text:
GOG.com (колишній Good Old Games, ) — інтернет-сервіс цифрової дистрибуції для продажу і розповсюдження відеоігор, який належить польській компанії CD Projekt Investment. Ресурс, в основному, поширює старі ігри 80-х і 90-х років. Всі ігри оптимізовані для запуску на Windows XP і Windows Vista.

На відміну від інших сервісів, на GOG.com не використовується DRM,і для завантаження і запуску ігор не потрібна установка будь-якого спеціального клієнта.

Окрім ігор, на сайті можна також завантажити безліч додаткових матеріалів, таких як саундтрек, шпалери для робочого столу і керівництва.

GOG Ltd є дочірньою компанією CD Projekt, яка знаходиться у Варшаві, Польща. GOG спочатку був сконцентрований на випуск «класичних» ігор для Windows без будь-якого захисту, але пізніше почав продавати і новіші ігри, а також підтримувати Mac OS X і Linux.

Особливості

Продукти можна купувати і завантажувати онлайн, і всі вони надаються без захисту. Цінова політика спочатку передбачала дві категорії ігор — за $5 і $10. Але з початком продажу нових ігор, а пізніше і підтримкою регіональних цін, цінових категорій з'явилося набагато більше. На окремі ігри або збірки регулярно проводяться знижки, як на вихідні та свята, так і посеред тижня.

Користувачам не потрібно завантажувати якісь спеціальні клієнти або програми, для того щоб завантажувати та оновлювати ігри. Але є опціональний менеджер завантажень, який дозволяє автоматизувати завантаження і перевірку правильності отриманих пакунків ігор. Всі ігри можна зберігати на будь-яких носіях і заново завантажувати необмежену кількість разів. Викачані ігри ніяк не прив'язані до призначеного для користувача профілю на GOG.com, але юридично все ще потрапляють під угоду.

Для того, щоб ігри нормально працювали на новітніх версіях операційних систем і апаратного забезпечення, деякі ігри оновлені і надаються з відкритими для використання програмами емуляції і поліпшення сумісності, типу ScummVM і DOSBox. Іноді, в ігри вже вбудовані додаткові оновлення, створені не оригінальними розробниками — наприклад Outcast або Dungeon Keeper 2.

Разом з самою грою, користувач також отримує бонусні матеріали, які додаються до гри і список яких є на сторінці продукту. Зазвичай у такі матеріали входять саундтрек гри, шпалери, аватари і керівництва. Також, на GOG працює повноцінна підтримка користувачів на декількох мовах, і є програма 30-денної гарантії повернення грошей за куплені продукти. А для користувачів, у яких регіональна ціна вище ціни в США, існує програма «справедливої ціни», по якій користувачі отримують «переплачену» суму у вигляді кредиту на наступні покупки.

Історія
26 березня 2009 GOG повідомили про підписання контракту з Ubisoft про видання на сервісі старих ігор видавця — це був перший великий контракт для сервісу після початкового договору з Interplay. За контрактом, на GOG стали доступні ігри, які не були доступні на інших сервісах цифрової дистрибуції. З 27 серпня 2014 до каталогу ігор на сервісі додали і окремий каталог фільмів, які також доступні без захисту. У той же день сервіс змінив дизайн і почав надавати кілька нових можливостей, на кшталт можливості зміни місцевої валюти на долари США.

Повідомлення про закриття сервісу
У період з 19 по 22 вересня 2010 GOG сайт припинив роботу, залишивши тільки повідомлення, що сайт і Twitter сервісу будуть закриті. Представник сервісу пізніше заявив, що сайт не закривається назовсім, а лише тимчасово закритий через прийдешніх змін на сервісі. 20 вересня 2010 року на сайті було опубліковано пояснення, що сайт закритий з «бізнес і технічних причин», що мотивувало ігрових журналістів припустити, що сайт закрився через акцент на випуск ігор без DRM.

22 вересня 2010, GOG повідомили, що «закриття» сайту було лише маркетинговим ходом, яким вони хотіли акцентувати вихід сервісу з бета статусу. Керівництво сайту, знаючи про реакцію користувачів на псевдозакриття, написали: Для початку, ми би хотіли вибачитися перед тими, кого зачепило або хто відчуває себе обдуреним через закриття GOG.com. Будучи маленькою компанією без великих бюджетів для маркетингу, ми не хотіли пропускати можливості якось дуже голосно заявити про появу нової версії нашого сайту і, що ще важливіше, відродження серії Baldur's Gate!

Перезапуск

Сайт знову запрацював до 23 вересня 2010, з оновленим дизайном, новими функціями, про які сервіс повідомив під час онлайн презентації. Під час презентації, один з творців GOG і CD Projekt Марчін ІвинскІ і генеральний директор Гійом Рамбур одяглися в чернечі ряси, щоб «покаятися за свої гріхи». Перезапуск сайту, на думку Рамбура, був успішним і привів на сайт багато нових користувачів, які до цього не знали про сервісі.

Як і обіцялося, після перезапуску на GOG випустили кілька ігор від Black Isle Studios і Bioware, таких як Baldur's Gate, Planescape: Torment і Icewind Dale, які до цього не були доступні в цифрової дистрибуції через конфлікти за ліцензії на Dungeons & Dragons ігор між Atari, Hasbro та іншими компаніями.

Перейменування в GOG.com
27 березня 2012 GOG відкинули стару розшифровку Good old games і почали пропонувати на своєму сервісі гри ААА-класу і від незалежних розробників крім тільки класичних релізів. Сервіс став називатися просто GOG.com.

Підтримка OS X
З жовтня 2012 GOG.com з'явилася підтримка OS X версій ігор без захисту. Це включало як і раніше екслюзивні для Steam OS X версії серії Відьмак, створені «спорідненою» студією CD Projekt RED.

Гарантія повернення грошей
9 грудня 2013 на GOG.com з'явилася гарантія повернення грошей у перші 30 днів після покупки продукту, якщо у користувача виникають несподівані невиправні технічні проблеми з грою.

Підтримка Linux
GOG.com збирали думки користувачів у списку бажаного спільноти і однією з найбільш бажаних речей з майже 15 000 голосами, була нативна підтримка Linux. Спочатку представник GOG заявив, що є технічні та інші причини з реалізацією такої функції, але сервіс дуже хотів би це втілити в життя і давно про це думає.

18 березня 2014 GOG офіційно заявили про підтримку платформи Linux, з початкової підтримкою Ubuntu і Linux Mint до осені 2014.

25 липня 2014 підтримка Linux була додана раніше очікуваного часу з 50 іграми, які були на той момент сумісні з операційною системою. Деякі з ігор отримали підтримку Linux вперше, але більшість вже були раніше доступні для цієї ОС на інших сервісах цифрової дистрибуції.

Також за допомогою платформи PlayOnLinux реалізована підтримка декількох десятків ігор з GoG.com не портованих на Linux.

Продаж кіно без DRM
27 серпня 2014 GOG заявили про запуск нового сервісу — продажу фільмів без DRM. Скачувати Фільми можна без захисту у форматі mp4 або ж дивитися їх прямо в браузері без будь-яких обмежень і прив'язок до платформ або пристрою. Фільми доступні в Full HD 1080p, 720p і 576p форматах. Для початку, GOG додали 21-хвилинний документальний фільм по інтернет культури і геймінг. Але за словами Гійома Рамбура, компанія зараз розмовляє з різними великими студіями і в майбутньому планує додавати найрізноманітніші фільми. На жаль, багато студії поки не дуже хочуть ризикувати з підходом розповсюдження фільмів без захисту, хоч їм і дуже подобається ідея, і всі чекають, поки якась з великих студій зробить таке рішення першою. Але GOG планують подолати ці перешкоди і вже почали працювати з різними незалежними режисерами і студіями, які випускають не документальне кіно.

Ігри на Amiga / Cinemaware
5 вересня 2014 на GOG.com стали продавати старі ігри для платформи Amiga від Cinemaware, почавши з Defender of the Crown. Це було можливим завдяки власне написаному Cinemaware емулятору, який називається «Rocklobster».

GOG Galaxy
У великому оновленні від CD Projekt RED і GOG в червні 2014 був анонсований новий опціональний клієнт під назвою GOG Galaxy. Клієнт має замінити нинішню опціональну програму для скачування ігор, і додати величезну кількість нових функцій, включаючи можливість зручної мережевої гри з гравцями зі Steam версії тієї ж гри.

15 жовтня 2014 почалося відкрите мережеве бета-тестування клієнта GOG Galaxy, яке супроводжувалася безкоштовною роздачею Aliens versus Predator, оновленої з підтримкою клієнта для мережевої гри.

25 квітня 2017 відбувся реліз GOG Galaxy 1.2, клієнт вийшов із стадії бети, було додано функцію оверлею, хмарного зберігання збережень деяких ігор та багато інших функцій.

Disney Interactive / LucasArts
28 жовтня 2014 на GOG.com були випущені без захисту класичні ігри від Disney Interactive / LucasArts, які давно чекала спільнота сайту. Спочатку було випущено кілька ігор Star Wars: X-Wing, Star Wars: TIE Fighter, Sam & Max Hit the Road, The Secret of Monkey Island, Indiana Jones and the Fate of Atlantis і Star Wars: Knights of the Old Republic — деякі з яких до цього не були доступні на сервісах цифрової дистрибуції.

Запуск локалізованих версій сайту
7 листопада 2014 GOG офіційно запустили підтримку локалізованих версій сайтів, почавши з французької мови. З'явилася можливість змінювати мову сайту (хоч деякі його частини поки залишаються не перекладені), багато ігор отримали додаткові локалізації. В честь запуску локалізованої версії сайту сервіс почав французький розпродаж і в день запуску роздавав безкоштовну копію класичної гри від французького розробника Little Big Adventure.

Місце на ринку
Оскільки GOG.com як і багато інших сервісів цифрової дистрибуції, не робить продаж публічним, дізнатися точне місце сервісу, у порівнянні з іншими практично неможливо. Але іноді окремі розробники і видавці роблять продажі своєї гри публічними, з чого можна дізнатися деякі подробиці.

У статті 11 листопада 2011 PC Gamer назвав онлайн-продажу Відьмака 2. За їх даними, Direct2Drive, Impulse і Gamersgate разом продали 10 000 (4 %) копій, GOG продав 40 000 копій (16 %), а Steam за той же період продав 200 000 копій (80 %).

20 лютого 2013 розробник Defender's Quest Ларс Дусі назвав продажу своєї гри за перші три місяці на 6 різних сервісах цифрової дистрибуції, включаючи 4 великі сервісі та 2 способи покупки і завантаження гри безпосередньо від розробника. За результатом вийшло, що GOG.com був другим великим сервісом з продажу, поступаючись тільки Steam.

