Oregon Commercial Building pressurization testing. We test and analyze pressures to diagnose existing pressures. Air flow balancing, room pressures, duct supply lines, learn more about positive and negative pressures.
Critical Room Pressurization Verification (CRPV) We feel this is the best way to test individual rooms or spaces for climate control and air tightness. For years we used the blower door to test the whole structure. While under pressure we then use a thermal camera to pinpoint leaky areas. However by utilizing better techniques it becomes simple by just using a manometer. I can tell you which room is more leakier than the other. Or which side of the duct system is more leakier.
By just getting a set of leak numbers when conducting a blower test is not the answer. Need to locate the leaks then fix them. By testing each room it is much simpler to pinpoint the leakier areas.
All buildings operate under some level of positive or negative pressure. Offices are generally troubled by pressures. While one office has ideal climate control the office next to it has terrible climate control.
To ensure positive building pressures, normally we have to introduce fresh or outside air into the system. This pressurizes the building, because if a 1,200 cfm fan returns 200 cfm through an outside air duct, it can only pull 1,000 cfm from the building. Meanwhile, the supply side of the fan is delivering the full 1,200 cfm. This leaves 1,000 cfm return and 1,200 cfm supply, causing a 200 cfm positive pressure in the building. With more supply air than return air in the building, the building is under a negative pressure providing the duct system is outside of the building envelop. If the duct system is inside of the building envelop then we have a more positive result. However depending on duct leakage will the pressures be measured.
However, it's hard to say how great the positive pressure reading in the building will be. It depends on how tight (or leaky) the building envelope is, and what other pressure generating forces exist, including the wind, appliances, exhaust fans, and the stack effect of the building.
Because pressures are invisible and variable, we must measure them to understand their effect on a building. In addition to the overall building pressure, pressure measurements can be taken between rooms in the building to further identify the effect of the HVAC system on the performance of a building.
Pressures between rooms are a different issue than the overall pressure of a building, although they're often related. Measuring pressure between rooms will reveal pressure imbalances that may have a significant impact on the comfort and air quality in the rooms.
For example, a room with an extremely negative pressure will be drawing in outside air through leaky doors, vents, ducts and windows. A room with too much positive pressure will be forcing conditioned air out of the building, leaving and uncomfortable room.
Exhaust hoods in commercial kitchens cause a negative pressure to remove smoke from the room. To high of a negative pressure will result in HVAC run time. To little pressure will result in a smoky kitchen and dinning room. These hoods have to be carefully balanced. It will effect efficiency and higher energy bills.
Rooms with a negative pressure containing combustion appliances may result in serious injury or even death due to carbon monoxide poisoning. It pays to measure room pressures.
All pressure balancing requires a differential pressure measurement instrument. The manometers we use all have two pressure ports; a manometer reads the difference between the pressures exerted on either of the pressure ports on the tool. We use hoses to channel the pressures from a duct or a room into each of the ports on a manometer.
In industrial sites when the building is under a negative pressure and chemicals are stored within a room, the chemicals will be drawn throughout the building causing serious injury.
Commercial building also have to provide fresh air to the interior. Usually a large fan connected to the duct system supplies this air. This will cause a positive pressure if the pressures are not released as fast as they are supplied. This also causes an un-balanced climate zone. Resulting in higher energy bills. So each room should be measured with reference too. Meaning we use a manometer to measure the pressures between rooms and the outdoors.
Determining the best leakage rate is much more difficult because every building leaks and the engineering goal is to minimize the rate as much as possible for the lowest construction cost. Leakage is due to the permeability of the air barrier itself and how the air barrier is applied, especially due to transitions and penetrations such as pipes and conduits and due to how many doors and windows a building envelope has.
Buildings normally should operate at a positive pressure of 4 to 5 PA (0.016 to 0.02 in.). At a test pressure of 75 PA (0.3 in.), a leakage rate of 0.4 cfm/sq ft of air barrier would be a very high leakage rate and 0.1 cfm/sq ft would be a very low leakage rate. By specification, these leakage rates are measured while the building is at a pressure difference of 50 to 75 PA, between the interior and exterior. The air barrier area is defined as the area of the first floor, the top floor ceiling, and the total area of the exterior wall air barrier. Table 1 indicates standard leakage rates for various types of structures at either 50 PA or 75 PA building pressures.


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text:
Madeeha Attari
Medium: Acrylic on Canvas
Image Courtesy: Madiha Attari
Image Copyright: Madiha Attari
Madiha Attari's abstract art work and photography are representative of her attempt to reconcile her inner consciousness with the outside world as she treads the path of self-discovery, all the while documenting (on canvas and film) a myriad experiences – from the years of conflicted quiet desperation of being a faceless identity as a Pakistani living in India to the joy of finding love and embracing a culture and community which has given her both a new sense of wonder and belonging.
Taking inspiration from the 50's Abstract expressionist movement, Madeeha attempts to use contrasting colours, irregular linear form and at times an aggressive mix of texture for spontaneous personal emotional expression. Her work is often interspersed with multiple layers of gritty paint work creating a unique tactile experience.
Madeeha Naeem Attari is a Visual Artist born and raised in Pakistan. She completed her schooling in Lahore after which she went on to do her BSc Honours in International Relations from the London School of Economics and Political Sciences. After working at the House of Lords in London, she returned back to Lahore in 2006. She is now married and settled in Mumbai for the past twelve years.
Meghna Patpatia Alignment

redpajama_set_name: RedPajamaCommonCrawl


text:
National Recreation Area NY, NJ
On This Page Navigation
The 250-year-old Sandy Hook Lighthouse. The Lighthouse Keepers Quarters next door, built in 1883, is currently serving as the Sandy Hook Visitor Center.
The park is open 6 am through 9 pm daily, except by permit.
There is a $20 charge per day for beach parking from Memorial Day weekend through Labor Day. Credit cards are the preferred method of payment. Parking at Fort Hancock is always free.
All navigable marine waters within the park boundary are open 24 hours.
Sandy Hook Lighthouse: closed
Lighthouse Keepers Quarters and Visitor Center: open weekends from 10 am to 4 pm.
Keep it with you.
Sandy Hook follows a carry in, carry out policy. If you brought it with you, take it with you.
Can I bring my pet to Sandy Hook?
Pets and their owners may visit bayside beaches throughout the year. Pets are only allowed on oceanside beaches from September 15 - March 15. Pets are not permitted on oceanside beaches during the spring and summer because that is when the threatened piping plover builds nests in those locations.
Pets must always be on a leash no longer than 6 feet.
GPS address: 128 South Hartshorne Drive, Highlands, NJ 07732. At the entrance to the park you may find speed bumps. Please drive at 10 mph for our employees who work in the entrance booths.
From North Jersey and New York: - South on the New Jersey Turnpike - Exit 11 onto the Garden State Parkway (South) - Exit 117 (Keyport/Hazlet) - After toll, bear left and follow Route 36 East - 12 miles to park entrance (after crossing the Highlands Bridge)-Stay right to enter the park.
From South Jersey: - North on Garden State Parkway - Exit 105 (Eatontown), bear left for Route 36 East - Continue on Route 36 East, pass Monmouth Mall and across Route 35 through Eatontown and Long Branch (Route 36 is called Joline Avenue here). - Continue on Route 36, which turns left and follows north along ocean. (Route 36 is called Ocean Blvd. here) - North for six miles through the towns of Long Branch, Monmouth Beach and Sea Bright to park.
From Philadelphia & Camden, New Jersey: - New Jersey Turnpike north to Exit 7A (Shore Points) onto I-195 - East on I-195, to Garden State Parkway north. - GSP north to Exit 105 (Eatontown). - Then follow "From South Jersey" directions above.
The closest New Jersey Transit Bus stop to Sandy Hook is on the 834. However, that is still seven miles from the entrance. Call 973-275-5555 for more information.
Academy Lines bus service operates from New York City to Highlands, NJ. Call 732-291-1300 or 800-242-1339 for schedules. You can also visit their website.
Seastreak offers visitors a high speed ferry to Sandy Hook from Manhattan between Memorial Day weekend and Labor Day.
New Jersey Transit's North Jersey Coast Line to Red Bank, then NJT Bus 834 to Highlands. The drop off is seven miles from the entrance to the park, there is no transportation directly to the entrance of the park.
210 New York Avenue
Staten Island , NY 10305


