OpenRaiser/Intern-Atlas huggingface dataset:
Good Distance Lattices from High Dimensional Expanders
Venue: arXiv cs.CC 2018
Year: 2018

We show a new framework for constructing good distance lattices from high dimensional expanders. For error-correcting codes, which have a similar flavor as lattices, there is a known framework that yields good codes from expanders. However, prior to our work, there has been no framework that yields good distance lattices directly from expanders. Interestingly, we need the notion of high dimensional expansion (and not only one dimensional expansion) for obtaining large distance lattices which are dense. Our construction is obtained by proving the existence of bounded degree high dimensional cosystolic expanders over any ring, and in particular over $\mathbb{Z}$. Previous bounded degree cosystolic expanders were known only over $\mathbb{F}_2$. The proof of the cosystolic expansion over any ring is composed of two main steps, each of an independent interest: We show that coboundary expansion over any ring of the links of a bounded degree complex implies that the complex is a cosystolic expander over any ring. We then prove that all the links of Ramanujan complexes (which are called spherical buildings) are coboundary expanders over any ring. We follow the strategy of [LMM16] for proving that the spherical building is a coboundary expander over any ring. Besides of generalizing their proof from $\mathbb{F}_2$ to any ring, we present it in a detailed way, which might serve readers with less background who wish to get into the field.
On the round-trip efficiency of an HVAC-based virtual battery
Venue: arXiv cs.SY 2018
Year: 2018

Flexible loads, especially heating, ventilation, and air-conditioning (HVAC) systems can be used to provide a battery-like service to the power grid by varying their demand up and down over a baseline. Recent work has reported that providing virtual energy storage with HVAC systems lead to a net loss of energy, akin to a low round-trip efficiency (RTE) of a battery. In this work we rigorously analyze the RTE of a virtual battery through a simplified physics-based model. We show that the low RTEs reported in recent experimental and simulation work are an artifact of the experimental/simulation setup. When the HVAC system is repeatedly used as a virtual battery, the asymptotic RTE is 1. Robustness of the result to assumptions made in the analysis is illustrated through a simulation case study.
A Brandom-ian view of Reinforcement Learning towards strong-AI
Venue: arXiv cs.AI 2018
Year: 2018

The analytic philosophy of Robert Brandom, based on the ideas of pragmatism, paints a picture of sapience, through inferentialism. In this paper, we present a theory, that utilizes essential elements of Brandom's philosophy, towards the objective of achieving strong-AI. We do this by connecting the constitutive elements of reinforcement learning and the Game Of Giving and Asking For Reasons. Further, following Brandom's prescriptive thoughts, we restructure the popular reinforcement learning algorithm A3C, and show that RL algorithms can be tuned towards the objective of strong-AI.
Comparison of Noisy Channels and Reverse Data-Processing Theorems
Venue: arXiv cs.IT 2018
Year: 2018

This paper considers the comparison of noisy channels from the viewpoint of statistical decision theory. Various orderings are discussed, all formalizing the idea that one channel is "better" than another for information transmission. The main result is an equivalence relation that is proved for classical channels, quantum channels with classical encoding, and quantum channels with quantum encoding.
Semantical Equivalence of the Control Flow Graph and the Program Dependence Graph
Venue: arXiv cs.PL 2018
Year: 2018

The program dependence graph (PDG) represents data and control dependence between statements in a program. This paper presents an operational semantics of program dependence graphs. Since PDGs exclude artificial order of statements that resides in sequential programs, executions of PDGs are not unique. However, we identified a class of PDGs that have unique final states of executions, called deterministic PDGs. We prove that the operational semantics of control flow graphs is equivalent to that of deterministic PDGs. The class of deterministic PDGs properly include PDGs obtained from well-structured programs. Thus, our operational semantics of PDGs is more general than that of PDGs for well-structured programs, which are already established in literature.
On First-Order Meta-Learning Algorithms
Venue: arXiv cs.LG 2018
Year: 2018

This paper considers meta-learning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i.e., learns quickly) when presented with a previously unseen task sampled from this distribution. We analyze a family of algorithms for learning a parameter initialization that can be fine-tuned quickly on a new task, using only first-order derivatives for the meta-learning updates. This family includes and generalizes first-order MAML, an approximation to MAML obtained by ignoring second-order derivatives. It also includes Reptile, a new algorithm that we introduce here, which works by repeatedly sampling a task, training on it, and moving the initialization towards the trained weights on that task. We expand on the results from Finn et al. showing that first-order meta-learning algorithms perform well on some well-established benchmarks for few-shot classification, and we provide theoretical analysis aimed at understanding why these algorithms work.
Learning Effective Binary Visual Representations with Deep Networks
Venue: arXiv cs.CV 2018
Year: 2018

