text:
The Helm of Midnight
The Five Penalties (Volume 1)
Author: Marina Lostetter
Worst of all, I am afraid. Even with the bracers on, their red garnets and yellow topaz blazing with stolen emotion, I am afraid. Perhaps the gemstones do not help because this is a special fear: fear of disappointing. Fear of known failure. I could go to an Emotioteur and have them extract the fear permanently. But I can't shake my suspicion of the needles—that the prick can take more than the enchanters claim.
The night pressed against the conservatory's windows like a wall of water, humid and thick. Looking out into the blackness, De-Krona felt squeezed. Pressured. As though one careless tap on the pretty green glass would cause it to crack and shatter, allowing the night to rush in, smothering partygoers and servants alike.
But the quartet played a joyful tune, and smiles flashed all around. She let herself sway in time to the music while scanning the room's rear left quadrant for suspicious behavior.
Tonight's celebration was in honor of the Chief Magistrate's Silver Jubilee. He was the head of all security divisions in the city-state of Lutador, from the food inspectors all the way up through the ranks of the Watch, to the Regulators, the Martinets, and, of course, the Marchonian Guard.
Twelve articles of enchantment, each taken into custody during the Chief Magistrate's tenure, and typically hidden away in the city-state's vaults, were on display. De-Krona carefully wove her way through the tall cases. The sampling of contraband included one syringe, five masks, three necklaces, one brooch, one poorly blown glass globe, and one bronze penknife.
All seemingly normal items.
All incredibly dangerous.
Six Regulators had been assigned to the collection's security, and Krona had been excited for the opportunity. Though she was accustomed to field work—three years and counting—she was the newest member of this team, under the direction of Captain De-Lia Hirvath—her older sister (and the reason why she often dropped the familial De in her name).
De-Lia's team was well respected. Often given the most high-profile concerns.
The glass shell sculpted to fit over De-Krona's ear vibrated. "I don't like the look of that man in red" came Tray's voice in her helm. Sucking on an enchanted reverb bead allowed the Regulators to communicate with each other at a distance, and without being overheard. Tray stood nearest the door, observing as each guest showed the doorman their invitation. "This is a party," he continued. "Red. It's … unseemly."
Tan and gray and white and brown swirled around the room in vivacious synchronicity. The crowd was spotted through with figures in black, such as De-Krona, but a lone man in crimson strolled through the throngs of nobility. His mourning suit screamed out for attention like a swath of blood on a white child's knee. The path he cut indicated his goal was the exhibit.
Too conspicuous for trouble, Krona thought. Licking out, she caught the reverb bead where it dangled on a thread near her lips, and drew it under her tongue. "I'll keep a watch on him," she said before spitting the bead out again.
"Call if you need assistance," Royu said from the other side of the massive room. Zhe and Sasha passed each other, patrolling the perimeter.
As he drew nearer to Krona, the man's features resolved. The light brown of his face was made darker by the circles around his eyes and the sunkeness of his cheeks. He was perhaps thirty, thirty-five, and would have been handsome if not for the deep gloom about him. Though his steps found firm footing, his gaze jumped and swirled. His attire and posture were well-kempt. Small feathers lined the plunging neckline of his tunic—they looked like little red teeth at her distance.
De-Krona was sure his mourning attire was not chosen out of jest or disrespect. And he wore no jewelry that she could see—no opals. The grief appeared genuine.
He approached the display case farthest from her, then wove in and out of the tall podiums that held the items at eye level, studying each artifact in detail without really seeing them at all.
"How may I aid you?" Krona asked when he came near, aware that her oversized helm and androgynous uniform often made her a daunting figure to the public (noble or otherwise).
His gaze trailed away from the misshapen globe, falling on the rounded, dark-berry-colored glass of the visor that hid her face from view. The rims of his eyes were as red as his formal coat. "There is no aid a Regulator can give, unless you can turn back time." Far from sounding distant or removed, he instead sounded … hot. Intense, like a furnace. Like a coiled fever ran through him, and with one touch he could set the drapes on fire.
"That is not aid anyone can give. The goddess Time insists on a steady course," she said softly, maintaining her authoritative posture.
"Just as turning up the gas does not make it morning, so turning back the clock's hands does not make it yesterday," he said. Each word flaunted a biting edge.
A servant carrying a tray of champagne flutes twirled by, pausing to offer the man a drink before whisking off again. With the many platters of exotic, high-end foods waving about, Krona wished her sense of smell wasn't so muted by her freshly scrubbed helmet. The aroma of alcohol-washed metal rode above all else.
She appreciated fine food and libations when she could get them, which on her salary wasn't often. The lavishness of the dresses and the decorations at the party, she could leave.
The man refused the drink, and looked as though he wished he'd been offered a soft bed and a long lie-down instead. "If I may be so bold," she said. "Perhaps you would be better off in the lounge where it's quiet, instead of in here with all the—the noise." A duet of violinists had just taken up in the arboretum nook. Their merry tune was not unpleasant, but it wasn't as soothing as a dark room.
He smiled humorlessly to himself. "The Chief Magistrate's speech is soon. I'd never hear the end of it if I took leave."
Ah, a relative of tonight's honoree, then. He did look a touch like the Magistrate. Same nose, similar hair and eyes. "A Monsieur Iyendar, are you?"
He made no indication either way. "Tell me about the brooch. I've never seen an enchanted ruby so big."
Docent was not her favorite role, even though she didn't play it often, but at least he genuinely appeared interested. Easing herself between the pedestals, she waved a black-gloved hand at the case harboring the gold-and-ivory brooch. Nestled at its center was a gem of exceptional clarity, gazing out from behind the protective glass like a judgmental eye. "Ten-point-eight-carat weight, containing point nine five grams of despair."
"Nearly twenty times the legal limit." His eyes darted to her copper bracers, inlaid with state-issued enchanted garnets and topaz.
Yes, they're enchanted as well, she thought. One filled with courage and one with resolve. Nearly every gemstone could be enchanted to hold a specific emotion. "The legal limit for your average emotion, yes. Despair can be removed from a person as a therapy, but it is illegal for enchanters to sell despairstones, as despair is an insidious emotion that can tell a person a multitude of lies. All despairstones are to be turned over to the government for safekeeping as soon as they are made. But this one was inlaid in a brooch. It was created by an Emotioteur specifically as a tool for executing her revenge against the terrorists responsible for the Council Bombings fifteen years ago. The ones who wanted all of the work camps closed. Her mother died in the evacuation—trampled." Krona paused. This didn't seem like a good party story for a grieving man.
"I was still young at the time, but I remember. My father was in the north wing when it happened." He glanced toward the front of the conservatory, where the Magistrate would make his speech. "Please, continue."
"Three men committed suicide while wearing the brooch. The Emotioteur was apprehended before she could transfer it to a fourth victim."
As though suddenly disinterested in jewelry, the man turned away, spinning, like he was drunk. "And this?"