Доступні ігри
На 18 грудня 2014, на сайті доступно 886 ігор. До 12 червня 2015, кількість доступних ігор сягнула 1123. Станом на 23 червня 2017 на GoG.com доступними є 2034 гри. Нові ігри додаються на сервіс кілька разів на тиждень. У GOG.com є контракти з великою кількістю видавців, включаючи:

Співробітники

 Адам Ольдаковський () — керуючий директор GOG.com

Примітки

Посилання

Сайти, пов'язані з відеоіграми
Інтернет в Польщі
Електронна комерція
Цифрова дистрибуція
Англійська мова
CD Projekt Investment


text:
\section{Introduction}
Pouring a specific amount of liquid into a container is an important manipulation skill for service robots in applications such as bartending or housekeeping and also in industrial environments.
Compared to humans who can effortlessly pour the liquid into a container without spilling,
predicting the liquid height in the target container is still challenging for a robot.
For the robust and accurate perception of robot pouring, recent approaches mainly rely on vision~\cite{visualpouring2, visualpouring3, visualpouring4}. They take the RGB image as input, then infer the liquid height, liquid amount, even pouring trajectories.
However, vision fails under occlusions or in the dark.
Using audio~\cite{audio_corl, wilson2019analyzing, liang2019AudioPouring} is a second option for robot pouring perception,
but the performance of this method will degrade in a noisy environment.
Besides, if the robot is equipped with force/torque sensing, the amount of liquid poured out from the source container can be measured directly~\cite{huang2017learning},
but the liquid height in a target container can only be predicted if the initial fill level and the shape of the container are known.
These drawbacks in existing perception methods suggest that
to estimate the liquid height precisely, it is necessary
to find a robust approach that still works in conditions where one modality
becomes incomplete or deteriorated.

Therefore, we propose to tackle the issues of robust robotic pouring by a multimodal perception method.
This multimodal system should be robust in a wild environment, for example, against different levels of noise, types of noise, or light conditions.
The perception system should be able to generalize to different target containers, liquid types, and initial liquid height in the target containers.

\begin{figure}[t]
    \centering
    \includegraphics[width=0.486\textwidth]{pic/pipeline-mp.pdf}
    \caption{Our multimodal pouring pipeline. (Left) The goal of our pouring task is to pour the desired amount of liquid into a target container.
The generated sounds depend on the height of the remaining air column ($H_a$) in the container.
(Center) We collected a multimodal robotic pouring dataset, which contains 300 pouring sequences with audio and force/torque sensor data for training our network. A scale recorded the weight of the poured liquid. We then calculated $H_a$ to serve as ground truth. (Right) By giving the audio and haptic as input, our Multimodal PouringNet (MP-Net) predicts $\hat{H}_a$ as output. This output is then used to control the robotic pouring.}
    \vskip -0.15in
    \label{fig:mp_pipeline}
\end{figure}

In this paper, we develop a novel multimodal neural network called MP-Net that takes preprocessed audio spectrograms and force/torque data as the input and estimates the length of the air column in the target container.
This idea is inspired by human
experiences, as
humans can infer whether the target container is almost full from the pouring sound
and use their proprioceptive haptic feedback to estimate how much liquid is poured out from the source container~\cite{ikeno2015change, pouring-dark}.
Fig.~\ref{fig:mp_pipeline} illustrates the proposed perception pipeline.

The main structure of MP-Net is a recurrent neural network that implicitly integrates the prior knowledge that the liquid level rises monotonically during the pouring.
A multimodal pouring dataset with audio and haptics was built up to train this model.
To further improve the adaptability to the noisy environment of MP-Net, we augmented the audio data by adding different noise levels of robot noise.
The benefits of force/torque data and audio data augmentation are verified in both our dataset and a wide range of robotic experiments, respectively.

To sum up, our main contributions are:
\begin{enumerate}
    \item We propose a multimodal neural network MP-Net to estimate the liquid height robustly and to enhance the precision and generalization ability of robotic pouring.
    Not only the haptic input but also the audio data augmentation facilitate to produce a robust model that can generalize broadly.
    \item System assessment and comparison across eight robotic experiments, \textit{e}.\textit{g}., pouring water into different containers, in various noisy environments with different noise levels and noise sources, revealed that high accuracy could be achieved by our proposed method.
    \item The shape of the target container is generated by combining the force data and realtime height prediction.
\end{enumerate}

\section{Related Work}
\textbf{Visual sensing for robotic pouring.}
Vision remains the most popular modality in robot pouring perception,
as humans also highly rely on vision to perform the same task.
Pithadiya \textit{et al}.~\cite{pithadiya2011selecting} compared several edge detection techniques for the filling height inspection of convex target containers.
Schenck \textit{et al}.~\cite{visualpouring1} used a thermal camera to generate pixel-level ground truth data of heated water.
The RGB camera is then used to determine the water volume using both a model-based method and a neural network method.
This method suffers from poor estimation accuracy due to the varied liquid types and the complex shapes of the water flow.
To predict the absolute height of the liquid, Dong \textit{et al}.~\cite{Dong2019DepthPouring} used a point cloud to model the target container and a proportional-derivative controller to perform the pouring action.
Do \textit{et al}.~\cite{dochau2019} utilized a Kalman filter dealing with opaque and transparent liquids based on an RGB-D camera.
However, the mean height errors for ten pourings of three transparent liquids were larger than 4\,mm.
Recently, \cite{dochau2018learning, sermanet2016unsupervised}  deployed reinforcement learning methods for generating motion trajectories during the pouring process.

\textbf{Audio sensing for robotic pouring.}
The auditory signal generated when liquid or granular materials interact with other objects
also comprises reliable clues, such as resonance frequency and vibration behavior of the liquids and the air.
Griffith \textit{et al}.~\cite{griffith2012object} indicated that auditory and proprioceptive data enhance the classification tasks for the interaction between objects and water.
Clarke \textit{et al}.~\cite{audio_corl} used audio-frequency vibration generated by shaking a granular material to evaluate the weight that is poured out.
However, the weight of the poured out granular materials does not allow us to estimate the filling height of the target container.

None of the above work explored the fact that the audio vibration of the air in the target container
can be analyzed to solve the liquid height regression in robotic pouring.
In our previous work~\cite{liang2019AudioPouring}, we used audio as input and trained an RNN based network to directly predict the length of the air column, including generalization to different target containers.
However, the network failed in a noisy environment or when pouring liquids with high viscosity
as the audio signal is weak in these conditions.

\textbf{Haptic sensing for robotic pouring.}
Haptic sensing, especially force and torque sensing, is also an essential signal of robotic pouring.
The goal of learning robust robot pouring from human demonstrations including hand trajectories and force/torque data is explicitly discussed by Huang and Sun ~\cite{huang2019dataset}, as part of their multi-task dataset on human daily interactive manipulation.
Rozo \textit{et al}.~\cite{rozo2013force} used a parametric hidden Markov model to retrieve joint-level commands given the force/torque inputs from the human demonstration.
Saal \textit{et al}.~\cite{saal2010active} examined the viscosity estimation of various liquids inside a bottle from tactile sensory data.
Matl \textit{et al}.~\cite{matl2019haptic} utilized a force sensor mounted at the end of a robotic arm with a container grasped by a gripper to get the change of
wrenches, and they used a physics-based model to estimate the mass and volume of the liquid.
In the above three papers, perception happens before the pouring action begins.
Although the force signal from the pouring container can explicitly represent the volume of the poured-out liquid,
it cannot measure the liquid height in an unseen target container.

\begin{figure}[t]
    \centering
    \includegraphics[width=0.486\textwidth]{pic/mp_pouring_setup}
    \caption{Pouring setup used to collect our multimodal dataset and to perform the robot experiment.
The three target containers used to collect the dataset are shown in the bottom left corner.
We put these containers on a calibrated scale to get the realtime label of the liquid height. A force/torque sensor is mounted on the source container to collect force and torque information while pouring, and a microphone was set on the table to collect the pouring audio.
Loudspeakers were used to create ambient noise for robot experiments.
S1, S2, S3 in the left represent three different source containers where S1 is the container used to collect dataset, S2, and S3 are novel source containers.
}
    \vskip -0.25in
    \label{fig:dataset_setup}
\end{figure}

\textbf{Multimodal fusion for robotic pouring.}
Recent studies have shown that multimodal sources represent the environmental features better than single modality in pouring tasks \cite{sanchez2020} as weel as other tasks~\cite{gan2019look}.
Wu \textit{et al}.~\cite{wu2018liquid} presented a hierarchical long short-term memory~\cite{hochreiter1997long} model, which could detect if a pouring sequence was successful or failed. This model is based on a pouring dataset, including visual sequences and IMU data.
However, this work only classifies whether a pouring action was successful but cannot predict the precise height of the poured-out liquid.
Wilson \textit{et al}.~\cite{wilson2019analyzing} implemented a multimodal convolutional neural network to fuse audio and visual data to predict the weight of the poured liquid, detect overflow and to classify the liquid and the target container.
Park \textit{et al}.~\cite{multimodal-anomaly-detection} proposed another interesting robot application for anomaly detection during robot manipulation using haptic, visual, auditory, and kinematic sensing.

Based on how humans rely on the correlations of haptic and audio while pouring, in this paper, we take advantage of audio and force/torque data as the input to promote the robustness of robotic pouring.

\section{Multimodal Pouring Dataset}
\subsection{Dataset Collection Setup}
To create the multimodal dataset for the height estimation task, we designed a robot pouring setup, as shown in Fig.~\ref{fig:dataset_setup}.
It consists of a UR5 robot arm with a Robotiq 3-finger hand,
and a custom 3D-printed bottle holder with an embedded ATI force/torque sensor.
A standard plastic bottle (S1) is used as the source container, with an optional bottle spout to limit liquid flow.
The target container is placed on a MAULlogic digital scale which measures the combined weight of the container and the liquid inside.
Audio is recorded with a microphone in an environment with only UR5 robot ego-noise.
The dataset setup contains three different target containers with three different materials and heights.
The properties of these containers are shown in Table~\ref{tab:containers} with ID 1-3.

Unlike our previous work that used human pouring sequences as training data~\cite{liang2019AudioPouring},
we use the robot to perform the pouring task.
The main reason is that the haptic data coming from human pouring is massively different from the robot pouring data, which makes it hard to transform human pouring to robotic pouring.

We set a fixed pouring trajectory and place the mouth center of the source container 310\,mm above the scale plane.
In this manner, we collected 100 trials each for three different target containers.
For each trial, we recorded both audio and force/torque data starting just before the scale reading begins to change, and stopping after the reading of the scale becomes stable again.
The lengths of one pouring recording varied from 24-40 seconds according to
the capacity of the different target containers.

\subsection{Data Analysis}

\textbf{Audio-frequency data.}
To better extract audio information from the microphone,
we resampled all audio data from 44.1\,kHz to 16\,kHz and computed spectrograms with a window length of 32\,ms and 50\% overlap without zero-padding.
For network training, we randomly chose 4 seconds of audio clips from one complete pouring audio sequence.
Using fixed length training samples allows for batch processing for better GPU performance,
and random starts correspond to different initial liquid amounts,
so that the network can generalize to partially filled containers.

