text:
Canine biphasic synovial sarcoma: case report and immunohistochemical characterization
Panayiotis Loukopoulos, Hock Gan Heng, Habibah Arshad
Agricultural, Environmental and Veterinary Sciences
The clinical, radiological and pathologic features of a biphasic synovial sarcoma in the left elbow joint of a two-year-old male Rottweiler are presented. The tumor showed positive immunoreactivity for vimentin, Epithelial Membrane Antigen (EMA), p53 and PCNA, while it was negative for the cytokeratin used, S-100, Rb and p21. Immunohistochemistry for EMA allowed the identification of epithelioid components of synovial sarcoma, and may, therefore, contribute in establishing a diagnosis of biphasic synovial sarcoma. Intratumoral variation in PCNA immunoreactivity was minimal, indicating that the various tumor components proliferate at more or less similar rates. Overall, the characterized immunohistochemical profile for canine synovial sarcoma, not defined previously, may provide clues to the histogenesis of the phenotypically mesenchymal and epithelial elements of the tumor, and may be of value in the differential diagnosis of challenging cases, decreasing the risk of under- and mis-diagnosis. Although more cases need to be studied to determine whether there is a consistent pattern of immunostaining in canine synovial sarcoma, its potential significance is discussed in relation to the histogenesis, molecular pathology and differential diagnosis of canine synovial sarcoma.
Journal of Veterinary Science
Link to PubMed entry
Dive into the research topics of 'Canine biphasic synovial sarcoma: case report and immunohistochemical characterization'. Together they form a unique fingerprint.
Synovial Sarcoma Medicine and Dentistry 100%
Neoplasm Medicine and Dentistry 42%
Diagnosis Medicine and Dentistry 28%
Differential Diagnosis Medicine and Dentistry 28%
Immunoreactivity Medicine and Dentistry 28%
Epithelial Membrane Antigen Medicine and Dentistry 28%
Histogenesis Medicine and Dentistry 28%
Male Medicine and Dentistry 28%
1 Visiting an external organisation
Panos Loukopoulos (Visiting researcher)
25 Aug 1999 → 06 Oct 1999
Activity: Visiting an external institution › Visiting an external organisation › Academic
Loukopoulos, P., Heng, H. G., & Arshad, H. (2004). Canine biphasic synovial sarcoma: case report and immunohistochemical characterization. Journal of Veterinary Science, 5(2), 173-80. [15192346].
Loukopoulos, Panayiotis ; Heng, Hock Gan ; Arshad, Habibah. / Canine biphasic synovial sarcoma: case report and immunohistochemical characterization. In: Journal of Veterinary Science. 2004 ; Vol. 5, No. 2. pp. 173-80.
@article{936f661bddd64868af1e78a29f6a33e3,
title = "Canine biphasic synovial sarcoma: case report and immunohistochemical characterization",
abstract = "The clinical, radiological and pathologic features of a biphasic synovial sarcoma in the left elbow joint of a two-year-old male Rottweiler are presented. The tumor showed positive immunoreactivity for vimentin, Epithelial Membrane Antigen (EMA), p53 and PCNA, while it was negative for the cytokeratin used, S-100, Rb and p21. Immunohistochemistry for EMA allowed the identification of epithelioid components of synovial sarcoma, and may, therefore, contribute in establishing a diagnosis of biphasic synovial sarcoma. Intratumoral variation in PCNA immunoreactivity was minimal, indicating that the various tumor components proliferate at more or less similar rates. Overall, the characterized immunohistochemical profile for canine synovial sarcoma, not defined previously, may provide clues to the histogenesis of the phenotypically mesenchymal and epithelial elements of the tumor, and may be of value in the differential diagnosis of challenging cases, decreasing the risk of under- and mis-diagnosis. Although more cases need to be studied to determine whether there is a consistent pattern of immunostaining in canine synovial sarcoma, its potential significance is discussed in relation to the histogenesis, molecular pathology and differential diagnosis of canine synovial sarcoma.",
keywords = "p21, PCNA, oncology, cancer, pathology, Joints, comparative oncology",
author = "Panayiotis Loukopoulos and Heng, {Hock Gan} and Habibah Arshad",
journal = "Journal of Veterinary Science",
publisher = "Korean Society of Veterinary Science",
Loukopoulos, P, Heng, HG & Arshad, H 2004, 'Canine biphasic synovial sarcoma: case report and immunohistochemical characterization', Journal of Veterinary Science, vol. 5, no. 2, 15192346, pp. 173-80.
Canine biphasic synovial sarcoma: case report and immunohistochemical characterization. / Loukopoulos, Panayiotis; Heng, Hock Gan; Arshad, Habibah.
In: Journal of Veterinary Science, Vol. 5, No. 2, 15192346, 2004, p. 173-80.
T1 - Canine biphasic synovial sarcoma: case report and immunohistochemical characterization
AU - Loukopoulos, Panayiotis
AU - Heng, Hock Gan
AU - Arshad, Habibah
N2 - The clinical, radiological and pathologic features of a biphasic synovial sarcoma in the left elbow joint of a two-year-old male Rottweiler are presented. The tumor showed positive immunoreactivity for vimentin, Epithelial Membrane Antigen (EMA), p53 and PCNA, while it was negative for the cytokeratin used, S-100, Rb and p21. Immunohistochemistry for EMA allowed the identification of epithelioid components of synovial sarcoma, and may, therefore, contribute in establishing a diagnosis of biphasic synovial sarcoma. Intratumoral variation in PCNA immunoreactivity was minimal, indicating that the various tumor components proliferate at more or less similar rates. Overall, the characterized immunohistochemical profile for canine synovial sarcoma, not defined previously, may provide clues to the histogenesis of the phenotypically mesenchymal and epithelial elements of the tumor, and may be of value in the differential diagnosis of challenging cases, decreasing the risk of under- and mis-diagnosis. Although more cases need to be studied to determine whether there is a consistent pattern of immunostaining in canine synovial sarcoma, its potential significance is discussed in relation to the histogenesis, molecular pathology and differential diagnosis of canine synovial sarcoma.
AB - The clinical, radiological and pathologic features of a biphasic synovial sarcoma in the left elbow joint of a two-year-old male Rottweiler are presented. The tumor showed positive immunoreactivity for vimentin, Epithelial Membrane Antigen (EMA), p53 and PCNA, while it was negative for the cytokeratin used, S-100, Rb and p21. Immunohistochemistry for EMA allowed the identification of epithelioid components of synovial sarcoma, and may, therefore, contribute in establishing a diagnosis of biphasic synovial sarcoma. Intratumoral variation in PCNA immunoreactivity was minimal, indicating that the various tumor components proliferate at more or less similar rates. Overall, the characterized immunohistochemical profile for canine synovial sarcoma, not defined previously, may provide clues to the histogenesis of the phenotypically mesenchymal and epithelial elements of the tumor, and may be of value in the differential diagnosis of challenging cases, decreasing the risk of under- and mis-diagnosis. Although more cases need to be studied to determine whether there is a consistent pattern of immunostaining in canine synovial sarcoma, its potential significance is discussed in relation to the histogenesis, molecular pathology and differential diagnosis of canine synovial sarcoma.
KW - p21
KW - PCNA
KW - oncology
KW - cancer
KW - pathology
KW - Joints
KW - comparative oncology
UR - http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15192346
JO - Journal of Veterinary Science
JF - Journal of Veterinary Science
M1 - 15192346
Loukopoulos P, Heng HG, Arshad H. Canine biphasic synovial sarcoma: case report and immunohistochemical characterization. Journal of Veterinary Science. 2004;5(2):173-80. 15192346.


