Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Integer Balancing Under Weight Constraints in the Presence of Transmission Delays

We consider the distributed weight balancing problem in networks of nodes that are interconnected via directed edges, each of which admits a positive integer weight. A digraph with positive integer weights on its edges is weight balanced if, for each node, the sum of the weights of the incoming edges equals the sum of the weights of the outgoing edges. In this article, we develop a distributed iterative algorithm, which solves the integer weight balancing problem in the presence of arbitrary (time-varying and inhomogeneous) time delays that might affect transmissions at particular links. We assume that each positive weight is constrained to lie within a certain interval, captured by individual lower and upper limits and that communication between neighboring nodes is bidirectional. We show that even when different transmissions on communication links are affected from bounded delays, the proposed distributed algorithm allows nodes to obtain a set of weights that solves the integer weight balancing problem, after a finite number of iterations, as long as a feasible solution exists. Finally, we provide examples to illustrate the operation and performance of the proposed algorithm.
Implicit Discourse Relation Identification for Open-domain Dialogues

Discourse relation identification has been an active area of research for many years, and the challenge of identifying implicit relations remains largely an unsolved task, especially in the context of an open-domain dialogue system. Previous work primarily relies on a corpora of formal text which is inherently non-dialogic, i.e., news and journals. This data however is not suitable to handle the nuances of informal dialogue nor is it capable of navigating the plethora of valid topics present in open-domain dialogue. In this paper, we designed a novel discourse relation identification pipeline specifically tuned for open-domain dialogue systems. We firstly propose a method to automatically extract the implicit discourse relation argument pairs and labels from a dataset of dialogic turns, resulting in a novel corpus of discourse relation pairs; the first of its kind to attempt to identify the discourse relations connecting the dialogic turns in open-domain discourse. Moreover, we have taken the first steps to leverage the dialogue features unique to our task to further improve the identification of such relations by performing feature ablation and incorporating dialogue features to enhance the state-of-the-art model.
Molecular gas and dust properties of galaxies from the Great Observatories All-sky LIRG Survey

We present IRAM-30 m Telescope 12CO and 13CO observations of a sample of 55 luminous and ultraluminous infrared galaxies (LIRGs and ULIRGs) in the local universe. This sample is a subset of the Great Observatory All-Sky LIRG Survey (GOALS), for which we use ancillary multi-wavelength data to better understand their interstellar medium and star formation properties. Fifty-three (96%) of the galaxies are detected in 12CO, and 29 (52%) are also detected in 13CO above a 3σ level. The median full width at zero intensity (FWZI) velocity of the CO line emission is 661 km s−1, and ∼54% of the galaxies show a multi-peak CO profile. Herschel photometric data is used to construct the far-IR spectral energy distribution of each galaxy, which are fit with a modified blackbody model that allows us to derive dust temperatures and masses, and infrared luminosities. We make the assumption that the gas-to-dust mass ratio of (U)LIRGs is comparable to local spiral galaxies with a similar stellar mass (i.e., gas/dust of mergers is comparable to their progenitors) to derive a CO-to-H2 conversion factor of ⟨α⟩ = 1.8−0.8+1.3 M⊙ (K km s−1 pc2)−1; such a value is comparable to that derived for (U)LIRGs based on dynamical mass arguments. We derive gas depletion times of 400 − 600 Myr for the (U)LIRGs, compared to the 1.3 Gyr for local spiral galaxies. Finally, we re-examine the relationship between the 12CO/13CO ratio and dust temperature, confirming a transition to elevated ratios in warmer systems.
Uncertainty and causal emergence in complex networks

The connectivity of a network conveys information about the dependencies between nodes. We show that this information can be analyzed by measuring the uncertainty (and certainty) contained in paths along nodes and links in a network. Specifically, we derive from first principles a measure known as effective information and describe its behavior in common network models. Networks with higher effective information contain more information within the dependencies between nodes. We show how subgraphs of nodes can be grouped into macro-nodes, reducing the size of a network while increasing its effective information, a phenomenon known as causal emergence. We find that causal emergence is common in simulated and real networks across biological, social, informational, and technological domains. Ultimately, these results show that the emergence of higher scales in networks can be directly assessed, and that these higher scales offer a way to create certainty out of uncertainty.
The State and Future of Genetic Improvement

We report the discussion session at the sixth international Genetic Improvement workshop, GI-2019 @ ICSE, which was held as part of the 41st ACM/IEEE International Confer- ence on Software Engineering on Tuesday 28th May 2019. Topics included GI representations, the maintainability of evolved code, automated software testing, future areas of GI research, such as co-evolution, and existing GI tools and benchmarks.
Table Understanding in Structured Documents