\textbf{Force/Torque data.}
The force/torque sensor we use is ATI Mini45 (500\,Hz).
As the weight of the source container is far below the maximum payload of this sensor (580\,N in Fx/Fy),
the raw data is rather noisy but becomes usable after simple low-pass filtering.
See Fig.~\ref{fig:ft_vis_data} for typical raw force/torque data (in N/Nm) during a sample pouring sequence.
In each sub-figure, we plot the noisy raw signal from the sensor shown as a light color
together with a low-pass filtered signal shown as a dark color.
The change of the bottle weight during pouring can clearly be seen in the force/torque readings
for the x- and y-axis, while the z-axis measurements show only little change.
This is due to the specific orientation of the sensor in our 3D-printed holder,
dictated by the cable routing.
While the sensor z-axis is almost horizontal (orthogonal to gravity),
the x- and y-axes of the sensor are pointing diagonally upwards,
so that the raw sensor readings actually increase when pouring from the source container.
The pouring motion from the robot slightly rotates the bottle around the z-axis.

\begin{figure}[t]
    \centering
    \includegraphics[width=0.486\textwidth]{pic/force_torque_data.pdf}
    \vskip -3mm
    \caption{A sample of force/torque data (N/Nm) in our pouring dataset. In each sub-figure, we show the raw sensor data (light line) and the filtered data using a Butterworth low pass filter (dark line). See the text for details.}
    \vskip -0.5mm
    \label{fig:ft_vis_data}
\end{figure}

\begin{figure}[t]
    \centering

    {\includegraphics[height=0.18\textwidth]{pic/scale_reading.pdf}

    }
     \hspace{2mm}

    {\includegraphics[height=0.18\textwidth]{pic/weight_to_height.pdf}

    }
    \vskip -1mm
    \caption
    (a, left) Sample scale readings of a pouring sequence in our dataset from bottle ID 1. Each red dot is one weight measurement from the scale. The blue line is the interpolated curve.

    (b, right) Converting liquid weights (kg) into length of the air column (mm) for bottle ID 1. The dotted points are the manually measured data, where the red points indicate the empty and completely filled container. The blue line is the fitting curve that calculates from the scale reading to the length of the air column.}
    \label{fig:scale_data_process}
    \vskip -4mm
\end{figure}

\begin{figure*}[t]
    \centering
    \includegraphics[width=1\textwidth]{pic/spec_with_diff_noise.pdf}
    \caption{
    Examples of audio spectrograms that add different noise levels of the same audio signal in our dataset.
    While the $\rm SNR_{dB}$ is high (\textit{e}.\textit{g}., $\rm SNR_{dB}=15$, right), which indicates the audio signal is rather clear, we can clearly see rising frequency curves between 256\,Hz-2048\,Hz. But when the $\rm SNR_{dB}$ is low (\textit{e}.\textit{g}., $\rm SNR_{dB}=-5$, left), there is no meaningful structure of the resonance frequency.}
    \vskip -0.15in
    \label{fig:audio}
\end{figure*}

\textbf{Scale data.}
To get the ground truth of the pouring perception problem, we used a MAULlogic digital scale
to measure the weight of the target container (accuracy $\pm 2$\,g).
Because the publishing frequency of the scale is only 1\,Hz,
we deployed a linear interpolation (Fig.~\ref{fig:scale_data_process}(a))
to get realtime scale readings.
To convert the measured weights into the heights $H_a$ of the air column needed for network training,
we sampled 10-15 random amounts of liquid for each target container, up to full container capacity,
and manually measured the length of the air column.
A polynomial curve fitting is utilized using this data to calculate the approximate air column length for different amounts of liquid and corresponding measured weights (Fig.~\ref{fig:scale_data_process}(b)).

\subsection{Audio Data Augmentation}
Considering that the variability of the audio data is significant enough, models trained on it will better generalize in different audio conditions.
Therefore, we augmented the audio data in our dataset by adding noise.
We recorded an ego noise from a humanoid PR2 robot, which is commonly used for research in household environments.
In detail, we employed the signal-to-noise ratio in decibels (${\rm SNR_{dB}}$) as our reference on how much noise was added.
\begin{equation}
    \label{eq1}
    {\rm SNR_{dB}} = 10 \log\left( \frac{A_{\rm signal}}{ \alpha_{\rm nf} \times A_{\rm noise} }\right)^2
\end{equation}
\begin{equation}
    \label{eq2}
    \alpha_{\rm nf} = \frac{A_{\rm signal}}{A_{\rm noise}} \times 10^{\frac{{\rm SNR_{dB}}}{-20}}
\end{equation}
\begin{equation}
    \label{eq3}
    \rm audio = signal + noise \times \alpha_{nf}
\end{equation}
Equations~(\ref{eq1}) - (\ref{eq3}) illustrate how noise was added,
where $A_{\rm signal}$ and $A_{\rm noise}$ are the root mean square (RMS) amplitude for the recorded pouring audio and noise, respectively.
$\alpha_{\rm nf}$ is the ratio of the input noise.

We mixed the original audio sequences with noise
of dif\-fer\-ent levels ranging from -20-20 $\rm SNR_{dB}$ with a step size of 5\,dB.
The number of generated audio clips, of 4 seconds length each, is proportional to the length of a pouring trial.
Fig.~\ref{fig:audio} shows examples of mixed audio spectro\-grams for different $\rm SNR_{dB}$.
When $\rm SNR_{dB} > 0$, one high-energy and rising curve between 256\,Hz and 2048\,Hz is clearly visible during pouring.
This curve represents the resonance frequency of the air.
But when $\rm SNR_{dB} \le 0$, the spectro\-gram has little structure of the resonance frequency, which indicates this audio sequence contains more noise than signal.

\section{Multimodal Pouring Network}\label{sec:net}
Our goal is to design a robust network architecture to acquire the liquid filling height by making sense of audition and haptics together.
Audio vibration results in the changes in the resonance frequency of the air in the target container.
Force/torque changes result from the liquid poured out of the source container, which yields additional guidance about the liquid in the target container, especially when the audio signal is deteriorated.

The pouring process is a typical sequential problem and the predicted length has a temporal relationship. Intuitively, we choose a recurrent network~\cite{cho2014properties} as our model architecture.
The multimodal network architecture MP-Net is shown in Fig.~\ref{fig:net}.
In detail, we utilize the long short-term memory (LSTM) unit to process the multimodal inputs.
We mix audio clips with noise using Equation~(\ref{eq3})
and transform it into a $257 \times n$ matrix using Short-Time Fourier Transform (STFT).
The haptic data is processed to a $6 \times 8 \times n$ matrix
($6$ F/T sensor channels, and $8$ F/T samples arrive during each $16$\,msec STFT interval)
and then concatenated with the audio data to a $305\times n$ matrix for input.
Here, $n$ is the number of the time slice, and we get $n = 251$ when using audio clips
of $4$ seconds duration from our dataset.
Then each time slice of the fusion data is progressively fed into the encoder module (2-layer LSTM unit) to a layer of 56 recurrent features $A_h$.

\begin{figure}[t]
    \centering
    \includegraphics[width=0.486\textwidth]{pic/mp_net_noise.pdf}
    \caption{MP-Net architecture. The raw audio data is transformed into a spectrogram with 257 descriptors.
Then the encoder module (a recurrent neural unit) is progressively fed each time slice of the audio-frequency spectrogram and the corresponding haptic data.
Finally, the height predictor module produces the 1D length of the air column of the target containers.
    The blue rectangle denotes a fully-connected layer followed by a batch normalization layer and a rectified linear unit. $n = 251$ for a 4\,s pouring sample.
}
    \vskip -0.15in
    \label{fig:net}
\end{figure}

Besides, it is crucial to find a well-suited ground truth in supervised learning.
Due to the fact that when the length of the air column gets shorter in an organ pipe, the air vibrates faster and the resonance frequency of the air increases~\cite{french1983vino, webster2010use}, it is more indicative to choose the length of the air column $H_a$ as the ground truth of our model instead of the liquid height.
Thus, the height predictor (a 2-layer multilayer perceptron) in MP-Net takes the recurrent vector $A_h$ as input and performs a regression of the length of the air column $H_a$.
The height predictor is supervised with a mean squared error (MSE) loss $\mathcal{L}_{height}$

\begin{equation}
\label{heightloss}
\mathcal{L}_{height} = \|\hat{H}_a - H_a\|^{2}\text{.}
\end{equation}

In addition, an auxiliary $\mathcal{L}_{mono}$ is introduced to enforce the decrease of the estimated length of the air column over time $t$
\begin{equation}
\label{monoloss}
\mathcal{L}_{mono} = \sum\limits_t[\max(0, (\hat{H}_{a_{t+1}} - \hat{H}_{a_t}))]\text{.}
\end{equation}

\noindent
\textbf{Overall loss.} Combining with $\mathcal{L}_{height}$ and $\mathcal{L}_{mono}$, the complete training objective for MP-Net is defined by $\mathcal{L}_{mp}$
\begin{equation}
\label{audioloss}
\mathcal{L}_{mp}(\theta)=\mathcal{L}_{height} + \alpha \cdot \mathcal{L}_{mono}\text{,}
\end{equation}
where $\alpha$ is a hyperparameter for balancing these two loss functions.
In our implementation, we set it to $0.01$ for the best performance via some preliminary experiments.

\section{EXPERIMENT}
\subsection{MP-Net Evaluation}
\label{network}

We examined our proposed multimodal network MP-Net against the following baseline methods:
1) MP-Net*: the same network architecture as MP-Net but trained without adding noise to the audio data,
2) AP-Net: only audio branch of MP-Net and training on augmented audio data with noise,
3) AP-Net*: same network architecture as AP-Net without audio augmentation~\cite{liang2019AudioPouring},
4) FT-Net: only force/torque branch of MP-Net that only takes force/torque data as input.
All the models mentioned above were trained on our whole dataset which contains pouring samples collected from three different target containers.

First, we evaluated MP-Net and four baselines using the fraction of the input sequences
whose length prediction error $|\hat{H}_a - H_a| $ is below a threshold $e$.
We trained MP-Net and AP-Net on noisy audio, with noise levels from the $\rm SNR_{dB}$ set [0, 5, 10, 15, 20].
Except for FT-Net, MP-Net and the other three baseline models were then tested on audio data with $\rm SNR_{dB}=5$ noise level.
Fig.~\ref{fig:network_eval}(a) shows that MP-Net has a stable advantage of about 8\% over AP-Net
over the full range of tested length error thresholds,
which indicates that the force data provide a positive gain to the training result.
The performances of MP-Net* and AP-Net* are both poor compared to MP-Net and AP-Net, respectively, which indicates that augmenting audio data also contributes a lot to the networks.
FT-Net was not trained on audio and has learned to predict the air column length averaged over all containers.

To compare the robustness of MP-Net, AP-Net, FT-Net with regard to audio noise,
we evaluate the percentage of input sequences whose length prediction error stays below 5\,mm,
when both the training and the testing data were augmented with the same noise levels.
Each model was trained and tested on audio data augmented with 9 different $\rm SNR_{dB}$ levels from the set [-20, -15, -10, -5, 0, 5, 10, 15, 20] respectively.
Fig.~\ref{fig:network_eval}(b) illustrates that the accuracy of MP-Net and AP-Net increases with $\rm SNR_{dB}$.
The accuracy curve of FT-Net is straight because force/torque data is independent of audio noise.
We can see that MP-Net has at least 27\% higher accuracy than AP-Net when $\rm SNR_{dB} \le -15$, and has 8\% higher accuracy when $\rm SNR_{dB} = 0$.
Note that the performance of AP-Net degrades quickly for  $\rm SNR_{dB} \le -15$,
which indicates that the noise interferes with the audio and the network cannot gain useful information from it anymore.
Nevertheless, with the help of force/torque data, MP-Net can still perform comparably to FT-Net in such a noisy situation.
This phenomenon proves that MP-Net has learned to predict liquid height by conditioning on both audio and haptic input.

\begin{figure}[t]
    \centering

    {\includegraphics[height=0.23\textwidth]{pic/fig_model_eval.pdf}

    }
    \hskip 2mm

    {\includegraphics[height=0.23\textwidth]{pic/multi_diff_noise_acc5.pdf}

    }
    \vspace{-2mm}
    \caption{Network evaluation results of MP-Net and four baselines.