text:
Neustar Research Shows A DDoS Attack Can Cost An Organization On Average More Than $2.5 Million in Revenue
Neustar Building 10Tbps Global Defense Network to Combat Growing Problem STERLING, Va.-Tuesday, May 2nd 2017 [ ME NewsWire ] (BUSINESS WIRE)-- Neustar, Inc. (NYSE: NSR), a trusted, neutral provider of real-time information services, today released the findings from its fourth annual Worldwide DDoS Attacks and Cyber Insights Research Report. The report documents the frequency and cost of attacks and what measures are being taken to counter these threats. The Neustar May 2017 Worldwide DDoS Attacks and Cyber Insights Research Report, a 52-page global report, analyzes the responses of more than one thousand CISOs, CSO, CTOs security directors and managers. The report also includes, for the first time, Q1 attack data and trends captured from the Neustar DDoS Security Operations Center. "Distributed Denial of Service (DDoS) attacks are the zeitgeist of today's Internet," said Barrett Lyon, pioneer of the DDoS defense industry and Head of Research and Development at Neustar Security Sol ... Read more »
MATRIXX Software lance une plateforme de commerce numérique de classe opérateur pour le cloud
Une plateforme unique fournit des performances et une fiabilité de classe réseau au sein des environnements de cloud, de cloud privé et sur site SARATOGA, Californie -Mardi 2 Mai 2017 [ ME NewsWire ] (BUSINESS WIRE)--MATRIXX Software (http://www.matrixx.com/) a annoncé aujourd'hui le lancement de MATRIXX Digital Commerce, une plateforme unique qui fournit des performances et une fiabilité de classe opérateur nécessaires aux fournisseurs de services de communications (FSC) pour les applications métier vitales au sein des environnements de cloud, de cloud privé et sur site. MATRIXX Digital Commerce place la barre très haut et anticipe de nouveaux indicateurs de performance clés (IPC) numériques. Les FSC s'attendent à ce que leurs fournisseurs respectent ces IPC pour le cloud et la virtualisation, dans quatre domaines principaux : évolutivité, fiabilité, prise en charge de l'écosystème numérique et fidélisation des clients. Jusqu'à présent, les FSC ont été prudents quant à la migratio ... Read more »
Views: 1054 | Added by: uaeonlinenews | Date: 05.02.2017
UAE Icebergs Project to Tip the Scales towards Climate Change in the Region and Transform the Rub' El Khali Desert (Empty Quarter) into a Th
ABU DHABI, United Arab Emirates-Tuesday, May 2nd 2017 [ ME NewsWire ] The National Advisor Bureau Limited, which plans to tow icebergs from Antarctica to Fujairah coast as a new source of freshwater in the region, revealed that the towed icebergs may cause a significant and radical climate change in the region. Cold air gushing out from an iceberg close to the shores of the Arabian Sea would cause a trough and rainstorms across the Arabian Gulf and the southern region of the Arabian Peninsula all year round. When icebergs make contact with humid climate (Arabian Sea), moist air is forced to rise. Rising air expands; cools and condenses due to the decrease in air pressure. When enough water vapor is collected in the clouds, they become heavy and fall as rain. Icebergs are expected to cause a tremendous increase in the amount of water vapor in the atmosphere. Clouds approaching from the Arabian Sea will flow around a circular low pressure center above the iceberg and create a vortex. I ... Read more »
HH Sheikh Maktoum bin Mohammed bin Rashid Al Maktoum Honore 10 Lauréats de 8 Pays au Prix Mondial de l'Eau lancé par Mohammed bin Rashid Al
Dubaï, Émirats Arabes Unis-Mardi 2 Mai 2017 [ ME NewsWire ] HH Sheikh Maktoum bin Mohammed bin Rashid Al Maktoum, Adjoint du Souverain de Dubaï, a honoré 10 lauréats de 8 pays au Prix Mondial de Mohammed bin Rashid Al Maktoum, qui s'élevé à 1 million de dollars américains. HH Mansoor bin Mohammed bin Rashid Al Maktoum a aussi assisté à la cérémonie de la remise des prix. Le Prix a été lancé par HH Sheikh Mohammed bin Rashid Al Maktoum, Vice-Président et Premier Ministre des UAE et Souverain de Dubaï, pour encourager les centres de recherche, les individus et les innovateurs du monde entier à trouver des solutions innovantes et durables pour la pénurie d'eau propre autour du monde, en utilisant l'énergie solaire. Le prix est supervisé par UAE Water Aid Foundation (Suqia), sous le patronage des Initiatives Mondiales de Mohammed bin Rashid Al Maktoum. Les trois catégories principales du prix sont : Prix des Projets Innovants, Prix de Recherche et de Développement Innovants et Prix de la ... Read more »
HH Sheikh Maktoum bin Mohammed bin Rashid Al Maktoum Honours 10 Winners from 8 Countries at Mohammed bin Rashid Al Maktoum Global Water Awar
Dubai, United Arab Emirates -Friday, April 28th 2017 [ ME NewsWire ] HH Sheikh Maktoum bin Mohammed bin Rashid Al Maktoum, Deputy Ruler of Dubai, honoured 10 winners from 8 countries at the Mohammed bin Rashid Al Maktoum Global Award, which is worth USD 1 million. Also present was HH Mansoor bin Mohammed bin Rashid Al Maktoum. The Award was launched by HH Sheikh Mohammed bin Rashid Al Maktoum, Vice President and Prime Minister of the UAE and Ruler of Dubai, to encourage research centres, individuals and innovators from around the world to find innovative and sustainable solutions for clean-water scarcity around the world, using solar power. The award is overseen by the UAE Water Aid Foundation (Suqia), under the umbrella of the Mohammed bin Rashid Al Maktoum Global Initiatives. It has three main categories: Innovative Projects Award, Innovative Research and Development Award, and the Innovative Youth Award. The first place in the Innovative Research and Development Award – National I ... Read more »
Takeda Announces FDA Accelerated Approval of ALUNBRIGTM (brigatinib)
− ALUNBRIG Approved for ALK+ Metastatic Non-Small Cell Lung Cancer (NSCLC) Patients Who Have Progressed on or are Intolerant to Crizotinib – CAMBRIDGE, Mass. & OSAKA, Japan-Monday, May 1st 2017 [ ME NewsWire ] (BUSINESS WIRE)-- Takeda Pharmaceutical Company Limited (TSE: 4502) today announced that ALUNBRIG™ (brigatinib) has received Accelerated Approval from the U.S. Food and Drug Administration (FDA) for the treatment of patients with anaplastic lymphoma kinase-positive (ALK+) metastatic non-small cell lung cancer (NSCLC) who have progressed on or are intolerant to crizotinib. This indication is approved under Accelerated Approval based on tumor response rate and duration of response. Continued approval for this indication may be contingent upon verification and description of clinical benefit in a confirmatory trial. ALUNBRIG, which previously received Breakthrough Therapy Designation from the FDA, is a once-daily oral therapy that may be taken with or without food. "In recent ... Read more »

redpajama_set_name: RedPajamaCommonCrawl


text:
Missing Children, Females, 1990's
Shakeima Cabbagestalk
Revision as of 17:28, 7 March 2020 by 108.17.137.167 (contribs)
Ten-year-old Shakeima was last seen at the Turning Point Grocery Store in Dillon, South Carolina on July 22, 1993 with her little brother and hasn't been seen since. Her stepfather, Sam Harmon was the last one to see Shakeima and he says that he left her & her little brother at the store.
In August of 2005, Sam was charged with murder, kidnapping and criminal sexual conduct with a minor in connection with Shakeima's case. In 2008, he was convicted of kidnapping, but not murder and was sentenced to 12 years in prison.
In 1992, Shakeima accused Sam of inappropriate touching, but charges weren't filed against him because of lack of evidence, plus Shakeima's story changed several times.
At the time of her disappearance, Shakeima was close to her brother & grandmother and she would've contacted them if she could.
At Sam's trial in October 2008, an uncle of Shakeima says that two weeks before she disappeared, Sam told him ways he could dispose of a dead body. There's no physical evidence to prove Shakeima's dead, but Sam maintains his innocence in her disappearance.
Due to the circumstances involved, foul play is suspected in Shakeima's disappearance, but her relatives believed that Shakeima's still alive and her remains have never been found.
Shakeima had a double tooth on the upper side of her mouth and moles near the corner of her mouth & below her neck.
Retrieved from "https://missingchildren.wikia.org/wiki/Shakeima_Cabbagestalk?oldid=7596"


text:
ChompTalk.com
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Gators in the NBA: Horford, 76ers break losing streak, Beal day-to-day
By mstinejr on January 10, 2020 • ( Leave a comment )
Photo by Jesse D. Garrabrant / Getty Images
The recent four-game road trip proved difficult for five-time All-Star Al Horford (center/power forward, Philadelphia 76ers).
Big Al didn't register a single game in the stretch with at least 10 points or 10 rebounds, as the 76ers (24-14) dropped all four contests to slide to fifth in the Eastern Conference.
However, order was restored when Phili took its home floor against Oklahoma City on Monday night. Horford had 13 points, four rebounds and four assists, as the 76ers returned to their winning ways in a 120-113 triumph.
If you look on the surface of his statistics, yes, Horford's production has taken a nosedive. His 12.3 points per game is the lowest average since his second year in the Association (11.5, 2008-09), and his 6.5 rebounds per game is a career low. However, Horford's in a position with Philadelphia that he has never been in before, and aside from his recent road-trip woes, he has been a decent offensive presence for his squad.
Since entering the NBA, Big Al's been a primary scoring option for every team in which he's played. He was a reliable inside presence with the Hawks (alongside Jeff Teague and Josh Smith) and with the Celtics, where he complimented Isaiah Thomas then Kyrie Irving. But with the 76ers, he's the fifth option, falling behind Joel Embiid, Tobias Harris, Josh Richardson and Ben Simmons.
But look at Horford's stats per 100 possessions, and it's impressive that he is hanging around his career averages despite taking an offensive back seat to Philly's youth.
This year, Horford is averaging an estimated 113 points produced per 100 possessions with a field goal attempt. That's actually on par with his 2015-16 All-Star season. And sure, he's averaging a career-worst 10.2 rebounds per 100 possessions, but that's very close to the 10.5 he averaged in his first season in Boston.
As Horford continues to gel with Philadelphia, expect his consistency to improve, and therefore, his numbers. He'll look to do so Thursday against the Boston Celtics at 7 p.m.
Update: With star big man Joel Embiid out with a finger injury, Horford filled in at the five admirably. In 32 minutes of action, he came away with 17 points on 7-11 from the field and grabbed eight rebounds. He also dished out six assists in th 76ers 109-98 victory.
Meanwhile, Bradley Beal's (guard, Washington Wizards) production has ground to a halt, as he has been day-to-day with a lower leg injury. Beal was inactive for five of the last six games and is questionable to play against the Hawks on Friday night.
Dorian Finney-Smith (forward, Dallas Mavericks) continues to be a productive cog in the NBA's top-ranked offense. He averages 9.0 points per game, good for fifth on the team, and is first in offensive rebounding with 1.9. He scored in double digits in Dallas' last two games, and he'll look to make it three-straight for the second time this season when the Lakers come to town on Friday night.
It has been a while since Chandler Parsons, Chris Chiozza or Udonis Haslem have seen the floor. Parsons last played for Atlanta in 26-point loss against Milwaukee on Dec. 27. Chiozza last saw action for the Wizards in a 17-point loss at Memphis on Dec. 14. And Haslem has yet to play for Miami since a 24-point win over Cleveland on Nov. 20.
Mark Stine is a contributing writer for Chomp Talk. Follow him on Twitter @mstinejr.
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text:
import * as path from 'path';
import * as crypto from 'crypto';
import * as pfs from 'vs/base/node/pfs';
import { URI as Uri } from 'vs/base/common/uri';
import { ResourceQueue } from 'vs/base/common/async';
import { IBackupFileService, BACKUP_FILE_UPDATE_OPTIONS, BACKUP_FILE_RESOLVE_OPTIONS } from 'vs/workbench/services/backup/common/backup';
import { IFileService, ITextSnapshot } from 'vs/platform/files/common/files';
import { readToMatchingString } from 'vs/base/node/stream';
import { ITextBufferFactory } from 'vs/editor/common/model';
import { createTextBufferFactoryFromStream, createTextBufferFactoryFromSnapshot } from 'vs/editor/common/model/textModel';
import { keys } from 'vs/base/common/map';
import { Schemas } from 'vs/base/common/network';

export interface IBackupFilesModel {
	resolve(backupRoot: string): Promise<IBackupFilesModel>;

	add(resource: Uri, versionId?: number): void;
	has(resource: Uri, versionId?: number): boolean;
	get(): Uri[];
	remove(resource: Uri): void;
	count(): number;
	clear(): void;
}

export class BackupSnapshot implements ITextSnapshot {
	private preambleHandled: boolean;

	constructor(private snapshot: ITextSnapshot, private preamble: string) { }

	read(): string | null {
		let value = this.snapshot.read();
		if (!this.preambleHandled) {
			this.preambleHandled = true;

			if (typeof value === 'string') {
				value = this.preamble + value;
			} else {
				value = this.preamble;
			}
		}

		return value;
	}
}

export class BackupFilesModel implements IBackupFilesModel {
	private cache: { [resource: string]: number /* version ID */ } = Object.create(null);

	resolve(backupRoot: string): Promise<IBackupFilesModel> {
		return pfs.readDirsInDir(backupRoot).then(backupSchemas => {

			// For all supported schemas
			return Promise.all(backupSchemas.map(backupSchema => {