Table detection and extraction has been studied in the context of documents like reports, where tables are clearly outlined and stand out from the document structure visually. We study this topic in a rather more challenging domain of layout-heavy business documents, particularly invoices. Invoices present the novel challenges of tables being often without outlines - either in the form of borders or surrounding text flow - with ragged columns and widely varying data content. We will also show, that we can extract specific information from structurally different tables or table-like structures with one model. We present a comprehensive representation of a page using graph over word boxes, positional embeddings, trainable textual features and rephrase the table detection as a text box labeling problem. We will work on our newly presented dataset of pro forma invoices, invoices and debit note documents using this representation and propose multiple baselines to solve this labeling problem. We then propose a novel neural network model that achieves strong, practical results on the presented dataset and analyze the model performance and effects of graph convolutions and self-attention in detail.
Formation of Shocks for 2D Isentropic Compressible Euler

We consider the 2D isentropic compressible Euler equations, with pressure law p(ρ) = (1γ)ργ, with γ > 1. We provide an elementary constructive proof of shock formation from smooth initial data of finite energy, with no vacuum regions, and with nontrivial vorticity. We prove that for initial data which has minimum slope −1ε, for ε > 0 taken sufficiently small relative to the O1 amplitude, there exist smooth solutions to the Euler equations which form a shock in time Oε . The blowup time and location can be explicitly computed and solutions at the blowup time are of cusp‐type, with Hölder C13 regularity.
Better Sample - Random Subset Sum in 20.255n and its Impact on Decoding Random Linear Codes

We propose a new heuristic algorithm for solving random subset sum instances $a_1, \ldots, a_n, t \in \mathbb{Z}_{2^n}$, which play a crucial role in cryptographic constructions. Our algorithm is search tree-based and solves the instances in a divide-and-conquer method using the representation method. From a high level perspective, our algorithm is similar to the algorithm of Howgrave-Graham-Joux (HGJ) and Becker-Coron-Joux (BCJ), but instead of enumerating the initial lists we sample candidate solutions. So whereas HGJ and BCJ are based on combinatorics, our analysis is stochastic. Our sampling technique introduces variance that increases the amount of representations and gives our algorithm more optimization flexibility. This results in the remarkable and natural property that we improve with increasing search tree depth. Whereas BCJ achieves the currently best known (heuristic) run time $2^{0.291n}$ for random subset sum, we improve (heuristically) down to $2^{0.255n}$ using a search tree of depth at least $13$. We also apply our subset algorithm to the decoding of random binary linear codes, where we improve the best known run time of the Becker-Joux-May-Meurer algorithm from $2^{0.048n}$ in the half distance decoding setting down to $2^{0.042n}$.
Gaia Data Release 2 catalogue of extremely low-mass white dwarf candidates

Extremely low-mass white dwarf stars (ELMs) are M < 0.3 M⊙ helium-core white dwarfs born either as a result of a common-envelope phase or after a stable Roche lobe overflow episode in a multiple system. The Universe is not old enough for ELMs to have formed through single-star evolution channels. As remnants of binary evolution, ELMs can shed light onto the poorly understood phase of common-envelope evolution and provide constraints to the physics of mass accretion. Most known ELMs will merge in less than a Hubble time, providing an important contribution to the signal to be detected by upcoming space-based gravitational wave detectors. There are currently less than 150 known ELMs; most were selected by colour, focusing on hot objects, in a magnitude-limited survey of the Northern hemisphere only. Recent theoretical models have predicted a much larger space density for ELMs than estimated observationally based on this limited sample. In order to perform meaningful comparisons with theoretical models and test their predictions, a larger well-defined sample is required. In this work, we present a catalogue of ELM candidates selected from the second data release of Gaia (DR2). We have used predictions from theoretical models and analysed the properties of the known sample to map the space spanned by ELMs in the Gaia Hertzsprung–Russell diagram. Defining a set of colour cuts and quality flags, we have obtained a final sample of 5762 ELM candidates down to Teff ≈ 5000 K.
Spectral Numerical Mode-Matching Method for 3-D Layered Multiregion Structures

The spectral numerical mode-matching (SNMM) method is developed to simulate the 3-D layered multiregion structures. The SNMM method is a semi-analytical solver having the properties of dimensionality reduction to reduce the computational costs; it is especially useful for microwave and optical integrated circuits where fabrication is often done in a layered structure. Furthermore, at some layer interfaces, very thin surfaces such as good conductor surfaces and metasurfaces can be deposited to achieve desired properties such as high absorbance and/or anomalous reflection/refraction. In this article, the 3-D SNMM method is further extended from a single interface to multiple layers so that the electromagnetic propagation and scattering in the longitudinal direction is treated analytically through reflection and transmission matrices by using the eigenmode expansions in the transverse directions. Therefore, it effectively reduces the original 3-D problem into a series of 2-D eigenvalue problems for periodic structures. We apply this method to characterize metasurfaces and lithography models and show that the SNMM method is especially efficient when the longitudinal layer thicknesses are large compared with wavelength. Numerical experiments indicate that the SNMM method is highly efficient and accurate for the metasurfaces and the lithography models.
How bad can a bug get? an empirical analysis of software failures in the OpenStack cloud computing platform