    (a, left) The fraction of the input sequence whose length prediction error is below a threshold between five models.
    (b, right) The fraction of the input sequence whose length prediction error is below 5\,mm between MP-Net, AP-Net, FT-Net.
    MP-Net and AP-Net trained and tested on nine datasets, where the only difference between each dataset is the noise level of audio data. }
    \label{fig:network_eval}
    \vskip -2mm
\end{figure}

\subsection{Robotic Experiments}
To verify and compare the reliability and robustness of the proposed MP-Net in robotic pouring tasks, we carried out eight evaluation experiments on
different target containers,
different pouring heights,
different source containers,
different noise levels,
varying positions of the noise source,
different initial liquid heights,
different types of liquid,
and different noise sources.

All experiments used both MP-Net and AP-Net to compare their real-world performance, except for the different source containers experiments.
To adapt to different noisy environments,
MP-Net and AP-Net were trained on noisy audio data with noise levels $\rm SNR_{dB}$ from 0-20.
The experiment setup was the same as in our dataset collection setup
and the initial water level of the source container was random.
MP-Net took the latest 4\,s sequence of audio and haptic data.
While the sequence length of the input signal was less than 4\,s, all the input signals were fed into the network.
Once the estimated length of the air column was smaller than the desired one,
the robot immediately
stopped pouring.
To compensate for the liquid poured out during the stopping motion, a corresponding correction was applied.

\subsubsection{Evaluation of Different Target Containers}
We used nine different target containers for robot experiments.
The first five rows in Table~\ref{tab:containers} illustrate the properties of these nine containers, where
containers 1-3 are included in the training dataset while 4-9 are only used for testing.
We kept the distance between the target containers and the microphone the same as in our original dataset.
Then we set the desired length of the air column to 60\,mm.
We synthesized a $\rm SNR_{dB}\!=\!5$ audio signal by playing~the~PR2 robot noise from a pair of loud\-speakers lo\-cated at positions 1\&1 as shown in Fig.~\ref{fig:pos}.
Here, 1\&1 means that both loudspeakers were placed at position 1.
We repeated the experiments five times for each container.

\renewcommand{\arraystretch}{.95}
\begin{table*}[ht]

    \begin{center}
        \caption{The properties of nine target containers and controlling results of pouring water into a desired length of the air column.}
        \vskip -0.15in
        \begin{tabular}{m{1.5cm}<{\centering}*{9}{m{1.3cm}<{\centering}}}
            \multicolumn{10}{c}{}\\
            \toprule

            ID & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\
            \midrule

            \raisebox{-0.15cm}{Container} & \raisebox{-0.4cm}{\includegraphics[height=0.0582\textwidth]{pic/bottle1}}& \raisebox{-0.4cm}{\includegraphics[height=0.0582\textwidth]{pic/bottle3}} & \raisebox{-0.4cm}{\includegraphics[height=0.0582\textwidth]{pic/bottle4}} & \raisebox{-0.4cm}{\includegraphics[height=0.0582\textwidth]{pic/bottle6}} & \raisebox{-0.4cm}{\includegraphics[height=0.0582\textwidth]{pic/bottle7}} & \raisebox{-0.4cm}{\includegraphics[height=0.0582\textwidth]{pic/bottle8}} & \raisebox{-0.4cm}{\includegraphics[height=0.0582\textwidth]{pic/bottle9}} & \raisebox{-0.4cm}{\includegraphics[height=0.0582\textwidth]{pic/bottle10}}& \raisebox{-0.4cm}{\includegraphics[height=0.0582\textwidth]{pic/bottle11}}\\
            \midrule

            Material & Glass & Ceramics & Steel & Ceramics & Ceramics & Plastic & Plastic & Enamel & Glass\\
            \midrule

            Height (mm) &127 &99 &150 &97 &94 &103 &115 &78 &135 \\
            \midrule

            Volume (ml) &460 &428 &766 &310 &298 &416 &408 &382 &358 \\
            \bottomrule

            \multicolumn{10}{c}{Error in volume (ml) with $\rm SNR_{dB} = 5 $, $H_a=60$\,mm } \\
            \toprule

            MP-Net &$15.8 \pm 14.0$ &$11.5 \pm 8.1$ &$6.3 \pm 6.1$ &$16.6 \pm 19.0$ &$8.2 \pm 9.3$&$12.8 \pm 11.5$ &$23.7 \pm 18.7$ &$8.5 \pm 9.6$ &$13.9 \pm 4.4$ \\
            \midrule

            AP-Net &$23.1 \pm 8.2$ &$7.5 \pm 8.2$ &$6.0 \pm 4.7$ &$25.3 \pm 14.5$ &$5.2 \pm 2.3$ &$14.3 \pm 10$ &$46.6 \pm 70.6$ &$41.6 \pm 10.4$ &$26.6 \pm 45.4$ \\
            \bottomrule

        \end{tabular}
        \label{tab:containers}
    \end{center}
    \vskip -0.2in
\end{table*}

Fig.~\ref{fig:diff_cups} displays that the absolute mean errors of the liquid height are below 8\,mm and the standard deviations are below 4\,mm for both MP-Net and AP-Net among the known target containers.
Container 3 performs best for both networks due to the stainless steel material which makes the crispest sound.
Furthermore, we converted the height error of each cup to a weight error, shown in rows seven and eight of Table~\ref{tab:containers}.
We can see that AP-Net performs well on known containers and even outperforms MP-Net a little on containers 2 and 3.
Regarding the unseen containers, especially for container 7 and 9, MP-Net exhibits a stronger generalization ability than AP-Net in a noisy environment.

\begin{figure}[t]
    \centering
    \subfigure[]
    {\includegraphics[height=0.21\textwidth]{pic/fig_error_bar_diff_bottle.pdf}
    \label{fig:diff_cups}
    }
    \subfigure[]
    {\includegraphics[height=0.21\textwidth]{pic/fig_error_bar_diff_pouring_height.pdf}
    \label{fig:diff_pouring_height}
    }
    \subfigure[]
    {\includegraphics[height=0.21\textwidth]{pic/fig_error_bar_diff_source_container.pdf}
    \label{fig:diff_source_container}}
    \vskip -2mm
    \caption{ In the following robot experiments, the $\rm SNR_{dB}$ were set to 5\,dB. Robot experiment result \subref{fig:diff_cups} on pouring water into different target containers ($H_a=60$\,mm, ID 1-3 are in the training dataset, ID 4-9 are novel),   \subref{fig:diff_pouring_height} on four different pouring heights (pouring height 310\,mm was the height for dataset collection, $H_a=40$\,mm),
    \subref{fig:diff_source_container} on one known source container S1 and two novel source containers S2, S3 ($H_a=40$\,mm).
    }
    \vskip -0.25in
\end{figure}

\subsubsection{Evaluation of Different Pouring Heights}
In this experiment, we placed the source container at four different heights from the set [310, 260, 210, 160]\,mm respectively.
The height of the source container is the vertical distance from the mouth center of the source container to the scale plane.
We carried out five robot experiments on each height using target container 2 when $\rm SNR_{dB} = 5$.
Fig. ~\ref{fig:diff_pouring_height} indicates that our algorithm performs well for the three higher heights but not at the lowest height.
As the pouring height decreases, the volume of the pouring sound also decreases. With the source container at height 160\,mm
the pouring sound is too low, and haptics only is not sufficient for the network to perceive the height of the air column.

\subsubsection{Evaluation of Different Source Containers}
The source container influences the flow-rate and the initial force/torque data.
Therefore, we tested two novel source containers S2 (44\,g) and S3 (484\,g), which are shown in the left side of Fig.~\ref{fig:dataset_setup}, to compare with the container S1 (64\,g) used for network training and all other experiments. We kept the pouring height at 310\,mm, and the other experimental setup was the same as in the evaluation of the different pouring height.
Fig.~\ref{fig:diff_source_container} suggests that different source containers hardly affect network performance.

\subsubsection{Evaluation of Varying Noise Conditions}
To further verify the performance of our MP-Net model in different noise conditions, we implemented a set of experiments using target container 2 under six different noise
levels of \mbox{[-5, 0, 5, 10, 15, 20]} $\rm SNR_{dB}$.
The loudspeakers were again located at positions 1\&1.
    For each $\rm SNR_{dB}$ level, we tested five different target lengths of air column, namely [40, 50, 60, 70, 80]\,mm.
    We carried out five robot experiments on each audio and target length condition.

    As visualized in Fig.~\ref{fig:three_model}, MP-Net has a substantial advantage over AP-Net when $\rm SNR_{dB} = -5$, which further indicates the advantages of multimodal fusion.
In this experiment, we also tested AP-Net* under different noise conditions.
AP-Net* performed well while $\rm SNR_{dB}\ge10$, but when $\rm SNR_{dB}<10$, the robot either stopped pouring immediately or overfilled the target containers.
Therefore we did not list the experiment results of AP-Net* tested on audio with $\rm SNR_{dB}<10$.

\begin{figure}[t]
    \includegraphics[width=0.48\textwidth]{pic/fig_error_bar_all.pdf}
    \caption{Robot experiment results of the performance of MP-Net, AP-Net, and AP-Net* in environments with different levels of noise (measured by $\rm SNR_{dB}$). We evaluate with five different target liquid heights and the results are demonstrated in five different colors, respectively. The dashed lines represent the desired lengths of the air column, while the solid dots (with error bars) show the actual ones when the pouring terminates.}
    \label{fig:three_model}
    \vskip -0.25in
\end{figure}

\begin{figure*}[t]
    \centering
    \subfigure[]
    {\includegraphics[height=0.24\textwidth]{pic/noise_position.pdf}
    \label{fig:pos}\hspace{.37cm}}
    \subfigure[]
    {\includegraphics[height=0.23\textwidth]{pic/fig_error_bar_diff_noise_source.pdf}
    \label{fig:pos_eval}\hspace{.48cm}}
    \subfigure[]
    {\includegraphics[height=0.23\textwidth]{pic/fig_error_bar_diff_init_height.pdf}
    \label{fig:inital}\hspace{.48cm}}
    \subfigure[]
    {\includegraphics[height=0.23\textwidth]{pic/fig_error_bar_diff_liquid.pdf}
    \label{fig:diff_liquid}\hspace{.48cm}}
    \subfigure[]
    {\includegraphics[height=0.23\textwidth]{pic/fig_error_bar_diff_noise_type.pdf}
    \label{fig:diff_noise_type}}
    \vskip -1mm
    \caption{
    \subref{fig:pos} Schematic diagram of 4 different loudspeaker positions relative to the target containers, the UR5 robot position, and the control box of the UR5 robot.
     Evaluation results of \subref{fig:pos_eval} six combinations of two loudspeakers positions, \subref{fig:inital} varying initial heights of the source container, \subref{fig:diff_liquid} different types of liquids and \subref{fig:diff_noise_type} different types of noise sources. In these experiments, the $\rm SNR_{dB}$ was set to 5\,dB. The target height $H_a$ was set to 40\,mm.
    }
    \vskip -0.2in
\end{figure*}

    \subsubsection{Evaluation of Varying Positions of Noise Source}
To assess whether MP-Net is sensitive to the direction of the noise source, we set up six different position combinations [1\&1, 2\&2, 3\&3, 4\&4, 2\&4, 1\&3] of the two loudspeakers.