text:
Jakub Parusinski
co-founder and editor The Fix
Jakub Parusinski is a co-founder and editor at The Fix, a media industry publication and research centre covering the European media sector, as well as managing partner at Jnomics media consultancy. He has over a decade of experience working in the media sector, including as a journalist, editor and media manager.
Jakub is a board member of Warsaw-based, award-winning media start-up Outriders, a board member of the Media Development Foundation in Kyiv and an advisory board of the Stockholm School of Economics in Riga, where he is a program director, media strategy lecturer and mentor to a range of media from across the post-Soviet region.
Life in exile: how migrations, war and revolution are impacting CEE media
Daniil Bogdanovich
Daria Minsky
Daryna Shevchenko
The tumultuous events of the past 2 years - war in Ukraine, a crushed revolution in Belarus, a last gasp for Russia's independent media - have left the CEE media struggling to adapt. Audiences have b...
Ukraine media crisis response: lessons and challenges ahead
Gaygysyz Geldiyev
Mariia Leonova
In the hours after the Russian invasion started a coalition including The Fix Media, Are We Europe, Jnomics, and the Media Development Foundation, together with multiple media partners, started workin...
Journalism on many front lines: the case of Ukraine
Jakub Gornicki
Anastasiia Stanko
Inna Varenytsia
Journalism in Ukraine faces many challenges, from conflict reporting about the war with Russia, through disinformation and infowar, to freedom of press issues, oligarchy influence and ownership. But o...
Century of truth: building positive attitudes towards journalists
Anna Babinets
Tamas Bodoky
Recent years have been marked by growing antipathy toward journalists, particularly when they challenge respected figures or in times of national crisis. Journalists are associated with increasingly v...
#ijf22
Photos are licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License (CC BY-ND 4.0).


At Canadian Family Pharmacy, our number one goal is to provide outstanding service and value to our customers — while meeting their healthcare needs and making their overall shopping experience as easy as possible.
Having the products our customers want in stock is a large part of meeting our goal. We understand that working successfully with suppliers who share our values, our promise to deliver outstanding service, and to uphold the highest standard and level of integrity help to make our commitment a reality.
Canadian Family Pharmacy is positioned to improve access for patients, promote better health outcomes, and control payor costs in a way that no pharmacy retailer or PBM could do separately.
The way we work enables us to provide end-to-end solutions that impact everything from pharmacy plan design to the ultimate delivery of products and services to customers. Our capabilities include industry-leading clinical and health management programs, specialty pharmacy expertise, leadership in retail clinics, customer service excellence, and our deep knowledge of the consumer gained through the more than thousands of customers who visit our stores every day.
In Canadian Family Pharmacy, you get a company with the potential to have a major impact on the way pharmacy and health care services are delivered. We plan to leverage our unique combination to help payors control costs more effectively, improve patient access, and promote better health outcomes in a way that no other company can.
Canadian Family Pharmacy, being a leader in pharmacy business provides you with high-quality products, on-time delivery, prompt and efficient customer support. These three elements guaranteed us vigorous development, and thousands of pleased customers. We highly honor every customer and treat everyone as an individual. Each one of you is very special for us. That is why we want you to be pleased with the order, purchased from us and want you to come back in future. Every customer gets significant discount on the second order. It's truly impossible to find more beneficial offer than the one, provided by us.


text:
\section{Introduction}

\noeditage{
\figC
}

\SW{
This paper considers the problem of scalable change detection  using  a  novel, compact  representation  of an  environment map (Fig. \ref{fig:C}). With recent progress in large-scale map maintenance \cite{2} and long-term map learning \cite{3}, the task of change detection on a large-scale map, from a visual image captured by a mobile robot, has become a problem of increasing criticality \cite{4}. We  aim to enhance the scalability from the novel perspective of map compactness while maintaining the effectiveness of the change detection system.
}{
}

\SW{
In general, change detection has the goal of detecting changes between a robot's view image and a previously constructed map. A major challenge in change detection is to effectively address appearance variations of the change objects. As the variations are inherently place-specific \cite{5} and attributed to various factors (e.g., objects, viewpoint, background, illumination conditions, occlusions), it is challenging to obtain a general-purpose change model. Till the present, the most fundamental scheme reported for addressing this challenge is to directly compare each view image against the corresponding reference (i.e., mapped) image using handcrafted features \cite{6} or deep-learning techniques \cite{4d}. However, this fundamental scheme requires explicit memorization of every possible mapped image and exhaustive many-to-many image comparisons, which severely limits the scalability in time and space.
}{
}

\SW{
Our approach is inspired by the recent success in the community of change-classifier-learning \cite{7,8,8d,9}. Instead of memorizing the large collection of mapped images, the change-classifier-learning approach simply learns an essential change classifier that is often significantly compact and nonetheless exhibits generalization capability. In \cite{7}, Gueguen et al. presented a change detection method for overhead imagery wherein they train a support vector machine (SVM) with linear kernel with L1-regularization and L2-loss as a semi-supervised scene-specific change classifier from manually labeled positive examples (i.e., changes) and plenty of available negative examples (i.e., no-changes). Moreover, it is preferable to use only a single change classifier; however, it is challenging for a single classifier to capture the place-specific variations of changes \cite{5} or to flexibly incorporate the latest local changes in the map \cite{10}. Therefore, we opt to use multiple place-specific change classifiers, each of which is responsible for each specific place region and the place-specific change classifiers can be flexibly and locally updated, added, or deleted at a marginal fixed cost. Each classifier receives local feature-descriptors (e.g., ORB descriptors) as input and evaluates their likelihood of change. However, it is not straightforward to apply the typical framework of change-classifier-learning to our application domain of autonomous robotics, wherein we are not provided examples with labels (i.e., change/no-change). Instead, the mapper-robot itself should collect examples in an unsupervised manner.
}{
}

\SW{
In this study, we address the above issue from the viewpoint of zero-shot learning (ZSL) \cite{11d}. ZSL is a domain-adaptation technique in the field of machine learning and was originally proposed as a strategy to obtain classifiers for arbitrary, novel categories when no labeled example is available. In particular, we are inspired by mining-based ZSL \cite{11}, wherein training examples for novel unseen categories (in the target domain) are collected by mining an external knowledge base (EKB) such as a search engine. To realize our ZSL-based change-classification framework, it is necessary to address the following questions: (1) How does one train classifiers? (2) How does one represent examples? (3) How does one prepare an EKB?
}{
}