Cloud management systems provide abstractions and APIs for programmatically configuring cloud infrastructures. Unfortunately, residual software bugs in these systems can potentially lead to high-severity failures, such as prolonged outages and data losses. In this paper, we investigate the impact of failures in the context widespread OpenStack cloud management system, by performing fault injection and by analyzing the impact of the resulting failures in terms of fail-stop behavior, failure detection through logging, and failure propagation across components. The analysis points out that most of the failures are not timely detected and notified; moreover, many of these failures can silently propagate over time and through components of the cloud management system, which call for more thorough run-time checks and fault containment.
Quenching estimates for a non-Newtonian filtration equation with singular boundary conditions

This study concerns with the quenching features of solutions of the non-Newtonian filtration equation. Various conditions on the initial condition are shown to guarantee quenching at either the left or right boundary. Theoretical quenching rates and lower bounds to the quenching time are determined are certain cases. Numerical experiments are provided to illustrate and provide additional validation of the theoretical predictions to the quenching rates and times.
SVGD: A Virtual Gradients Descent Method for Stochastic Optimization

Inspired by dynamic programming, we propose Stochastic Virtual Gradient Descent (SVGD) algorithm where the Virtual Gradient is defined by computational graph and automatic differentiation. The method is computationally efficient and has little memory requirements. We also analyze the theoretical convergence properties and implementation of the algorithm. Experimental results on multiple datasets and network models show that SVGD has advantages over other stochastic optimization methods.
Using Metadata to Integrate Medical Knowledge in a Clinical Information System

Abstract The clinical information system under development at Columbia Presbyterian Medical Center includes a relational patient database and a decision support system. The design incorporates a database of system information, or “Metadatabase”, which serves to integrate three forms of medical knowledge with clinical applications: medical entities, events, and decision logic. The Metadatabase is implemented in standard relational technology, and is intended to supply information to all phases of application development.
Manticore: A User-Friendly Symbolic Execution Framework for Binaries and Smart Contracts

An effective way to maximize code coverage in software tests is through dynamic symbolic execution—a technique that uses constraint solving to systematically explore a program's state space. We introduce an open-source dynamic symbolic execution framework called Manticore for analyzing binaries and Ethereum smart contracts. Manticore's flexible architecture allows it to support both traditional and exotic execution environments, and its API allows users to customize their analysis. Here, we discuss Manticore's architecture and demonstrate the capabilities we have used to find bugs and verify the correctness of code for our commercial clients.
Counting and Finding Homomorphisms is Universal for Parameterized Complexity Theory

Counting homomorphisms from a graph $H$ into another graph $G$ is a fundamental problem of (parameterized) counting complexity theory. In this work, we study the case where \emph{both} graphs $H$ and $G$ stem from given classes of graphs: $H\in \mathcal{H}$ and $G\in \mathcal{G}$. By this, we combine the structurally restricted version of this problem, with the language-restricted version. Our main result is a construction based on Kneser graphs that associates every problem $\tt P$ in $\#\mathsf{W[1]}$ with two classes of graphs $\mathcal{H}$ and $\mathcal{G}$ such that the problem $\tt P$ is \emph{equivalent} to the problem $\#{\tt HOM}(\mathcal{H}\to \mathcal{G})$ of counting homomorphisms from a graph in $\mathcal{H}$ to a graph in $\mathcal{G}$. In view of Ladner's seminal work on the existence of $\mathsf{NP}$-intermediate problems [J.ACM'75] and its adaptations to the parameterized setting, a classification of the class $\#\mathsf{W[1]}$ in fixed-parameter tractable and $\#\mathsf{W[1]}$-complete cases is unlikely. Hence, obtaining a complete classification for the problem $\#{\tt HOM}(\mathcal{H}\to \mathcal{G})$ seems unlikely. Further, our proofs easily adapt to $\mathsf{W[1]}$. In search of complexity dichotomies, we hence turn to special graph classes. Those classes include line graphs, claw-free graphs, perfect graphs, and combinations thereof, and $F$-colorable graphs for fixed graphs $F$: If the class $\mathcal{G}$ is one of those classes and the class $\mathcal{H}$ is closed under taking minors, then we establish explicit criteria for the class $\mathcal{H}$ that partition the family of problems $\#{\tt HOM}(\mathcal{H}\to\mathcal{G})$ into polynomial-time solvable and $\#\mathsf{W[1]}$-hard cases. In particular, we can drop the condition of $\mathcal{H}$ being minor-closed for $F$-colorable graphs.
Contextual One-Class Classification in Data Streams