He pointed at one of the more grotesque masks. Asymmetrical, painted in mismatched colors, it was the countenance of a demon, maw wide, fangs long and thick. The jaw was crooked, as though broken, and five horns jutted out at odd angles from one another—two in its forehead, with the other three alternating down the sides of its face. The Teleoteur had been a skilled artisan, no question about that. No matter how many times Krona looked at it, the thing still sent shivers through her core.
"Louis Charbon's Mayhem Mask," she said.
"Oh? Oh? The killer?"
"Lutador's most disturbed mass murderer, yes. The mask's magnitude is unknown—as it is illegal for anyone to ever put it on, even to score it—and its knowledge capacity is of the most sensitive rating: Tenth Tier. No one is quite sure how the death mask was even enchanted, given that the Teleoteur who is credited with its creation never had access to Charbon's body after he was hanged. Indeed, the enchanter was in an asylum at the time."
"And what knowledge does the mask preserve? How to kill?"
Krona had been called to a fair few scenes of violence in her three years as a Regulator. Crimes of passion often took place around enchanted items—usually arguments over ownership rather than any sort of magically aided conflict—and in her opinion it took little skill to kill. "No, not exactly. You see, it was Charbon's knowledge of anatomy that allowed him to dissect and rearrange the bodies as he did. His will to kill might very well be engrained in his echo, but it was his intricate knowledge of the internal workings of a human, and his capacity to dismember a body just so, that Eric Matisse preserved."
The man nodded, as though considering this. He hemmed and hawed for a few more moments, then blurted, "And which of these baubles would you say Magistrate Iyendar is the most proud of?"
Perhaps an innocent question. Perhaps not. "I don't know him personally," she said carefully.
The man took a small, sliding step back toward the brooch. New tears brimmed at the edges of his eyes, and he used his sleeve to dab at his nose. "Surly it's not the ruby," he said, jaw clenched. "The feelings of others never did interest him." Trembling fingers clenched and unclenched. "Tell me, Regulator, where would you be the evening after your granddaughter's passing? At a gala?" He reached for the glass.
"Monsieur, please move back." She put a warning hand on the hilt of her saber, simultaneously sucking in the reverb bead. "I may have a problem," she murmured.
"Understood," chimed three replies in her ear.
Monsieur Iyendar the younger—for now she was sure—did not touch the case, but turned on her. "Where would you be?" he demanded. "Would you make your son leave his child's side? Would you make him endure pleasantries for the sake of face?"
Krona's chest tightened, but it was not her place to inquire after—or, save the Five, make judgments about—the Chief Magistrate's family. "Monsieur—"
With a strangled sob, he skipped backward, toward another pillar. "He likes the masks best. I know he does. This one with the fish, what's so dangerous about it?"
She caught sight of Tray moving in from the front, and Sasha walking stiffly from the other side of the collection, both dodging potted palms and guests alike.
"The mask belonged to Lord Birron. He was very skilled in opiate refinery," she said placatingly, taking Iyendar by the elbow. "Please, monsieur. Come away with me. To that seat just over there."
He threw her off. "No. If these trinkets mean more to him than my daughter, I shall examine them until I am content." His palms smacked against the case, leaving sweaty smears.
Wishing the others would move faster, she unsheathed her saber. "You know I am fully within the order of the law to remove you."
"So remove me!" he shouted.
Faces snapped in their direction, carrying various expressions from irritation to interest.
"Do you want to cause a scene?"
"A scene?" he scoffed, feigning scandalization. "I resent the idea that I would in any way desire to disrupt the Magistrate's perfect evening."
"How may we be of aid?" Tray asked as he and Sasha drew up on the opposite side of the pillar.
In contrast to the frills, alternating high-low collars, and soft lines of the attendees' clothing, Regulator uniforms were simple, yet imposing. The three of them looked like black pieces from an artisan chess board. Tall, wide helms—roomy enough to accommodate an enchanted mask beneath, though one wasn't always worn—spanned shoulder to shoulder, making the Regulators look like neckless, faceless, multihorned beasts. Long leather coats gave them strong, box-like proportions, and many Regulators, like Krona, chose to bind their chests beneath. The coat topped a pair of umanori, which made for easy movement and encompassed knee-high boots with thick-heeled soles.
Copyright © 2021 by Little Lost Stories LLC
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Hannibal meets Mistborn in Marina Lostetter's THE HELM OF MIDNIGHT, the dark and stunning first novel in a new trilogy that combines the intricate worldbuilding and rigorous...
Hannibal meets Mistborn in Marina Lostetter's THE HELM OF MIDNIGHT, the dark and stunning first novel in a new trilogy that combines the intricate worldbuilding and rigorous magic system of the best of epic fantasy with a dark and chilling thriller.
In a daring and deadly heist, thieves have made away with an artifact of terrible power—the death mask of Louis Charbon. Made by a master craftsman, it is imbued with the spirit of a monster from history, a serial murderer who terrorized the city.
Now Charbon is loose once more, killing from beyond the grave. But these murders are different from before, not simply random but the work of a deliberate mind probing for answers to a sinister question.
It is up to Krona Hirvath and her fellow Regulators to enter the mind of madness to stop this insatiable killer while facing the terrible truths left in his wake.
Praise for The Helm of Midnight
"Marina Lostetter brings together a cast of relatable, remarkably human characters across three separate timelines to tell a beautiful story of struggle, loss and, eventually, triumph... Tears, smiles and surprise await any reader that opens this book."—Bookpage, starred review
"The Helm of Midnight transports readers to an intensely unique and creative world, with interwoven secrets and heart-pounding action. Bloody, ambitious, and absolutely riveting."—Andrea Stewart, author of The Bone Shard Daughter
"A mysterious and mind-ripping journey through the mystic depths of time and the darkness of the human psyche." —Matt Wallace, author of the Sin du Jour and Savage Rebellion series
"Beautiful and vicious, The Helm of Midnight will snare you with its intriguing mystery and then enthrall you with its rich characters and inventive world."—Megan E. O'Keefe, author of Velocity Weapon
"An utterly enthralling mystery of magic, masks, and murder. Marina Lostetter weaves together three stories to a stunning conclusion with a skill that will leave you breathless."—K. B. Wagers, author of the Farian War series
"A tense and atmospheric mystery in an inventive fantasy setting, Lostetter weaves three stories of trauma and grief together with skill and suspense to a thrilling conclusion."—Sam Hawke, author of City of Lies
"A firecracker.... What makes this book a captivating read is not merely the tight prose and exquisite use of language to create tension, but the master craft level of world building that offers much more to the fantasy genre than the usual fare. Don't pass this one by, it's too good."—New York Journal of Books
"This tale has creative worldbuilding and an intense focus on the psychology of a killer."—Library Journal
Praise for Noumenon:
"Lostetter expertly balances the thrill of discovery with the interpersonal consequences of an isolated community. The tools of speculative fiction are deployed with heart-rending attention to emotional reality in this enthralling odyssey. A striking adventure story that could hold a galaxy in its scope." —Kirkus Reviews, starred review
"[An] ambitious and stunning debut...Lostetter handles a complex and fractured narrative masterfully, never allowing her novel to become confusing or unconvincing. There are no easy answers to the book's questions, but the lingering sense of wonder and discovery thoroughly justifies its title." —Publishers Weekly, starred review
"Noumenon...offers a dreamlike exploration of evolving societies and the many ways humans can control, fight and love each other." —The Washington Post