The two loudspeakers played a synthetic $\rm SNR_{dB}\!= \!5$ noise signal at each position.
We used the target container 2 and a desired air column length of 40\,mm.
The other experimental setup was the same as in the evaluations of the different target containers.
Then at each combined position of the two loudspeakers, we poured water five times.
As shown in Fig.~\ref{fig:pos_eval},
when the loudspeakers are at position 1\&1, both models perform best as the loudspeakers are behind the microphone.
MP-Net generalizes better than AP-Net to the different positions of the loudspeakers due to the lower consistent mean height error among all tested positions.

\subsubsection{Evaluation of Varying Initial Liquid Height in Target Containers}
In this experiment, we poured water into the target container 2, starting from five different initial liquid heights [0, 10, 20, 30, 40]\,mm.
We tested five times from each initial level.
We put two loudspeakers at 1\&1 positions and kept the other test setups the same as in the evaluations of the varying direction of noise sources.
The results in Fig.~\ref{fig:inital} demonstrate that MP-Net is again more robust than AP-Net.
Force and torque data yield a meaningful indication of how much water was poured out.

\subsubsection{Evaluation of Different Types of Liquid}
    We conducted pouring experiments with different liquids: pure water, orange juice and 1.8\% fat milk.
    We used the same experimental setting as in the evaluations of different microphone positions and poured each type of liquid for five times.
    As manifested in Fig.~\ref{fig:diff_liquid}, MP-Net can generalize to common household liquids like water and orange juice while AP-Net cannot handle the task of
    pouring orange juice well under $\rm SNR_{dB} = 5$.
    However, similar to \cite{liang2019AudioPouring}, due to the high viscosity of milk, both models cannot generate correct height prediction.

\subsubsection{Evaluation of Different Types of Noise Sources}
    We also assessed our model with three noise types: PR2 robot noise, human voices and a continuous piece of piano music.
    The human voice is represented by discrete sounds of a man counting numbers in English.
    We poured water under each type of noise five times.
    All experimental settings were the same as in the evaluations of different types of liquid.
    Fig.~\ref{fig:diff_noise_type} shows that MP-Net is not affected by different noise types, but the accuracy of AP-Net has a small fluctuation under a musical disturbance.

\subsection{Shape Prediction of Target Containers}
\begin{figure*}[ht]

    \begin{minipage}[b]{0.15\textwidth}
    \centering
    \includegraphics[height=3.5cm]{pic/bottle_shape.pdf}
    \end{minipage}
    \begin{minipage}[b]{0.84\textwidth}
    \centering
    \includegraphics[height=3.5cm]{pic/predict_bottle_shape.pdf}

    \end{minipage}
    \caption{(a, left) Schematic diagram of a symmetric container.
    In a specified time interval $\Delta t$, the change in mass $\Delta m$ can be determined by the F/T sensor and the change in height $\Delta h$ can be derived
    through MP-Net. Then the radius $r$ at each height can be calculated to form an edge-profile of this container.

    (b, right) Prediction result of estimating the target container shape. The black curve is the ground truth, while the five different colored curves are the estimated target container shape in five trials.
    The mean error of the estimated container radius at different heights is plotted in the middle of each subplot (the shaded magenta area).

    \label{bottle_shape}
    }
    \vskip -0.15in
\end{figure*}

In this section, we applied MP-Net to predict the shape of symmetric target containers.
In this case, the edge profile is sufficient to describe the shape of the containers,
which is determined by the correlation between height and radius~\cite{kennedy2019autonomous}.
Fig.~\ref{bottle_shape}(a) shows a volume profile filled with liquid of density $\rho$, where $\Delta V, \Delta m, \Delta h$ are the poured liquid volume, and the weight and liquid height differences during a time interval $\Delta t$ respectively.
Assuming that $\Delta t$ is very small, then $\Delta m$ can be calculated by approximating the shape of $\Delta V$ as a cylinder,
\vskip -0.2in

\begin{equation}
    \label{eq7}
    \Delta m = \rho \Delta V
    = \rho \pi r^2 \Delta h
\end{equation}
We can determine $\Delta m$ by the $(f_x, f_y, f_z)$ values from the force/torque sensor and $\Delta h$ through our neural network output $\hat{H}_a$.
In the robot experiments, the frequency of $\Delta m$ and $\hat{H}_a$ was 500\,Hz and 12\,Hz, respectively. To get a smooth and accurate estimation of the container shape, we used a quadratic function to fit the scatter points,
\begin{equation}
    \label{eq8}
    r(h) \approx \sqrt{\frac{\Delta m}{\Delta h}\frac{1}{\rho\pi}}
\end{equation}
For target containers 1, 2, 4, 5, 6, 7, we conducted five trials of the experiment in which the robot pours into these target containers.
We recorded the realtime estimation of $\hat{H}_a$ and force data into a rosbag.
When the target container was filled to about 90\% of its total height, we stopped the pouring and the recording.
Using the data from these rosbags, we calculated the edge-profiles of the target containers.
In Fig.~\ref{bottle_shape}(b),
the thick black curves are the ground truth profiles, while five colored curves around black curves depict the experimental results.
The magenta area in the middle of each target container visualizes the mean error of the radius prediction.
As expected, the mean radius estimation error is highest for an empty container,
when our recursive network cannot yet rely on its memory but stabilizes as the liquid level rises.
Due to the restriction to quadratic functions,
the reconstruction works best for containers with low edge curvature (such as containers 1, 7).

\section{Conclusion and Future Work}
In this paper, we motivate the need for combining audio and haptic information for robot pouring tasks.
We recorded a robot pouring dataset that includes 300 complete robot pouring sequences with audio and force/torque data.
We propose a novel audio-haptic recurrent deep network (MP-Net) trained on this dataset that predicts liquid height in realtime.
The multimodal perception system is systematically tested across four baselines and a wide range of robotic pouring experiments in a noisy environment.
The results substantiate that MP-Net is quite robust against noise and against changes in different tasks and varying environments.
Finally, the multimodal nature of our network lets us reconstruct the shape of the target container.
The dataset and associated software are public and are available at
 \href{https://lianghongzhuo.github.io/MultimodalPouring}{https://lianghongzhuo.github.io/MultimodalPouring}.

One surprising limitation of our approach is the poor generalization to liquids like milk or fruit juices, which would be considered quite similar to water by many humans, while the pouring noises are actually quite different.
Training on different liquid types would improve network performance, but MP-Net will still fail in situations where the auditory signal is too weak.
Another issue is our use of raw force/torque data as the network input,
which changes significantly for different grasp types and pouring motions. This could be resolved by training on many grasps, or simply by feeding preprocessed weight data into the network.

For future work, using audio and haptic information for dynamic control of robotic pouring would be an exciting research direction.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


text:
Chromosomes & mtDNA
Help Me Understand Genetics
Crigler-Najjar syndrome
Printable PDF Open All Close All
Enable Javascript to view the expand/collapse boxes.
Crigler-Najjar syndrome is a severe condition characterized by high levels of a toxic substance called bilirubin in the blood (hyperbilirubinemia). Bilirubin is produced when red blood cells are broken down. This substance is removed from the body only after it undergoes a chemical reaction in the liver, which converts the toxic form of bilirubin (called unconjugated bilirubin) to a nontoxic form called conjugated bilirubin. People with Crigler-Najjar syndrome have a buildup of unconjugated bilirubin in their blood (unconjugated hyperbilirubinemia).
Bilirubin has an orange-yellow tint, and hyperbilirubinemia causes yellowing of the skin and whites of the eyes (jaundice). In Crigler-Najjar syndrome, jaundice is apparent at birth or in infancy. Severe unconjugated hyperbilirubinemia can lead to a condition called kernicterus, which is a form of brain damage caused by the accumulation of unconjugated bilirubin in the brain and nerve tissues. Babies with kernicterus are often extremely tired (lethargic) and may have weak muscle tone (hypotonia). These babies may experience episodes of increased muscle tone (hypertonia) and arching of their backs. Kernicterus can lead to other neurological problems, including involuntary writhing movements of the body (choreoathetosis), hearing problems, or intellectual disability.
Crigler-Najjar syndrome is divided into two types. Type 1 (CN1) is very severe, and affected individuals can die in childhood due to kernicterus, although with proper treatment, they may survive longer. Type 2 (CN2) is less severe. People with CN2 are less likely to develop kernicterus, and most affected individuals survive into adulthood.
What does it mean if a disorder seems to run in my family?
What is the prognosis of a genetic condition?
Genetic and Rare Diseases Information Center
Crigler-Najjar syndrome is estimated to affect fewer than 1 in 1 million newborns worldwide.
What information about a genetic condition can statistics provide?
Why are some genetic conditions more common in particular ethnic groups?
Mutations in the UGT1A1 gene cause Crigler-Najjar syndrome. This gene provides instructions for making the bilirubin uridine diphosphate glucuronosyl transferase (bilirubin-UGT) enzyme, which is found primarily in liver cells and is necessary for the removal of bilirubin from the body.
The bilirubin-UGT enzyme performs a chemical reaction called glucuronidation. During this reaction, the enzyme transfers a compound called glucuronic acid to unconjugated bilirubin, converting it to conjugated bilirubin. Glucuronidation makes bilirubin dissolvable in water so that it can be removed from the body.
Mutations in the UGT1A1 gene that cause Crigler-Najjar syndrome result in reduced or absent function of the bilirubin-UGT enzyme. People with CN1 have no enzyme function, while people with CN2 have less than 20 percent of normal function. The loss of bilirubin-UGT function decreases glucuronidation of unconjugated bilirubin. This toxic substance then builds up in the body, causing unconjugated hyperbilirubinemia and jaundice.
Learn more about the gene associated with Crigler-Najjar syndrome
UGT1A1
What is a gene?
What is a gene mutation and how do mutations occur?
How can gene mutations affect health and development?
More about Mutations and Health
Inheritance Pattern
Crigler-Najjar syndrome is inherited in an autosomal recessive pattern, which means both copies of the UGT1A1 gene in each cell have mutations. A less severe condition called Gilbert syndrome can occur when one copy of the UGT1A1 gene has a mutation.
What are the different ways in which a genetic condition can be inherited?
More about Inheriting Genetic Conditions
Diagnosis & Management Links
Genetic Testing Information (3 links)
What is genetic testing?
Genetic Testing Registry: Crigler-Najjar syndrome
Genetic Testing Registry: Crigler-Najjar syndrome, type II
Research Studies from ClinicalTrials.gov (1 link)
Other Diagnosis and Management Resources (1 link)
Centers for Disease Control and Prevention: Facts About Jaundice and Kernicterus
How we cover diagnosis and management of health conditions
How are genetic conditions diagnosed?
How are genetic conditions treated or managed?
How can I find a genetics professional in my area?
Other Names for This Condition
Crigler Najjar syndrome
familial nonhemolytic unconjugated hyperbilirubinemia
hereditary unconjugated hyperbilirubinemia
How are genetic conditions and genes named?
Additional Information & Resources
Health Information from MedlinePlus (4 links)
Encyclopedia: Bilirubin - Blood
Encyclopedia: Kernicterus
Health Topic: Jaundice
Health Topic: Liver Diseases
Genetic and Rare Diseases Information Center (2 links)
Crigler-Najjar syndrome type 2
Crigler Najjar syndrome, type 1
Educational Resources (6 links)
MalaCards: crigler-najjar syndrome, type i
MalaCards: crigler-najjar syndrome, type ii
Orphanet: Crigler-Najjar syndrome
Orphanet: Crigler-Najjar syndrome type 1
Patient Support and Advocacy Resources (2 links)
American Liver Foundation
National Organization for Rare Disorders (NORD)
Scientific Articles on PubMed (1 link)
Catalog of Genes and Diseases from OMIM (2 links)
CRIGLER-NAJJAR SYNDROME, TYPE I
CRIGLER-NAJJAR SYNDROME, TYPE II
Sources for This Page
Bosma PJ. Inherited disorders of bilirubin metabolism. J Hepatol. 2003 Jan;38(1):107-17. Review.
Citation on PubMed
Servedio V, d'Apolito M, Maiorano N, Minuti B, Torricelli F, Ronchi F, Zancan L, Perrotta S, Vajro P, Boschetto L, Iolascon A. Spectrum of UGT1A1 mutations in Crigler-Najjar (CN) syndrome patients: identification of twelve novel alleles and genotype-phenotype correlation. Hum Mutat. 2005 Mar;25(3):325.
Sneitz N, Bakker CT, de Knegt RJ, Halley DJ, Finel M, Bosma PJ. Crigler-Najjar syndrome in The Netherlands: identification of four novel UGT1A1 alleles, genotype-phenotype correlation, and functional analysis of 10 missense mutants. Hum Mutat. 2010 Jan;31(1):52-9. doi: 10.1002/humu.21133.
Udomuksorn W, Elliot DJ, Lewis BC, Mackenzie PI, Yoovathaworn K, Miners JO. Influence of mutations associated with Gilbert and Crigler-Najjar type II syndromes on the glucuronidation kinetics of bilirubin and other UDP-glucuronosyltransferase 1A substrates. Pharmacogenet Genomics. 2007 Dec;17(12):1017-29.
More from Genetics Home Reference
Au-Kline syndrome
10q26 deletion syndrome
PLCG2-associated antibody deficiency and immune dysregulation
All Recently Added Pages
GeneEd resources available on Genetics Home Reference
Genetics Home Reference results available from MedlinePlus Connect
What is direct-to-consumer genetic testing?
What are genome editing and CRISPR-Cas9?
What is precision medicine?
What is newborn screening?
Reviewed: February 2012
The resources on this site should not be used as a substitute for professional medical care or advice. Users with questions about a personal health condition should consult with a qualified healthcare professional.
Selection Criteria for Links
Data Files & API
8600 Rockville Pike, Bethesda, MD 20894, USA