				// Read backup directory for backups
				const backupSchemaPath = path.join(backupRoot, backupSchema);
				return pfs.readdir(backupSchemaPath).then(backupHashes => {

					// Remember known backups in our caches
					backupHashes.forEach(backupHash => {
						const backupResource = Uri.file(path.join(backupSchemaPath, backupHash));
						this.add(backupResource);
					});
				});
			}));
		}).then(() => this, error => this);
	}

	add(resource: Uri, versionId = 0): void {
		this.cache[resource.toString()] = versionId;
	}

	count(): number {
		return Object.keys(this.cache).length;
	}

	has(resource: Uri, versionId?: number): boolean {
		const cachedVersionId = this.cache[resource.toString()];
		if (typeof cachedVersionId !== 'number') {
			return false; // unknown resource
		}

		if (typeof versionId === 'number') {
			return versionId === cachedVersionId; // if we are asked with a specific version ID, make sure to test for it
		}

		return true;
	}

	get(): Uri[] {
		return Object.keys(this.cache).map(k => Uri.parse(k));
	}

	remove(resource: Uri): void {
		delete this.cache[resource.toString()];
	}

	clear(): void {
		this.cache = Object.create(null);
	}
}

export class BackupFileService implements IBackupFileService {

	private static readonly META_MARKER = '\n';

	_serviceBrand: any;

	private backupWorkspacePath: string;

	private isShuttingDown: boolean;
	private ready: Promise<IBackupFilesModel>;
	private ioOperationQueues: ResourceQueue; // queue IO operations to ensure write order

	constructor(
		backupWorkspacePath: string,
		@IFileService private readonly fileService: IFileService
	) {
		this.isShuttingDown = false;
		this.ioOperationQueues = new ResourceQueue();

		this.initialize(backupWorkspacePath);
	}

	initialize(backupWorkspacePath: string): void {
		this.backupWorkspacePath = backupWorkspacePath;

		this.ready = this.init();
	}

	private init(): Promise<IBackupFilesModel> {
		const model = new BackupFilesModel();

		return model.resolve(this.backupWorkspacePath);
	}

	hasBackups(): Promise<boolean> {
		return this.ready.then(model => {
			return model.count() > 0;
		});
	}

	loadBackupResource(resource: Uri): Promise<Uri | undefined> {
		return this.ready.then(model => {

			// Return directly if we have a known backup with that resource
			const backupResource = this.toBackupResource(resource);
			if (model.has(backupResource)) {
				return backupResource;
			}

			return undefined;
		});
	}

	backupResource(resource: Uri, content: ITextSnapshot, versionId?: number): Promise<void> {
		if (this.isShuttingDown) {
			return Promise.resolve();
		}

		return this.ready.then(model => {
			const backupResource = this.toBackupResource(resource);
			if (model.has(backupResource, versionId)) {
				return undefined; // return early if backup version id matches requested one
			}

			return this.ioOperationQueues.queueFor(backupResource).queue(() => {
				const preamble = `${resource.toString()}${BackupFileService.META_MARKER}`;

				// Update content with value
				return this.fileService.updateContent(backupResource, new BackupSnapshot(content, preamble), BACKUP_FILE_UPDATE_OPTIONS).then(() => model.add(backupResource, versionId));
			});
		});
	}

	discardResourceBackup(resource: Uri): Promise<void> {
		return this.ready.then(model => {
			const backupResource = this.toBackupResource(resource);

			return this.ioOperationQueues.queueFor(backupResource).queue(() => {
				return pfs.del(backupResource.fsPath).then(() => model.remove(backupResource));
			});
		});
	}

	discardAllWorkspaceBackups(): Promise<void> {
		this.isShuttingDown = true;

		return this.ready.then(model => {
			return pfs.del(this.backupWorkspacePath).then(() => model.clear());
		});
	}

	getWorkspaceFileBackups(): Promise<Uri[]> {
		return this.ready.then(model => {
			const readPromises: Promise<Uri>[] = [];

			model.get().forEach(fileBackup => {
				readPromises.push(
					readToMatchingString(fileBackup.fsPath, BackupFileService.META_MARKER, 2000, 10000).then(Uri.parse)
				);
			});

			return Promise.all(readPromises);
		});
	}

	resolveBackupContent(backup: Uri): Promise<ITextBufferFactory> {
		return this.fileService.resolveStreamContent(backup, BACKUP_FILE_RESOLVE_OPTIONS).then(content => {

			// Add a filter method to filter out everything until the meta marker
			let metaFound = false;
			const metaPreambleFilter = (chunk: string) => {
				if (!metaFound && chunk) {
					const metaIndex = chunk.indexOf(BackupFileService.META_MARKER);
					if (metaIndex === -1) {
						return ''; // meta not yet found, return empty string
					}

					metaFound = true;
					return chunk.substr(metaIndex + 1); // meta found, return everything after
				}

				return chunk;
			};

			return createTextBufferFactoryFromStream(content.value, metaPreambleFilter);
		});
	}

	toBackupResource(resource: Uri): Uri {
		return Uri.file(path.join(this.backupWorkspacePath, resource.scheme, hashPath(resource)));
	}
}

export class InMemoryBackupFileService implements IBackupFileService {

	_serviceBrand: any;

	private backups: Map<string, ITextSnapshot> = new Map();

	hasBackups(): Promise<boolean> {
		return Promise.resolve(this.backups.size > 0);
	}

	loadBackupResource(resource: Uri): Promise<Uri | undefined> {
		const backupResource = this.toBackupResource(resource);
		if (this.backups.has(backupResource.toString())) {
			return Promise.resolve(backupResource);
		}

		return Promise.resolve(undefined);
	}

	backupResource(resource: Uri, content: ITextSnapshot, versionId?: number): Promise<void> {
		const backupResource = this.toBackupResource(resource);
		this.backups.set(backupResource.toString(), content);

		return Promise.resolve();
	}

	resolveBackupContent(backupResource: Uri): Promise<ITextBufferFactory | undefined> {
		const snapshot = this.backups.get(backupResource.toString());
		if (snapshot) {
			return Promise.resolve(createTextBufferFactoryFromSnapshot(snapshot));
		}

		return Promise.resolve(undefined);
	}

	getWorkspaceFileBackups(): Promise<Uri[]> {
		return Promise.resolve(keys(this.backups).map(key => Uri.parse(key)));
	}

	discardResourceBackup(resource: Uri): Promise<void> {
		this.backups.delete(this.toBackupResource(resource).toString());

		return Promise.resolve();
	}

	discardAllWorkspaceBackups(): Promise<void> {
		this.backups.clear();

		return Promise.resolve();
	}

	toBackupResource(resource: Uri): Uri {
		return Uri.file(path.join(resource.scheme, hashPath(resource)));
	}

}

/*
 * Exported only for testing
 */
export function hashPath(resource: Uri): string {
	const str = resource.scheme === Schemas.file ? resource.fsPath : resource.toString();
	return crypto.createHash('md5').update(str).digest('hex');
}

redpajama_set_name: RedPajamaGithub


text:
\section{Introduction}
One of the most fundamental trade-offs in stochastic decision theory is the well celebrated Exploration vs. Exploitation dilemma. Should one acquire new knowledge on the expense of possible sacrifice in the immediate reward (Exploration), or leverage past knowledge in order to maximize instantaneous reward (Exploitation)? Solutions that have been demonstrated to perform well are those which succeed in balancing the two. First proposed by~\citet{ThompsonLikelihood} in the context of drug trials, and later formulated in a more general setting by~\citet{SeqDesignExp}, MAB problems serve as a distilled framework for this dilemma. In the classical setting of the MAB, at each time step, the decision maker must choose (pull) between a fixed number of arms. After pulling an arm, she receives a reward which is a realization drawn from the arm's underlying reward distribution. The decision maker's objective is to maximize her cumulative expected reward over the time horizon. An equivalent, more typically studied, is the \textit{regret}, which is defined as the difference between the optimal cumulative expected reward (under full information) and that of the policy deployed by the decision maker.

MAB formulation has been studied extensively, and was leveraged to formulate many real-world problems. Some examples for such modeling are online advertising \citep{BanditsTaxonomies}, routing of packets \citep{AdaptiveRouting}, and online auctions \citep{ValueKnowingDemand}.

Most past work (Section~\ref{sec_Rel_work}) on the MAB framework has been performed under the assumption that the underlying distributions are stationary, or possibly quasi-stationary. In many real-world scenarios, this assumption may seem simplistic. Specifically, we are motivated by real-world scenarios where the expected reward of an arm decreases over time instances that it has been pulled. We term this variant \textit{Rotting Bandits}. For motivational purposes, we present the following two examples.
\begin{itemize}
	\item Consider an online advertising problem where an agent must choose which ad (arm) to present (pull) to a user. It seems reasonable that the effectiveness (reward) of a specific ad on a user would deteriorate over exposures. Similarly, in the content recommendation context, \citet{SpatioTemporal} showed that articles' CTR decay over amount of exposures.
	\item Consider the problem of assigning projects through crowdsourcing systems \citep{EfficientCrowd}. Given that the assignments primarily require human perception, subjects may fall into boredom and their performance would decay (e.g., license plate transcriptions \citep{AutoLicPlate}).
\end{itemize}

As opposed to the stationary case, where the optimal policy is to always choose some specific arm, in the case of Rotting Bandits the optimal policy consists of choosing different arms. This results in the notion of \textit{adversarial regret} vs. \textit{policy regret} \citep{RegretToPolicyRegret} (see Section~\ref{sec_Rel_work}). In this work we tackle the harder problem of minimizing the policy regret.

The main contributions of this paper are the following:
\begin{itemize}
	\item Introducing a novel, real-world oriented MAB\ formulation, termed \textit{Rotting Bandits}.
	\item Present an easy-to-follow algorithm for the general case, accompanied with theoretical guarantees.
	\item Refine the theoretical guarantees for the case of existing prior knowledge on the rotting models, accompanied with suitable algorithms.
\end{itemize}

The rest of the paper is organized as follows: in Section~\ref{sec_model} we present the model and relevant preliminaries. In Section~\ref{sec_non_param} we present our algorithm along with theoretical guarantees for the general case. In Section~\ref{sec_param} we do the same for the parameterized case, followed by simulations in Section~\ref{sec_sim}. In Section~\ref{sec_Rel_work} we review related work, and conclude with a discussion in Section~\ref{Sec_dis}.

