OpenRaiser/Intern-Atlas huggingface dataset:
Efficient PAC Learning from the Crowd
Venue: arXiv cs.LG 2017
Year: 2017

In recent years crowdsourcing has become the method of choice for gathering labeled training data for learning algorithms. Standard approaches to crowdsourcing view the process of acquiring labeled data separately from the process of learning a classifier from the gathered data. This can give rise to computational and statistical challenges. For example, in most cases there are no known computationally efficient learning algorithms that are robust to the high level of noise that exists in crowdsourced data, and efforts to eliminate noise through voting often require a large number of queries per example. In this paper, we show how by interleaving the process of labeling and learning, we can attain computational efficiency with much less overhead in the labeling cost. In particular, we consider the realizable setting where there exists a true target function in $\mathcal{F}$ and consider a pool of labelers. When a noticeable fraction of the labelers are perfect, and the rest behave arbitrarily, we show that any $\mathcal{F}$ that can be efficiently learned in the traditional realizable PAC model can be learned in a computationally efficient manner by querying the crowd, despite high amounts of noise in the responses. Moreover, we show that this can be done while each labeler only labels a constant number of examples and the number of labels requested per example, on average, is a constant. When no perfect labelers exist, a related task is to find a set of the labelers which are good but not perfect. We show that we can identify all good labelers, when at least the majority of labelers are good.
On Corecursive Algebras for Functors Preserving Coproducts
Venue: arXiv cs.LO 2017
Year: 2017

For an endofunctor $H$ on a hyper-extensive category preserving countable coproducts we describe the free corecursive algebra on $Y$ as the coproduct of the final coalgebra for $H$ and the free $H$-algebra on $Y$. As a consequence, we derive that $H$ is a cia functor, i.e., its corecursive algebras are precisely the cias (completely iterative algebras). Also all functors $H(-) + Y$ are then cia functors. For finitary set functors we prove that, conversely, if $H$ is a cia functor, then it has the form $H = W \times (-) + Y$ for some sets $W$ and $Y$.
HTC Vive MeVisLab integration via OpenVR for medical applications
Venue: arXiv cs.GR 2017
Year: 2017

Virtual Reality, an immersive technology that replicates an environment via computer-simulated reality, gets a lot of attention in the entertainment industry. However, VR has also great potential in other areas, like the medical domain, Examples are intervention planning, training and simulation. This is especially of use in medical operations, where an aesthetic outcome is important, like for facial surgeries. Alas, importing medical data into Virtual Reality devices is not necessarily trivial, in particular, when a direct connection to a proprietary application is desired. Moreover, most researcher do not build their medical applications from scratch, but rather leverage platforms like MeVisLab, MITK, OsiriX or 3D Slicer. These platforms have in common that they use libraries like ITK and VTK, and provide a convenient graphical interface. However, ITK and VTK do not support Virtual Reality directly. In this study, the usage of a Virtual Reality device for medical data under the MeVisLab platform is presented. The OpenVR library is integrated into the MeVisLab platform, allowing a direct and uncomplicated usage of the head mounted display HTC Vive inside the MeVisLab platform. Medical data coming from other MeVisLab modules can directly be connected per drag-and-drop to the Virtual Reality module, rendering the data inside the HTC Vive for immersive virtual reality inspection.
Query-to-Communication Lifting for BPP
Venue: arXiv cs.CC 2017
Year: 2017

For any $n$-bit boolean function $f$, we show that the randomized communication complexity of the composed function $f\circ g^n$, where $g$ is an index gadget, is characterized by the randomized decision tree complexity of $f$. In particular, this means that many query complexity separations involving randomized models (e.g., classical vs. quantum) automatically imply analogous separations in communication complexity.
A Weakest Pre-Expectation Semantics for Mixed-Sign Expectations
Venue: arXiv cs.LO 2017
Year: 2017