redpajama_set_name: RedPajamaCommonCrawl


View real photos of patients who had Breast Augmentation with Saline Implants from Dr. Robinson at Indiana Plastic Surgery located in Munster Indiana!
Many women visit Dr. Wagner for breast augmentation with implants in Indianapolis, Indiana, to have bigger breasts, but really that's only part of the story. He also uses breast implants to correct asymmetric breasts and restore volume following childbirth, breastfeeding, or significant weight loss.
Below are before and after pictures of Dr. Severinac's breast implant augmentation in Fort Wayne, Indiana. These patients have had either the Mentor Moderate Plus Profile saline implant or the Ideal structured saline implant. Breast Implant Options. Tweet. One of the most important aspects of your breast augmentation is the choice of breast implants. In Valparaiso and Schererville, Indiana, board-certified plastic surgeon Dr. Gus Galante understands that this can make a big difference in the final appearance of your breasts. If a saline implant does leak, the.
I am wicked, it not sincere approaches me. Who in addition, suchlike may perhaps prompt?


Current research areas emphasize: biogeochemical and mineralogical processes in seasonally reduced soils and riparian zones focusing on the mobility of P and the role of "green rust" minerals on water quality; management of viticultural soils.
1986-present Associate Professor, Department of Soil Science, OSU.
1979-86 Assistant Professor, Department of Crop and Soil Science, OSU.


FLOUR, NIACIN, REDUCED IRON, THIAMIN MONONITRATE, RIBOFLAVIN, FOLIC ACID), DEXTROSE, SALT, YEAST, SOYBEAN OIL, MALTED BARLEY FLOUR.
Gonnella French Style bread crumbs deliver a level of quality and consistency no other bread crumb can approach. There are no added sugars, just old-fashioned, hearth baked goodness that enhances the flavor and quality of any dish.


text:
TO ROW
Adult Learn to Row courses
Are you looking for a new challenge? Rowing is an exciting and fun sport that can help you to get fit quickly, gain new skills in a boat and meet new friends. Our Learn to Row courses for beginners will get you started with the basics of the sport and get you out on the water. Where you take it from there depends on how far you want to push yourself.
Courses run every summer from July through to August and last five weeks.
All sessions are led by a qualified coach, with support from experienced club members.
The course culminates in a fun 'regatta' (that's a type of race – we'll explain much more about when you start!)
Who is this suitable for?
Anyone who hasn't rowed before or has limited experience who wants to try the sport.
You must be able to swim – although we should point out its very rare for people to fall in, we just need to know that you can in case of an emergency.
The course is for people aged 18 and up (our oldest rowers are in their 80s – age is no barrier). If you are under 18 you might be interested in our Junior or Schools programme.
What will the course cover?
The main purpose of the course is to give you experience rowing on the water – we aim to get people into a boat by the end of their first session.
The course will involve both water and land-based training in our air-conditioned gym.
Water based sessions are in our special training boats, which are for five people, and are very stable for beginners.
We'll give you an introduction to the Rowing Club, the Boathouse and its layout.
Familiarisation with the river – the training environment.
Full water and boat safety briefing to make sure you're comfortable and stay safe.
Explain the types of boat, the important parts of the boat, oars and other equipment.
Learn and practise British Rowing Technique on indoor rowing machines.
By the end of the course you're not going to be a world beating rower, but you should be confident rowing and steering a boat.
What happens after the course?
We hope that everyone who completes the Learn to Row course will go on to become a club member.
Depending on where you want to go with your rowing, you can either go forward as a competitive rower, training to take part in regional and national events, or you can row recreationally for fun and good exercise.
How much does it cost, and what do I get for that?
The Learn to Row course costs £180. This covers:
5 coach led training sessions plus a fun regatta.
All necessary equipment and insurance.
Three months introductory club membership.
Once you've completed the course the introductory membership (which commences from the start of the course) gives you full use of the club facilities, and means you can spend some more time practising and enjoying rowing before deciding whether you want to sign up as a club member.
If you like the sound of Learn to Row, you can register an interest in a place on the next course we have available.
Please note: we will send out invites to people on the waiting list around the end of April. Places are allocated on a first-come-first-serve basis.
No payment is required until you are offered a position on the course.
About Learn to Row courses
Gill Parker
Learn to Row course leader, and a former Olympian!
Gill Parker is a former British Olympic rower, having taken part in the 1976 Montreal Olympics, the first time women rowed in a British Olympic team. She has been training at Lea Rowing Club since we opened the doors for the first time in 1980, and has coached our senior men and women over the years.
Lea R.C. learn to row course
Learn to row participants in the gym
Getting out on the water at Lea R.C.
Sign up to our Learn to Row mailing list to register you interest in our next course:
I'd like to Learn to Row
If you'd like to find out more or have any questions about learning to row, please get in touch with our Secretary via our Contact page:


Our Specialist Leaders of Education (SLE) are outstanding middle and senior leaders who have the skills, knowledge and experience to support individuals or teams in other schools. They understand what outstanding leadership practice in their area of expertise looks like and they can help others to achieve it in their own setting.
If your school requires the support of an SLE Aspire Teaching School will help broker the support you need. SLEs can be requested by name or we can advise who is best suited for your school.
SLEs are able to deliver a range of support activities; school to school support work, delivering professional development, audits, diagnostics, working one on one with staff or with small teams. You discuss your needs with us and we can recommend a bespoke package to meet those needs. SLE deployments may also be requested by the Local Authority to ensure improvements identified by Ofsted are met. The length of each deployment will depend on the needs of the school.
Our SLEs can offer a wide range of expertise and support for EYFS, KS1 and KS2. They come from a variety of schools across the Wakefield and Kirklees districts and specialise in a variety of areas ranging from Phonics, Literacy and Numeracy to School Business Management For details of our SLEs please see the School to School Support Menu.
Aspire Teaching School will liaise closely with the school requesting the deployment of an SLE to ensure all agreed outcomes at the start of the deployment have been satisfactorily achieved. A brokerage contract will be completed to outline specifically what the area of focus is, the agreed timescale of the deployment, funding agreement and impact measures to ensure improved outcomes for children.
Identifying priorities within our alliance - prioritising, marketing and advertising details of SLE.
Setting up a panel of headteachers and Local Authority representatives to review applications and assess SLE applicants through face-to-face activities.
Notify the College of designations and confirm outcomes to applicants.
Work with lead training schools to register SLEs onto training.
Broker SLE support within their alliance.
Deploying SLEs and monitoring their deployments.
Details of how to apply to become an SLE can be found here.


