Estimating Depth from RGB and Sparse Sensing

We present a deep model that can accurately produce dense depth maps given an RGB image with known depth at a very sparse set of pixels. The model works simultaneously for both indoor/outdoor scenes and produces state-of-the-art dense depth maps at nearly real-time speeds on both the NYUv2 and KITTI datasets. We surpass the state-of-the-art for monocular depth estimation even with depth values for only 1 out of every ~10000 image pixels, and we outperform other sparse-to-dense depth methods at all sparsity levels. With depth values for 1/256 of the image pixels, we achieve a mean absolute error of less than 1% of actual depth on indoor scenes, comparable to the performance of consumer-grade depth sensor hardware. Our experiments demonstrate that it would indeed be possible to efficiently transform sparse depth measurements obtained using e.g. lower-power depth sensors or SLAM systems into high-quality dense depth maps.Comment: European Conference on Computer Vision (ECCV) 2018. Updated to camera-ready version with additional experiment

On Optimization Modulo Theories, MaxSMT and Sorting Networks

Optimization Modulo Theories (OMT) is an extension of SMT which allows for finding models that optimize given objectives. (Partial weighted) MaxSMT --or equivalently OMT with Pseudo-Boolean objective functions, OMT+PB-- is a very-relevant strict subcase of OMT. We classify existing approaches for MaxSMT or OMT+PB in two groups: MaxSAT-based approaches exploit the efficiency of state-of-the-art MAXSAT solvers, but they are specific-purpose and not always applicable; OMT-based approaches are general-purpose, but they suffer from intrinsic inefficiencies on MaxSMT/OMT+PB problems. We identify a major source of such inefficiencies, and we address it by enhancing OMT by means of bidirectional sorting networks. We implemented this idea on top of the OptiMathSAT OMT solver. We run an extensive empirical evaluation on a variety of problems, comparing MaxSAT-based and OMT-based techniques, with and without sorting networks, implemented on top of OptiMathSAT and {\nu}Z. The results support the effectiveness of this idea, and provide interesting insights about the different approaches.Comment: 17 pages, submitted at Tacas 1

Generalized Totalizer Encoding for Pseudo-Boolean Constraints

Pseudo-Boolean constraints, also known as 0-1 Integer Linear Constraints, are used to model many real-world problems. A common approach to solve these constraints is to encode them into a SAT formula. The runtime of the SAT solver on such formula is sensitive to the manner in which the given pseudo-Boolean constraints are encoded. In this paper, we propose generalized Totalizer encoding (GTE), which is an arc-consistency preserving extension of the Totalizer encoding to pseudo-Boolean constraints. Unlike some other encodings, the number of auxiliary variables required for GTE does not depend on the magnitudes of the coefficients. Instead, it depends on the number of distinct combinations of these coefficients. We show the superiority of GTE with respect to other encodings when large pseudo-Boolean constraints have low number of distinct coefficients. Our experimental results also show that GTE remains competitive even when the pseudo-Boolean constraints do not have this characteristic.Comment: 10 pages, 2 figures, 2 tables. To be published in 21st International Conference on Principles and Practice of Constraint Programming 201

A Gpr120-selective agonist improves insulin resistance and chronic inflammation in obese mice.

It is well known that the ω-3 fatty acids (ω-3-FAs; also known as n-3 fatty acids) can exert potent anti-inflammatory effects. Commonly consumed as fish products, dietary supplements and pharmaceuticals, ω-3-FAs have a number of health benefits ascribed to them, including reduced plasma triglyceride levels, amelioration of atherosclerosis and increased insulin sensitivity. We reported that Gpr120 is the functional receptor for these fatty acids and that ω-3-FAs produce robust anti-inflammatory, insulin-sensitizing effects, both in vivo and in vitro, in a Gpr120-dependent manner. Indeed, genetic variants that predispose to obesity and diabetes have been described in the gene encoding GPR120 in humans (FFAR4). However, the amount of fish oils that would have to be consumed to sustain chronic agonism of Gpr120 is too high to be practical, and, thus, a high-affinity small-molecule Gpr120 agonist would be of potential clinical benefit. Accordingly, Gpr120 is a widely studied drug discovery target within the pharmaceutical industry. Gpr40 is another lipid-sensing G protein-coupled receptor, and it has been difficult to identify compounds with a high degree of selectivity for Gpr120 over Gpr40 (ref. 11). Here we report that a selective high-affinity, orally available, small-molecule Gpr120 agonist (cpdA) exerts potent anti-inflammatory effects on macrophages in vitro and in obese mice in vivo. Gpr120 agonist treatment of high-fat diet-fed obese mice causes improved glucose tolerance, decreased hyperinsulinemia, increased insulin sensitivity and decreased hepatic steatosis. This suggests that Gpr120 agonists could become new insulin-sensitizing drugs for the treatment of type 2 diabetes and other human insulin-resistant states in the future