We present a weakest-precondition-style calculus for reasoning about the expected values (pre-expectations) of \emph{mixed-sign unbounded} random variables after execution of a probabilistic program. The semantics of a while-loop is well-defined as the limit of iteratively applying a functional to a zero-element just as in the traditional weakest pre-expectation calculus, even though a standard least fixed point argument is not applicable in this context. A striking feature of our semantics is that it is always well-defined, even if the expected values do not exist. We show that the calculus is sound, allows for compositional reasoning, and present an invariant-based approach for reasoning about pre-expectations of loops.
Formal Methods for Adaptive Control of Dynamical Systems
Venue: arXiv cs.SY 2017
Year: 2017

We develop a method to control discrete-time systems with constant but initially unknown parameters from linear temporal logic (LTL) specifications. We introduce the notions of (non-deterministic) parametric and adaptive transition systems and show how to use tools from formal methods to compute adaptive control strategies for finite systems. For infinite systems, we first compute abstractions in the form of parametric finite quotient transition systems and then apply the techniques for finite systems. Unlike traditional adaptive control methods, our approach is correct by design, does not require a reference model, and can deal with a much wider range of systems and specifications. Illustrative case studies are included.
Independently Controllable Features
Venue: arXiv cs.LG 2017
Year: 2017

Finding features that disentangle the different causes of variation in real data is a difficult task, that has nonetheless received considerable attention in static domains like natural images. Interactive environments, in which an agent can deliberately take actions, offer an opportunity to tackle this task better, because the agent can experiment with different actions and observe their effects. We introduce the idea that in interactive environments, latent factors that control the variation in observed data can be identified by figuring out what the agent can control. We propose a naive method to find factors that explain or measure the effect of the actions of a learner, and test it in illustrative experiments.
Quantum Communication-Query Tradeoffs
Venue: arXiv cs.CC 2017
Year: 2017

For any function $f: X \times Y \to Z$, we prove that $Q^{*\text{cc}}(f) \cdot Q^{\text{OIP}}(f) \cdot (\log Q^{\text{OIP}}(f) + \log |Z|) \geq Ω(\log |X|)$. Here, $Q^{*\text{cc}}(f)$ denotes the bounded-error communication complexity of $f$ using an entanglement-assisted two-way qubit channel, and $Q^{\text{OIP}}(f)$ denotes the number of quantum queries needed to learn $x$ with high probability given oracle access to the function $f_x(y) \stackrel{\text{def}}{=} f(x, y)$. We show that this tradeoff is close to the best possible. We also give a generalization of this tradeoff for distributional query complexity. As an application, we prove an optimal $Ω(\log q)$ lower bound on the $Q^{*\text{cc}}$ complexity of determining whether $x + y$ is a perfect square, where Alice holds $x \in \mathbf{F}_q$, Bob holds $y \in \mathbf{F}_q$, and $\mathbf{F}_q$ is a finite field of odd characteristic. As another application, we give a new, simpler proof that searching an ordered size-$N$ database requires $Ω(\log N / \log \log N)$ quantum queries. (It was already known that $Θ(\log N)$ queries are required.)
Classification-based RNN machine translation using GRUs
Venue: arXiv cs.NE 2017
Year: 2017

We report the results of our classification-based machine translation model, built upon the framework of a recurrent neural network using gated recurrent units. Unlike other RNN models that attempt to maximize the overall conditional log probability of sentences against sentences, our model focuses a classification approach of estimating the conditional probability of the next word given the input sequence. This simpler approach using GRUs was hoped to be comparable with more complicated RNN models, but achievements in this implementation were modest and there remains a lot of room for improving this classification approach.
An iBeacon based Proximity and Indoor Localization System
Venue: arXiv cs.NI 2017
Year: 2017