\SW{
The key concepts required for answering these questions are as follows:
\begin{enumerate}
\item We train classifiers with the latest examples when necessary to incorporate the latest changes in the map;
\item We use bag-of-words (BoW) as a compact and discriminative representation of examples;
\item We employ a visual word vocabulary as the EKB,
\end{enumerate}
which are inspired by the use of BoW as a compressed form of place-specific classifier in our previous study \cite{23}.
}{
}
\SW{
The above concepts exhibit several advantages: (1) We only have to memorize training examples rather than the classifier themselves because typical classifier-learning algorithms such as SVM \cite{12} and nearest neighbor (NN) \cite{13} allow us to reproduce
a classifier, given the same training examples \cite{23}. (2) We can incorporate the latest changes into the classifiers by simply adding or deleting a few training examples that correspond to these classifiers, while the training-time overhead per place can be reasonably low by using efficient training algorithms such as NN and SVM. (3) We can represent each training example in a significantly compact form of a visual word \cite{23}, implying that the training example is approximated by its nearest neighbor exemplar feature from a visual word vocabulary. (4) We can share the vocabulary with other related task-scenarios including BoW-based self-localization, SLAM, lifelong learning, and open-set recognition, wherein the vocabulary is generally designed as a rich, continuously growing, and domain-adaptive knowledge base. In experiments, the proposed framework is evaluated in a cross-season change detection setting using the publicly available NCLT dataset \cite{1}.
}{
}

\subsection{Relation to Other Works}

\SW{
A majority of the present state-of-the-art change detection systems are based on image differencing or pairwise image comparison \cite{5}.
In \cite{26},
a scene alignment method for image differencing
is proposed based on ground surface reconstruction, texture projection, image rendering, and registration refinement.
In \cite{4}, a deep deconvolutional network for pixel-wise change detection was trained and used for comparing query and reference image patches.

Some recent studies use change-classifier-learning to realize more efficient and compact change detection \cite{7,8,8d,9}. Our algorithm is inspired by these classifier-learning approaches and advances a step further from the perspective of unsupervised ZSL. The work in \cite{7} can be considered as one of the most relevant works to our study. In their work, change detection in overhead imagery is addressed. A BoW model with tree-of-shape features is employed to achieve more effective accuracy-efficiency tradeoff. Based on these features, linear canonical correlation analysis is employed to learn a subspace to encode the notion of change between images. To reduce the cost of label acquisition by human photo-interpreters, a semi-supervised SVM framework is introduced. In contrast, we address unsupervised settings, wherein examples have to be labeled automatically by the robot itself rather than by human labelers.
}{
}

\SW{
Our study focuses on a monocular camera as the sole input device. This is a significantly challenging setting compared with other change detection settings, which assume a more richer information source, including 3D model \cite{14}, stereo or image sequence \cite{6}, 3D data \cite{15d}, multi-spectral overhead imagery \cite{16}, and object model \cite{ren2017faster}.
}{
}

\SW{
In the area of field robotics, there is substantial work on change detection systems for patrolling \cite{17}, agriculture \cite{18}, tunnel inspection \cite{18d}, and damage detection \cite{7}. We consider that our extension would also contribute to these applications from the novel perspective of map compactness.
}{
}

\SW{
This work is a part of our study on compact map model for scalable change detection and long-term map maintenance \cite{acpr17kanji,23,21,icra04kanji,icra15kanji}. In previous studies, two scenarios have been considered. One is the ``global localization" scenario \cite{icra04kanji}, wherein the change detection system is required to work under global viewpoint uncertainty. As this scenario requires access to the entire large-size maps in memory, individual images were used in their compressed form of bag-of-words \cite{acpr17kanji}. In contrast, the current study focuses on the alternative ``pose-tracking" scenario, wherein the system can assume that the robot's viewpoint is tracked over time. A similar setting is addressed in our recent study in \cite{21} with a key variation being that classifiers are not compressed. As the pose-tracking scenario requires access to only a marginal portion of the submaps that correspond to the robot's surroundings, we are allowed to use mapped images in a less-compact form of the decompressed classifiers. Nevertheless, it is necessary to maintain the other classifiers compressed. Hence, the efficiency of compression/decompression is another critical topic, as also discussed in this paper.
}{

}

\section{Approach}

\SW{
Our goal is to enhance the compactness of map representation for scalable change detection. As mentioned, this rules out typical image-differencing approaches that require memorization of a number of high-resolution mapped images proportional to the map size. Therefore, we decide to use the change-classifier approach. More specifically, we employ multiple place-specific change classifiers to capture the place-specific nature of changes and to enable flexible and local update of the change model. The key concept is to represent each classifier by their training examples and compress each training example to a visual word; this enables a significantly compact and domain-adaptive change model.
}{

}

\SW{
Based on the above consideration, we use a set of place-specific change classifiers and represent each by a BoW. Following literature \cite{bow}, a BoW is represented as an unordered collection of visual words, or visual features vector quantized by a codebook of exemplar features called vocabulary.
In our approach, an $i$-th classifier is represented by positive (i.e., change) and negative (i.e., no-change) sets of visual words, $S^+_i$ and $S^-_i$, respectively. Each visual word $w\in S^{\{+,-\}}_i$ is represented in the form:
\begin{equation}
w=
\langle
w^a, w^{r}
\rangle.
\end{equation}
$w^a$ is a $B$-bit code called appearance word and is an identifier for its nearest neighbor local-feature-descriptor in the vocabulary. We employ ORB feature descriptor \cite{orb} as local-feature-descriptor because it is a rapid binary keypoint descriptor that has been applied to numerous real-time computer-vision and robotic-mapping problems \cite{orbslam}. The number of ORB descriptors per image is set to 2000. We denote the appearance-word vocabulary as $V$ (i.e., $B=\log_2 |V|$). $w^{r}$ is called pose word and represents the spatial location of the feature keypoint descriptor with respect to the object region; it incurs a small constant space cost $B'$=$\log_2|A|$ (bits) where $|A|$ is the area [pixels] of the object region. Consequently, the total space-cost (bits) of our BoW-based model approximately sums up to
\begin{equation}
C = \sum_{x\in X} \left[ \sum_{r\in R_x} \left[ \sum_{w\in W_r} \log_2|V| + B' \right] \right],
\end{equation}
where $X$ is the set of place regions, $R_x$ is the object regions
(described in \ref{sec:learning})
in the $x$-th place, and $W_r$ are the visual words belonging to the object $r$.
}{
}

\SW{
Apparently, it is necessary to minimize the sizes of $X$, $R_x$, $W_r$ and $V$ without compromising change-classification accuracy. Joint minimization of these is generally intractable, and in this study, we straightforwardly consider each minimization problem separately. Minimizing $R_x$, $W_r$ and $V$ are the tasks of change-aware object proposal, feature extraction, and vocabulary design. We have also addressed the issue of vocabulary design and the use of BoW as a compressed form of classifiers in an alternative context of visual robot localization in \cite{23}. Minimizing $P$ is called unsupervised place-definition and workspace-partitioning discovery. We have addressed this problem in our previous studies in the context of place recognition \cite{mva17kanji} and change recognition \cite{21}. In this study, we straightforwardly partition the entire sequence of mapped images in the workspace into equal-length subsequences, each of which corresponds to each place class.
}{
}

\noeditage{
\figD
}

\noeditage{
\figF
}

\SW{
Our algorithm consists of two distinct phases: training and classification. The training procedure runs through the following steps: (t1) Train classifiers. (t2) Represent classifiers by their positive/negative examples. (t3) Approximate the examples by BoWs. (t4) Memorize the examples in the form of BoW. The classification procedure runs through the following steps: (c1) Lookup the vocabulary to determine examples that correspond to the specified reference image. (c2) Reproduce classifiers from the examples. (c3) Classify query features using the reproduced classifiers. (c4) Delete classifiers. Note that steps (c2) and (c4) function as decompression and compression of classifiers, respectively (Fig. \ref{fig:D}). The time overhead of the compression task is more or less negligible, while that of the decompression task is marginal albeit noticeable. Therefore, it is necessary to perform task scheduling of decompression task adequately. In this study, we follow a straightforward strategy: When the robot approaches or enters a new submap's region, decompress the classifiers of its surrounding submaps.

