Right for the Right Reason: Training Agnostic Networks

We consider the problem of a neural network being requested to classify images (or other inputs) without making implicit use of a "protected concept", that is a concept that should not play any role in the decision of the network. Typically these concepts include information such as gender or race, or other contextual information such as image backgrounds that might be implicitly reflected in unknown correlations with other variables, making it insufficient to simply remove them from the input features. In other words, making accurate predictions is not good enough if those predictions rely on information that should not be used: predictive performance is not the only important metric for learning systems. We apply a method developed in the context of domain adaptation to address this problem of "being right for the right reason", where we request a classifier to make a decision in a way that is entirely 'agnostic' to a given protected concept (e.g. gender, race, background etc.), even if this could be implicitly reflected in other attributes via unknown correlations. After defining the concept of an 'agnostic model', we demonstrate how the Domain-Adversarial Neural Network can remove unwanted information from a model using a gradient reversal layer.Comment: Author's original versio

Network-wide Configuration Synthesis

Computer networks are hard to manage. Given a set of high-level requirements (e.g., reachability, security), operators have to manually figure out the individual configuration of potentially hundreds of devices running complex distributed protocols so that they, collectively, compute a compatible forwarding state. Not surprisingly, operators often make mistakes which lead to downtimes. To address this problem, we present a novel synthesis approach that automatically computes correct network configurations that comply with the operator's requirements. We capture the behavior of existing routers along with the distributed protocols they run in stratified Datalog. Our key insight is to reduce the problem of finding correct input configurations to the task of synthesizing inputs for a stratified Datalog program. To solve this synthesis task, we introduce a new algorithm that synthesizes inputs for stratified Datalog programs. This algorithm is applicable beyond the domain of networks. We leverage our synthesis algorithm to construct the first network-wide configuration synthesis system, called SyNET, that support multiple interacting routing protocols (OSPF and BGP) and static routes. We show that our system is practical and can infer correct input configurations, in a reasonable amount time, for networks of realistic size (> 50 routers) that forward packets for multiple traffic classes.Comment: 24 Pages, short version published in CAV 201

Post-spinel transformations and equation of state in ZnGa2O4: Determination at high-pressure by in situ x-ray diffraction

Room temperature angle-dispersive x-ray diffraction measurements on spinel ZnGa2O4 up to 56 GPa show evidence of two structural phase transformations. At 31.2 GPa, ZnGa2O4 undergoes a transition from the cubic spinel structure to a tetragonal spinel structure similar to that of ZnMn2O4. At 55 GPa, a second transition to the orthorhombic marokite structure (CaMn2O4-type) takes place. The equation of state of cubic spinel ZnGa2O4 is determined: V0 = 580.1(9) A3, B0 = 233(8) GPa, B0'= 8.3(4), and B0''= -0.1145 GPa-1 (implied value); showing that ZnGa2O4 is one of the less compressible spinels studied to date. For the tetragonal structure an equation of state is also determined: V0 = 257.8(9) A3, B0 = 257(11) GPa, B0'= 7.5(6), and B0''= -0.0764 GPa-1 (implied value). The reported structural sequence coincides with that found in NiMn2O4 and MgMn2O4.Comment: 20 pages, 4 figures, 2 Table

From natural language processing to neural databases

In recent years, neural networks have shown impressive performance gains on long-standing AI problems, such as answering queries from text and machine translation. These advances raise the question of whether neural nets can be used at the core of query processing to derive answers from facts, even when the facts are expressed in natural language. If so, it is conceivable that we could relax the fundamental assumption of database management, namely, that our data is represented as fields of a pre-defined schema. Furthermore, such technology would enable combining information from text, images, and structured data seamlessly. This paper introduces neural databases, a class of systems that use NLP transformers as localized answer derivation engines. We ground the vision in NeuralDB, a system for querying facts represented as short natural language sentences. We demonstrate that recent natural language processing models, specifically transformers, can answer select-project-join queries if they are given a set of relevant facts. However, they cannot scale to non-trivial databases nor answer set-based and aggregation queries. Based on these insights, we identify specific research challenges that are needed to build neural databases. Some of the challenges require drawing upon the rich literature in data management, and others pose new research opportunities to the NLP community. Finally, we show that with preliminary solutions, NeuralDB can already answer queries over thousands of sentences with very high accuracy

Structural, electronic, and magnetic characteristics of Np_2Co_(17)

A previously unknown neptunium-transition-metal binary compound Np_2Co_(17) has been synthesized and characterized by means of powder x-ray diffraction, ^(237)Np Mössbauer spectroscopy, superconducting-quantum-interference-device magnetometry, and x-ray magnetic circular dichroism (XMCD). The compound crystallizes in a Th_2Ni_(17)-type hexagonal structure with room-temperature lattice parameters α=8.3107(1) Å and c=8.1058(1) Å. Magnetization curves indicate the occurrence of ferromagnetic order below T_C>350 K. Mössbauer spectra suggest a Np^(3+) oxidation state and give an ordered moment of μ_(Np)=1.57(4) μ_B and μ_(Np)=1.63(4) μ_B for the Np atoms located, respectively, at the 2b and 2d crystallographic positions of the P6_3/mmc space group. Combining these values with a sum-rule analysis of the XMCD spectra measured at the neptunium M_(4,5) absorption edges, one obtains the spin and orbital contributions to the site-averaged Np moment [μ_S=−1.88(9) μ_B, μ_L=3.48(9) μ_B]. The ratio between the expectation value of the magnetic-dipole moment and the spin magnetic moment (m_(md)/μS=+1.36) is positive as predicted for localized 5f electrons and lies between the values calculated in intermediate-coupling (IC) and jj approximations. The expectation value of the angular part of the spin-orbit-interaction operator is in excellent agreement with the IC estimate. The ordered moment averaged over the four inequivalent Co sites, as obtained from the saturation value of the magnetization, is μ_(Co)≃1.6 μ_B. The experimental results are discussed against the predictions of first-principles electronic-structure calculations based on the spin-polarized local-spin-density approximation plus the Hubbard interaction

Crop Knowledge Discovery Based on Agricultural Big Data Integration

Nowadays, the agricultural data can be generated through various sources, such as: Internet of Thing (IoT), sensors, satellites, weather stations, robots, farm equipment, agricultural laboratories, farmers, government agencies and agribusinesses. The analysis of this big data enables farmers, companies and agronomists to extract high business and scientific knowledge, improving their operational processes and product quality. However, before analysing this data, different data sources need to be normalised, homogenised and integrated into a unified data representation. In this paper, we propose an agricultural data integration method using a constellation schema which is designed to be flexible enough to incorporate other datasets and big data models. We also apply some methods to extract knowledge with the view to improve crop yield; these include finding suitable quantities of soil properties, herbicides and insecticides for both increasing crop yield and protecting the environment.Comment: 5 page

Database reasoning over text

Neural models have shown impressive performance gains in answering queries from natural language text. However, existing works are unable to support database queries, such as “List/Count all female athletes who were born in 20th century”, which require reasoning over sets of relevant facts with operations such as join, filtering and aggregation. We show that while state-of-the-art transformer models perform very well for small databases, they exhibit limitations in processing noisy data, numerical operations, and queries that aggregate facts. We propose a modular architecture to answer these database-style queries over multiple spans from text and aggregating these at scale. We evaluate the architecture using WikiNLDB, a novel dataset for exploring such queries. Our architecture scales to databases containing thousands of facts whereas contemporary models are limited by how many facts can be encoded. In direct comparison on small databases, our approach increases overall answer accuracy from 85% to 90%. On larger databases, our approach retains its accuracy whereas transformer baselines could not encode the context