Indoor localization and Location Based Services (LBS) can greatly benefit from the widescale proliferation of communication devices. The basic requirements of a system that can provide the aforementioned services are energy efficiency, scalability, lower costs, wide reception range, high localization accuracy and availability. Different technologies such as WiFi, UWB, RFID have been leveraged to provide LBS and Proximity Based Services (PBS), however they do not meet the aforementioned requirements. Apple's Bluetooth Low Energy (BLE) based iBeacon solution primarily intends to provide Proximity Based Services (PBS). However, it suffers from poor proximity detection accuracy due to its reliance on Received Signal Strength Indicator (RSSI) that is prone to multipath fading and drastic fluctuations in the indoor environment. Therefore, in this paper, we present our iBeacon based accurate proximity and indoor localization system. Our two algorithms Server-Side Running Average (SRA) and Server-Side Kalman Filter (SKF) improve the proximity detection accuracy of iBeacons by 29% and 32% respectively, when compared with Apple's current moving average based approach. We also present our novel cascaded Kalman Filter-Particle Filter (KFPF) algorithm for indoor localization. Our cascaded filter approach uses a Kalman Filter (KF) to reduce the RSSI fluctuation and then inputs the filtered RSSI values into a Particle Filter (PF) to improve the accuracy of indoor localization. Our experimental results, obtained through experiments in a space replicating real-world scenario, show that our cascaded filter approach outperforms the use of only PF by 28.16% and 25.59% in 2-Dimensional (2D) and 3-Dimensional (3D) environments respectively, and achieves a localization error as low as 0.70 meters in 2D environment and 0.947 meters in 3D environment.
Robust Polynomial Reconstruction via Chinese Remainder Theorem in the Presence of Small Degree Residue Errors
Venue: arXiv cs.IT 2017
Year: 2017

Based on unique decoding of the polynomial residue code with non-pairwise coprime moduli, a polynomial with degree less than that of the least common multiple (lcm) of all the moduli can be accurately reconstructed when the number of residue errors is less than half the minimum distance of the code. However, once the number of residue errors is beyond half the minimum distance of the code, the unique decoding may fail and lead to a large reconstruction error. In this paper, assuming that all the residues are allowed to have errors with small degrees, we consider how to reconstruct the polynomial as accurately as possible in the sense that a reconstructed polynomial is obtained with only the last $τ$ number of coefficients being possibly erroneous, when the residues are affected by errors with degrees upper bounded by $τ$. In this regard, we first propose a multi-level robust Chinese remainder theorem (CRT) for polynomials, namely, a trade-off between the dynamic range of the degree of the polynomial to be reconstructed and the residue error bound $τ$ is formulated. Furthermore, a simple closed-form reconstruction algorithm is also proposed.
Nonconvex Regularization Based Sparse Recovery and Demixing with Application to Color Image Inpainting
Venue: arXiv cs.IT 2017
Year: 2017

This work addresses the recovery and demixing problem of signals that are sparse in some general dictionary. Involved applications include source separation, image inpainting, super-resolution, and restoration of signals corrupted by clipping, saturation, impulsive noise, or narrowband interference. We employ the $\ell_q$-norm ($0 \le q < 1$) for sparsity inducing and propose a constrained $\ell_q$-minimization formulation for the recovery and demixing problem. This nonconvex formulation is approximately solved by two efficient first-order algorithms based on proximal coordinate descent and alternative direction method of multipliers (ADMM), respectively. The new algorithms are convergent in the nonconvex case under some mild conditions and scale well for high-dimensional problems. A convergence condition of the new ADMM algorithm has been derived. Furthermore, extension of the two algorithms for multi-channels joint recovery has been presented, which can further exploit the joint sparsity pattern among multi-channel signals. Various numerical experiments showed that the new algorithms can achieve considerable performance gain over the $\ell_1$-regularized algorithms.
Image-based Localization using Hourglass Networks
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we propose an encoder-decoder convolutional neural network (CNN) architecture for estimating camera pose (orientation and location) from a single RGB-image. The architecture has a hourglass shape consisting of a chain of convolution and up-convolution layers followed by a regression part. The up-convolution layers are introduced to preserve the fine-grained information of the input image. Following the common practice, we train our model in end-to-end manner utilizing transfer learning from large scale classification data. The experiments demonstrate the performance of the approach on data exhibiting different lighting conditions, reflections, and motion blur. The results indicate a clear improvement over the previous state-of-the-art even when compared to methods that utilize sequence of test frames instead of a single frame.
On strongly chordal graphs that are not leaf powers
Venue: arXiv cs.DM 2017
Year: 2017