\section{Model and Preliminaries}
\label{sec_model}
We consider the problem of Rotting Bandits (RB); an agent is given $K$ arms and at each time step $t=1,2,..$ one of the arms must be pulled. We denote the arm that is pulled at time step $t$ as ${i\left(t\right) \in \left[K\right]=\{1,..,K\}}$. When arm $i$ is pulled for the $n^{\textrm{th}}$ time, the agent receives a time independent, $\sigma^{2}$ sub-Gaussian random reward, $r_{t}$, with mean ${\mu_{i}\left(n\right)}$.\footnote{Our results hold for pulls-number dependent variances $\sigma^{2}\left(n\right)$, by upper bound them ${\sigma^{2} \geq \sigma^{2}\left(n\right), \forall n}$. It is fairly straightforward to adapt the results to pulls-number dependent variances, but we believe that the way presented conveys the setting in the clearest way.}

In this work we consider two cases: (1) There is no prior knowledge on the expected rewards, except for the `rotting' assumption to be presented shortly, i.e., a non-parametric case (NPC). (2) There is prior knowledge that the expected rewards comprised of an unknown constant part and a \textit{rotting} part which is known to belong to a set of rotting models, i.e., a parametric case (PC).

Let $N_{i}\left(t\right)$ be the number of pulls of arm $i$ at time $t$ not including this round's choice (${N_{i}\left(1\right)=0}$), and $\Pi$ the set of all sequences ${i\left(1\right), i\left(2\right),..}$, where ${i\left(t\right) \in \left[K\right], \forall t \in \mathbb{N}}$. i.e., ${\pi \in \Pi}$ is an infinite sequence of actions (arms), also referred to as a policy. We denote the arm that is chosen by policy $\pi$ at time $t$ as $\pi\left(t\right)$. The objective of an agent is to maximize the expected total reward in time $T$, defined for policy $\pi \in \Pi$ by,
\begin{equation}
J\left(T ; \pi\right) = \mathbb{E}\left[\sum_{t=1}^{T} \mu_{\pi\left(t\right)}\left(N_{\pi\left(t\right)}\left(t\right)+1\right) \right]
\label{total_reward}
\end{equation}
We consider the equivalent objective of minimizing the regret in time $T$ defined by,
\begin{equation}
\mathcal{R}\left(T ; \pi\right) = \max_{\tilde{\pi} \in \Pi} \{ J\left(T ; \tilde{\pi}\right) \} - J\left(T ; \pi\right).
\label{regret}
\end{equation}

\begin{assumption}
\textbf{(Rotting)} $\forall i \in \left[K\right]$, $\mu_{i}\left(n\right)$ is positive, and non-increasing in $n$.
\label{assm_rotting}
\end{assumption}

\subsection{Optimal Policy}
Let $\pi^{\textrm{max}}$ be a policy defined by,
\begin{equation}
\pi^{\textrm{max}}\left(t\right) \in \argmax_{i\in\left[K\right]} \{\mu_{i}\left(N_{i}\left(t\right)+1\right)\}
\label{opt_policy}
\end{equation}
where, in a case of tie, break it randomly.

\begin{lemma}
\label{lemma_opt_policy}
$\pi^{\normalfont{\textrm{max}}}$ is an optimal policy for the RB\ problem. \\
\textbf{Proof:} See Appendix B of the supplementary material.
\end{lemma}

\section{Non-Parametric Case}
\label{sec_non_param}
In the NPC\ setting for the RB\ problem, the only information we have is that the expected rewards sequences are positive and non-increasing in the number of pulls. The Sliding-Window Average (\textbf{SWA}) approach is a heuristic for ensuring with high probability that, at each time step, the agent did not sample significantly sub-optimal arms too many times. We note that, potentially, the optimal arm changes throughout the trajectory, as Lemma~\ref{lemma_opt_policy} suggests. We start by assuming that we know the time horizon, and later account for the case we do not.

\underline{\textbf{Known Horizon}}\\
The idea behind the SWA\ approach is that after we pulled a significantly sub-optimal arm ``enough" times, the empirical average of these ``enough" pulls would be distinguishable from the optimal arm for that time step and, as such, given any time step there is a bounded number of significantly sub-optimal pulls compared to the optimal policy. Pseudo algorithm for SWA\ is given by Algorithm~\ref{alg_swa}.

\begin{algorithm}
\caption{SWA} \label{alg_swa}
\begin{algorithmic}
\STATE $\textbf{Input}: K, T, \alpha > 0$
\STATE $\textbf{Initialize}: M \leftarrow \ceil{\alpha 4^{2/3} \sigma^{2/3} K^{-2/3} T^{2/3} \ln ^{1/3} \left(\sqrt{2} T \right)} \text{, and } N_{i} \leftarrow 0 \text{ for all } i \in \left[K\right]$
\FOR {$t = 1,2,..,KM$}
\STATE $\textbf{Ramp up}: i\left(t\right) \text{by Round-Robin, receive } r_{t} \text{, and set } N_{i\left(t\right)} \leftarrow N_{i\left(t\right)} + 1 \text{ ; } r_{i\left(t\right)}^{N_{i\left(t\right)}} \leftarrow r_{t}$
\ENDFOR
\FOR {$t = KM+1,...,T$}
\STATE $\textbf{Balance}: i\left(t\right) \in \argmax_{i\in\left[K\right]} \bigg\{ \frac{1}{M} \sum_{n=N_{i}-M+1}^{N_{i}} r_{i}^{n} \bigg\}$
\STATE $\textbf{Update}: \text{receive } r_{t} \text{, and set } N_{i\left(t\right)} \leftarrow N_{i\left(t\right)} + 1 \text{ ; } r_{i\left(t\right)}^{N_{i\left(t\right)}} \leftarrow r_{t}$
\ENDFOR
\end{algorithmic}
\end{algorithm}

\begin{theorem}
Suppose Assumption~\ref{assm_rotting} holds. SWA\ algorithm achieves regret bounded by,
\begin{equation}
\mathcal{R}\left(T ; \pi^{\normalfont{\textrm{SWA}}}\right) \leq \left(\alpha \max_{i \in \left[K\right]} \mu_{i}\left(1\right) + \alpha^{-1/2}\right) 4^{2/3} \sigma^{2/3} K^{1/3} T^{2/3} \ln ^{1/3} \left(\sqrt{2} T \right) + 3K \max_{i \in \left[K\right]} \mu_{i}\left(1\right)
\end{equation}
\label{thm_swa_known_horizon}
\textbf{Proof:} See Appendix C.1 of the supplementary material.
\end{theorem}
We note that the upper bound obtains its minimum for $\alpha = \left(2 \max_{i \in \left[K\right]} \mu_{i}\left(1\right)\right)^{-2/3}$, which can serve as a way to choose $\alpha$ if $\max_{i \in \left[K\right]} \mu_{i}\left(1\right)$ is known, but $\alpha$ can also be given as an input to SWA\ to allow control on the averaging window size.

\underline{\textbf{Unknown Horizon}}\\
In this case we use \textit{doubling trick} in order to achieve the same horizon-dependent rate for the regret. We apply the SWA\ algorithm with a series of increasing horizons (powers of two, i.e., $1,2,4,..$) until reaching the (unknown) horizon. We term this Algorithm wSWA\ (wrapper SWA).

\begin{corollary}
Suppose Assumption~\ref{assm_rotting} holds. wSWA\ algorithm achieves regret bounded by,
\begin{multline}
\mathcal{R}\left(T ; \pi^{\normalfont{\textrm{wSWA}}}\right) \leq \left(\alpha \max_{i \in \left[K\right]} \mu_{i}\left(1\right) + \alpha^{-1/2}\right) 8 \sigma^{2/3} K^{1/3} T^{2/3} \ln^{1/3} \left(\sqrt{2}T \right) \\ + 3K \max_{i \in \left[K\right]} \mu_{i}\left(1\right) \left( \log_{2}T+1 \right)
\end{multline}
\label{crl_swa_unknown_horizon}
\textbf{Proof:} See Appendix C.2 of the supplementary material.
\end{corollary}

\section{Parametric Case}
\label{sec_param}
In the PC\ setting for the RB\ problem, there is prior knowledge that the expected rewards comprised of a sum of an unknown constant part and a rotting part known to belong to a set of models, $\Theta$. i.e., the expected reward of arm $i$ at its $n^{\textrm{th}}$ pull is given by, ${\mu_{i}\left(n\right) = \mu_{i}^{c} + \mu\left(n ; \theta_{i}^{*} \right)}$, where ${\theta_{i}^{*} \in \Theta}$. We denote ${\{\theta_{i}^{*}\}_{i=1}^{\left[K\right]}}$ by $\Theta^{*}$. We consider two cases: The first is the asymptotically vanishing case (AV), i.e., ${\forall i : \mu_{i}^{c}=0}$. The second is the asymptotically non-vanishing case (ANV), i.e., ${\forall i : \mu_{i}^{c} \in \mathbb{R}}$.

We present a few definitions that will serve us in the following section.

\begin{definition}
For a function ${f : \mathbb{N} \rightarrow \mathbb{R}}$, we define the function ${f^{\star \downarrow} : \mathbb{R} \rightarrow \mathbb{N} \cup \{\infty\}}$ by the following rule: given ${\zeta \in \mathbb{R}}$, ${f^{\star \downarrow}\left(\zeta\right)}$ returns the smallest ${N \in \mathbb{N}}$ such that ${\forall n \geq N : f\left(n\right) \leq \zeta}$, or $\infty$ if such $N$ does not exist.
\end{definition}

\begin{definition} For any $\theta_{1} \neq \theta_{2} \in \Theta^{2}$, define $det_{\theta_{1}, \theta_{2}}, Ddet_{\theta_{1}, \theta_{2}} : \mathbb{N} \rightarrow \mathbb{R}$ as,
\begin{equation} \nonumber
\begin{split}
det_{\theta_{1}, \theta_{2}}\left(n\right) & = \frac{n \sigma^{2}}{\left( \sum_{j=1}^{n} \mu\left(j ; \theta_{1}\right) - \sum_{j=1}^{n} \mu\left(j ; \theta_{2}\right) \right)^{2}} \\
Ddet_{\theta_{1}, \theta_{2}}\left(n\right) & = \frac{n \sigma^{2}}{\left( \sum_{j=1}^{\floor{n/2}} \left[ \mu\left(j ; \theta_{1}\right) - \mu\left(j ; \theta_{2}\right) \right] - \sum_{j=\floor{n/2}+1}^{n} \left[ \mu\left(j ; \theta_{1}\right) - \mu\left(j ; \theta_{2}\right) \right] \right)^{2}}
\end{split}
\end{equation}
\end{definition}

\begin{definition}
Let ${bal : \mathbb{N} \cup \infty \rightarrow \mathbb{N} \cup \infty}$ be defined at each point $n \in \mathbb{N}$ as the solution for,
\begin{equation} \nonumber
\begin{split}
\min \text{ } \alpha \qquad
\normalfont \text{s.t, }
\max_{\theta \in \Theta} \mu\left(\alpha ; \theta\right) \leq \min_{\theta \in \Theta} \mu\left(n ; \theta\right)
\end{split}
\end{equation}
We define $bal\left(\infty\right) = \infty$.
\end{definition}

\begin{assumption}
\textbf{(Rotting Models)} $\mu\left(n ; \theta\right)$ is positive, non-increasing in $n$, and ${\mu\left(n ; \theta\right) \in o\left(1\right)}$, ${\forall \theta \in \Theta}$, where $\Theta$ is a discrete known set.
\label{assm_param_rotting}
\end{assumption}