DataJoint: managing big scientific data using MATLAB or Python

The rise of big data in modern research poses serious challenges for data management: Large and intricate datasets from diverse instrumentation must be precisely aligned, annotated, and processed in a variety of ways to extract new insights. While high levels of data integrity are expected, research teams have diverse backgrounds, are geographically dispersed, and rarely possess a primary interest in data science. Here we describe DataJoint, an open-source toolbox designed for manipulating and processing scientific data under the relational data model. Designed for scientists who need a flexible and expressive database language with few basic concepts and operations, DataJoint facilitates multi-user access, efficient queries, and distributed computing. With implementations in both MATLAB and Python, DataJoint is not limited to particular file formats, acquisition systems, or data modalities and can be quickly adapted to new experimental designs. DataJoint and related resources are available at http://datajoint.github.com

On Tackling the Limits of Resolution in SAT Solving

The practical success of Boolean Satisfiability (SAT) solvers stems from the CDCL (Conflict-Driven Clause Learning) approach to SAT solving. However, from a propositional proof complexity perspective, CDCL is no more powerful than the resolution proof system, for which many hard examples exist. This paper proposes a new problem transformation, which enables reducing the decision problem for formulas in conjunctive normal form (CNF) to the problem of solving maximum satisfiability over Horn formulas. Given the new transformation, the paper proves a polynomial bound on the number of MaxSAT resolution steps for pigeonhole formulas. This result is in clear contrast with earlier results on the length of proofs of MaxSAT resolution for pigeonhole formulas. The paper also establishes the same polynomial bound in the case of modern core-guided MaxSAT solvers. Experimental results, obtained on CNF formulas known to be hard for CDCL SAT solvers, show that these can be efficiently solved with modern MaxSAT solvers

In vitro bioactivity of various pure flavonoids in ruminal fermentation, with special reference to methane formation

Polyphenols, like flavonoids, have been investigated when present in intact plants or in extracts as methane mitigating dietary supplements in ruminants. The aim of the present study was to examine pure compounds in a short-term in vitro experiment using the Hohenheim Gas Test method. We focused on the group of the flavonoids and tested which of them had the potential to mitigate methane without negatively affecting ruminal fermentation. Eight flavonoids were tested: epicatechin, luteolin-7-glucoside, quercetin, and isoquercetin in Experiment 1; catechin, gallocatechin, epigallocatechin, and epigallocatechin gallate in Experiment 2. Tannic acid, no flavonoid but a phenolic acid with known methane mitigating properties, served as positive control, and the unsupplemented basal diet as negative control. In both experiments, each of these compounds (including tannic acid) was tested at dosages of 0.5, 5.0, and 50.0 mg/g basal diet dry matter (DM) in four runs each. Gallocatechin, tannic acid, and epigallocatechin gallate (50 mg/g DM) lowered fermentation gas formation and in vitro organic matter digestibility relative to the negative control (Experiment 2). Apart from tannic acid, epicatechin, quercetin, isoquercetin, and luteolin-7-glucoside (5 and 50 mg/g DM) reduced the amount of CH4 produced in relation to total gas produced (Experiment 1). The incubation fluid ammonia concentration was decreased with luteolin-7-glucoside and tannic acid (50 mg/g DM). From the flavonoids tested especially luteolin-7-glucoside seems to have a similar potential as tannic acid to mitigate methane and ammonia formation during ruminal fermentation in vitro, both favourable in environmental respect. These results need to be confirmed in live animals