Although traditionally binary visual representations are mainly designed to reduce computational and storage costs in the image retrieval research, this paper argues that binary visual representations can be applied to large scale recognition and detection problems in addition to hashing in retrieval. Furthermore, the binary nature may make it generalize better than its real-valued counterparts. Existing binary hashing methods are either two-stage or hinging on loss term regularization or saturated functions, hence converge slowly and only emit soft binary values. This paper proposes Approximately Binary Clamping (ABC), which is non-saturating, end-to-end trainable, with fast convergence and can output true binary visual representations. ABC achieves comparable accuracy in ImageNet classification as its real-valued counterpart, and even generalizes better in object detection. On benchmark image retrieval datasets, ABC also outperforms existing hashing methods.
Sample Complexity of Total Variation Minimization
Venue: arXiv cs.IT 2018
Year: 2018

This work considers the use of Total variation (TV) minimization in the recovery of a given gradient sparse vector from Gaussian linear measurements. It has been shown in recent studies that there exist a sharp phase transition behavior in TV minimization in asymptotic regimes. The phase transition curve specifies the boundary of success and failure of TV minimization for large number of measurements. It is a challenging task to obtain a theoretical bound that reflects this curve. In this work, we present a novel upper-bound that suitably approximates this curve and is asymptotically sharp. Numerical results show that our bound is closer to the empirical TV phase transition curve than the previously known bound obtained by Kabanava.
QREME - Quality Requirements Management Model for Supporting Decision-Making
Venue: arXiv cs.SE 2018
Year: 2018

[Context and motivation] Quality requirements (QRs) are inherently diffi-cult to manage as they are often subjective, context-dependent and hard to fully grasp by various stakeholders. Furthermore, there are many sources that can provide input on important QRs and suitable levels. Responding timely to customer needs and realizing them in product portfolio and product scope decisions remain the main challenge. [Question/problem] Data-driven methodologies based on product usage data analysis gain popularity and enable new (bottom-up, feedback-driven) ways of planning and evaluating QRs in product development. Can these be effi-ciently combined with established top-down, forward-driven management of QRs? [Principal idea / Results] We propose a model for how to handle decisions about QRs at a strategic and operational level, encompassing product deci-sions as well as business intelligence and usage data. We inferred the model from an extensive empirical investigation of five years of decision making history at a large B2C company. We illustrate the model by assessing two in-dustrial case studies from different domains. [Contribution] We believe that utilizing the right approach in the right situa-tion will be key for handling QRs, as both different groups of QRs and do-mains have their special characteristics.
Carnap: An Open Framework for Formal Reasoning in the Browser
Venue: arXiv cs.HC 2018
Year: 2018

This paper presents an overview of Carnap, a free and open framework for the development of formal reasoning applications. Carnap's design emphasizes flexibility, extensibility, and rapid prototyping. Carnap-based applications are written in Haskell, but can be compiled to JavaScript to run in standard web browsers. This combination of features makes Carnap ideally suited for educational applications, where ease-of-use is crucial for students and adaptability to different teaching strategies and classroom needs is crucial for instructors. The paper describes Carnap's implementation, along with its current and projected pedagogical applications.
Applicability and interpretation of the deterministic weighted cepstral distance
Venue: arXiv cs.SY 2018
Year: 2018

Quantifying similarity between data objects is an important part of modern data science. Deciding what similarity measure to use is very application dependent. In this paper, we combine insights from systems theory and machine learning, and investigate the weighted cepstral distance, which was previously defined for signals coming from ARMA models. We provide an extension of this distance to invertible deterministic linear time invariant single input single output models, and assess its applicability. We show that it can always be interpreted in terms of the poles and zeros of the underlying model, and that, in the case of stable, minimum-phase, or unstable, maximum-phase models, a geometrical interpretation in terms of subspace angles can be given. We then devise a method to assess stability and phase-type of the generating models, using only input/output signal information. In this way, we prove a connection between the extended weighted cepstral distance and a weighted cepstral model norm. In this way, we provide a purely data-driven way to assess different underlying dynamics of input/output signal pairs, without the need for any system identification step. This can be useful in machine learning tasks such as time series clustering. An iPython tutorial is published complementary to this paper, containing implementations of the various methods and algorithms presented here, as well as some numerical illustrations of the equivalences proven here.
Modeling Activation Processes in Human Memory to Improve Tag Recommendations
Venue: arXiv cs.IR 2018
Year: 2018