A common task in phylogenetics is to find an evolutionary tree representing proximity relationships between species. This motivates the notion of leaf powers: a graph G = (V, E) is a leaf power if there exist a tree T on leafset V and a threshold k such that uv is an edge if and only if the distance between u and v in T is at most k. Characterizing leaf powers is a challenging open problem, along with determining the complexity of their recognition. This is in part due to the fact that few graphs are known to not be leaf powers, as such graphs are difficult to construct. Recently, Nevries and Rosenke asked if leaf powers could be characterized by strong chordality and a finite set of forbidden subgraphs. In this paper, we provide a negative answer to this question, by exhibiting an infinite family \G of (minimal) strongly chordal graphs that are not leaf powers. During the process, we establish a connection between leaf powers, alternating cycles and quartet compatibility. We also show that deciding if a chordal graph is \G-free is NP-complete, which may provide insight on the complexity of the leaf power recognition problem.
A Bag-of-Words Equivalent Recurrent Neural Network for Action Recognition
Venue: arXiv cs.CV 2017
Year: 2017

The traditional bag-of-words approach has found a wide range of applications in computer vision. The standard pipeline consists of a generation of a visual vocabulary, a quantization of the features into histograms of visual words, and a classification step for which usually a support vector machine in combination with a non-linear kernel is used. Given large amounts of data, however, the model suffers from a lack of discriminative power. This applies particularly for action recognition, where the vast amount of video features needs to be subsampled for unsupervised visual vocabulary generation. Moreover, the kernel computation can be very expensive on large datasets. In this work, we propose a recurrent neural network that is equivalent to the traditional bag-of-words approach but enables for the application of discriminative training. The model further allows to incorporate the kernel computation into the neural network directly, solving the complexity issue and allowing to represent the complete classification system within a single network. We evaluate our method on four recent action recognition benchmarks and show that the conventional model as well as sparse coding methods are outperformed.
Further Generalisations of Twisted Gabidulin Codes
Venue: arXiv cs.IT 2017
Year: 2017

We present a new family of maximum rank distance (MRD) codes. The new class contains codes that are neither equivalent to a generalised Gabidulin nor to a twisted Gabidulin code, the only two known general constructions of linear MRD codes.
Well-Behaved Model Transformations with Model Subtyping
Venue: arXiv cs.PL 2017
Year: 2017

In model-driven engineering, models abstract the relevant features of software artefacts and model transformations act on them automating complex tasks of the development process. It is, thus, crucially important to provide pragmatic, reliable methods to verify that model transformations guarantee the correctness of generated models in order to ensure the quality of the final end product. In this paper, we build on an object-oriented algebraic encoding of metamodels and models as defined in the standard Meta-Object Facility and in tools, such as the Eclipse Modeling Framework, to specify a domain-specific language for representing the action part of model transformations. We introduce the big-step operational structural semantics of this language and its type system, which includes a notion of polymorphic model subtyping, showing that well-typed model transformations are well behaved. That is, that metamodel-conformant model transformations never go wrong.
Combinatorial Entropy Encoding
Venue: arXiv cs.IT 2017
Year: 2017

This paper proposes a novel entropy encoding technique for lossless data compression. Representing a message string by its lexicographic index in the permutations of its symbols results in a compressed version matching Shannon entropy of the message. Commercial data compression standards make use of Huffman or arithmetic coding at some stage of the compression process. In the proposed method, like arithmetic coding entire string is mapped to an integer but is not based on fractional numbers. Unlike both arithmetic and Huffman coding no prior entropy model of the source is required. Simple intuitive algorithm based on multinomial coefficients is developed for entropy encoding that adoptively uses low number of bits for more frequent symbols. Correctness of the algorithm is demonstrated by an example.
Optimal lower bounds for universal relation, samplers, and finding duplicates
Venue: arXiv cs.CC 2017
Year: 2017