Last week one of my site's got hacked and I got a huge negative SEO attack. The hack itself was primarily my fault as it was running an out of date WordPress theme from 2008. The theme developer no longer supports it and I haven't had the time to migrate the site to a new theme. The hacker managed to insert some PHP spamming "viagra" everywhere. They also managed to hack a network of other sites, 204 domains to be exact all with "viagra" terms in the anchor text against my domain (thousands of backlinks).
Good thing I use Web CEO and am constantly checking my backlink profile (they pull their backlink data straight from Majestic) otherwise I would have never caught it. With Penguin 3 about to hit, I need my sites to be as clean as possible. Follow my tutorial below using Web CEO to see how I cleaned up my negative SEO attack and backlink profile.
I am a huge fan of Web CEO as an all around SEO software suite. I highly suggest reading my Web CEO All-In-One SEO Software Suite Review if you aren't familiar with their product. I switched to it from Raven Tools and never looked back.
First in Web CEO we are going to click into the backlink area (link sources distribution). As you can see my linking pages jumped up to a whopping 237k! Not good as over half was from the negative SEO attack. At first you might feel overwhelmed. But don't worry, this is where a tool like Web CEO really comes in handy. My linking domains also jumped way up, by an additional 204 (Please note I rescanned a couple times before taking these screenshots so the data might be a tad off).
Web CEO gives us a quick an easy way to see a variety of different aspects of our backlinks. The first is they allow us to see how the link text is distributed. As you can see my backlink profile was suddenly spammed with all sorts of viagra related terms (sorted by highest to lowest totals). By link text it means the text that is used in the hyperlink itself on the link from the other website.
You can always expand the link text to see the domains in question.
Web CEO also allows us to see all of the linking domains on a completely unique view. These are all the domains that contain backlinks to my site. My domain is located in the US and is targeting the US, so to have any backlinks from foreign domains is usually a red flag. You can always expand the domain itself and it will show you a list of the text link being used with a link to the actual backlink.
So for smaller or general cleanup I would normally use the "toxic link" feature in Web CEO paired with their linking pages view. It is awesome. You can go through your backlink profile and on the right hand side mark the link as toxic. You can see below the URL that the backlink resides, the link text, and the target page (which is the page on my website it is hitting). They also let you see the Google PR of the linking domain. Web CEO has an amazing setting where you can set the "toxicity settings" based upon number of internal links, external links, and a number of other factors. This allows it to automatically scan to detect "suspected toxic" links. It actually works very well. However in the end I always touch everything myself.
The reason I would mark each one individually is because Web CEO can then track the history of each backlink as reported to Google as disavowed. As you can see on the right hand side it timestamps it with a date. This is great for long term cleanup and tracking. However in this case because my negative SEO attack was so huge I had to go with the other option.
In this case I had thousands of bad backlinks against me with 204 bad domains. So in this instance I don't have time to individually mark each bad backlink as toxic, so I actually chose to disavow the entire domain. So in the linking domain view I go to the top right and export all the domains as a CSV after sorting through it to leave the remaining good domains.
Now that I have my CSV I actually need to append something on each line. To disavow an entire domain Google requires that you have "domain:" prefixed to each URL. So in Excel you can use the following formula to quickly add this to all the domains.
Cleaning up a couple thousand bad backlinks with over 200 domains only took about 45 minutes! And now I can sleep at night.
The funny thing is, some of these domains were for actual legitimate businesses. I looked through a couple of them. They had actually been hacked as well and then the bad backlink targeted/pointed against my domain. Am I going to contact all of these business owners? Unfortunately I don't have time, but I urge anyone who reads this to check your backlink profiles! If you don't know how, hire someone who does. If you value your site, then you should protect your hard work.
Don't forget to check out my in-depth review on Web CEO.
That's indeed a great stuff put-forth by you @Brian!! You made eveyone to understand how we can clean bad links by following your steps!!
Thanks Parker! Glad it was helpful.
Brian, one of my websites got hit by penguin 2.0 last year and I tried to best to get it back.
We re-wrote almost 300 article (as those were over SEO optimized), removed many backlinks manually, uploaded file for disavow but still that site is not getting recovered.
I am going to try your suggestion and see if this helps.
Thanks for introducing Web CEO.
Thanks for your comment! In my experience what I have seen is usually people clean up their backlink profiles partially and miss some bad links and usually when trying to recover Google wants to see that everything has been cleaned up 100%. Are you positive you cleaned up and disavowed everything? Also, from the chatter over Thanksgiving… it appears that Penguin might still be rolling out. So I wouldn't give up hope yet.
If Google looks natural on changes to the site, then it was free backlinks. I am not guilty because someone sends spam links to my site. Why can check backlinks charged and again there is no guarantee that it is 100% pure?
Is there an option to check bad links in some other way? Tnx !
Great lessons to protect one's website against negative SEO and with Web CEO you can quickly recover from Google pengium. This guide is very useful and I will implement same for some of my clients' websites that get natural spammy backlinks from web directories. Thanks for the great stuff.