Figure \ref{fig:F} demonstrates our strategy. In the figure, the frame IDs ranging from 137 to 237 correspond to the classifiers for the robot's surroundings. As illustrated, the space cost of the compressed classifiers is significantly lower than that of non-compressed or decompressed classifiers, which demonstrates the effectiveness of our approach.
}{
}

\subsection{Classifier-learning}\label{sec:learning}

\SW{
The place-specific change model aims to detect changes at the level of object. It consists of a set of object-level classifiers, each of which aims to learn the appearance of a known object (i.e., no-change object) in the reference image of a specific place and then, to classify an unseen query feature as either change or no-change with respect to the learned object. To this end, object segmentation both at training and at classification tasks significantly influences the performance of the object-level change detection.
}{
}

\noeditage{
\figH
}

\SW{
The training phase begins with the extraction of a collection of object proposals from the specified reference image (Fig. \ref{fig:H}b left). We use BING object-proposal algorithm \cite{bing} to achieve this because it is highly efficient to provide object proposals with category-independent image windows. Then, the proposed object-regions are further grouped into clusters of spatially-near regions, termed object clusters (Fig. \ref{fig:H}b right). We use a criterion in this clustering wherein any two object regions belong to the same cluster if their bounding boxes overlap each other.
Then, an additional ``background" object cluster,
which treats the entire image region as the object region, is added.
Then, a change classifier is trained for each object cluster. We will implement and evaluate two types of classifiers: nearest neighbor, or SVM, and four types of SVM kernel: linear, sigmoid, polynomial, or RBF. The above methods of object proposal and classifier-learning are detailed below.
}{
}

\SW{
The BING algorithm generally produces a large number of object proposals (e.g., 2$\times 10^3$ proposals per image). The proposals generally contain numerous false positives. Evaluating all the proposals is computationally intractable. Therefore, we decide to select a marginal portion of the object proposals. From the viewpoint of effectiveness of our BoW model, we evaluate spatial density of visual words $N/A$, where $A$ and $N$ are the area of the object proposal's bounding box and the number of visual words inside the bounding box, respectively. Then, we select 400 object proposals with highest density. Subsequently, near-duplicate object regions that overlap significantly with object regions with higher density are eliminated. We consider two object regions $i$ and $j$ to be in the near-duplicate condition if their overlap ratio
$A_{ij}/\min(A_i, A_j)$
exceeds a threshold of 0.5; here, $A_i$, $A_j$ and $A_{ij}$ are the areas [pixels] of the objects $i$ and $j$ and their overlap region.
}{
}

\SW{
The change classifier addresses one-sided classification problem, wherein only negative examples (i.e., no-change) are provided during the training stage. In order to obtain a sufficiently large number of positive examples, we consider the task that we call change mining \cite{21}, the aim of which is to mine features of potential change objects from a large feature collection of the EKB. In this study, we consider three strategies for change mining: ``uniform," ``farthest," and ``nearest." The ``uniform" strategy uniformly samples positive examples from the EKB, while the ``farthest" and ``nearest" strategies sample positive examples that are respectively the farthest and nearest in Hamming distance from the specified negative examples. For all the strategies, each example in the EKB that is nearer than 10 bit from its nearest neighbor negative is considered inappropriate as a positive example and eliminated from the candidates of positive examples prior to the change mining.
To enable efficient training, the number of training examples
is truncated to a maximum number 400.
}{
}

\subsection{Change Classification}\label{sec:classification}

\SW{
The change classification process aims to rank local feature-descriptors in query images in the order of likelihood of changes. It consists of two distinct steps: (1) image registration and (2) change ranking. Both the steps are detailed below.
}{

}

\SW{
The image registration step, which aims to align query and reference images into the same coordinate frame (Fig. \ref{fig:H}c), is a necessary pre-processing step for a substantial majority of change detection tasks \cite{5}. Note that image alignment from monocular image-pairs is significantly ill-posed, particularly when we are provided only BoW representations of the images. We have tested three strategies for feature matching---ORB keypoint matching with and without post-verifications using RANSAC \cite{24} and using vector field consensus (VFC) \cite{25}---and observed that these two post-verification strategies (i.e., RANSAC and VFC) are not adequately stable in our scenario of highly-complex scenes. Therefore, we use ORB keypoint matching as a method for image alignment.
}{
}

\SW{
In this study, we follow a fundamental procedure for image registration \cite{26}, which assumes a linear transformation from reference- to query-image coordinate (Fig. \ref{fig:H}c),
although a key difference is that we are given the BoW representation instead of raw feature descriptors. Note that the transformation algorithm requires pairs of feature keypoints matched between query and reference images. In order to filter out outlier matches to the maximum, an image region commonly visible in both images is estimated in the form of bounding box (Fig. \ref{fig:H}a). More formally, keypoints of matched visual words $M$ are sorted in ascending order of $x$- or $y$-coordinates, and we define $\lfloor \delta|M| \rfloor$-th and $\lceil (1-\delta)|M| \rceil$-th elements ($\delta=0.1$) in the sorted lists as the $x$- and $y$-locations of the boundary of the visible regions.
}{

}

\SW{
Two types of additional spatial cues are obtained as a byproduct of the image registration. The first one is global spatial-information for visibility analysis (Fig. \ref{fig:H}a), wherein local features outside the commonly visible region are not considered as change-object candidates. The second is local spatial-information by which each object region (i.e., bounding box) in the reference image is first transformed to the query image's coordinate (Fig. \ref{fig:H}c); following this, the query image's local features outside the transformed bounding box (Fig. \ref{fig:H}d) are not considered as matching candidates of the specific object in the reference image. Taking into account the transformation errors, we expand the transformed bounding box by $\Delta L$ [pixel], wherein the margin $\Delta L$ is currently set to 10\% of the image width.
}{

}

\SW{
The change-ranking step aims to rank query features by the likelihood of change, given the learned place-specific object-level change classifiers. First, each query feature is assigned to the spatially-near object cluster in the corresponding reference image (Fig. \ref{fig:H}d). We assign each query feature to an object cluster if its keypoint is located within one of the object cluster's bounding box. Then, each query feature is input to the assigned object cluster's change classifier, and the classifier outputs the probability $p_r$ of the query feature not originating from the learned object cluster $r$. We employ Platt scaling \cite{27} with five-fold cross-validation to convert an output value of the SVM classifier to the probability estimate or use a Gaussian $[1-\exp(-d^2/\sigma_d^2)]$ to convert an output distance of the nearest neighbor classifier $d$ into a probability value. Given the output probability value $p_c$ from each object cluster $c$, the probability of the query feature of interest being changed is computed by
\begin{equation}
p=\min_{c} p_c.
\end{equation}
}{