In the communication problem $\mathbf{UR}$ (universal relation) [KRW95], Alice and Bob respectively receive $x$ and $y$ in $\{0,1\}^n$ with the promise that $x\neq y$. The last player to receive a message must output an index $i$ such that $x_i\neq y_i$. We prove that the randomized one-way communication complexity of this problem in the public coin model is exactly $Θ(\min\{n, \log(1/δ)\log^2(\frac{n}{\log(1/δ)})\})$ bits for failure probability $δ$. Our lower bound holds even if promised $\mathop{support}(y)\subset \mathop{support}(x)$. As a corollary, we obtain optimal lower bounds for $\ell_p$-sampling in strict turnstile streams for $0\le p < 2$, as well as for the problem of finding duplicates in a stream. Our lower bounds do not need to use large weights, and hold even if it is promised that $x\in\{0,1\}^n$ at all points in the stream. Our lower bound demonstrates that any algorithm $\mathcal{A}$ solving sampling problems in turnstile streams in low memory can be used to encode subsets of $[n]$ of certain sizes into a number of bits below the information theoretic minimum. Our encoder makes adaptive queries to $\mathcal{A}$ throughout its execution, but done carefully so as to not violate correctness. This is accomplished by injecting random noise into the encoder's interactions with $\mathcal{A}$, which is loosely motivated by techniques in differential privacy. Our correctness analysis involves understanding the ability of $\mathcal{A}$ to correctly answer adaptive queries which have positive but bounded mutual information with $\mathcal{A}$'s internal randomness, and may be of independent interest in the newly emerging area of adaptive data analysis with a theoretical computer science lens.
Single Image Super-resolution via a Lightweight Residual Convolutional Neural Network
Venue: arXiv cs.CV 2017
Year: 2017

Recent years have witnessed great success of convolutional neural network (CNN) for various problems both in low and high level visions. Especially noteworthy is the residual network which was originally proposed to handle high-level vision problems and enjoys several merits. This paper aims to extend the merits of residual network, such as skip connection induced fast training, for a typical low-level vision problem, i.e., single image super-resolution. In general, the two main challenges of existing deep CNN for supper-resolution lie in the gradient exploding/vanishing problem and large numbers of parameters or computational cost as CNN goes deeper. Correspondingly, the skip connections or identity mapping shortcuts are utilized to avoid gradient exploding/vanishing problem. In addition, the skip connections have naturally centered the activation which led to better performance. To tackle with the second problem, a lightweight CNN architecture which has carefully designed width, depth and skip connections was proposed. In particular, a strategy of gradually varying the shape of network has been proposed for residual network. Different residual architectures for image super-resolution have also been compared. Experimental results have demonstrated that the proposed CNN model can not only achieve state-of-the-art PSNR and SSIM results for single image super-resolution but also produce visually pleasant results. This paper has extended the mmm 2017 oral conference paper with a considerable new analyses and more experiments especially from the perspective of centering activations and ensemble behaviors of residual network.
On Desirable Semantics of Functional Dependencies over Databases with Incomplete Information
Venue: arXiv cs.DB 2017
Year: 2017

Codd's relational model describes just one possible world. To better cope with incomplete information, extended database models allow several possible worlds. Vague tables are one such convenient extended model where attributes accept sets of possible values (e.g., the manager is either Jill or Bob). However, conceptual database design in such cases remains an open problem. In particular, there is no canonical definition of functional dependencies (FDs) over possible worlds (e.g., each employee has just one manager). We identify several desirable properties that the semantics of such FDs should meet including Armstrong's axioms, the independence from irrelevant attributes, seamless satisfaction and implied by strong satisfaction. We show that we can define FDs such that they have all our desirable properties over vague tables. However, we also show that no notion of FD can satisfy all our desirable properties over a more general model (disjunctive tables). Our work formalizes a trade-off between having a general model and having well-behaved FDs.