Click on play button to listen high quality(320 Kbps) mp3 of "Cutting Crew - (I Just) Died in Your Arms". This audio song duration is 4:28 minutes. This song is sing by Cutting Crew. You can download Cutting Crew - (I Just) Died in Your Arms high quality audio by clicking on "Download Mp3" button.


I would like to request all information relating to 999 emergency calls made to your force over the past 24 months (June 2013 to June 2015, or as near to those dates as possible) that concern any kind of paranormal or supernatural activity.
This would include any reference to ghosts, UFOs, vampires, ghouls, zombies and any other kind of supernatural being or spirit. To be clear, I am chief writer at FHM and this is for an article I am putting together about the paranormal.
I should prefer to receive these in electronic format at this email address, however, I am happy to receive them by post if that is more convenient (address below).
It also would be helpful if you could provide any brief notes which might be necessary to understand the context of the information provided, although I recognise that you are not obliged to do this.
If the information requested contains sections of confidential information, please blank out or remove those sections, and mark clearly that they have been removed.


As long as their not 3D I don't mind. I always have the orginals to play if I don't like the remakes. I can't stand how all games are 3D these days. I enjoy my rpgs colorful and 2D. So I guess I'm old fashion.
I agree in the fact that 2D colourful RPG's are amazing, though the thing with that is reviewers. If a PS2 RPG were to be released in full 2D, that would sink its review due to dated graphics.
2D not necesarily = dated. If you think about it, we've really only recently figured out how to use 2D to its full potential. With today's generation of systems, we could have a mind boggling number of sprites on screen and tons of effects. Think of it this way, what if they remade FFVI, but fully animated EVERYTHING. Every enemy with upwards of 150 frames of animation (standing position, moving over to attack, attacking, using special skill, casting spells, getting hit, dying, etc.) Imagine even MORE frames of animation than has ever been used in a sprite based game. Imagine animated background environments (we still don't get a whole lot of that in current FFs). Imagine spells so beautifully animated that you can actually stand watching them be cast over and over. Imagine enemy bosses giving speeches while you fight them, and actually watching them move while they talk. You see, it's not 2D that makes the games look "dated", it's the fact that they don't make very good use of the hardware's potential.
If I were rich I would try to buy every copy of FFVI that I could. Then buy some TV time so the country can watch me burn the games.
Only if they do something like DJzen said. 3D is awful if it's only for graphics, and not actually taking advantage of the 3D-ness.
Ug-grade FF7 certainly, but FF1-6 (Including MQ) should be updated for awesome 2D graphics with 3D battles and FMV sequences. Now THAT would be cool.
Not to mention release Tactics in NZ.
I think remakes like Origins are pretty nice... when done tastefully I like it.
However a lot of your graphic junkies can't do without the FMVs ever 5 seconds and the super 3D environments. It's pretty sad. There are some great games before FFs hit PSX.
Now DJZen has a point... with the engine of the PS2 one could remake the 2D games with incredible animations. It's honestly something I've not seen. Right now it seems like cel-shading is the big thing to try. It's nice and different but how about the idea of sprite based graphics with the incredible processing power to make them come alive?
I don't think game play should be taking a backseat to graphics as it has in some of the past few games.


text:
package org.camunda.bpm.cycle.configuration;

import org.camunda.bpm.security.SecurityConfiguration;
import org.springframework.stereotype.Component;

/**
 * Application configuration component
 *
 * @author nico.rehwaldt
 * @author Daniel Meyer
 */
@Component
public class CycleConfiguration extends SecurityConfiguration {

  private String mailSessionName;
  private String emailFrom = "cycle@localhost";
  private String defaultCommitMessage = "Changed using camunda cycle";

  /**
   * The mail session name is used for looking up a mail session in JNDI
   *
   * @return the JNDI name for looking up a mail session
   */
  public String getMailSessionName() {
    return mailSessionName;
  }

  public void setMailSessionName(String mailSessionUrl) {
    this.mailSessionName = mailSessionUrl;
  }

  /**
   * The name that is used by cycle to send emails.
   */
  public String getEmailFrom() {
    return emailFrom;
  }

  public void setEmailFrom(String emailFrom) {
    this.emailFrom = emailFrom;
  }

  public String getDefaultCommitMessage() {
    return defaultCommitMessage;
  }

  public void setDefaultCommitMessage(String defaultCommitMessage) {
    this.defaultCommitMessage = defaultCommitMessage;
  }

}

redpajama_set_name: RedPajamaGithub


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However you choose to get involved, it is totally at your pace… and you will receive contextually appropriate information – in text, audio, or video formats – that will help educate and guide you on your personal path to healing and empowerment.
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text:
"""R-FCN meta-architecture definition.

R-FCN: Dai, Jifeng, et al. "R-FCN: Object Detection via Region-based
Fully Convolutional Networks." arXiv preprint arXiv:1605.06409 (2016).

The R-FCN meta architecture is similar to Faster R-CNN and only differs in the
second stage. Hence this class inherits FasterRCNNMetaArch and overrides only
the `_predict_second_stage` method.

Similar to Faster R-CNN we allow for two modes: number_of_stages=1 and
number_of_stages=2.  In the former setting, all of the user facing methods
(e.g., predict, postprocess, loss) can be used as if the model consisted
only of the RPN, returning class agnostic proposals (these can be thought of as
approximate detections with no associated class information).  In the latter
setting, proposals are computed, then passed through a second stage
"box classifier" to yield (multi-class) detections.

Implementations of R-FCN models must define a new FasterRCNNFeatureExtractor and
override three methods: `preprocess`, `_extract_proposal_features` (the first
stage of the model), and `_extract_box_classifier_features` (the second stage of
the model). Optionally, the `restore_fn` method can be overridden.  See tests
for an example.

See notes in the documentation of Faster R-CNN meta-architecture as they all
apply here.
"""
import tensorflow.compat.v1 as tf

from object_detection.core import box_predictor
from object_detection.meta_architectures import faster_rcnn_meta_arch
from object_detection.utils import ops

class RFCNMetaArch(faster_rcnn_meta_arch.FasterRCNNMetaArch):
  """R-FCN Meta-architecture definition."""

  def __init__(self,
               is_training,
               num_classes,
               image_resizer_fn,
               feature_extractor,
               number_of_stages,
               first_stage_anchor_generator,
               first_stage_target_assigner,
               first_stage_atrous_rate,
               first_stage_box_predictor_arg_scope_fn,
               first_stage_box_predictor_kernel_size,
               first_stage_box_predictor_depth,
               first_stage_minibatch_size,
               first_stage_sampler,
               first_stage_non_max_suppression_fn,
               first_stage_max_proposals,
               first_stage_localization_loss_weight,
               first_stage_objectness_loss_weight,
               crop_and_resize_fn,
               second_stage_target_assigner,
               second_stage_rfcn_box_predictor,
               second_stage_batch_size,
               second_stage_sampler,
               second_stage_non_max_suppression_fn,
               second_stage_score_conversion_fn,
               second_stage_localization_loss_weight,
               second_stage_classification_loss_weight,
               second_stage_classification_loss,
               hard_example_miner,
               parallel_iterations=16,
               add_summaries=True,
               clip_anchors_to_image=False,
               use_static_shapes=False,
               resize_masks=False,
               freeze_batchnorm=False,
               return_raw_detections_during_predict=False,
               output_final_box_features=False):
    """RFCNMetaArch Constructor.