text:
"From the Corner of St. Johns and Emerson to Barber Street: 100 Years of the Ithaca Church of God, 1918-2018" Part IV: A Young and Vibrant Leader, a Growing and Active Church, 1950-1970
August 21, 2018 / goodspeedgratiotcountyhistorical
Above: Earl and Charlotte Van Sipe pastored the church for almost exactly twenty years from 1950-1970; a 1950s shot of the old church; a 1964 shot of the new Church of God on Barber Street; in 1970 the Board of Trustees celebrated a mortgage burning ceremony – the new church was paid off.
As 1950 began, Ithaca, like many other towns across America, had seen great changes in society and in the world. The Second World War ended in 1945 and the church had many men and women in its midst who had faithfully served their country. In Ithaca, a terrible polio epidemic had started in mid-1949 which terrified the community because of the number of people who were stricken in the wake of this terrible disease. Nothing had gripped Gratiot County on this scale since the Flu Epidemic of 1918-1919. By the summer of 1950, another conflict in Korea threatened the United States with another world war. When would peace and stability find come? In some ways, the Ithaca Church of God needed a shepherd who would turn out to be a long-term pastor, and they found one in a young Earl Van Sipe.
Earl Van Sipe was 25 years old. He had pastored the Church of God in Petoskey for two years, was married to his wife, Charlotte, and had a nine-month-old daughter, Judy. He and his wife would also be the first pastors to have a child in Ithaca with the birth of their son, Rodney. Van Sipe was originally from Alpena and he had spent 2 ½ years in the United States Maritime Service during World War II. Both he and his wife were Anderson College graduates and it was there that Earl won letters in several sports and he coached the debate team. What many did not know was that he had turned down signing with the Cincinnati Reds baseball team in order to follow his calling as a pastor. Van Sipe had a knack with people and during his twenty years in Ithaca he became well known to many in the county, whether he was an auctioneer for a charitable cause, heading the county ministerial association, being a high school sports official, a leader of the Gratiot County Civil Defense Program, or serving as chaplain for the county sheriff's department.
As the church moved into the 1950s it averaged 90 people a Sunday and continued its ministry within the community. Holding annual or seasonal revivals were a signature of the church and notable evangelists came from across the Church of God to Ithaca to hold meetings. In 1952, a special dedication service was held for the purchase and installation of new pews, which cost $2485.00. It was one of many steps in the 1950s that the church took to deal with its growth. In early 1957, the church undertook a drive to raise $30,000 to add a new educational unit for new classrooms, fellowship hall and pastor's office. Attendance was now reaching an average of 160 people each week.
Still, there seemed to be more for the Ithaca Church of God. In the midst of the 1957 drive a new vision was presented to do something better: build a new, modern church and then relocate. Parishioners were asked to think bigger and to commit deeply to the idea of building this new church. As leaders looked for a place to build this church, one day Pastor Van Sipe and trustee Ed Papendick ventured several blocks northeast of the church to what would eventually be called Barber Street. At the time the area was largely corn fields and there was yet to be a subdivision or a high school nearby. The church decided to enter into a mortgage for $45,000 and construction of a new church started in July 1963. In late December, the congregation moved to the church on Barber Street and another church, the Ithaca Free Methodist, purchased the old Church of God properties. Coincidentally, the two churches moved on the same day. Services at Barber Street were held in the completed sanctuary even though the rest of the church was not completed until April 1964. On Sunday, June 14, 1964, the new church was officially dedicated. The new church had a larger sanctuary which was fully carpeted, had new pews, modern acoustics, a new fellowship area with new classrooms. The property also offered the public something that the older church never had: more than enough parking! The congregation also took another step and purchased the house across the road which became the new parsonage. Still, many wondered, "How will we pay for this?"
As the 1960s went on, the church grew, many well-known speakers, evangelists, and singers visited the church and the mortgage was soon paid off. In late April 1970, the church held a mortgage burning ceremony and the church celebrated having paid off this new church. It was a time that celebrated that God was good and that his people were faithful to their commitment to having a new church.
Within a few months, the church began a new stage when Earl Van Sipe announced his resignation to accept a call to become part of the pastoral staff at Pennway Church of God in Lansing. The Van Sipes had grown with the church and the community for almost exactly 20 years. In July, the church entered the next decade of its history with another young pastor and his family.
Copyright 2018 James M Goodspeed
← "From the Corner of St. Johns and Emerson to Barber Street: 100 Years of the Ithaca Church of God, 1918-2018" Part III: Navigating the 1940s
"From the Corner of St. Johns and Emerson to Barber Street: 100 Years of the Ithaca Church of God, 1918-2018" Part IV: A Young and Vibrant Leader, a Growing and Active Church, 1950-1970 →


Missy is totally housebroken ! I haven't had one accident with her. She is very good with our 2 pugs, our cat and our son's Boston Terrier. The only time she gets a little skittish is if a bigger dog is too aggressive or if there are to many new people around. I would say older kids would be okay. She adores us and is always waiting at the door when we get home. She is not a big toy girl but does like a rawhide bone and loves to play "chase me". I just thought a little more info on the website might help. I am sitting here writing this and she is sleeping at my feet. She is a devoted sweetheart.


The Wise Tails' facility is located in the hills above Simi Valley, California, providing beautiful views of the surrounding mountains and city below. The grounds are equipped with play and relaxation areas, as well as walking paths. Our goal is to provide a comforting and healthful environment for senior dogs, so that they may experience as enjoyable a quality of life as possible.
Wise Tails – A Senior Dog Sanctuary, a non-profit 501(c)(3) organization, rescues mature dogs from kill shelters, provides necessary services and locates permanent homes for elderly dogs in need. The Wise Tails' team of volunteers and staff members provide care, comfort, and a loving environment to elderly/senior dogs, while constantly seeking forever homes for its residents.
Make a difference and set an example.
Dogs have been part of my life since I was old enough to remember. They have been and continue to be wonderful friends and companions. Receiving their love and reciprocating it back to them is one of my life's greatest pleasures.
I have never had a dog actually speak the words to me but I do believe that if we were to ask a dog whether they want to live or not, they would say, ``Yes, just as much as humans do``. Unfortunately, there are the cases where dogs and animals may be in constant pain or diseased to the point that their suffering should be taken into account. In many cases however, senior dogs just require more patience and care.
Throughout my life I grew up with dogs and as I've gotten older, I have only grown to love and appreciate them even more with every passing year. As a person that whole-heartedly loves every dog I cross paths with, I could not be more excited to be a part of Wise Tails - A Senior Dog Sanctuary.


Ferbane Tidy Towns & Offaly County Council have introduced the Mutt Mitt Bags and Dispensers throughout the town in the coming weeks to combat the problem of Dog Fouling.
Dog owners should note that it is the responsiblilty of the dog owner or the person in charge of the dog to clear up any dog foul left by their dog. Dog waste is a threat to the health of children, transmits diseases and degrades the town. There is an on the spot fine of €150 euro up to €3,000 for re-offenders. Your co-operation in this matter is greatly appreciated.


text:
To Bear Witness: Extraordinary Lives - Opening Reception
December 12, 2021 | Noon Pacific | Oregon Jewish Museum & Center for Holocaust Education
724 NW Davis St., Portland OR 97209
The Immigrant Story and the Oregon Jewish Museum and Center for Holocaust Education invite you to visit "To Bear Witness – Extraordinary Lives," a multimedia exhibition that celebrates the lives of men and women who endured unthinkable cruelty elsewhere in the world, only to resume productive lives in Oregon. For visitor information, click here.
The exhibition takes its name from the words of the late Nobel Prize–winning writer, activist, and Holocaust survivor Elie Wiesel, who emphatically proclaimed, "For the dead and the living, we must bear witness." The exhibition features profiles of survivors of the Nazi Holocaust; genocides in Europe, Africa, and Asia; and unimaginable atrocities of war. The profiles are united by the troubling truth that human despotism sometimes knows no bounds, but each is also a portrait of courage and human resilience. We present these stories in hopes that they will inspire, inform, and possibly instruct.
This multimedia exhibition opens on December 12, 2021, at the Oregon Jewish Museum and Center for Holocaust Education. The exhibition will run through May 15, 2022.
This exhibition is made possible in part by a grant from Oregon Humanities.
The Immigrant Story
https://theimmigrantstory.org/to-bear-witness-extraordinary-lives/


text:
Clear to partly cloudy. Low 22F. Winds NNW at 5 to 10 mph..
Clear to partly cloudy. Low 22F. Winds NNW at 5 to 10 mph.
Joaquin Phoenix as Arthur Fleck in "Joker."
Niko Tavernise/Warner Bros.
Oscar nominations 2020: 'Joker' leads with 11
"Joker," the controversial drama about the mentally ill Batman villain that sparked backlash with its realistic depictions of extreme violence, triumphed at the 92nd annual Academy Awards nominations on Monday morning, earning 11 nods, the most of any film.
Three films were close behind with 10 nominations: "Once Upon a Time in Hollywood," Quentin Tarantino's fictional ode to 1960s Hollywood; "The Irishman," Martin Scorsese's mob drama starring Robert De Niro, Al Pacino and Joe Pesci that clocks in at three and a half hours; and "1917," the World War I epic that centers on two British soldiers on a dangerous trip to deliver a critical message that could save 1,600 troops.
All four of those movies also earned best picture nominations. Rounding out the prestigious category is "Little Women," Greta Gerwig's version of Louisa May Alcott's tale of four sisters growing up in Massachusetts during the Civil War; "Marriage Story," which centers on an excruciating divorce and custody battle; "Parasite," the South Korean psychological thriller-slash-dark comedy; "Jojo Rabbit," about a young German boy who counts Hitler as an imaginary friend; and "Ford v Ferrari," based on the true story of Ford's goal to make a faster car than the Ferrari.
For the second year in a row, there were no women nominated in the best director category: Nominees included Martin Scorsese, Quentin Tarantino, Bong Joon-ho, Sam Mendes and Todd Phillips, with the notable snub of Gerwig.
Once again, the Oscars ceremony will be host-free.
The nominations were announced Monday morning. The Academy Awards air Sunday, Feb. 9 on ABC.
Oscar nominations by movie:
"Joker" — 11
"Once Upon a Time In Hollywood" — 10
"The Irishman" — 10
"Parasite" — 6
"Marriage Story" 6
"Little Women" — 6
"Bombshell" — 3
The nominations for the 92nd Academy Awards:
Renée Zellweger, "Judy"
Charlize Theron, "Bombshell"
Scarlett Johansson, "Marriage Story"
Saoirse Ronan, "Little Women"
Cynthia Erivo, "Harriet"
Joaquin Phoenix, "Joker"
Adam Driver, "Marriage Story"
Leonardo DiCaprio, "Once Upon a Time in Hollywood"
Antonio Banderas, "Pain and Glory"
Jonathan Pryce, "The Two Popes"
Martin Scorsese, "The Irishman"
Quentin Tarantino, "Once Upon a Time in Hollywood"
Bong Joon-ho, "Parasite"
Sam Mendes, "1917"
Todd Phillips, "Joker"
Brad Pitt, "Once Upon a Time in Hollywood"
Al Pacino, "The Irishman"
Joe Pesci, "The Irishman"
Tom Hanks, "A Beautiful Day in the Neighborhood"
Anthony Hopkins, "The Two Popes"
Laura Dern, "Marriage Story"
Margot Robbie, "Bombshell"
Florence Pugh, "Little Women"
Scarlett Johansson, "Jojo Rabbit"
Kathy Bates, "Richard Jewell"
Best international feature film
South Korea, "Parasite"
Spain, "Pain and Glory"
France, "Les Misérables"
North Macedonia, "Honeyland"
Poland, "Corpus Christi"
"1917," Roger Deakins
"Once Upon a Time in Hollywood," Robert Richardson
"The Irishman," Rodrigo Prieto
"Joker," Lawrence Sher
"The Lighthouse," Jarin Blaschke
"I'm Standing With You," from "Breakthrough"
"Into the Unknown," from "Frozen II"
"Stand Up," from "Harriet"
"(I'm Gonna) Love Me Again," from "Rocketman"
"I Can't Let You Throw Yourself Away," from "Toy Story 4"
Country singer Chase Rice explains how he got pulled into that cruel twist on 'The Bachelor'
In the fall, country singer Chase Rice had a rare and exciting opportunity: He was scheduled for a performance that would be filmed for an episode of "The Bachelor." ABC's long-running reality series, which averages about 6 million viewers per episode, is a coveted promotional platform for m…
College football coach suspended after saying Hitler was undeniably 'a great leader'
Grand Valley State University suspended a newly hired member of its football coaching staff after he made laudatory remarks about Adolf Hitler during an interview with the Michigan school's student newspaper, saying, "The way he was able to lead was second-to-none."
Super Bowl to feature Trump, Bloomberg and corporate America battling for attention
When the San Francisco 49ers and Kansas City Chiefs face off in Super Bowl LIV in Miami on Sunday, billionaire presidential candidates will battle for attention while big brands bearing positive messages hope to bring balance to the divisive political atmosphere.
Blind Bulgarian artist finds a way to keep painting
PLOVDIV, Bulgaria — Stamen Karamfilov bends low over a canvas, adding the finishing touches to a woodland landscape — no mean feat considering the Bulgarian artist is almost totally blind.
Queen Elizabeth's statement on future of Prince Harry and Meghan
Canada's Trudeau makes waves online with new beard
Milo the cat went missing at the airport. Two months later, he's been reunited with his owner.
Runaway husbands in India have no place to hide
10 min to read
As 'Dancing With the Stars' judges seem to lose patience with Sean Spicer, Trump tweets his support


Call 307-783-8398 or visit EvanstonAnytime.com to make an appointment in Evanston or Mountain View!
Our surgeons have experience in many facets of surgery from tumor removal and hernia repair to gallbladder surgery and emergency surgery. They specialize in minimally invasive (Laparoscopic) surgical techniques that typically result in less pain, minimal scarring and faster recoveries. They also perform colonoscopies and other endoscopies.