}

\noeditage{
\figB
}

\SW{
In order to render the set of top-ranked features more diverse, we introduce the idea of non-maximal suppression and penalize features that are considered similar to a higher-ranked feature. In our view, two features are similar if they belong to the same object cluster. Based on the concept, we extract object clusters from a specified query image and group query features into clusters in a similar manner as in the reference images; then, features in each cluster are ranked in descending order of the probability $p$. Then, the entire set of query features is re-ranked in the ascending order of augmented score $r+(1-p)$, where $r(\in[1, C])$ is the abovementioned intra-cluster rank, $C$ is the size of the largest cluster, and $p(\in [0,1])$ is the original probability-estimate.
}{

}

\SW{
Note that all query features do not always belong to object clusters, and there may be isolated query features that do not belong to any object cluster. We empirically determined that such isolated features are generally less-reliable. Therefore, we assign them a worst intra-cluster rank $r=C+1$.
}{

}

\section{Experiments}

\SW{
We evaluated various change detection strategies using NCLT dataset \cite{1}. NCLT dataset is a long-term autonomy dataset for robotics research collected on the University of Michigan's North Campus. The dataset consists of omnidirectional imagery, 3D lidar, planar lidar, GPS, and odometry, and we use the monocular images from the front-directed camera (``camera \#5") for our change detection tasks. Fig. \ref{fig:B} shows some examples of scene recognition.

During the vehicle's travel through both indoor and outdoor environments (Fig. \ref{fig:C}a), it encounters various types of changes, which originate from the movement of individuals, parking of cars, furniture, building construction, opening/closing of doors, and placement/removal of posters (Fig. \ref{fig:C}b). There are also nuisance changes that originate from illumination alterations, viewpoint-dependent changes of objects' appearances and occlusions, weather variations, and falling leaves and snow. A critical and significant  challenge in a substantial majority of change detection tasks is to discriminate changes of interest from nuisances. This renders our change detection task significantly more demanding.
}{

}

\noeditage{
\figA
}

\SW{
We use four datasets ``2012/1/22," ``2012/3/31," ``2012/8/4," and ``2012/11/7" that correspond to four sessions of vehicle navigation. These datasets consist of 5095, 3994, 4877, and 5118 images of size 1232 $\times$ 1616.
We manually created 7571 pairs of corresponding query and reference images, which correspond to ``revisiting" or ``loop-closing" situations \cite{icra04kanji}, using the global viewpoint information. While 548 of the pairs are change image-pairs, 6714 pairs are no-change image-pairs; the remaining 309 pairs are
not independent of the 548 change pairs and are not used in the experiments. We categorized small changes (e.g., 10$ \times $10 [pixels]) that typically originated from distant objects, into no-change because it is challenging to detect such small changes by visual change detection algorithm. As a result, such small objects are likely to cause pseudo false-positive detection by change detection algorithms. We use specific combinations of query and reference image sets: (query, reference) = (2012/1/22, 2012/3/31), (2012/3/31, 2012/8/4), (2012/8/4, 2012/11/7), or (2012/11/7, 2012/1/22). The vocabulary is sized 1 M (i.e., 20-bit visual words) by default, and its exemplar features are randomly sampled from the reference set's visual features. Change objects in the 548 change image pairs are manually annotated in the form of bounding boxes.
}{
}

\SW{
The average time-overhead for compression and decompression per classifier was 0.3 s and time-overhead per place was 3.0 s using a non-optimized implementation of SVM from SVM light \cite{joachims1999svmlight} on a laptop PC (Intel Core i5-4200 2.5 GHz). This implies that the compression/decompression can function in real-time when the robot moves at the speed of 1 m/s and the workspace is divided into 3-m-length places. The total travel distance was 22,181 m.
}{
}

\SW{
We consider a straightforward change detection task scenario: Given a collection of 100 query images acquired by a robot, identify the image that is the changed one and the locations of the change features within that image. The size of an image collection is set to 100, and it consists of a change image and 99 random no-change images. We created 548 collections based on the above 548 change image pairs, for each of which 99 no-change images are randomly sampled from the 6714 no-change pairs, and the collections are commonly used as dataset by each change detection algorithm.
}{
}

\SW{
We consider various strategies of change mining, classifiers, and image registration. For the change mining, we implemented three methods: ``uniform," ``farthest," and ``nearest," which are described in II-A. For the classifier, we implemented the SVM classifier with four kernels---linear, sigmoid, polynomial, and RBF---and the nearest neighbor classifier with Hamming distance metric. For the image registration, we aim to verify the efficacy of the visibility analysis strategy presented in II-B and implemented the common visible region in the form of bounding box (``horizontal-vertical") as well as two additional methods for visibility analysis. One of these two is a method that omits $y$-direction boundaries, which is motivated by an observation that $y$-direction spatial information is generally unreliable (``horizontal"). The other is a method that completely omits the bounding-box information, which corresponds to not using the visibility analysis strategy (``none").
By default, we use SVM with RBF kernel as the classifier, uniform sampling with 10-bit distance threshold as the change mining strategy, and the 20-bit vocabulary.
}{
}

\SW{
Figures \ref{fig:A}, \ref{fig:Aa}, \ref{fig:Ab}, \ref{fig:Ac} and \ref{fig:Ad} exhibit the performance results. We evaluated the various methods over the independent 548 image collections. Each image collection consists of 100 pairing of query and reference images, and each query image contains 2000 features to score. We assess whether the change detection task on an image collection is successful or not and compute the success ratio over all the image collections. For the assessment, all the $100$$\times$$2,000$$=$$200,000$ features are sorted in the ascending order of the augmented score (described in \ref{sec:classification}), and if a ground-truth change feature is ranked within the top-$X$ (\%) with respect to the sorted list, the change detection is considered as success; otherwise, it is considered as failure. We performed the evaluation for various $X$ values:
0.1\%, 0.25\%, 0.5\%, 1\%, 2.5\%, and 5\%,
which respectively correspond to the top
200, 500, 1000, 2000, 5000, and 10000
ranked query features.
}{
}

\SW{
Figure \ref{fig:A} illustrates results of evaluating the fundamental effects of the proposed strategies---object-level change detection (\ref{sec:learning}) and non-maximal suppression (\ref{sec:classification}). We developed a comparison method termed ``non-object," which employs a single image-level classifier rather than the object-level classifier. The image-level classifier is implemented as a single object-level classifier that treats the entire image as the object region. We also developed another comparison method termed ``non-suppression," which does not employ the non-maximal suppression technique. In the figure, ``object" indicates the proposed method (classifier: SVM w/ RBF kernel, vocabulary size 20 bit), and ``non-object" and ``non-suppression" indicate the two comparison methods. It can be observed that the proposed ``object" method evidently outperforms the other two, and it is verified the object-level change classifiers are substantially more powerful than the comparison feature-level methods.
}{
}

