a mobility model that performs an extensive analysis of different human mobility scenarios, where each scenario has different data rate demands and movement patterns. We observe that for mobile users, there are optimal beamwidths that are affected by the mobility type (high mobility, constrained mobility, and low mobility) and AP placement.
Kinesthetic Learning -- Haptic User Interfaces for Gyroscopic Precession Simulation
Venue: arXiv cs.HC 2019
Year: 2019

Some forces in nature are difficult to comprehend due to their non-intuitive and abstract nature. Forces driving gyroscopic precession are invisible, yet their effect is very important in a variety of applications, from space navigation to motion tracking. Current technological advancements in haptic interfaces, enables development of revolutionary user interfaces, combining multiple modalities: tactile, visual and auditory. Tactile augmented user interfaces have been deployed in a variety of areas, from surgical training to elementary education. This research provides an overview of haptic user interfaces in higher education, and presents the development and assessment of a haptic-user interface that supports the learner's understanding of gyroscopic precession forces. The visual-haptic simulator proposed, is one module from a series of simulators targeted at complex concept representation, using multi-modal user interfaces. Various higher education domains, from classical physics to mechanical engineering, will benefit from the mainstream adoption of multi-modal interfaces for hands-on training and content delivery. Experimental results are promising, and underline the valuable impact that haptic user interfaces have on enabling abstract concepts understanding, through kinesthetic learning and hands-on practice.
Query-Based Named Entity Recognition
Venue: arXiv cs.CL 2019
Year: 2019

In this paper, we propose a new strategy for the task of named entity recognition (NER). We cast the task as a query-based machine reading comprehension task: e.g., the task of extracting entities with PER is formalized as answering the question of "which person is mentioned in the text ?". Such a strategy comes with the advantage that it solves the long-standing issue of handling overlapping or nested entities (the same token that participates in more than one entity categories) with sequence-labeling techniques for NER. Additionally, since the query encodes informative prior knowledge, this strategy facilitates the process of entity extraction, leading to better performances. We experiment the proposed model on five widely used NER datasets on English and Chinese, including MSRA, Resume, OntoNotes, ACE04 and ACE05. The proposed model sets new SOTA results on all of these datasets.
Hyper-Path-Based Representation Learning for Hyper-Networks
Venue: arXiv cs.SI 2019
Year: 2019

Network representation learning has aroused widespread interests in recent years. While most of the existing methods deal with edges as pairwise relationships, only a few studies have been proposed for hyper-networks to capture more complicated tuplewise relationships among multiple nodes. A hyper-network is a network where each edge, called hyperedge, connects an arbitrary number of nodes. Different from conventional networks, hyper-networks have certain degrees of indecomposability such that the nodes in a subset of a hyperedge may not possess a strong relationship. That is the main reason why traditional algorithms fail in learning representations in hyper-networks by simply decomposing hyperedges into pairwise relationships. In this paper, we firstly define a metric to depict the degrees of indecomposability for hyper-networks. Then we propose a new concept called hyper-path and design hyper-path-based random walks to preserve the structural information of hyper-networks according to the analysis of the indecomposability. Then a carefully designed algorithm, Hyper-gram, utilizes these random walks to capture both pairwise relationships and tuplewise relationships in the whole hyper-networks. Finally, we conduct extensive experiments on several real-world datasets covering the tasks of link prediction and hyper-network reconstruction, and results demonstrate the rationality, validity, and effectiveness of our methods compared with those existing state-of-the-art models designed for conventional networks or hyper-networks.
Texture and Structure Two-view Classification of Images
Venue: arXiv cs.CV 2019
Year: 2019

Textural and structural features can be regraded as "two-view" feature sets. Inspired by the recent progress in multi-view learning, we propose a novel two-view classification method that models each feature set and optimizes the process of merging these views efficiently. Examples of implementation of this approach in classification of real-world data are presented, with special emphasis on medical images. We firstly decompose fully-textured images into two layers of representation, corresponding to natural stochastic textures (NST) and structural layer, respectively. The structural, edge-and-curve-type, information is mostly represented by the local spatial phase, whereas, the pure NST has random phase and is characterized by Gaussianity and self-similarity. Therefore, the NST is modeled by the 2D self-similar process, fractional Brownian motion (fBm). The Hurst parameter, characteristic of fBm, specifies the roughness or irregularity of the texture. This leads us to its estimation and implementation along other features extracted from the structure layer, to build the "two-view" features sets used in our classification scheme. A shallow neural net (NN) is exploited to execute the process of merging these feature sets, in a straightforward and efficient manner.
4-Bit High-Speed Binary Ling Adder
Venue: arXiv cs.AR 2019
Year: 2019