This thesis was submitted by Dr. Dominik Kowald to the Institute of Interactive Systems and Data Science of Graz University of Technology in Austria on the 5th of September 2017 for the attainment of the degree 'Dr.techn'. The supervisors of this thesis have been Prof. Stefanie Lindstaedt and Ass.Prof. Elisabeth Lex from Graz University of Technology, and the external assessor has been Prof. Tobias Ley from Tallinn University. In the current enthusiasm around Data Science and Big Data Analytics, it is important to mention that only theory-guided approaches will truly enable us to fully understand why an algorithm works and how specific results can be explained. It was the goal of this dissertation research to follow this path by demonstrating that a recommender system inspired by human memory theory can have a true impact in the field.
Knowledge Graphs in the Libraries and Digital Humanities Domain
Venue: arXiv cs.DL 2018
Year: 2018

Knowledge graphs represent concepts (e.g., people, places, events) and their semantic relationships. As a data structure, they underpin a digital information system, support users in resource discovery and retrieval, and are useful for navigation and visualization purposes. Within the libaries and humanities domain, knowledge graphs are typically rooted in knowledge organization systems, which have a century-old tradition and have undergone their digital transformation with the advent of the Web and Linked Data. Being exposed to the Web, metadata and concept definitions are now forming an interconnected and decentralized global knowledge network that can be curated and enriched by community-driven editorial processes. In the future, knowledge graphs could be vehicles for formalizing and connecting findings and insights derived from the analysis of possibly large-scale corpora in the libraries and digital humanities domain.
Multilevel Illumination Coding for Fourier Transform Interferometry in Fluorescence Spectroscopy
Venue: arXiv cs.IT 2018
Year: 2018

Fourier Transform Interferometry (FTI) is an interferometric procedure for acquiring HyperSpectral (HS) data. Recently, it has been observed that the light source highlighting a (biologic) sample can be coded before the FTI acquisition in a procedure called Coded Illumination-FTI (CI-FTI). This turns HS data reconstruction into a Compressive Sensing (CS) problem regularized by the sparsity of the HS data. CI-FTI combines the high spectral resolution of FTI with the advantages of reduced-light-exposure imaging in biology. In this paper, we leverage multilevel sampling scheme recently developed in CS theory to adapt the coding strategy of CI-FTI to the spectral sparsity structure of HS data in Fluorescence Spectroscopy (FS). This structure is actually extracted from the spectral signatures of actual fluorescent dyes used in FS. Accordingly, the optimum illumination coding as well as the theoretical recovery guarantee are derived. We conduct numerous numerical experiments on synthetic and experimental data that show the faithfulness of the proposed theory to experimental observations.
Super Compaction and Pluripotent Shape Transformation via Algorithmic Stacking for 3D Deployable Structures
Venue: arXiv cs.CG 2018
Year: 2018

Origami structures enabled by folding and unfolding can create complex 3D shapes. However, even a small 3D shape can have large 2D unfoldings. The huge initial dimension of the 2D flattened structure makes fabrication difficult, and defeats the main purpose, namely compactness, of many origami-inspired engineering. In this work, we propose a novel algorithmic kirigami method that provides super compaction of an arbitrary 3D shape with non-negligible surface thickness called "algorithmic stacking". Our approach computationally finds a way of cutting the thick surface of the shape into a strip. This strip forms a Hamiltonian cycle that covers the entire surface and can realize transformation between two target shapes: from a super compact stacked shape to the input 3D shape. Depending on the surface thickness, the stacked structure takes merely 0.001% to 6% of the original volume. This super compacted structure not only can be manufactured in a workspace that is significantly smaller than the provided 3D shape, but also makes packing and transportation easier for a deployable application. We further demonstrate that, the proposed stackable structure also provides high pluripotency and can transform into multiple 3D target shapes if these 3D shapes can be dissected in specific ways and form a common stacked structure. In contrast to many designs of origami structure that usually target at a particular shape, our results provide a universal platform for pluripotent 3D transformable structures.
Generalization in Metric Learning: Should the Embedding Layer be the Embedding Layer?
Venue: arXiv cs.CV 2018
Year: 2018

This work studies deep metric learning under small to medium scale data as we believe that better generalization could be a contributing factor to the improvement of previous fine-grained image retrieval methods; it should be considered when designing future techniques. In particular, we investigate using other layers in a deep metric learning system (besides the embedding layer) for feature extraction and analyze how well they perform on training data and generalize to testing data. From this study, we suggest a new regularization practice where one can add or choose a more optimal layer for feature extraction. State-of-the-art performance is demonstrated on 3 fine-grained image retrieval benchmarks: Cars-196, CUB-200-2011, and Stanford Online Product.
A Polynomial Kernel for Diamond-Free Editing
Venue: arXiv cs.DS 2018
Year: 2018