\SW{
Figure \ref{fig:Aa} illustrates the result of comparing classifiers and kernels. We evaluated different classifiers and kernels (nearest neighbor, SVM with linear, sigmoid, polynomial, and RBF kernels). It can be seen that SVM with RBF kernel outperforms the other classifiers and kernels.
}{
}

\noeditage{
\figAa
\figAb
\figAc
\figAd
\figG
}

\SW{
Figure \ref{fig:Ab} shows a result of comparing different change mining strategies.
We evaluated different change mining strategies (uniform with 10-bit, 20-bit, and 30-bit distance threshold, farthest, and nearest). It can be seen that uniform sampling with 10-bit distance threshold outperforms the other methods. The reason might be that the uniform sampling strategy tends to produce a more diverse set of positive examples than other strategies, and thus the resulted classifier exhibits adequate generalization capability.
}{
}

\SW{
Figure \ref{fig:Ac} illustrates the result of evaluating the visibility analysis strategies. We evaluated different visibility analysis strategies (horizontal-vertical, horizontal, and none).
It can be observed that the horizontal-vertical strategy outperforms the other two strategies when the parameter $X$ is sufficiently marginal.
}{
}

\SW{
Figure \ref{fig:G} illustrates examples of successful and failed change detection. Here, we used the abovementioned default settings. As illustrated, changes originating from moving objects such as cars and pedestrians are generally easy to detect owing to the fact that their visual appearances are significantly discriminative from other background objects such as roads and trees. However, the detection of moving objects is rendered a substantially challenging task when a similar moving object appears in the corresponding reference image also (Fig. \ref{fig:G} failure examples). Although spatial information (e.g., location and size) of such similar objects are generally dissimilar to that of the query object, they are ordinarily accepted as matching candidates of the query object because the search region is expanded to overcome coordinate transformation error (as explained in \ref{sec:classification}). Other challenging cases include change objects such as boxes and tables (Fig. \ref{fig:G} failure examples) whose visual appearance are significantly similar to background objects such as floors and walls.
}{
}

\section{Conclusions}

\SW{
We presented a change detection framework that realizes map compactness while maintaining detection efficiency. Rather than memorizing pre-trained classifiers, our ZSL-based approach has to memorize only the compact indexes to their training examples that are mined from EKB. Experimental results on place-specific object-level change classifiers have demonstrated high potential. The proposed algorithm is efficient and very simple to implement. It should therefore be convenient to integrate it into existing frameworks of change detection (e.g., change detection in 3D, stereo images) to enhance their compactness.
}{
}

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


text:
Home Book Reviews One True Way
One True Way
by Shannon Hitchcock
New York: Scholastic Press, 2018
Reviewed by Casey O'Leary
I devour books like food. Reading kept me alive as much as actual food did during my childhood, when I searched for reflections of myself in the pages of my favorite titles. I longed to understand myself and the world around me. I sometimes wonder: If I had known as a young woman that I was lesbian, would I have ever come across an age-appropriate book that accurately reflected my feelings and experiences?
Even the most voracious young reader in the 1980s would have been hard pressed to find a book like One True Way on the shelf. A middle grade book (for a core audience of 8- to 12-year-olds) about a lesbian relationship? The cover even shows two girls holding hands under their school desks!
One True Way is set in North Carolina in the 1970s. Allie is the new kid in seventh grade, having moved from New Jersey after the death of her brother Eric and her parents' separation. On her first day of school, Allie meets Sam (short for Samantha) and the two become friends. Sam is athletic and popular but not a great student, while Allie wants to be a reporter and joins the school paper. After interviewing Sam for her first column, Allie slowly realizes she is attracted to Sam, and eventually Sam admits her attraction to Allie. But their burgeoning relationship is threatened from all sides. Sam comes from a strict, religious family (the book title is also the name of her family church). Allie's mother is more understanding but still objects to the relationship. There's even a boy whose crush on Allie pulls her away from Sam.
Homosexuality is not the only taboo topic openly discussed in One True Way; Christianity plays a central role in the story and its characters, a subject not often discussed in mainstream middle grade fiction. The depictions of people's religious beliefs are quite diverse, reflecting the realities of differing beliefs under the umbrella of Christianity. Sam's mother, for example, portrays the intolerance often associated with religious judgment of homosexuality; she calls a lesbian character an "abomination" and a "pervert" and reports alleged homosexual behavior to church leaders. However, the character Allie's Methodist minister Reverend Walker, female and friends with a local lesbian couple, clearly represents a more open-minded Christian view. Reverend Walker talks openly with Allie about the flaws in Biblical arguments against homosexuality. "First Corinthians 14:35 says it's shameful for a woman to speak in church. Can you imagine? And there are over two hundred verses dealing with slavery, but those verses don't apply to our lives today. Maybe the same could be said for the Bible's stance on homosexuality" (p. 132). Both characters come across as realistic, dimensional human beings rather than caricatures of narrow viewpoints. The result is a gentle portrayal of differing beliefs, a welcome change from more heavy-handed rhetoric.
The supporting characters are also realistic, and I particularly related to Allie's relationship with her mother. Upon hearing her daughter is attracted to another girl, Allie's mother isn't supportive, but she doesn't outright object, saying, "I'm sorry, Allie. I'm usually so tolerant, but somehow it's different when it's your own daughter" (p. 112).
When Allie talks about her fear of hurting her mother by being openly gay, I was struck by the memory of my own fear about hurting my mom by coming out, and balancing that fear with the desire to be completely, wholly myself.
These familiar pangs of understanding reinforce my belief that One True Way is sorely needed, for not just young gay and lesbian readers but for all young readers. As authors and illustrators begin to ask difficult questions about writing from someone else's viewpoint (see Grace Lin's excellent blog post here), Shannon Hitchcock's perspective of same-sex first love might be pushed aside for a book written by an openly lesbian author. But Hitchcock has tried to create work that speaks to gay kids and those who care about them. Hitchcock notes that her manuscript was reviewed by "a minister, a lesbian, a gay man, and the mother of a gay son" (p. 207) prior to publication. The result is a story that appeals to any reader who is interested in a young person's journey to self-awareness through relationships with peers, family, and religious communities.
But the best part of One True Way is the gentle tickle of first love, the moment when two people realize the other feels something too. When Sam and Allie acknowledge their feelings for each other and touch hands, the moment fairly lifts off the page, taking my young, romantic heart with it. One True Way works because it reinforces the universality of the human experience, in religion and in love. Readers are fortunate to have a book like this one.
Casey O'Leary
http://afterthecloset.com
Casey O'Leary is a writer and children's librarian. She is passionate about reading and helping children find the "perfect book." In 2012, she fulfilled a dream by completing her master's degree in library science from Indiana University. Casey blogs at After the Closet and is a frequent contributor to Christian Feminism Today.
OtherWise Christian — A Guidebook for Transgender Liberation
UnUnited: LGBTQ Inclusion in the United Methodist Church
A Lamb's Exodus: Overcoming Religious Fundamentalism, Sexism, Racism, Fatphobia, and Conversion Therapy
Pride Month Interviews with LGBTQ+ Clergy
An Interview with Susan Cottrell
With hundreds of reviews on the Christian Feminism Today website we suggest using the search function above if you are looking for something specific.
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Feeling adventurous? Click here to access the reviews presented in a random order.
Oriented to Love— The Huge Knot of Misunderstanding
Where She Is Blog February 27, 2014