    Args:
      is_training: A boolean indicating whether the training version of the
        computation graph should be constructed.
      num_classes: Number of classes.  Note that num_classes *does not*
        include the background category, so if groundtruth labels take values
        in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the
        assigned classification targets can range from {0,... K}).
      image_resizer_fn: A callable for image resizing.  This callable always
        takes a rank-3 image tensor (corresponding to a single image) and
        returns a rank-3 image tensor, possibly with new spatial dimensions.
        See builders/image_resizer_builder.py.
      feature_extractor: A FasterRCNNFeatureExtractor object.
      number_of_stages:  Valid values are {1, 2}. If 1 will only construct the
        Region Proposal Network (RPN) part of the model.
      first_stage_anchor_generator: An anchor_generator.AnchorGenerator object
        (note that currently we only support
        grid_anchor_generator.GridAnchorGenerator objects)
      first_stage_target_assigner: Target assigner to use for first stage of
        R-FCN (RPN).
      first_stage_atrous_rate: A single integer indicating the atrous rate for
        the single convolution op which is applied to the `rpn_features_to_crop`
        tensor to obtain a tensor to be used for box prediction. Some feature
        extractors optionally allow for producing feature maps computed at
        denser resolutions.  The atrous rate is used to compensate for the
        denser feature maps by using an effectively larger receptive field.
        (This should typically be set to 1).
      first_stage_box_predictor_arg_scope_fn: Either a
        Keras layer hyperparams object or a function to construct tf-slim
        arg_scope for conv2d, separable_conv2d and fully_connected ops. Used
        for the RPN box predictor. If it is a keras hyperparams object the
        RPN box predictor will be a Keras model. If it is a function to
        construct an arg scope it will be a tf-slim box predictor.
      first_stage_box_predictor_kernel_size: Kernel size to use for the
        convolution op just prior to RPN box predictions.
      first_stage_box_predictor_depth: Output depth for the convolution op
        just prior to RPN box predictions.
      first_stage_minibatch_size: The "batch size" to use for computing the
        objectness and location loss of the region proposal network. This
        "batch size" refers to the number of anchors selected as contributing
        to the loss function for any given image within the image batch and is
        only called "batch_size" due to terminology from the Faster R-CNN paper.
      first_stage_sampler: The sampler for the boxes used to calculate the RPN
        loss after the first stage.
      first_stage_non_max_suppression_fn: batch_multiclass_non_max_suppression
        callable that takes `boxes`, `scores` and optional `clip_window`(with
        all other inputs already set) and returns a dictionary containing
        tensors with keys: `detection_boxes`, `detection_scores`,
        `detection_classes`, `num_detections`. This is used to perform non max
        suppression  on the boxes predicted by the Region Proposal Network
        (RPN).
        See `post_processing.batch_multiclass_non_max_suppression` for the type
        and shape of these tensors.
      first_stage_max_proposals: Maximum number of boxes to retain after
        performing Non-Max Suppression (NMS) on the boxes predicted by the
        Region Proposal Network (RPN).
      first_stage_localization_loss_weight: A float
      first_stage_objectness_loss_weight: A float
      crop_and_resize_fn: A differentiable resampler to use for cropping RPN
        proposal features.
      second_stage_target_assigner: Target assigner to use for second stage of
        R-FCN. If the model is configured with multiple prediction heads, this
        target assigner is used to generate targets for all heads (with the
        correct `unmatched_class_label`).
      second_stage_rfcn_box_predictor: RFCN box predictor to use for
        second stage.
      second_stage_batch_size: The batch size used for computing the
        classification and refined location loss of the box classifier.  This
        "batch size" refers to the number of proposals selected as contributing
        to the loss function for any given image within the image batch and is
        only called "batch_size" due to terminology from the Faster R-CNN paper.
      second_stage_sampler: The sampler for the boxes used for second stage
        box classifier.
      second_stage_non_max_suppression_fn: batch_multiclass_non_max_suppression
        callable that takes `boxes`, `scores`, optional `clip_window` and
        optional (kwarg) `mask` inputs (with all other inputs already set)
        and returns a dictionary containing tensors with keys:
        `detection_boxes`, `detection_scores`, `detection_classes`,
        `num_detections`, and (optionally) `detection_masks`. See
        `post_processing.batch_multiclass_non_max_suppression` for the type and
        shape of these tensors.
      second_stage_score_conversion_fn: Callable elementwise nonlinearity
        (that takes tensors as inputs and returns tensors).  This is usually
        used to convert logits to probabilities.
      second_stage_localization_loss_weight: A float
      second_stage_classification_loss_weight: A float
      second_stage_classification_loss: A string indicating which loss function
        to use, supports 'softmax' and 'sigmoid'.
      hard_example_miner:  A losses.HardExampleMiner object (can be None).
      parallel_iterations: (Optional) The number of iterations allowed to run
        in parallel for calls to tf.map_fn.
      add_summaries: boolean (default: True) controlling whether summary ops
        should be added to tensorflow graph.
      clip_anchors_to_image: The anchors generated are clip to the
        window size without filtering the nonoverlapping anchors. This generates
        a static number of anchors. This argument is unused.
      use_static_shapes: If True, uses implementation of ops with static shape
        guarantees.
      resize_masks: Indicates whether the masks presend in the groundtruth
        should be resized in the model with `image_resizer_fn`
      freeze_batchnorm: Whether to freeze batch norm parameters during
        training or not. When training with a small batch size (e.g. 1), it is
        desirable to freeze batch norm update and use pretrained batch norm
        params.
      return_raw_detections_during_predict: Whether to return raw detection
        boxes in the predict() method. These are decoded boxes that have not
        been through postprocessing (i.e. NMS). Default False.
      output_final_box_features: Whether to output final box features. If true,
        it crops the feauture map based on the final box prediction and returns
        in the dict as detection_features.