Binary addition is one of the most primitive and most commonly used applications in computer arithmetic. A large variety of algorithms and implementations have been proposed for binary addition. Huey Ling proposed a simpler form of CLA equations which rely on adjacent pair bits. Along with bit generate and bit propagate, we introduce another prefix bit, the half sum bit. Ling adder increases the speed of n-bit binary addition, which is an upgrade from the existing Carry-Look-Ahead adder. Several variants of the carry look-ahead equations, like Ling carries, have been presented that simplify carry computation and can lead to faster structures. Ling adders, make use of Ling carry and propagate bits, in order to calculate the sum bit. As a result, dependency on the previous bit addition is reduced; that is, ripple effect is lowered. This paper provides a comparative study on the implementation of the above mentioned high-speed adders.
Graph Stories in Small Area
Venue: arXiv cs.DS 2019
Year: 2019

We study the problem of drawing a dynamic graph, where each vertex appears in the graph at a certain time and remains in the graph for a fixed amount of time, called the window size. This defines a graph story, i.e., a sequence of subgraphs, each induced by the vertices that are in the graph at the same time. The drawing of a graph story is a sequence of drawings of such subgraphs. To support readability, we require that each drawing is straight-line and planar and that each vertex maintains its placement in all the drawings. Ideally, the area of the drawing of each subgraph should be a function of the window size, rather than a function of the size of the entire graph, which could be too large. We show that the graph stories of paths and trees can be drawn on a $2W \times 2W$ and on an $(8W + 1) \times (8W + 1)$ grid, respectively, where $W$ is the window size. These results are constructive and yield linear-time algorithms. Further, we show that there exist graph stories of planar graphs whose subgraphs cannot be drawn within an area that is only a function of $W$.
Efficient Bidirectional Neural Machine Translation
Venue: arXiv cs.CL 2019
Year: 2019

The encoder-decoder based neural machine translation usually generates a target sequence token by token from left to right. Due to error propagation, the tokens in the right side of the generated sequence are usually of poorer quality than those in the left side. In this paper, we propose an efficient method to generate a sequence in both left-to-right and right-to-left manners using a single encoder and decoder, combining the advantages of both generation directions. Experiments on three translation tasks show that our method achieves significant improvements over conventional unidirectional approach. Compared with ensemble methods that train and combine two models with different generation directions, our method saves 50% model parameters and about 40% training time, and also improve inference speed.
Unsupervised Construction of Knowledge Graphs From Text and Code
Venue: arXiv cs.LG 2019
Year: 2019

The scientific literature is a rich source of information for data mining with conceptual knowledge graphs; the open science movement has enriched this literature with complementary source code that implements scientific models. To exploit this new resource, we construct a knowledge graph using unsupervised learning methods to identify conceptual entities. We associate source code entities to these natural language concepts using word embedding and clustering techniques. Practical naming conventions for methods and functions tend to reflect the concept(s) they implement. We take advantage of this specificity by presenting a novel process for joint clustering text concepts that combines word-embeddings, nonlinear dimensionality reduction, and clustering techniques to assist in understanding, organizing, and comparing software in the open science ecosystem. With our pipeline, we aim to assist scientists in building on existing models in their discipline when making novel models for new phenomena. By combining source code and conceptual information, our knowledge graph enhances corpus-wide understanding of scientific literature.
LightMC: A Dynamic and Efficient Multiclass Decomposition Algorithm
Venue: arXiv cs.LG 2019
Year: 2019