text:
Month: Jun 2018
London: NHS 70th Anniversary March (30.6.218)
This NHS March marked the 70th anniversary of the founding of the NHS by the Labour Party in 1948. It was also a protest registered
Annual Report of the Communist Party of Lugansk & the Communist Union of the Lugansk Region (28.8.2016)
Communist Party of Lugansk – Russian Language Annual Report CPL & CULR- Russian Language (Translated by Adrian Chan-Wyles PhD) The revival of the Communist Party
Lenin Honoured in Lugansk! (23.1.2017)
Communist Party of Lugansk – Russian Language Lenin Honoured in Lugansk! – Russian Language (Translated by Adrian Chan-Wyles PhD) On January 21st , 2017 in
LNR: First Important Announcement of the Communist Party of Lugansk (22.8.2015)
Communist Party of Lugansk – Russian Language LNR: Important Announcement (22.8.2015) – Russian Language (Translated by Adrian Chan-Wyles PhD) On August 22nd , 2015 there
Lugansk: Important Announcement: (15.6.2018)
Communist Party of Lugansk – Russian Language Important Announcement: (15.6.2018) – Russian Language (Translated by Adrian Chan-Wyles PhD) The Lugansk People's Republic (LNR) was visited
Lugansk Communists Deliver Humanitarian Aid to the 1st Defence Line of the Lugansk People's Republic (LNR) [23.6.2018]
…the distinguished fighters of the 16th BTRO of the People's Militia of the People's Liberation Army, received medals celebrating '100 years of the Red Army'.


We have been in the house a few months now and have had lots of requests for pictures…. So, here are a few pictures of the house and the view.
Well….what a week. Yet again a huge amount of progress has been made on the house. The week started off with the heat pipes going in on the main floor. As you can see from the pictures there will not be a cold spot on the whole floor – which is great cos I hate cold feet. Whilst the pipes were being laid on the main floor the second storey floor was being put in – ready to have the heat pipes added the next day. On Wednesday the concrete was poured on the main floor followed by the second floor on Thursday.
Whilst the floor was drying, the contractors responsible for putting in the fireplace came to site and started work. by the end of the week they had half completed it.
On the Friday we were able to stand on the second floor of the house, and oh my, what a view. We can see right over the tops of the trees into the bay of Tatamagouche. We can see the Cobequid mountains out the back and as far as PEI out to the front. By the end of Friday we not only had the floor in on the second floor, but the framing was up for the external walls.
It was only after the floor had been poured and the framing was up that we spotted a little extra part of the house that we had missed on the plan. Next to the master bedroom there was a space that was not utilised. It was about 4 feet wide by 30 feet long. We decided that this would be great for a storage space and so we asked our builder to put a door in the bedroom so we could access the space. Just think of all that wasted space had we not seen this when we did.


text:
How to celebrate Routt County history during National Preservation Month
Eleanor C. Hasenbeck
ehasenbeck@SteamboatPilot.com
Crossan's Market in Yampa in 2014 before rehabilitating the building's exterior and in 2017, after exterior rehabilitation. Over 12 years, the Market has been revitalized into a community hub, serving as the town's visitor center and town hall, among other things.
STEAMBOAT SPRINGS — From the old mine shanties to the whispering pines of Routt National Forest and every historic building in between, local historic preservation organizations hope to celebrate the places that Routt County cherishes for May's National Preservation Month.
Take a photo to participate in This Place Matters
In May, the Steamboat Springs Historic Preservation Commission, Historic Routt County, Main Street Steamboat and the Tread of Pioneers Museum are launching an effort to highlight the places that are meaningful to the community — and worth preserving — as part of the "This Place Matters" campaign.
The social media campaign asks residents to post a photo holding a "This Place Matters" sign at the place that matters most to them, share a few sentences about why it matters and post it to social media with the hash tags #thisplacematters and #steamboatsnaps.
"Whether you love soaking at Strawberry Park, eating, shopping, running into friends in downtown Steamboat, hiking and biking in Routt National Forest or taking scenic drives to scope all the ranches around the county, you're encountering places steeped in history and stories everywhere you go," Historic Routt County Executive Director Emily Katzman wrote in an email to Steamboat Pilot & Today. "These places matter. They define our communities and our way of life. It's important to consider what our towns and county might look and feel like if those places were gone."
Katzman hasn't quite decided what place she'll choose to highlight as her place that matters. Yampa's revitalized Crossan's Market is among her favorite historic buildings, but she might choose one of the mineral springs, which she said has drawn people to the valley for a 100 years — including Katzman herself.
Tread of Pioneers Museum Curator Katie Adams is working to assemble an exhibit that will include community-submitted "This Place Matters" photos.
She hopes to get the property owners' permission to take her "This Place Matters" photo in front of Steamboat's last false front building, which once sat on Lincoln Avenue. It was moved and is now tucked away in an alley in Old Town.
Lincoln Avenue from about Fifth to 11th streets is a National Historic District. According to the National Parks Service, the building that holds Harwigs is the oldest in the district, built to house a drugstore around 1886. During the month of May, new banners on Lincoln Avenue will highlight this district.
How to participate in the "This Place Matters" campaign:
Pick a place that matters. There are no limits or restrictions: anywhere in Routt County.
Download and print a "This Place Matters" sign at the bottom of this webpage or by clicking here.
Snap a photo of yourself with the "This Places Matters" sign at the place that matters to you.
Share a few sentences about why This Place Matters.
Share your photo on social media with the hashtags #thisplacematters and #steamboatsnaps.
That's where Mainstreet Steamboat Executive Director Lisa Popovich is looking to as she chooses her place that matters.
"It's like asking someone who's your favorite child," she said of choosing a place. "I don't know. It might just be downtown in general — the historic district — because I can't name a place that matters more to me than my downtown … It's the heart of our community."
Popovich said it'll be interesting to see what people choose as places that matter.
The end goal "way down the line" is a city ordinance aimed at preserving these places, she added.
"We don't have any mechanism in place in Steamboat Springs to prevent places that we think are so wonderful from disappearing," she said. "For example, someone could potentially come downtown and buy one of our historic buildings and tear it down, even if it's on the national register, because we don't have any laws in place or any ordinance in place to prevent that from happening."
Visit the Tread of Pioneers Museum
The community photos shared as part of the campaign will be incorporated into "This Place Matters: The Economic, Cultural and Environmental Power of Heritage and Place," an exhibit highlighting historic preservation in Routt County at the Tread of Pioneers Museum.
In addition to community photos, the exhibit will feature a discussion space where visitors can weigh in on places that matter to them, a timeline of historic preservation efforts in Steamboat and a by-the-numbers exhibit explaining the value of historic preservation, Adams said. It will also explore local preservation success stories and an exhibit called "This Place Mattered" will be dedicated to unique structures that have been lost due to demolition.
The exhibit will open during the First Friday Art Walk on June 7.
The Museum will re-open on Tuesday, April 30, after spring cleaning and maintenance.
You can learn more about Steamboat's historic buildings and the people who built them anytime by completing the museum's downtown scavenger hunt. Visit the museum to pick up a scavenger hunt form to participate in the site-to-site scavenger hunt or download the form at treadofpioneers.org
Listen to Dana Crawford's presentation about award-winning preservation efforts
Preservationist Dana Crawford will close out National Preservation Month with a free presentation at 5:30 p.m. May 20 at the Chief Theater. Crawford organized investors to establish LoDo's Larimer Square as Denver's first Historic District in the early 1960s. More recently, she helped orchestrate the redevelopment of Denver's Union Station.
To reach Eleanor Hasenbeck, call 970-871-4210, email ehasenbeck@SteamboatPilot.com or follow her on Twitter @elHasenbeck.