    Raises:
      ValueError: If `second_stage_batch_size` > `first_stage_max_proposals`
      ValueError: If first_stage_anchor_generator is not of type
        grid_anchor_generator.GridAnchorGenerator.
    """
    # TODO(rathodv): add_summaries and crop_and_resize_fn is currently
    # unused. Respect that directive in the future.
    super(RFCNMetaArch, self).__init__(
        is_training,
        num_classes,
        image_resizer_fn,
        feature_extractor,
        number_of_stages,
        first_stage_anchor_generator,
        first_stage_target_assigner,
        first_stage_atrous_rate,
        first_stage_box_predictor_arg_scope_fn,
        first_stage_box_predictor_kernel_size,
        first_stage_box_predictor_depth,
        first_stage_minibatch_size,
        first_stage_sampler,
        first_stage_non_max_suppression_fn,
        first_stage_max_proposals,
        first_stage_localization_loss_weight,
        first_stage_objectness_loss_weight,
        crop_and_resize_fn,
        None,  # initial_crop_size is not used in R-FCN
        None,  # maxpool_kernel_size is not use in R-FCN
        None,  # maxpool_stride is not use in R-FCN
        second_stage_target_assigner,
        None,  # fully_connected_box_predictor is not used in R-FCN.
        second_stage_batch_size,
        second_stage_sampler,
        second_stage_non_max_suppression_fn,
        second_stage_score_conversion_fn,
        second_stage_localization_loss_weight,
        second_stage_classification_loss_weight,
        second_stage_classification_loss,
        1.0,  # second stage mask prediction loss weight isn't used in R-FCN.
        hard_example_miner,
        parallel_iterations,
        add_summaries,
        clip_anchors_to_image,
        use_static_shapes,
        resize_masks,
        freeze_batchnorm=freeze_batchnorm,
        return_raw_detections_during_predict=(
            return_raw_detections_during_predict),
        output_final_box_features=output_final_box_features)

    self._rfcn_box_predictor = second_stage_rfcn_box_predictor

  def _predict_second_stage(self, rpn_box_encodings,
                            rpn_objectness_predictions_with_background,
                            rpn_features,
                            anchors,
                            image_shape,
                            true_image_shapes):
    """Predicts the output tensors from 2nd stage of R-FCN.

    Args:
      rpn_box_encodings: 3-D float tensor of shape
        [batch_size, num_valid_anchors, self._box_coder.code_size] containing
        predicted boxes.
      rpn_objectness_predictions_with_background: 3-D float tensor of shape
        [batch_size, num_valid_anchors, 2] containing class
        predictions (logits) for each of the anchors.  Note that this
        tensor *includes* background class predictions (at class index 0).
      rpn_features: A 4-D float32 tensor with shape
        [batch_size, height, width, depth] representing image features from the
        RPN.
      anchors: 2-D float tensor of shape
        [num_anchors, self._box_coder.code_size].
      image_shape: A 1D int32 tensors of size [4] containing the image shape.
      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
        of the form [height, width, channels] indicating the shapes
        of true images in the resized images, as resized images can be padded
        with zeros.

    Returns:
      prediction_dict: a dictionary holding "raw" prediction tensors:
        1) refined_box_encodings: a 3-D tensor with shape
          [total_num_proposals, num_classes, 4] representing predicted
          (final) refined box encodings, where
          total_num_proposals=batch_size*self._max_num_proposals
        2) class_predictions_with_background: a 2-D tensor with shape
          [total_num_proposals, num_classes + 1] containing class
          predictions (logits) for each of the anchors, where
          total_num_proposals=batch_size*self._max_num_proposals.
          Note that this tensor *includes* background class predictions
          (at class index 0).
        3) num_proposals: An int32 tensor of shape [batch_size] representing the
          number of proposals generated by the RPN. `num_proposals` allows us
          to keep track of which entries are to be treated as zero paddings and
          which are not since we always pad the number of proposals to be
          `self.max_num_proposals` for each image.
        4) proposal_boxes: A float32 tensor of shape
          [batch_size, self.max_num_proposals, 4] representing
          decoded proposal bounding boxes (in absolute coordinates).
        5) proposal_boxes_normalized: A float32 tensor of shape
          [batch_size, self.max_num_proposals, 4] representing decoded proposal
          bounding boxes (in normalized coordinates). Can be used to override
          the boxes proposed by the RPN, thus enabling one to extract box
          classification and prediction for externally selected areas of the
          image.
        6) box_classifier_features: a 4-D float32 tensor, of shape
          [batch_size, feature_map_height, feature_map_width, depth],
          representing the box classifier features.
    """
    image_shape_2d = tf.tile(tf.expand_dims(image_shape[1:], 0),
                             [image_shape[0], 1])
    (proposal_boxes_normalized, _, _, num_proposals, _,
     _) = self._postprocess_rpn(rpn_box_encodings,
                                rpn_objectness_predictions_with_background,
                                anchors, image_shape_2d, true_image_shapes)

    box_classifier_features = (
        self._extract_box_classifier_features(rpn_features))

    if self._rfcn_box_predictor.is_keras_model:
      box_predictions = self._rfcn_box_predictor(
          [box_classifier_features],
          proposal_boxes=proposal_boxes_normalized)
    else:
      box_predictions = self._rfcn_box_predictor.predict(
          [box_classifier_features],
          num_predictions_per_location=[1],
          scope=self.second_stage_box_predictor_scope,
          proposal_boxes=proposal_boxes_normalized)
    refined_box_encodings = tf.squeeze(
        tf.concat(box_predictions[box_predictor.BOX_ENCODINGS], axis=1), axis=1)
    class_predictions_with_background = tf.squeeze(
        tf.concat(
            box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
            axis=1),
        axis=1)

    absolute_proposal_boxes = ops.normalized_to_image_coordinates(
        proposal_boxes_normalized, image_shape,
        parallel_iterations=self._parallel_iterations)

    prediction_dict = {
        'refined_box_encodings': refined_box_encodings,
        'class_predictions_with_background':
        class_predictions_with_background,
        'num_proposals': num_proposals,
        'proposal_boxes': absolute_proposal_boxes,
        'box_classifier_features': box_classifier_features,
        'proposal_boxes_normalized': proposal_boxes_normalized,
        'final_anchors': absolute_proposal_boxes
    }
    if self._return_raw_detections_during_predict:
      prediction_dict.update(self._raw_detections_and_feature_map_inds(
          refined_box_encodings, absolute_proposal_boxes))
    return prediction_dict

  def regularization_losses(self):
    """Returns a list of regularization losses for this model.

    Returns a list of regularization losses for this model that the estimator
    needs to use during training/optimization.

    Returns:
      A list of regularization loss tensors.
    """
    reg_losses = super(RFCNMetaArch, self).regularization_losses()
    if self._rfcn_box_predictor.is_keras_model:
      reg_losses.extend(self._rfcn_box_predictor.losses)
    return reg_losses

  def updates(self):
    """Returns a list of update operators for this model.

    Returns a list of update operators for this model that must be executed at
    each training step. The estimator's train op needs to have a control
    dependency on these updates.