An $H$-free editing problem asks whether we can edit at most $k$ edges to make a graph contain no induced copy of the fixed graph $H$. We obtain a polynomial kernel for this problem when $H$ is a diamond. The incompressibility dichotomy for $H$ being a 3-connected graph and the classical complexity dichotomy suggest that except for $H$ being a complete/empty graph, $H$-free editing problems admit polynomial kernels only for a few small graphs $H$. Therefore, we believe that our result is an essential step toward a complete dichotomy on the compressibility of $H$-free editing. Additionally, we give a cubic-vertex kernel for the diamond-free edge deletion problem, which is far simpler than the previous kernel of the same size for the problem.
A Quest for the Structure of Intra- and Postoperative Surgical Team Networks: Does the Small World Property Evolve over Time?
Venue: arXiv cs.SI 2018
Year: 2018

We examined the structure of intra- and postoperative case-collaboration networks among the surgical service providers in a quaternary-care academic medical center, using retrospective electronic medical record (EMR) data. We also analyzed the evolution of the network properties over time, as changes in nodes and edges can affect the network structure. We used de-identified intra- and postoperative data for adult patients, ages >= 21, who received nonambulatory/nonobstetric surgery at Shands at the University of Florida between June 1, 2011 and November 1, 2014. The intraoperative segment contained 30,245 surgical cases, and the postoperative segment considered 30,202 hospitalizations. Our results confirmed the existence of strict small world structure in both intra- and postoperative surgical team networks. A sudden declining trend is expected in the future in both intra- and postoperative networks, since the small world property is currently at its peak. In addition, high network density was observed in the intraoperative segment and partially in postoperative one, representing the existence of cohesive clusters of providers. We also observed that the small world property is exhibited more in the intraoperative compared to the postoperative network. Analyzing the temporal aspects of the networks revealed the postoperative segment tends to lose its cohesiveness as the time passes. Our results highlight the importance of stability of personnel in key positions. This highlights the important role of the central players in the network that offers change-leaders the opportunity to quantify and target those nodes as mediators of process change.
Review of Visual Saliency Detection with Comprehensive Information
Venue: arXiv cs.CV 2018
Year: 2018

Visual saliency detection model simulates the human visual system to perceive the scene, and has been widely used in many vision tasks. With the acquisition technology development, more comprehensive information, such as depth cue, inter-image correspondence, or temporal relationship, is available to extend image saliency detection to RGBD saliency detection, co-saliency detection, or video saliency detection. RGBD saliency detection model focuses on extracting the salient regions from RGBD images by combining the depth information. Co-saliency detection model introduces the inter-image correspondence constraint to discover the common salient object in an image group. The goal of video saliency detection model is to locate the motion-related salient object in video sequences, which considers the motion cue and spatiotemporal constraint jointly. In this paper, we review different types of saliency detection algorithms, summarize the important issues of the existing methods, and discuss the existent problems and future works. Moreover, the evaluation datasets and quantitative measurements are briefly introduced, and the experimental analysis and discission are conducted to provide a holistic overview of different saliency detection methods.
VR Content Capture using Aligned Smartphones
Venue: arXiv cs.HC 2018
Year: 2018

There are a number of dedicated 3D capture devices in the market, but generally they are unaffordable and do not make use of existing smartphone cameras, which are generally of decent quality. Due to this, while there are several means to consume 3D or VR content, there is currently lack of means to capture 3D content, resulting in very few 3D videos being publicly available. Some mobile applications such as Camerada enable 3D or VR content capture by combining the output of two existing smartphones, but users would have to hold the cameras in their hand, making it difficult to align properly. In this paper we present the design of a system to enable 3D content capture using one or more smartphones, taking care of alignment issues so as to get optimal alignment of the smartphone cameras. We aim to keep the distance between the cameras constant and equal to the inter-pupillary distance of about 6.5 cm. Our solution is applicable for one, two and three smartphones. We have a mobile app to generate a template given the dimensions of the smartphones, camera positions and other specifications. The template can be printed by the user and cut out on 2D cardboard, similar to Google cardboard. Alternatively, it can be printed using a 3D printer. During video capture, with the smartphones aligned using our printed template, we capture videos which are then combined to get the optimal 3D content. We present the details of a small proof of concept implementation. Our solution would make it easier for people to use existing smartphones to generate 3D content.