Multiclass decomposition splits a multiclass classification problem into a series of independent binary learners and recomposes them by combining their outputs to reconstruct the multiclass classification results. Three widely-used realizations of such decomposition methods are One-Versus-All (OVA), One-Versus-One (OVO), and Error-Correcting-Output-Code (ECOC). While OVA and OVO are quite simple, both of them assume all classes are orthogonal which neglect the latent correlation between classes in real-world. Error-Correcting-Output-Code (ECOC) based decomposition methods, on the other hand, are more preferable due to its integration of the correlation among classes. However, the performance of existing ECOC-based methods highly depends on the design of coding matrix and decoding strategy. Unfortunately, it is quite uncertain and time-consuming to discover an effective coding matrix with appropriate decoding strategy. To address this problem, we propose LightMC, an efficient dynamic multiclass decomposition algorithm. Instead of using fixed coding matrix and decoding strategy, LightMC uses a differentiable decoding strategy, which enables it to dynamically optimize the coding matrix and decoding strategy, toward increasing the overall accuracy of multiclass classification, via back propagation jointly with the training of base learners in an iterative way. Empirical experimental results on several public large-scale multiclass classification datasets have demonstrated the effectiveness of LightMC in terms of both good accuracy and high efficiency.
Optimal Curve Straightening is $\exists\mathbb{R}$-Complete
Venue: arXiv cs.CG 2019
Year: 2019

We prove that the following problem has the same computational complexity as the existential theory of the reals: Given a generic self-intersecting closed curve $γ$ in the plane and an integer $m$, is there a polygon with $m$ vertices that is isotopic to $γ$? Our reduction implies implies two stronger results, as corollaries of similar results for pseudoline arrangements. First, there are isotopy classes in which every $m$-gon with integer coordinates requires $2^{Ω(m)}$ bits of precision. Second, for any semi-algebraic set $V$, there is an integer $m$ and a closed curve $γ$ such that the space of all $m$-gons isotopic to $γ$ is homotopy equivalent to $V$.
Verifying Bit-vector Invertibility Conditions in Coq (Extended Abstract)
Venue: arXiv cs.LO 2019
Year: 2019

This work is a part of an ongoing effort to prove the correctness of invertibility conditions for the theory of fixed-width bit-vectors, which are used to solve quantified bit-vector formulas in the Satisfiability Modulo Theories (SMT) solver CVC4. While many of these were proved in a completely automatic fashion for any bit-width, some were only proved for bit-widths up to 65, even though they are being used to solve formulas over arbitrary bit-widths. In this paper we describe our initial efforts in proving a subset of these invertibility conditions in the Coq proof assistant. We describe the Coq library that we use, as well as the extensions that we introduced to it.
Supporting stylists by recommending fashion style
Venue: arXiv cs.LG 2019
Year: 2019

Outfittery is an online personalized styling service targeted at men. We have hundreds of stylists who create thousands of bespoke outfits for our customers every day. A critical challenge faced by our stylists when creating these outfits is selecting an appropriate item of clothing that makes sense in the context of the outfit being created, otherwise known as style fit. Another significant challenge is knowing if the item is relevant to the customer based on their tastes, physical attributes and price sensitivity. At Outfittery we leverage machine learning extensively and combine it with human domain expertise to tackle these challenges. We do this by surfacing relevant items of clothing during the outfit building process based on what our stylist is doing and what the preferences of our customer are. In this paper we describe one way in which we help our stylists to tackle style fit for a particular item of clothing and its relevance to an outfit. A thorough qualitative and quantitative evaluation highlights the method's ability to recommend fashion items by style fit.
Oprema -- The Relay Computer of Carl Zeiss Jena
Venue: arXiv cs.GL 2019
Year: 2019

The Oprema (Optikrechenmaschine = computer for optical calculations) was a relay computer whose development was initiated by Herbert Kortum and which was designed and built by a team under the leadership of Wilhelm Kaemmerer at Carl Zeiss Jena (CZJ) in 1954 and 1955. Basic experiments, design and construction of machine-1 were all done, partly concurrently, in the remarkably short time of about 14 months. Shortly after the electronic G 2 of Heinz Billing in Goettingen it was the 7th universal computer in Germany and the 1st in the GDR. The Oprema consisted of two identical machines. One machine consisted of about 8,300 relays, 45,000 selenium rectifiers and 250 km cable. The main reason for the construction of the Oprema was the computational needs of CZJ, which was the leading company for optics and precision mechanics in the GDR. During its lifetime (1955-1963) the Oprema was applied by CZJ and a number of other institutes and companies in the GDR. The paper presents new details of the Oprema project and of the arithmetic operations implemented in the Oprema. Additionally, it covers briefly the lives of the two protagonists, W. Kaemmerer and H. Kortum, and draws some comparisons with other early projects, namely Colossus, ASCC/Mark 1 and ENIAC. Finally, it discusses the question, whether Kortum is a German computer pioneer.
Towards Blockchain-enabled Searchable Encryption
Venue: arXiv cs.CR 2019
Year: 2019