    Returns:
      A list of update operators.
    """
    update_ops = super(RFCNMetaArch, self).updates()

    if self._rfcn_box_predictor.is_keras_model:
      update_ops.extend(
          self._rfcn_box_predictor.get_updates_for(None))
      update_ops.extend(
          self._rfcn_box_predictor.get_updates_for(
              self._rfcn_box_predictor.inputs))
    return update_ops


Some of our Year 5 and 6 children got the opportunity to go ice skating this week. Lots of wobbles, followed by growing confidence.
A few snaps from year 1's recent trip to Surrey Dock Farm featuring sheep, pigs and ponies. Everybody say 'awww'.
A smashing day experiencing life in the 18th Century as law-breaking Highwaymen as part of Year 5's work on Alfred Noyes poem 'The Highwayman.' Thanks to Timecapsule Education (www.timecapsule.education) for bringing the day to life. Oh, and look out for a rather disturbing 'Bess, the Landlord's Daughter' as featured in the poem.
We are pleased to announce that we now have guidance videos for parents to help teach your children handwriting. These can be found on our YouTube channel or at http://dkh.org.uk/handwriting/. We hope you find these useful.


text:
package io.grpc.benchmarks.proto;

public final class Services {
  private Services() {}
  public static void registerAllExtensions(
      com.google.protobuf.ExtensionRegistryLite registry) {
  }

  public static void registerAllExtensions(
      com.google.protobuf.ExtensionRegistry registry) {
    registerAllExtensions(
        (com.google.protobuf.ExtensionRegistryLite) registry);
  }

  public static com.google.protobuf.Descriptors.FileDescriptor
      getDescriptor() {
    return descriptor;
  }
  private static  com.google.protobuf.Descriptors.FileDescriptor
      descriptor;
  static {
    java.lang.String[] descriptorData = {
      "\n\033grpc/testing/services.proto\022\014grpc.test" +
      "ing\032\033grpc/testing/messages.proto\032\032grpc/t" +
      "esting/control.proto2\246\003\n\020BenchmarkServic" +
      "e\022F\n\tUnaryCall\022\033.grpc.testing.SimpleRequ" +
      "est\032\034.grpc.testing.SimpleResponse\022N\n\rStr" +
      "eamingCall\022\033.grpc.testing.SimpleRequest\032" +
      "\034.grpc.testing.SimpleResponse(\0010\001\022R\n\023Str" +
      "eamingFromClient\022\033.grpc.testing.SimpleRe" +
      "quest\032\034.grpc.testing.SimpleResponse(\001\022R\n" +
      "\023StreamingFromServer\022\033.grpc.testing.Simp" +
      "leRequest\032\034.grpc.testing.SimpleResponse0" +
      "\001\022R\n\021StreamingBothWays\022\033.grpc.testing.Si" +
      "mpleRequest\032\034.grpc.testing.SimpleRespons" +
      "e(\0010\0012\227\002\n\rWorkerService\022E\n\tRunServer\022\030.g" +
      "rpc.testing.ServerArgs\032\032.grpc.testing.Se" +
      "rverStatus(\0010\001\022E\n\tRunClient\022\030.grpc.testi" +
      "ng.ClientArgs\032\032.grpc.testing.ClientStatu" +
      "s(\0010\001\022B\n\tCoreCount\022\031.grpc.testing.CoreRe" +
      "quest\032\032.grpc.testing.CoreResponse\0224\n\nQui" +
      "tWorker\022\022.grpc.testing.Void\032\022.grpc.testi" +
      "ng.Void2^\n\030ReportQpsScenarioService\022B\n\016R" +
      "eportScenario\022\034.grpc.testing.ScenarioRes" +
      "ult\032\022.grpc.testing.VoidB$\n\030io.grpc.bench" +
      "marks.protoB\010Servicesb\006proto3"
    };
    com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner =
        new com.google.protobuf.Descriptors.FileDescriptor.    InternalDescriptorAssigner() {
          public com.google.protobuf.ExtensionRegistry assignDescriptors(
              com.google.protobuf.Descriptors.FileDescriptor root) {
            descriptor = root;
            return null;
          }
        };
    com.google.protobuf.Descriptors.FileDescriptor
      .internalBuildGeneratedFileFrom(descriptorData,
        new com.google.protobuf.Descriptors.FileDescriptor[] {
          io.grpc.benchmarks.proto.Messages.getDescriptor(),
          io.grpc.benchmarks.proto.Control.getDescriptor(),
        }, assigner);
    io.grpc.benchmarks.proto.Messages.getDescriptor();
    io.grpc.benchmarks.proto.Control.getDescriptor();
  }