We present an example for which, in Appendix~E, we demonstrate how the different following assumptions hold. By this we intend
to achieve two things: (i) show that the assumptions are not too harsh, keeping the problem relevant and non-trivial, and (ii) present a simple example on how to verify the assumptions.

\begin{example}
The reward of arm $i$ for its $n^{\textrm{th}}$ pull is distributed as ${\mathcal{N}\left(\mu_{i}^{c}+n^{-\theta_{i}^{*}}, \sigma^{2}\right)}$. Where ${\theta_{i}^{*} \in \Theta = \{\theta_{1},\theta_{2},...,\theta_{M}\}}$, and ${\forall \theta \in \Theta : 0.01 \leq\theta \leq 0.49}$.
\label{expl}
\end{example}

\subsection{Closest To Origin (AV)}
The Closest To Origin (CTO) approach for RB\ is a heuristic that simply states that we hypothesize that the true underlying model for an arm is the one that best fits the past rewards. The fitting criterion is proximity to the origin of the sum of expected rewards shifted by the observed rewards. Let ${r^{i}_{1}, r^{i}_{2},.., r^{i}_{N_{i}\left(t\right)}}$ be the sequence of rewards observed from arm $i$ up until time $t$. Define,
\begin{equation}
Y\left(i, t ; \Theta\right) = \bigg\{ \sum_{j=1}^{N_{i}\left(t\right)} r^{i}_{j} - \sum_{j=1}^{N_{i}\left(t\right)} \mu\left(j ; \theta\right) \bigg\}_{\theta \in \Theta}.
\label{wa_set}
\end{equation}
The CTO\ approach dictates that at each decision point, we assume that the true underlying rotting model corresponds to the following proximity to origin rule (hence the name),
\begin{equation}
\hat{\theta}_{i}\left(t\right) = \argmin_{\theta \in \Theta} \{| Y\left(i,t ; \theta\right)| \}.
\label{theta_choose}
\end{equation}
The CTO$_{\textrm{SIM}}$ version tackles the RB\ problem by simultaneously detecting the true rotting models and balancing between the expected rewards (following Lemma~\ref{lemma_opt_policy}). In this approach, every time step, each arm's rotting model is hypothesized according to the proximity rule~(\ref{theta_choose}). Then the algorithm simply follows an $\argmax$ rule, where least number of pulls is used for tie breaking (randomly between an equal number of pulls). Pseudo algorithm for CTO$_{\textrm{SIM}}$ is given by Algorithm~\ref{cto_sim_pure_alg}.

\begin{assumption}
\textbf{(Simultaneous Balance and Detection ability)}
\begin{equation} \nonumber
bal\left(\max_{\theta_{1} \neq \theta_{2} \in \Theta^{2}} \bigg\{det_{\theta_{1}, \theta_{2}}^{\star \downarrow}\left(\frac{1}{16}  \ln^{-1}\left(\zeta\right)\right)\bigg\}\right) \in o \left(\zeta\right)
\end{equation}
\label{assm_sim}
\end{assumption}

The above assumption ensures that, starting from some horizon $T$, the underlying models could be distinguished from the others, w.p $1-1/T^{2}$, by their sums of expected rewards, and the arms could then be balanced, all within the horizon.

\begin{theorem}
Suppose Assumptions~\ref{assm_param_rotting} and \ref{assm_sim} hold. There exists a finite step ${T^{*}_{\normalfont{\textrm{SIM}}}}$, such that for all ${T \geq T^{*}_{\normalfont{\textrm{SIM}}}}$, \textnormal{CTO}$_{\normalfont{\textrm{SIM}}}$ achieves regret upper bounded by $\mathbf{o\left(1\right)}$ (which is upper bounded by $\max_{\theta \in \Theta^{*}} \mu\left(1;\theta\right)$). Furthermore, ${T^{*}_{\normalfont{\textrm{SIM}}}}$ is upper bounded by the solution for the following,
\begin{equation}
\begin{split}
\min & \text{ } T \\
\text{s.t } &  \begin{cases}
T,b \in \mathbb{N}\cup{\{0\}}, t \in \mathbb{N}^{K} \\
\forall b, \exists t : \begin{cases}
\|t\|_{1} \leq T+b \\
t_{i} \geq \max_{\theta \in \Theta^{*}}{\bigg\{m^{*}\left(\frac{1}{K\left(T+b\right)^{2}} ; \theta\right)\bigg\}} \\
\mu\left(t_{i}+1 ; \theta^{*}_{i}\right) \leq  \min_{\tilde{\theta} \in \Theta} \left[\mu\left(\max_{\theta \in \Theta^{*}}{\bigg\{m^{*}\left(\frac{1}{K\left(T+b\right)^{2}} ; \theta\right)\bigg\}} ; \tilde{\theta}\right) \right]
\end{cases}
\end{cases}
\end{split}
\label{T_SIM_upper_bound}
\end{equation}
\label{thm_cto_sim_pure}
\textbf{Proof}: See Appendix D.1 of the supplementary material.
\end{theorem}

Regret upper bounded by $o\left(1\right)$ is achieved by proving that w.p of $1-1/T$ the regret vanishes, and in any case it is still bounded by a decaying term. The shown optimization bound stems from ensuring that the arms would be pulled enough times to be correctly detected, and then balanced (following the optimal policy, Lemma~\ref{lemma_opt_policy}). Another upper bound for ${T^{*}_{\normalfont{\textrm{SIM}}}}$ can be found in Appendix~D.1.

\subsection{Differences Closest To Origin (ANV)}
We tackle this problem by estimating both the rotting models and the constant terms of the arms. The Differences Closest To Origin (\textbf{D-CTO}) approach is composed of two stages: first, detecting the underlying rotting models, then estimating and controlling the pulls due to the constant terms. We denote ${a^{*} = \argmax_{i \in \left[K\right]} \{\mu_{i}^{c}\}}$, and $\Delta_{i} = \mu_{a^{*}}^{c} - \mu_{i}^{c}$.

\begin{assumption}
\textbf{(D-Detection ability)}
\begin{equation} \nonumber
\max_{\theta_{1} \neq \theta_{2} \in \Theta^{2}} \bigg\{Ddet^{\star \downarrow}_{\theta_{1}, \theta_{2}}\left(\epsilon\right)\bigg\} \leq D\left(\epsilon\right) < \infty, \quad \forall \epsilon > 0
\end{equation}
\label{assm_Bdistinction}
\vspace{-0.01in}
\end{assumption}
This assumption ensures that for any given probability, the models could be distinguished, by the differences (in pulls) between the first and second halves of the models' sums of expected rewards.

\underline{\textbf{Models Detection}}\\
In order to detect the underlying rotting models, we cancel the influence of the constant terms. Once we do this, we can detect the underlying models. Specifically, we define a criterion of proximity to the origin based on differences between the halves of the rewards sequences, as follows: define,
\begin{equation}
Z\left(i, t ; \Theta\right) = \left(\sum_{j=1}^{\floor*{N_{i}\left(t\right)/2}}r_{j}^{i} - \sum_{j=\floor*{N_{i}\left(t\right)/2}+1}^{N_{i}\left(t\right)}r_{j}^{i}\right) - \left(\sum_{j=1}^{\floor*{N_{i}\left(t\right)/2}}\mu\left(j ; \theta\right) - \sum_{j=\floor*{N_{i}\left(t\right)/2}+1}^{N_{i}\left(t\right)}\mu\left(j ; \theta\right)\right).
\label{wa_bias_set}
\end{equation}
The D-CTO\ approach is that in each decision point, we assume that the true underlying model corresponds to the following rule,
\begin{equation}
\hat{\theta}_{i}\left(t\right) = \argmin_{\theta \in \Theta} \{ | Z\left(i, t ; \theta\right) | \}
\label{theta_bias_choose}
\end{equation}
We define the following optimization problem, indicating the number of samples required for ensuring correct detection of the rotting models w.h.p. For some arm $i$ with (unknown) rotting model $\theta_{i}^{*}$,
\begin{equation}
\begin{split}
\min \text{ } m  \qquad
\text{s.t } \begin{cases}
P\left(\hat{\theta}_{i}\left(l\right) \neq \theta_{i}^{*} \right) \leq p, \quad \forall l \geq m \\
\text{while pulling only arm } i.
\end{cases}
\end{split}
\label{opt_prob_dcto}
\end{equation}
We denote the solution to the above problem, when we use proximity rule~(\ref{theta_bias_choose}), by $m^{*}_{\textrm{diff}}\left(p ; \theta_{i}^{*}\right)$, and define ${m^{*}_{\textrm{diff}}\left(p\right) = \max_{\theta \in \Theta}{\{m^{*}_{\textrm{diff}}\left(p ; \theta\right)\}}}$.