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Recent Developments on the Vitra Campus
New entrance and new building
In June 2016, a second entrance to the Campus was inaugurated – along with the Vitra Schaudepot, a new building by Herzog & de Meuron.
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Publication date: 3.6.2016
Image: Mark Niedermann


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text:
PTB may refer to:

Government and politics
 Bermuda Public Transportation Board
 Bessarabian Peasants' Party (), a political party in the Kingdom of Romania
 Brazilian Labour Party (historical) (), dismantled after the 1964 military-led coup d'état
 Brazilian Labour Party (current) (), founded 1981
 Pakistan Tobacco Board
 Party of Labour of Burkina (), a political party in Burkina Faso
 Physikalisch-Technische Bundesanstalt, the national metrology institute of Germany
 Workers' Party of Belgium (), a Belgian political party

Science and medicine
 N-Phenacylthiazolium bromide,  a cross-link breaker
 Permian-Triassic boundary, a mass extinction event
 Phosphotyrosine-binding domain (PTB-domain), a protein domain
 Polypyrimidine tract-binding protein, an RNA-binding protein
 Pulmonary tuberculosis, manifestation of tuberculosis in the lungs

Technology
 Project team builder, a project management simulator
 Public Test Build, of the game Dead by Daylight
 Push to break, electronics push switch
 Packet Too Big, an ICMPv6 message; see IPv6 packet
 Pounds of salt per thousand barrels of crude oil; see Desalter
 .ptb, a file extension used by musical tablature authoring tool Power Tab Editor

Transport
 Pentre-bach railway station, in Wales
 Petit train de banlieue, a passenger train in Senegal
 Petersburg station, an Amtrak station serving Petersburg, Virginia
 Voepass Linhas Aéreas, a Brazilian airline
 PTB Group, in Iran

Other uses
 P.T.B (album), by rapcore group Kingspade
 Placer Theatre Ballet, an American ballet company
 Portland Trail Blazers, an American professional basketball team
 Proto-Tibeto-Burman language
 Proto-Torres-Banks language, a reconstructed language from Vanuatu

See also
 The powers that be (disambiguation)

redpajama_set_name: RedPajamaWikipedia


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text:
Wilhelm Schwarz (* 2. April 1902 in Memmingen; † 25. Januar 1975 ebenda) war ein deutscher Politiker (NSDAP). Er war von 1930 bis 1945 Mitglied des Reichstags.

Leben
Schwarz besuchte das St.-Anna-Gymnasium in Augsburg und anschließend die Universitäten in Erlangen und Tübingen, wo er Rechtswissenschaft studierte. Nach dem Studium war er Referendar sowie Assessor und ab April 1930 als Rechtsanwalt wieder in Memmingen tätig.

1919 war Schwarz dem Deutschvölkischen Schutz- und Trutzbund beigetreten. Von 1921 bis 1923 war er Mitglied des Bundes Oberland, dem Nachfolger des gleichnamigen Freikorps. Eigenen Angaben zufolge will er 1923 "am aktiven Widerstand im besetzten Gebiet" teilgenommen haben. Der SA trat er 1925 bei; am 28. Mai 1926 wurde er Mitglied der Memminger NSDAP-Ortsgruppe (Mitgliedsnummer 37.394). Zwischen 1927 und 1932 war Schwarz der Bezirksleiter der NSDAP für Memmingen. 1928 trat Schwarz dem Bund Nationalsozialistischer Deutscher Juristen (BNSDJ, Mitgliedsnummer 37) bei. Von 1929 bis 1935 war Schwarz Stadtrat in Memmingen; die ersten vier Jahre führte er die dortige NSDAP-Fraktion.

Im September 1930 erhielt er ein Mandat im Reichstag, das er auch nach der Machtübertragung an die Nationalsozialisten bis 1945 im dann funktionslosen Parlament innehatte. Seit Juni 1934 war er Gauamtsleiter und Gauinspekteur im Gau Schwaben und Mitglied der Akademie für deutsches Recht. 1935 wurde er Ratsherr in Memmingen und 1936 NSDAP-Kreisleiter für den Stadt- und Landkreis Memmingen. Bei Kriegsende flüchtete er aus Memmingen und wurde am 26. April 1945 von amerikanischen Truppen in Markt Rettenbach gefangen genommen und anschließend interniert.

Schwarz wurde im Juli 1948 durch das Landgericht Memmingen zu zwei Jahren Gefängnis wegen Zerstörung der Memminger Synagoge verurteilt. Im Februar 1949 wurde er in der Entnazifizierung als Hauptschuldiger eingestuft und unter Anrechnung der politischen Haft durch die Spruchkammer Memmingen zu drei Jahren Arbeitslager verurteilt. Im Juli 1950 folgte die Einstufung als Belasteter durch die Berufungskammer. Schwarz wurde mit einem Verbot zur Bekleidung eines öffentlichen Amtes, einschließlich des Notariats und der Anwaltschaft belegt, welches im August 1958 aufgehoben wurde. Anschließend arbeitete er wieder als Rechtsanwalt in Memmingen. 1960 soll Schwarz an der Aufarbeitung des Schicksals der Memminger Juden in der Zeit des Nationalsozialismus mitgewirkt haben.

Literatur

Weblinks

Einzelnachweise

Person (Memmingen)
Reichstagsabgeordneter (Weimarer Republik)
Reichstagsabgeordneter (Deutsches Reich, 1933–1945)
Rechtsanwalt (Deutschland)
SA-Mitglied
NSDAP-Mitglied
Mitglied im Deutschvölkischen Schutz- und Trutzbund
Mitglied der Akademie für Deutsches Recht
Freikorps-Mitglied
Kreisleiter (NSDAP)
Deutscher
Geboren 1902
Gestorben 1975
Mann


text:
Q: Extracting values from dictionary using list Given a list of values e.g.
list1=[19, 25, 100]

and an index:
i = {'a': {10: 1}, 'b': {100: 3}, 'c': {19: 3, 25: 2, 26: 1, 28: 1, 30: 1}, 'd': {46:4, 100: 1}, 'e': {10: 1, 25: 1, 100: 1}, 'f': {4: 1, 19: 1}

What is the least expensive method (dealing with a much larger scale) with which I can look up a list value within the dict's first values, sum all of the second values of the value pairs belonging to the key it appears in and multiply this by the second value itself? I'd want these values to be collated in a structure like so.
{{19: [24, 2], 25: [16, 3], 100: [3, 3, 5]}}

where 24 = (3 * (3+2+1+1+1)) for example
Thank you.

A: Here is a double loop solution by looping through the keys and dictionaries, and constructing the dictionary on the go:
result = {}
for k in list1:
    result[k] = []
    for v in i.values():
        if k in v:
            result[k].append(v[k] * sum(v.values()))

result
# {19: [24, 2], 25: [16, 3], 100: [9, 3, 5]}

redpajama_set_name: RedPajamaStackExchange


text:
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