  // @@protoc_insertion_point(outer_class_scope)
}


Photo: Hong Kong is ranked as the world's freest economy.
Photo: Chinese mainland enterprises see Hong Kong as their preferred service platform when pursuing Belt and Road opportunities.
Photo: Hong Kong offers a wide range of high-quality professional services (1).
Photo: Hong Kong offers a wide range of high-quality professional services (2).
Hong Kong is ranked as the world's freest economy.
Chinese mainland enterprises see Hong Kong as their preferred service platform when pursuing Belt and Road opportunities.
Hong Kong offers a wide range of high-quality professional services (1).
Hong Kong offers a wide range of high-quality professional services (2).
Hong Kong, ranked by the Heritage Foundation as the world's freest economy for 23 consecutive years, is renowned for its straightforward business environment, one where most commercial activities can be conducted free from government intervention. Its transparent, low tax regime, as well as its free flows of capital and information, all combine to effectively lower overall operational costs, while enhancing efficiency, ultimately facilitating rapid access to international market opportunities. Although Hong Kong does not offer specific incentives to target foreign investors, mainland enterprises often see Hong Kong as their preferred service platform when pursuing Belt and Road opportunities, because of the economic and commercial benefits it provides.
As a result of globalisation, countries and regions are keen to compete for foreign investment. In order to attract investors, many countries – including China, Singapore and other ASEAN countries – have drawn up specific foreign investment policies. These often include items such as preferential terms of tax exemption or concession, discounts on the cost of resources such as land, or talent subsidies. They are designed to encourage multinational companies to set up business operations there, in the hope of stimulating local economic growth and employment.
In contrast, Hong Kong does not offer any financial or other specific incentives to attract foreign investment. Yet it is one of the major recipients of foreign direct investment (FDI) in the world. Hong Kong is the main stepping stone for foreign enterprises looking to invest on the Chinese mainland. Statistics show that, as of the end of 2016, 44.7% of approved foreign investment projects on the Chinese mainland were related to Hong Kong. Of the FDI that was actually utilised, a cumulative total of US$913.7 billion came from Hong Kong, 51.8% of the overall FDI inflow into China.
Hong Kong is also a major outbound investment platform for mainland enterprises. In 2016, the Chinese mainland made a total of US$114.2 billion worth of outbound direct investment (ODI) through Hong Kong, 58.2% of its overall ODI for the year. As of the end of 2016, a cumulative total of US$780.7 billion, or 57.5% of China's cumulative ODI, had been made through Hong Kong. The territory's ability to attract foreign investment has made it the preferred platform both for foreign companies looking to invest on the Chinese mainland, and also for mainland Chinese enterprises wanting to invest overseas.
Unlike some Asian economies, Hong Kong does not offer specific preferential treatment to foreign companies. Indeed, any concessions and subsidies are available to all companies incorporated in Hong Kong, be they local businesses, or ones from the Chinese mainland or further afield. To discover whether this affected Hong Kong's ability to compete for foreign investment against Asian economies which do offer preferential treatment, HKTDC Research and the Shanghai Municipal Commission of Commerce jointly interviewed a number of Hong Kong organisations and companies. The study found that although there were pressures on Hong Kong due to the competition in the region, this was more than offset by the territory focusing its development strategy on the provision of a favourable environment for businesses generally. Upholding the principles of impartiality and efficiency, Hong Kong offers a wide range of high-quality professional services to help businesses lower costs and take advantage of opportunities in international markets.
One example of this, often cited by Invest Hong Kong (InvestHK), is that no government approval is required to set up a regular company in Hong Kong. Another is that, because of its transparent low tax regime, the overall tax liability borne by a company in Hong Kong is lower than those in other economies, even those which offer specific tax incentives to foreign companies. Furthermore, Hong Kong's world-class infrastructure, including international traffic and transport networks and communication facilities, means companies can connect easily and quickly with the Chinese mainland and other parts of the world. Other factors, such as the free flows of capital and information in Hong Kong, have made the territory one of the least restrictive economies in the world. All this has attracted ever increasing numbers of foreign investors who rely on Hong Kong as a service platform for expanding their operations in the Chinese mainland and international markets.
Support for foreign investors is also available through agencies such as InvestHK and other public institutions. They help investors find the professional services and staff they need; assist in liaising with other government departments and completing formalities such as applying for business licences and employment visas; put them in contact with local trade sectors; and provide consultation services to help companies apply for subsidies and start-up support from the Hong Kong government.
Under China's Belt and Road Initiative, mainland enterprises have been stepping up their efforts to "go out" and go global. This is particularly true in the coastal areas that serve as China's major centres for economic co-operation with foreign countries, and especially so in Shanghai and the Yangtze River Delta (YRD) region. In 2016, 83.4% of the Chinese mainland's total ODI came from its eastern coastal areas, with Shanghai ranked first among all provinces and cities in terms of ODI volume. Companies in Shanghai and the YRD region have become increasingly active in working through Hong Kong to capture Belt and Road opportunities.
Against this backdrop, HKTDC Research and the Shanghai Municipal Commission of Commerce conducted a survey in Shanghai and the YRD region in the first quarter of 2017. Of those businesses who responded, nearly half (46%) said they regarded Hong Kong as their preferred destination for seeking professional services outside the mainland when looking to take advantage of Belt and Road opportunities.
Another survey, conducted by the same two organisations in Hong Kong in the 4th quarter of 2017, was aimed at assessing how Hong Kong can help mainland enterprises expand their international business. Many companies and organisations which responded to the survey acknowledged that local companies and foreign enterprises set up in Hong Kong enjoy the benefits offered by Hong Kong's business environment.
Investors lacking a thorough knowledge of the picture, however, may be prone to presume that the absence of specific incentives has a negative effect on foreign investment in Hong Kong. Yet once they evaluate the overall costs and benefits available to their projects, it becomes clear that Hong Kong is the ideal and efficient service platform for their investment.
Given that Shanghai and other mainland provinces and cities are stepping up their efforts to strengthen co-operation with Belt and Road countries, Hong Kong is the ideal service platform to support the Chinese mainland, both in making overseas investments and in attracting foreign investors.
 According to the World Investment Report 2017 published by the United Nations Conference on Trade and Development (UNCTAD), Hong Kong was the 4th largest recipient of FDI in the world in 2016, behind the US, UK and Chinese mainland.


text:
Home » Case Briefs Bank » Torts » Palsgraf v. The Long Island Railroad Company Case Brief
Palsgraf v. The Long Island Railroad Company Case Brief
Torts • Add Comment
Summary of Palsgraf v. The Long Island Railroad Company, 248 N.Y. 339; 162 n.e. 99; Court of Appeals of New York [1928]
Facts: Plaintiff was standing on a platform of defendant's railroad when a train stopped (which was headed in a different direction than the train plaintiff was boarding). Even though it was already moving, two men ran to catch the train. One got on the train with no problem. The other, who was carrying a 15-inch package wrapped in newspaper, appeared to lose his balance while attempting to board the moving train. A guard on the car reached out to help him, while another guard on the platform pushed him from behind onto the train. This act caused the package in the man's arm to dislodge and fall onto the rails.
Although the package appeared non-descript, it in fact contained fireworks. When the package fell, the fireworks exploded, causing some large scales at the other end of the platform to strike the plaintiff, causing injuries for which the plaintiff sued.
History: The trial court found for the plaintiff. Defendants appealed and the appellate court affirmed the judgment. The railroad then appealed to this court.
Issue: Did the railroad's negligence proximately cause plaintiff's injuries?
Ruling: No. The Court of Appeals of New York reversed the decision with Judge Andrews dissenting.
Rule/Analysis: Negligence is not a tort unless it results in the commission of a wrong by violating one's right. If the harm was not willful, it must be shown that the act had possibilities of apparent danger. Since the harm to plaintiff was not willful on the part of defendant, it had to be shown that the act of dropping a package had the apparent possibility of danger. Since there was nothing on the outside of the package which would cause the reasonable person to believe it contained explosives, there was no negligence and therefore railroad was also not the proximate cause of plaintiff's injuries.
Judge Andrews dissented, stating that negligence is a relative concept and proposed that every one owes to the world at large the duty of refraining from those acts that may unreasonably threaten the safety of others.
Summary: There was nothing in the situation to suggest, even to the most cautious person, that the package wrapped in newspaper would explode when dropped. It was the explosion that was the proximate cause of plaintiff's injuries – an act which could not have been foreseeable – and therefore the railroad was neither negligent nor the proximate cause of plaintiff's injuries. Judgment of the appellate court was reversed; Judge Andrews dissented.
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redpajama_set_name: RedPajamaCommonCrawl