Distributed Leger Technologies (DLTs), most notably Blockchain technologies, bring decentralised platforms that eliminate a single trusted third party and avoid the notorious single point of failure vulnerability. Since Nakamoto's Bitcoin cryptocurrency system, an enormous number of decentralised applications have been proposed on top of these technologies, aiming at more transparency and trustworthiness than their traditional counterparts. These applications spread over a lot of areas, e.g. financial services, healthcare, transportation, supply chain management, and cloud computing. While Blockchain brings transparency and decentralised trust intuitively due to the consensus of a (very large) group of nodes (or, miners), it introduces very subtle implications for other desirable properties such as privacy. In this work, we demonstrate these subtle implications for Blockchain-based searchable encryption solutions, which are one specific use case of cloud computing services. These solutions rely on Blockchain to achieve both the standard privacy property and the new fairness property, which requires that search operations are carried out faithfully and are rewarded accordingly. We show that directly replacing the server in an existing searchable encryption solution with a Blockchain will cause undesirable operational cost, privacy loss, and security vulnerabilities. The analysis results indicate that a dedicated server is still needed to achieve the desired privacy guarantee. To this end, we propose two frameworks which can be instantiated based on most existing searchable encryption schemes. Through analysing these two frameworks, we affirmatively show that a carefully engineered Blockchain-based solution can achieve the desired fairness property while preserving the privacy guarantee of the original searchable encryption scheme simultaneously.
End-To-End Measure for Text Recognition
Venue: arXiv cs.CV 2019
Year: 2019

Measuring the performance of text recognition and text line detection engines is an important step to objectively compare systems and their configuration. There exist well-established measures for both tasks separately. However, there is no sophisticated evaluation scheme to measure the quality of a combined text line detection and text recognition system. The F-measure on word level is a well-known methodology, which is sometimes used in this context. Nevertheless, it does not take into account the alignment of hypothesis and ground truth text and can lead to deceptive results. Since users of automatic information retrieval pipelines in the context of text recognition are mainly interested in the end-to-end performance of a given system, there is a strong need for such a measure. Hence, we present a measure to evaluate the quality of an end-to-end text recognition system. The basis for this measure is the well established and widely used character error rate, which is limited -- in its original form -- to aligned hypothesis and ground truth texts. The proposed measure is flexible in a way that it can be configured to penalize different reading orders between the hypothesis and ground truth and can take into account the geometric position of the text lines. Additionally, it can ignore over- and under- segmentation of text lines. With these parameters it is possible to get a measure fitting best to its own needs.
Mutual Information Approximation
Venue: arXiv cs.IT 2019
Year: 2019

To provide an efficient approach to characterize the input-output mutual information (MI) under additive white Gaussian noise (AWGN) channel, this short report fits the curves of exact MI under multilevel quadrature amplitude modulation (M-QAM) signal inputs via multi-exponential decay curve fitting (M-EDCF). Even though the definition expression for instanious MI versus Signal to Noise Ratio (SNR) is complex and the containing integral is intractable, our new developed fitting formula holds a neat and compact form, which possesses high precision as well as low complexity. Generally speaking, this approximation formula of MI can promote the research of performance analysis in practical communication system under discrete inputs.
Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?
Venue: arXiv cs.LG 2019
Year: 2019

We present an analysis of predictive uncertainty based out-of-distribution detection for different approaches to estimate various models' epistemic uncertainty and contrast it with extreme value theory based open set recognition. While the former alone does not seem to be enough to overcome this challenge, we demonstrate that uncertainty goes hand in hand with the latter method. This seems to be particularly reflected in a generative model approach, where we show that posterior based open set recognition outperforms discriminative models and predictive uncertainty based outlier rejection, raising the question of whether classifiers need to be generative in order to know what they have not seen.