\pagebreak

\underline{\textbf{D-CTO$_{\textrm{UCB}}$}}\\
We next describe an approach with one decision point, and later on remark on the possibility of having a decision point at each time step.
As explained above, after detecting the rotting models, we move to tackle the constant terms aspect of the expected rewards. This is done in a UCB1-like approach \citep{FiniteTimeAnalysisMAB}. Given a sequence of rewards from arm $i$, ${\{r_{k}^{i}\}_{k=1}^{N_{i}\left(t\right)}}$, we modify them using the estimated rotting model $\hat{\theta}_{i}$, then estimate the arm's constant term, and finally choose the arm with the highest estimated expected reward, plus an upper confident term. i.e., at time $t$, we pull arm $i\left(t\right)$, according to the rule,
\begin{equation}
i\left(t\right) \in \argmax_{i \in \left[K\right]} \left[\hat{\mu}_{i}^{c}\left(t\right) + \mu\left(N_{i}\left(t\right)+1 ; \hat{\theta}_{i}\left(t\right)\right) + c_{t,N_{i}\left(t\right)} \right]
\label{ucb_arm}
\end{equation}
where $\hat{\theta}_{i}\left(t\right)$ is the estimated rotting model (obtained in the first stage), and,
\begin{equation} \nonumber
\hat{\mu}_{i}^{c}\left(t\right) = \frac{\sum_{j=1}^{N_{i}\left(t\right)}\left(r_{j}^{i} - \mu\left(j ; \hat{\theta}_{i}\left(t\right)\right)\right)}{N_{i}\left(t\right)} , \qquad
c_{t,s} = \sqrt{\frac{8 \ln \left(t\right) \sigma^{2}}{s}}
\end{equation}
In a case of a tie in the UCB step, it may be arbitrarily broken. Pseudo algorithm for D-CTO$_{\textrm{UCB}}$ is given by Algorithm~\ref{bcto_alg}, accompanied with the following theorem.

\begin{theorem}
Suppose Assumptions~\ref{assm_param_rotting}, and \ref{assm_Bdistinction} hold. For $\delta \in\left(0,1\right)$, with probability of at least $1-\delta$, \textnormal{D-CTO}$_{\normalfont{\textrm{UCB}}}$ algorithm  achieves regret bounded at time $T$ by,
\begin{equation}
\sum_{\substack{i \in \left[K\right]\\ i \neq a^{*}}} \bigg[\max \bigg\{ m^{*}_{\normalfont{\textrm{diff}}}\left(\delta/K\right), \mu^{\star \downarrow}\left(\epsilon_{i} ; \theta_{i}^{*}\right), \frac{32 \sigma^{2} \ln T}{\left(\Delta_{i} - \epsilon_{i}\right)^{2}} \bigg\}
\times \left(\Delta_{i} + \mu\left(1 ; \theta_{a^{*}}^{*}\right)\right)\bigg] + C\left(\Theta^{*}, \{\mu_{i}^{c}\}\right)
\label{opt_prob_bcto_regret}
\end{equation}
for any sequence $\epsilon_{i} \in \left(0, \Delta_{i}\right), \forall i \neq a^{*}$. Where ${\frac{32 \sigma^{2} \ln T}{\left(\Delta_{i} - \epsilon_{i}\right)^{2}}}$ is the only time-dependent factor.
\label{thm_Bcto}
\\
\textbf{Proof}: See Appendix D.2 of the supplementary material.
\end{theorem}

\begin{table}[t]
\begin{tabular}{c|c}
\begin{minipage}{.54\textwidth}
\centering
\begin{algorithm}[H]
\caption{CTO$_{\textnormal{SIM}}$}
\label{cto_sim_pure_alg}
\begin{algorithmic}
\STATE $\textbf{Input}: K, \Theta$
\STATE $\textbf{Initialization}: N_{i}=0, \text{ } \forall i \in \left[K\right]$
\FOR {$t = 1,2,..,K$}
\STATE $\textbf{Ramp up}: i\left(t\right)=t \text{ ,and update } N_{i\left(t\right)}$
\ENDFOR
\FOR {$t = K+1,...,$}
\STATE $\textbf{Detect}: \text{determine } \{\hat{\theta}_{i}\} \text{ by Eq.~(\ref{theta_choose})} $
\STATE $\textbf{Balance}: {i\left(t\right) \in \argmax_{i\in\left[K\right]} \mu\left(N_{i}+1 ; \hat{\theta}_{i}\right) }$
\STATE $\textbf{Update}: N_{i\left(t\right)} \leftarrow N_{i\left(t\right)}+1$
\ENDFOR
\end{algorithmic}
\end{algorithm}
\end{minipage}
&
\begin{minipage}{.44\textwidth}
\centering
\begin{algorithm}[H]
\caption{D-CTO$_{\textnormal{UCB}}$} \label{bcto_alg}
\begin{algorithmic}
\STATE $\textbf{Input}: K, \Theta, \delta$
\STATE $\textbf{Initialization}: N_{i}=0, \text{ } \forall i \in \left[K\right]$
\FOR {$t = 1,2,..,K\times m^{*}_{\text{diff}}\left(\delta/K\right)$}
\STATE $\textbf{Explore}:$
\STATE ${i\left(t\right) \text{ by Round Robin, update } N_{i\left(t\right)}}$
\ENDFOR
\STATE $\textbf{Detect}: \text{determine } \{\hat{\theta}_{i}\} \text{ by Eq.~(\ref{theta_bias_choose})}$
\FOR {$t = K\times m^{*}_{\text{diff}}\left(\delta/K\right)+1,...,$}
\STATE $\textbf{UCB}: i\left(t\right) \text{ according to Eq.~(\ref{ucb_arm})}$
\STATE $\textbf{Update}: N_{i\left(t\right)} \leftarrow N_{i\left(t\right)}+1$
\ENDFOR
\end{algorithmic}
\end{algorithm}
\end{minipage}
\end{tabular}
\end{table}

A few notes on the result: Instead of calculating $m^{*}_{\textnormal{diff}}\left(\delta/K\right)$, it is possible to use any upper bound (e.g., as shown in Appendix E, ${\max_{\theta_{1} \neq \theta_{2} \in \Theta^{2}}  Ddet^{\star \downarrow}_{\theta_{1}, \theta_{2}}\left(\frac{1}{8}  \ln^{-1}\left(\frac{2K}{\delta}\right)\right) }$ rounded to higher even number). We cannot hope for a better rate than $\ln T$ as stochastic MAB is a special case of the RB\ problem. Finally, we can convert the \textnormal{D-CTO}$_{\textnormal{UCB}}$ algorithm to have a decision point in each step: at each time step, determine the rotting models according to proximity rule~(\ref{theta_bias_choose}), followed by pulling an arm according to Eq.~(\ref{ucb_arm}). We term this version \textnormal{D-CTO}$_{\textnormal{SIM-UCB}}$.

\section{Simulations}
\label{sec_sim}
We next compare the performance of the SWA and CTO approaches with benchmark algorithms.

\textbf{Setups} for all the simulations we use Normal distributions with ${\sigma^{2}=0.2}$, and ${T=30,000}$. \\
\textit{Non-Parametric:} ${K=2}$. As for the expected rewards: ${\mu_{1}\left(n\right) = 0.5, \forall n}$, and ${\mu_{2}\left(n\right)=1}$ for its first $7,500$ pulls and $0.4$ afterwards. This setup is aimed to show the importance of not relying on the whole past rewards in the RB\ setting.\\
\textit{Parametric AV\ \& ANV:} ${K=10}$. The rotting models are of the form ${\mu\left(j ; \theta\right) =\left(\textbf{int}\left(\frac{j}{100}\right)+1\right)^{-\theta}}$, where \textbf{int}$\left(\cdot\right)$ is the lower rounded integer, and ${\Theta = \{0.1,0.15,..,0.4\}}$ (i.e., plateaus of length $100$, with decay between plateaus according to $\theta$). ${\{\theta_{i}^{*}\}_{i=1}^{K}}$ were sampled with replacement from $\Theta$, independently across arms and trajectories. ${\{\mu_{i}^{c}\}_{i=1}^{K}}$ (ANV) were sampled randomly from ${\left[0,0.5\right]^{K}}$.

\textbf{Algorithms} we implemented standard benchmark algorithms for non-stationary MAB: UCB1 by \citet{FiniteTimeAnalysisMAB}, Discounted UCB (DUCB) and Sliding-Window UCB (SWUCB) by \citet{UpperConfBoundNonStat}. We implemented CTO$_{\textrm{SIM}}$, D-CTO$_{\textrm{SIM-UCB}}$, and wSWA\ for the relevant setups. We note that adversarial benchmark algorithms are not relevant in this case, as the rewards are unbounded.

\textbf{Grid Searches} were performed to determine the algorithms' parameters. For DUCB, following \citet{DiscountedUCB}, the discount factor was chosen from ${\gamma \in \{0.9,0.99,..,0.999999\}}$, the window size for SWUCB from ${\tau \in \{1e3,2e3,..,20e3\}}$, and $\alpha$ for wSWA\ from ${\{0.2,0.4,..,1\}}$.

\textbf{Performance} for each of the cases, we present a plot of the average regret over $100$ trajectories, specify the number of `wins' of each algorithm over the others, and report the p-value of a paired T-test between the (end of trajectories) regrets of each pair of algorithms. For each trajectory and two algorithms, the \textit{`winner'} is defined as the algorithm with the lesser regret at the end of the horizon.

\textbf{Results} the parameters that were chosen by the grid search are as follows: ${\gamma = 0.999}$ for the non-parametric case, and ${0.999999}$ for the parametric cases. ${\tau = 4e3}$, ${8e3}$, and ${16e3}$ for the non-parametric, AV, and ANV\ cases, respectively. ${\alpha=0.2}$ was chosen for all cases. \\
The average regret for the different algorithms is given by Figure~\ref{fig_sim}. Table~\ref{table_results} shows the number of `wins' and p-values. The table is to be read as the following: the entries under the diagonal are the number of times the algorithms from the left column `won' against the algorithms from the top row, and the entries above the diagonal are the p-values between the two. \\
While there is no clear `winner' between the three benchmark algorithms across the different cases, wSWA, which does not require any prior knowledge, consistently and significantly outperformed them. In addition, when prior knowledge was available and CTO$_{\textnormal{SIM}}$ or D-CTO$_{\textnormal{UCB-SIM}}$ could be deployed, they outperformed all the others, including wSWA.