In machine learning, the one-class classification problem occurs when training instances are only available from one class. It has been observed that making use of this class's structure, or its different contexts, may improve one-class classifier performance. Although this observation has been demonstrated for static data, a rigorous application of the idea within the data stream environment is lacking. To address this gap, we propose the use of context to guide one-class classifier learning in data streams, paying particular attention to the challenges presented by the dynamic learning environment. We present three frameworks that learn contexts and conduct experiments with synthetic and benchmark data streams. We conclude that the paradigm of contexts in data streams can be used to improve the performance of streaming one-class classifiers.
k-GANs: Ensemble of Generative Models with Semi-Discrete Optimal Transport

Generative adversarial networks (GANs) are the state of the art in generative modeling. Unfortunately, most GAN methods are susceptible to mode collapse, meaning that they tend to capture only a subset of the modes of the true distribution. A possible way of dealing with this problem is to use an ensemble of GANs, where (ideally) each network models a single mode. In this paper, we introduce a principled method for training an ensemble of GANs using semi-discrete optimal transport theory. In our approach, each generative network models the transportation map between a point mass (Dirac measure) and the restriction of the data distribution on a tile of a Voronoi tessellation that is defined by the location of the point masses. We iteratively train the generative networks and the point masses until convergence. The resulting k-GANs algorithm has strong theoretical connection with the k-medoids algorithm. In our experiments, we show that our ensemble method consistently outperforms baseline GANs.
Learning from Thresholds: Fully Automated Classification of Tumor Infiltrating Lymphocytes for Multiple Cancer Types

Deep learning classifiers for characterization of whole slide tissue morphology require large volumes of annotated data to learn variations across different tissue and cancer types. As is well known, manual generation of digital pathology training data is time consuming and expensive. In this paper, we propose a semi-automated method for annotating a group of similar instances at once, instead of collecting only per-instance manual annotations. This allows for a much larger training set, that reflects visual variability across multiple cancer types and thus training of a single network which can be automatically applied to each cancer type without human adjustment. We apply our method to the important task of classifying Tumor Infiltrating Lymphocytes (TILs) in H&E images. Prior approaches were trained for individual cancer types, with smaller training sets and human-in-the-loop threshold adjustment. We utilize these thresholded results as large scale "semi-automatic" annotations. Combined with existing manual annotations, our trained deep networks are able to automatically produce better TIL prediction results in 12 cancer types, compared to the human-in-the-loop approach.
FarSense

The past few years have witnessed the great potential of exploiting channel state information retrieved from commodity WiFi devices for respiration monitoring. However, existing approaches only work when the target is close to the WiFi transceivers and the performance degrades significantly when the target is far away. On the other hand, most home environments only have one WiFi access point and it may not be located in the same room as the target. This sensing range constraint greatly limits the application of the proposed approaches in real life. This paper presents FarSense--the first real-time system that can reliably monitor human respiration when the target is far away from the WiFi transceiver pair. FarSense works well even when one of the transceivers is located in another room, moving a big step towards real-life deployment. We propose two novel schemes to achieve this goal: (1) Instead of applying the raw CSI readings of individual antenna for sensing, we employ the ratio of CSI readings from two antennas, whose noise is mostly canceled out by the division operation to significantly increase the sensing range; (2) The division operation further enables us to utilize the phase information which is not usable with one single antenna for sensing. The orthogonal amplitude and phase are elaborately combined to address the "blind spots" issue and further increase the sensing range. Extensive experiments show that FarSense is able to accurately monitor human respiration even when the target is 8 meters away from the transceiver pair, increasing the sensing range by more than 100%. 1 We believe this is the first system to enable through-wall respiration sensing with commodity WiFi devices and the proposed method could also benefit other sensing applications.
Phonon and Shifton from a Real Modulated Scalar

We study a massive real scalar field that breaks translation symmetry dynamically. Higher-gradient terms favour modulated configurations and neither finite density nor temperature are needed. In the broken phase, the energy density depends on the spatial position and the linear fluctuations show phononic dispersion. We then study a related massless scalar model where the modulated vacua break also the field shift symmetry and give rise to an additional Nambu-Goldstone mode, the shifton. We discuss the independence of the shifton and the phonon and draw an analogy to rotons in superfluids. Proceeding from first-principles, we re-obtain and generalise some standard results for one-dimensional lattices. Eventually, we prove stability against geometric deformations extending existing analyses for elastic media to the higher-derivatives cases. ∗daniele.musso@usc.es †daniel.naegels@ulb.ac. be 1 ar X iv :1 90 7. 04 06 9v 1 [ he pth ] 9 J ul 2 01 9