UK's Leading Specialist of Promotional Products and Branded Merchandise customised with your logo and business message.
Buypromoproducts have over 20 years experience in the print and promotional products industry, their website offers 1000's of new promo items that can be customised with you logo and business message. Their dedicated account managers will oversee your marketing campaign from inception to completion, so you can relax safe in the knowledge that your project will receive the expertise and care it deserves.


Our Business Checking offers you the convenience, flexibility, and service you need!
BECAUSE YOU'RE OPEN FOR BUSINESS EVERY DAY.
Managing the day-to-day finances and cash flow of your business is critical to your success. Open a TAB Bank Business Checking Account, and you'll get the convenience, flexibility and service you need to keep things running smoothly from open to close — every single day.
Call 1-800-215-7128 to open a TAB Bank Business Checking Account today!
**Fees for the Analyzed Business Checking Account and corresponding Treasury Management Services (TMS) are only available by quote from a TMS Representative.
1. The definition of transactions includes any combination of deposits, paid items and deposited items, including paper and electronic. Refer to the fee schedule for all applicable fees.


Leuven, Belgium – ONCURIOUS NV, a Belgium-based biotech company focusing on the development of innovative oncology treatments, today announces that it has reached principle agreement with VIB to acquire exclusive licences to a portfolio of five unique next generation immuno-oncology assets, based on seminal work originating from the VIB-KULeuven labs of Massimiliano Mazzone and Gabriele Bergers, and from the VIB-VUB lab of Jo Van Ginderachter.
Oncurious broadens its ongoing clinical development activities in orphan pediatric oncology indications with preclinical research and drug development programs, resulting in an exciting pipeline of next-generation immuno-oncology drugs targeting a broad spectrum of cancers.
VIB Discovery Sciences will take the lead in the pre-clinical development of these new projects. As part of this agreement, VIB will increase its stake in Oncurious, with ThromboGenics remaining the majority shareholder. VIB will also receive a royalty on future sales of any of these assets. ThromboGenics invests an additional €2.1 million in Oncurious.
Oncurious is currently conducting a Phase I/IIa clinical trial for the evaluation of TB-403 in the treatment of medulloblastoma, in collaboration with BioInvent. TB-403 is a humanized monoclonal antibody against placental growth factor (PLGF). PLGF is expressed in several types of cancer, including medulloblastoma.


text:
Volcano Watch: Water was in Kilauea caldera before the 2018 summit collapse
By Hawaiian Volcano Observatory Special to West Hawaii Today | Saturday, August 15, 2020, 12:05 a.m.
The black streak on caldera wall (center) is about 50 yards long, and white steam plume (lower right) rises from northwestern part of Halemaʻumaʻu. Photo from Volcano House Hotel on July 4, 2018. The configuration of this area changed considerably after the photo was taken, as collapse continued into early August. (USGS/courtesy photo)
On July 4, 2018, an observer at the Volcano House Hotel was watching the evolving collapse of Halema'uma'u Crater, 2.5 miles away. Suddenly, he did a double take, blinked a couple of times, but couldn't erase the dark line descending the wall of Kilauea caldera above Halema'uma'u. Not knowing what it was, he dubbed it the "black streak."
Two ideas for this curious feature came to mind. One was that the streak was a fresh rockfall scar cutting across the dusty gray slope. The other was that the streak was made by water that washed away the dust.
Over the next few days, the black streak came and went. Finally, observations showed that the streak stayed black during a time when a lot of dust was billowing from Halema'uma'u. This was proof positive that it was made by water, not a rockfall.
The water flowed from a point 10 yard to 20 yards below the rim of the caldera, high above the groundwater body that today feeds the deepening lake described in the past two Volcano Watches. This point is within the national park's presently closed SW Rift wayside (marking the upper extent of Kilauea's Southwest Rift Zone), beneath which are many vertical dikes of solidified magma. How can water be so high in this area?
We put two and two together. During exceptionally heavy downpours, a river flows for an hour or so across the ground surface between the south end of Uekahuna Bluff and Southwest Rift, a distance of 600 yards to 800 yards. The flowing river, which we nicknamed Kilauea river, is several yards wide and a few inches deep. Most notably, the flowing river always ends before reaching Southwest Rift, sinking into alluvial sand. Where does the water go? Could that be the water that forms the black streak?
Yes, we think so. Beyond where it disappears, the river water probably flows underground but is dammed by dikes beneath the Southwest Rift area, forming a shallow perched aquifer. Faulting of the caldera wall during the 2018 summit collapse opened a pathway for this stored water to exit the aquifer and pour into the caldera.
The black streak, more prosaically called a water cascade, has reappeared sporadically in the past two years. In fact, a couple of such ephemeral cascades may streak the same area. Any photographs of a black streak taken by a reader since 2018 would be much appreciated. Recent photos show a cavity at the head of one cascade, either a lava tube or drained magma conduit. Volcanic ash has been washed from the cavity and forms a deposit next to the cascade.
The perched water body responsible for the water cascade is one of two such bodies existing before 2018. The other formed a tiny warm pond on the caldera floor 500 yards north of Halema'uma'u before it enlarged in 2018. Lush vegetation surrounded the pond, and microorganisms lived in the water.
The tiny pond drained as Halema'uma'u widened in 2018, though its site, marked by vegetation, remains. As the crater expanded in June and July, a white steam plume generally rose above the northwestern part of the crater, contrasting with the dusty brown clouds that engulfed most of the crater. The plume might have been generated by boiling of water in the same shallow aquifer that supported the pond.
Could other shallow water bodies exist unseen beneath the caldera floor? More rain falls on the northern part of the caldera than on the southern. Several caves below the northern caldera floor emit steam and are very hot, as cavers attest. Most likely the heat comes from solidified lava flows and lakes active in this area in the 19th and early 20th centuries, and it heats rainfall to steam. The steam persists even in dry weather. Does this suggest the presence of a deeper water body? Probably not, but we can't be sure.
Any perched water body would doubtless pale in importance when compared with the more extensive groundwater reservoir that supports the present lake. Such bodies would be both smaller and higher (farther from magma) than the main reservoir. Nonetheless, we would be remiss not to consider that even shallow water could contribute to explosive hazards at the summit of Kilauea.
Visit https://volcanoes.usgs.gov/observatories/hvo/ for past Volcano Watch articles, for past Volcano Watch articles, Kilauea and Mauna Loa updates, volcano photos, maps, recent earthquake info, and more. Email questions to askHVO@usgs.gov.
Volcano Watch is a weekly article and activity update written by U.S. Geological Survey Hawaiian Volcano Observatory scientists and affiliates.
Volcano Activity Updates
Kilauea Volcano is not erupting. Its USGS Volcano Alert level remains at NORMAL (https://volcanoes.usgs.gov/vhp/about_alerts.html). Kilauea updates are issued monthly.
Kilauea monitoring data for the past month show variable but typical rates of seismicity and ground deformation, low rates of sulfur dioxide emissions, and only minor geologic changes since the end of eruptive activity in September 2018. The water lake at the bottom of Halema'uma'u continues to slowly expand and deepen. For the most current information on the lake, see https://volcanoes.usgs.gov/volcanoes/Kilauea/summit_water_resources.html.
Mauna Loa is not erupting and remains at Volcano Alert Level ADVISORY. This alert level does not mean that an eruption is imminent or that progression to eruption from current level of unrest is certain. Mauna Loa updates are issued weekly.
This past week, about 164 small-magnitude earthquakes were recorded beneath the upper-elevations of Mauna Loa; most of these occurred at shallow depths of less than about 5 miles. GPS measurements show long-term slowly increasing summit inflation, consistent with magma supply to the volcano's shallow storage system. Gas concentrations and fumarole temperatures as measured at both Sulphur Cone and the summit remain stable. Webcams show no changes to the landscape. For more information on current monitoring of Mauna Loa Volcano, see: https://volcanoes.usgs.gov/volcanoes/mauna_loa/monitoring_summary.html.
There were two events with three or more felt reports in the Hawaiian Islands during the past week: a magnitude-1.9 earthquake 3 miles west-southwest of Kailua-Kona at 10:28 a.m. Aug. 10 and a magnitude-2.8 earthquake 2 miles east-northeast Leilani Estates at 11:06 a.m. Aug. 6.
Volcano Watch: How hot is Kilauea's summit water lake?
Volcano Watch: Doppler radar in Ka'u provides more than a weather radar


Australia's newest political movement, Flux, has engaged Sefiani Communications Group to help build their profile through an integrated communications campaign in the lead-up to the Federal election on 2 July.
Founded by technology entrepreneurs Nathan Spataro and Max Kaye, Flux is running two Senate candidates in each state and one in the ACT. The party's stated aim is to transform democracy by giving voters a direct say in legislation voted on by the Parliament. If elected, Flux Senators will vote on every Bill before the Senate only as they are directed by a majority vote of registered voters in the secure Flux app.


In a RGB color space, hex #043027 is composed of 1.6% red, 18.8% green and 15.3% blue. Whereas in a CMYK color space, it is composed of 91.7% cyan, 0% magenta, 18.8% yellow and 81.2% black. It has a hue angle of 167.7 degrees, a saturation of 84.6% and a lightness of 10.2%. #043027 color hex could be obtained by blending #08604e with #000000. Closest websafe color is: #003333.
● #043027 color description : Very dark (mostly black) cyan.
The hexadecimal color #043027 has RGB values of R:4, G:48, B:39 and CMYK values of C:0.92, M:0, Y:0.19, K:0.81. Its decimal value is 274471.
Below, you can see some colors close to #043027. Having a set of related colors can be useful if you need an inspirational alternative to your original color choice.
This text has a font color of #043027.
This paragraph has a background color of #043027.
This element has a border color of #043027.
A shade is achieved by adding black to any pure hue, while a tint is created by mixing white to any pure color. In this example, #010c0a is the darkest color, while #f9fefd is the lightest one.
A tone is produced by adding gray to any pure hue. In this case, #181c1b is the less saturated color, while #003429 is the most saturated one.
Below, you can see how #043027 is perceived by people affected by a color vision deficiency. This can be useful if you need to ensure your color combinations are accessible to color-blind users.