\begin{table*}[t]
\caption{Number of `wins' and p-values between the different algorithms}
\vskip 0.05in
\begin{center}
\begin{small}
\begin{tabular}{>{\centering\arraybackslash}m{0.01\textwidth} | p{0.09\textwidth} || >{\centering\arraybackslash}m{0.12\textwidth} | >{\centering\arraybackslash}m{0.12\textwidth} | >{\centering\arraybackslash}m{0.12\textwidth} | >{\centering\arraybackslash}m{0.12\textwidth} | >{\centering\arraybackslash}m{0.12\textwidth} |}
\multicolumn{2}{c |}{}  & UCB1 & DUCB & SWUCB & wSWA & (D-)CTO \\ \hline \hline
& UCB1 & \cellcolor{gray} & $<$1e-5 & $<$1e-5 & $<$1e-5& \cellcolor{gray} \\ \cline{2-7}
& DUCB & 100 & \cellcolor{gray} & $<$1e-5 & $<$1e-5 & \cellcolor{gray} \\ \cline{2-7}
& SWUCB & 100 & 100 & \cellcolor{gray} & $<$1e-5 &\cellcolor{gray} \\ \cline{2-7}
\multirow{-4}{*}{\begin{sideways}\textbf{NP}\end{sideways}} & wSWA & \textbf{100} & \textbf{100} & \textbf{100} & \cellcolor{gray} & \cellcolor{gray} \\ \hline \hline
& UCB1 & \cellcolor{gray} & 0.81 & $<$1e-5 & $<$1e-5 & $<$1e-5 \\ \cline{2-7}
& DUCB & 55 & \cellcolor{gray} & $<$1e-5 & $<$1e-5 & $<$1e-5 \\ \cline{2-7}
& SWUCB & 15 & 22 & \cellcolor{gray} & $<$1e-5 & $<$1e-5 \\ \cline{2-7}
& wSWA & \textbf{98} & \textbf{99} & \textbf{100} & \cellcolor{gray} & $<$1e-5 \\ \cline{2-7}
\multirow{-5}{*}{\begin{sideways}\textbf{AV}\end{sideways}} & CTO & \textbf{100} & \textbf{100} & \textbf{100} & \textbf{100} & \cellcolor{gray} \\ \hline \hline
& UCB1 & \cellcolor{gray} & 0.54 & 0.83 & $<$1e-5 & $<$1e-5 \\ \cline{2-7}
& DUCB & 40 & \cellcolor{gray} & 0.91 & $<1$e-5 & $<$1e-5 \\ \cline{2-7}
& SWUCB & 50 & 50 & \cellcolor{gray} & $<$1e-5 & $<$1e-5 \\ \cline{2-7}
& wSWA & \textbf{97} & \textbf{98} & \textbf{97} & \cellcolor{gray} & $<$1e-5 \\ \cline{2-7}
\multirow{-5}{*}{\begin{sideways}\textbf{ANV}\end{sideways}} & D-CTO & \textbf{100} & \textbf{100} & \textbf{100} & \textbf{66} & \cellcolor{gray} \\ \hline \hline \end{tabular}
\label{table_results}
\end{small}
\end{center}
\vskip -0.2in
\end{table*}

\begin{figure*}[t]
\vskip 0.05in
\begin{center}
\begin{minipage}{.32\textwidth}
  \centering
  \includegraphics[width=0.99\textwidth]{non_parametric_crop.pdf}

\end{minipage} \hfill
\begin{minipage}{.32\textwidth}
  \centering
  \includegraphics[width=0.99\textwidth]{parametric_av_crop.pdf}

\end{minipage}
\begin{minipage}{.32\textwidth}
  \centering
  \includegraphics[width=0.99\textwidth]{parametric_anv_crop.pdf}

\end{minipage}
\caption{Average regret. Left: non-parametric. Middle: parametric AV. Right: parametric ANV \label{fig_sim}}
\end{center}
\vskip -0.2in
\end{figure*}

\section{Related Work}
\label{sec_Rel_work}
We turn to reviewing related work while emphasizing the differences from our problem.

\textbf{Stochastic MAB} In the stochastic MAB setting \citep{AsymEfficient}, the underlying reward distributions are stationary over time. The notion of regret is the same as in our work, but the optimal policy in this setting is one that pulls a fixed arm throughout the trajectory. The two most common approaches for this problem are: constructing Upper Confidence Bounds which stem from the seminal work by \citet{gittins1979bandit} in which he proved that index policies that compute upper confidence bounds on the expected rewards of the arms are optimal in this case (e.g., see \citet{FiniteTimeAnalysisMAB, KLUCB, MABkldiv}), and Bayesian heuristics such as Thompson Sampling which was first presented by \citet{ThompsonLikelihood} in the context of drug treatments (e.g., see \citet{TSfinite, TSfurther, TScomplex}).

\textbf{Adversarial MAB} In the Adversarial MAB setting (also referred to as the Experts Problem, see the book of \citet{PredictionsLearningGames} for a review), the sequence of rewards are selected by an adversary (i.e., can be arbitrary). In this setting the notion of \textit{adversarial regret} is adopted \citep{NonStochasticMAB, BetterAlgs}, where the regret is measured against the best possible fixed action that could have been taken in hindsight. This is as opposed to the \textit{policy regret} we adopt, where the regret is measured against the best sequence of actions in hindsight.

\textbf{Hybrid models} Some past work consider settings between the Stochastic and the Adversarial settings. \citet{UpperConfBoundNonStat} consider the case where the reward distributions remain constant over epochs and change arbitrarily at unknown time instants, similarly to \citet{PiecewiseStatMAB} who consider the same setting, only with the availability of side observations. \citet{MortalMAB} consider the case where arms can expire and be replaced with new arms with arbitrary expected reward, but as long as an arm does not expire its statistics remain the same.

\textbf{Non-Stationary MAB} Most related to our problem is the so-called Non-Stationary MAB. Originally proposed by \citet{DynamicAllocationIndex}, who considered a case where the reward distribution of a chosen arm can change, and gave rise to a sequence of works (e.g., \citet{ArmAcquiring, OnlineLearningRestedRestless}) which were termed \textit{Restless Bandits} and \textit{Rested Bandits}. In the \textit{Restless Bandits} setting, termed by \citet{RestlessBanditsAllocation}, the reward distributions change in each step according to a known stochastic process. \citet{TimeDecayingBandits} consider the case where each arm decays according to a linear combination of decaying basis functions. This is similar to our parametric case in that the reward distributions decay according to possible models, but differs fundamentally in that it belongs to the \textit{Restless Bandits} setup (ours to the \textit{Rested Bandits}). More examples in this line of work are \citet{BrownianRestless} who consider evolution of rewards according to Brownian motion, and \citet{StocMABNonStat} who consider bounded total variation of expected rewards. The latter is related to our setting by considering the case where the total variation is bounded by a constant, but significantly differs by that it considers the case where the (unknown) expected rewards sequences are not affected by actions taken, and in addition requires bounded support as it uses the EXP3 as a sub-routine. In the \textit{Rested Bandits} setting, only the reward distribution of a chosen arm changes, which is the case we consider. An optimal control policy (reward processes are known, no learning required) to bandits with non-increasing rewards and discount factor was previously presented (e.g., \citet{mandelbaum1987continuous}, and \citet{kaspi1998multi}). \citet{TightPolicyRegret} (2016) consider the case where the reward decays (as we do), but with no statistical noise (deterministic rewards), which significantly simplifies the problem. Another somewhat closely related setting is suggested by \citet{MABKnownTrend}, in which statistical noise exists, but the expected reward shape is known up to a multiplicative factor.

\section{Discussion}
\label{Sec_dis}
We introduced a novel variant of the \textit{Rested Bandits} framework, which we termed \textit{Rotting Bandits}. This setting deals with the case where the expected rewards generated by an arm decay (or generally do not increase) as a function of pulls of that arm. This is motivated by many real-world scenarios.

We first tackled the non-parametric case, where there is no prior knowledge on the nature of the decay. We introduced an easy-to-follow algorithm accompanied by theoretical guarantees.

We then tackled the parametric case, and differentiated between two scenarios: expected rewards decay to zero (AV), and decay to different constants (ANV). For both scenarios we introduced suitable algorithms with stronger guarantees than for the non-parametric case: For the AV\ scenario we introduced an algorithm for ensuring, in expectation, regret upper bounded by a term that decays to zero with the horizon. For the ANV\ scenario we introduced an algorithm for ensuring, with high probability, regret upper bounded by a horizon-dependent rate which is optimal for the stationary case.

We concluded with simulations that demonstrated our algorithms' superiority over benchmark algorithms for non-stationary MAB. We note that since the RB\ setting is novel, there are not suitable available benchmarks, and so this paper also serves as a benchmark.

For future work we see two main interesting directions: (i) show a lower bound on the regret for the non-parametric case, and (ii) extend the scope of the parametric case to continuous parameterization.

\paragraph{Acknowledgment}
The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Program (FP/2007-2013) / ERC Grant Agreement n. 306638

\begin{small}
\bibliographystyle{abbrvnat}

redpajama_set_name: RedPajamaArXiv


text:
Daimyo experience
GOOD LUCK TRIP
Japanese castles aren't only gorgeous. They were strategically designed for war.
Japanese castles were built by daimyo (feudal lords) in the domains they governed. With the beginning of the Edo period, in 1603, and the peace it ushered in, Japanese castles which had been built during wartime as defensive strongholds became symbols of samurai rule. From the keep at the center of the castle, the stone walls and turrets for defense, the gate, the governing quarters, and the samurai residences to the defensive moat dug around the castle, Japanese castle architecture is both rich and spectacular.
You get to feel like a daimyo!? Take a private sightseeing tour by boat around the moat of Hikone Castle, a national treasure.
Construction began on Hikone Castle in 1604 and more than 400 years have passed since it was completed. It incorporated structures and stone walls from castles in the surrounding area that fell during wartime, and as such is known as a "recycled castle." You can take a private sightseeing tour around the castle moat in a reconstructed boat once reserved for use by daimyo! Viewing the unique stone walls, wooden bridges, the keep, and turrets that come into and go out of view, you can experience the unchanged style and air of the Edo period.
The boat that goes around in the moat has been reconstructed to look how it did in the past!
The boat that the daimyo used when inspecting his territory, or when entertaining other daimyo, has been almost perfectly reconstructed from existing documents and photos taken around 1863. You can take a sightseeing tour in Hikone Castle's inner moat on this boat that only high ranking officials would have been allowed to board. The boat seats 11 adults.
The pleasure boat you will board is the fourth boat, Ryuomaru.
There are two boatmen. On a private tour, the boatmen double as your guides.
You can experience how the nobility would have relaxed. The boat has a low ceiling so that it can go under the bridges over the moat.
Come on this cruise that takes about 90 minutes to go around the moat!
This 90 minute boat trip sets out from in front of Genkyuen Garden near Hikone Castle. Up to 11 people can board the boat, and you can dine on the boat with a reservation (reservations accepted from 8 people), allowing you to spend a wonderful time on board. You can also bring your own drinks including alcohol. If requested, a guide can explain various things inside the castle visible from the moat. Also, it is quite surprising that the moat's stone walls are original constructions that date back over 400 years.
This stone wall is called a "koshimaki stone wall," which is something rarely seen in the Kansai region.
The keep can be seen between a grove of trees. Don't miss a perfect chance to take a picture!
Experience what it feels like to be a daimyo on a private boat available for scenic sightseeing in all seasons!
In spring and autumn, private daimyo experience tours are also available in the evenings when the castle and moat are lit up. Taking a cruise at night with the cherry blossoms or fall leaves lit up is an exclusive pleasure only available with private reservations. Taking in the scenery while aboard a boat for a daimyo in the moat of the castle is sure to be a special memory of your trip to Japan! How would you like to experience what it feels like to be a daimyo from the past?
This is the spring pleasure boat. Cherry blossom viewing season is buzzing with large numbers of visitors.
During the fall leaves season, you can enjoy the best scenery possible with the contrast of red and green leaves.
You can experience these things here!
Name of facility | Hikone-jo Yakatabune
Name of experience|Go around the moat of Hikone Castle on a relaxing sightseeing tour in a private boat
Address | The wharf in front of Genkyuen Garden 3-40 Konkicho, Hikone City, Shiga 522-0061
Business hours |
10 a.m. tour, 11 a.m. tour, 12 p.m. tour, 1 p.m. tour, 2 p.m. tour, 3 p.m. tour, and a 4 p.m. tour (only on Saturdays, Sundays, and holidays)
*Additional tours are available during peak seasons
*Evening illumination tours during cherry blossom season in spring and during the fall leaves season are only available on weekends
*There may be a reduction in the number of tours offered between December and February, as well as during other times of the year.
Closed | No holidays
Estimate | 30,000 yen (1.5 hours per boat, tax included)
Boat meal: 5,000 yen per person, tax included *Separate reservation required, and orders are accepted starting from 8 people
Payment methods | Cash only
Number of people acceptable (group) | 11 people per group (at most 4 pleasure boats are available)
Telephone number| 080-1461-4123
Languages spoken| Japanese only
Official Website (Japanese)| https://yakatabune.info/
Notes | None
Written by GOOD LUCK TRIP


Minimum quantity for "Sweat Pants - Unisex" is 1.
Open Bottom Fleece Pant is 7.5 oz., 68% cotton / 32% polyester, has an inside drawstring waist closure and hidden pockets at side seams. These are a unisex pant and sit at the waistline.
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text:
Q: print_r() displays sql statement instead of printing the pdo result set? I am trying to debug why the below query does not return a result set.  When I tried debugging this using print_r to see what this sql returns , i get the sql statement itself instead of the result set.  Can someone help me with why this doesn't return a result set ?
Edit: I am using query function of the PHP Data Objects
  $query = "select * from categories order by categoryID";

  $categories = $db->query($query);

  print_r($categories);

A: Because that method returns a PDOStatement object, as stated in the manual, not an associative array. You have to get the resultset from this object first.
You could use fetchAll() to get all the results in an associative array (or any other PDO fetch method according to your taste):
print_r($categories->fetchAll());

When you encounter problems like this, it is always advisable to read the manual.

A: $sql = "select * from categories order by categoryID";

$query = $db->query($sql);

$categories = $query->fetchAll();

echo "<pre>";
print_r($categories);
echo "</pre>";

redpajama_set_name: RedPajamaStackExchange


Written by AD Garrett – Is this author's name new to you? Then take note – because you'll be hearing plenty more about AD Garrett, the pseudonym for the writing collaboration of prize-winning thriller author Margaret Murphy and Professor Dave Barclay, a world-renowned forensics expert and advisor to both police forces and the media. And it looks like a marriage made in crime fiction heaven, because this pair have produced a debut novel that might well take your breath away.
Everyone Lies introduces us to DI Kate Simms, a bright and intuitive officer who is on the fast track to nowhere. Five years ago, she bent the rules to help a colleague, got found out, and has been in the wilderness ever since. After clawing her way back from demotion, Kate is trying to make her mark in pastures new, and it is while crunching numbers in her new patch of Manchester that she spots an anomaly. An unusually large number of female drug addicts have been turning up dead. Her bosses dismiss her ideas as foolish, but then a reality TV star dies in the same manner and the media jump on the bandwagon.
Meanwhile, up in Aberdeen, Professor Nick Fennimore is in the midst of delivering a forensics lecture to rapt students. His story is another story of a fall from grace – Nick was once scientific advisor to the National Crime Faculty and was regarded as the best in his field. But ever since his wife was killed and his young daughter disappeared he has been hiding in Scotland, away from the limelight… and from Kate.
It is inevitable that they are thrown together in the search for whoever is responsible for the drugs deaths, but this relationship is never going to be an easy one. The pair have a history which is gradually drip fed to the reader as the story progresses. The water is muddied further by the fact that one of Kate's new bosses was there when her career imploded. Assistant Chief Constable Gifford is determined to see her fail again, and if he knew that Nick was back on the scene, Kate would probably be out of a job. Much of the story takes place on the mean streets of Manchester and Hull, and these areas are painted with deft brushstrokes. The characterisation is spot on and the action moves along at a cracking pace, but it is the sheer depth of forensic detail which sets this book apart.
Kate Simms and Nick Fennimore form a fascinating pairing. Both have their faults, but together they make a formidable pairing. Fennimore is helped by one of his students, Josh Brown, who is young, bright, energetic and something of an enigma. There are far too many loose ends for us not to be given a second Simms and Fennimore novel. Nick is still looking for his missing daughter, and just what is Josh's secret? I'll be devastated if we don't meet this lot again. Everyone lies is out on 20 June.


Approval ratings in North Carolina of Pres. Obama and GOP candidate for the U.S. Senate Thom Tillis, respectively, according to a poll released Oct. 31 by CNN/ORC International. The poll found a 49-48 percent favorable/unfavorable rating for Democratic Sen. Kay Hagan, the incumbent. The unfavorable rating for Tillis, who is also assembly leader, was 50 percent. Hagan holds a slight lead over Tillis, 48-46 percent, in the poll.


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Juneteenth: Let's Take It Outside Virtual Town Hall Recap
Conservation Lands Foundation partnered with Black Voice News to host a conversation about Race, Access, and America's Public Lands.
by Maricela Rosales
on June 22, 2020 < 1 min read
Watch Now: Juneteenth: Let's Take It Outside Virtual Town Hall co-hosted by Black Voice News and Conservation Lands Foundation on June 19.
"Juneteenth is significant because it marks the time the last of the enslaved learned they were free. That news literally was the difference between a life of freedom or bondage. As a legacy news platform, it is our mission to empower our community through information and this Town Hall was the continuation of an important conversation on access to all our shared public spaces." Paulette Brown-Hinds, PhD, Founder of Voice Media Ventures and Publisher of Black Voice News
A recording of the Town hall is available to watch here:
[embedlycard url="https://www.youtube.com/watch?v=MH7pcMUd8_I"]
We invite you to share this Town Hall conversation with colleagues, staff, friends and family and we look forward to continuing the deeper conversations and actions that are needed to create a future where access to public lands and outdoor spaces is equitable and welcoming for Black, Indigenous and all people of color.
About Maricela Rosales
Maricela is from Southern California and CLF's California Associate Program Director
Maricela Rosales published Juneteenth: Let's Take It Outside Virtual Town Hall in The Latest 2020-06-22 13:54:34 -0600


Chanse has been eating super healthy lately, so I have been trying to incorporate recipes that we can both enjoy. He really is so incredible though and I am so proud of him for making goals and keeping them. He looks great and has made amazing progress towards his goals! We both really like buffalo sauce, so when I found this recipe on Pinterest, I knew it would be a win.
This particular recipe uses the chicken for lettuce wraps, and they were amazing! However, you can eat the chicken in burritos with rice and beans, over a salad, or you can just dip them in extra buffalo sauce and eat with whatever sides you'd like! And best of all...the recipe is SO quick and easy!
You start off by cutting your thawed chicken into bite size pieces and then preparing a bowl of buffalo sauce and a bowl bread crumbs.
First, dip the chicken into the bowl of buffalo sauce.
Next, transfer these pieces of chicken into the bowl of bread crumbs.
Then you simply place them on a pan and stick 'em in the oven for 30 minutes at 375 degrees. I foil my pans for easier clean-up in case you were wondering.
Super easy right?! Seriously go make these right now! They are so good!
Saving this! I'm pretty obsessed with Franks and this sounds amazing. Thanks for sharing!
Such a great recipe, I cannot wait to try this!!
Omg! These look simply divine!!
This recipe seems simple enough, indeed! I love finding good recipes on Pinterest, too!
It fits a lot for evening movie with friend. Recipe is simple and fast-cooking.


1(a). In the diagram presented, eight effects were observed in the first marking scheme. The items depicted in the marking scheme are reminiscent of the bullwhip effect, which is an incident that occurs when some factors in a demand-motivated supply chain act up. The first observation is the distortion of information. Upon looking closely at the setup of the supply chain, it is easy to leave out and misinterpret bits and pieces of information as a result of the frequent transfer of information from one part of the chain to another.
The second and an obvious characteristic of the organization presented by the diagram is the fragmented supply chain. In the diagram, companies A,B,C,D, and E have an indirect but interdependent relationships as far as supply and demand are concerned. Companies A, B, C, D and E each "own" one particular stage that lead to the delivery of the finished product to the consumer. Taking into account the companies' relationships with the customer, only Company A has a clear-cut and transparent association with the end user.
The structure of the supply chain as seen in the diagram lends to the amplification of demand further away from real demand. The end consumer is in direct communication only with Company A. Company A, on the other hand has an exclusive partnership with Company B. Likewise, Company B has an exclusive partnership with Company C. The same setup is true for Company D and E. The real demand is what the end consumer requires from Company A. The existing arrangement complicates an otherwise simple business transaction between what is supposed to be a deal between Company A and the end customer.
1(b). Several solutions can be implemented to reduce the impact of the bullwhip effect.
2(a) Organizations use certain criteria to evaluate the performance of a logistics supply chain.
Storage and transportation are essential activities in a supply chain. A well-defined warehousing and transportation system will lead to reduced logistics support costs.
It is wise to measure supplier performance to ensure that products are always at top quality. Suppliers should be subject to inspections and evaluations from time to time to guarantee that raw materials or services availed from them are at a par to the company's standards.
2 (b) All business organizations are not without their fair share of obstacles. In developing a Logistics Information System or L.I.S. there are several common problems that organizations should look out for.
The first problem is inaccurate data. Inaccurate data results from an inefficient system of recording transaction information. In spite of the seemingly trivial, sometimes redundant nature of this activity, it is essential that problems with inaccurate data is dealt with at the onset to prevent a snowball effect of other problems that it may bring about.
Lee, Hau L., V. Padmanabhan, and Seungjin Whang. Comments on "information distortion in a supply chain: the bullwhip effect"; The bullwhip effect: reflections. December 1, 2004.
Wikipedia, the free encyclopedia. Enterprise resource planning. August 9, 2007. http://en.wikipedia.org/wiki/Enterprise_resource_planning#Before (accessed August 10, 2007).
Would Climate Change Lead to New Wars in Africa?