LWE search to decision reduction

1 Decision to Search Reduction for LWE The rst step is to come up with a way to reduce the search version of LWE to the decision version (which is the basis of cryptographic schemes, e.g., the public-key encryption schemes we already saw in Lecture 1). Later, we will show a reduction from worst-case lattice problems to search LWE DOI: 10.1109/ITW.2011.6089491 Corpus ID: 2905292. Search to decision reduction for the learning with errors over rings problem @article{Lyubashevsky2011SearchTD, title={Search to decision reduction for the learning with errors over rings problem}, author={Vadim Lyubashevsky}, journal={2011 IEEE Information Theory Workshop}, year={2011}, pages={410-414} applications in cryptography. Among these reductions is a search to decision reduction, showing that it suffices to distinguish LWE samples from entirely uniform samples, and a worst-case to average-case reduction, showing that it suffices to solve this distinguishing task for a uniform secret s 2Zn q Pseudorandom Knapsacks and the Sample Complexity of LWE Search-to-Decision Reductions. Daniele Micciancio and Petros Mol. Abstract: We study under what conditions the conjectured one-wayness of the knapsack function (with polynomially bounded inputs) over an arbitrary finite abelian group implies that the output of the function is pseudorandom, i.e

Search to decision reduction for the learning with errors

• Toolset for studying Search-to-Decision reductions for LWE with polynomially bounded noise. - Subsume and extend previously known ones - Reductions are in addition sample-preserving. Our results 8 • Powerful and usable criteria to establish Search-to The learning with errors (LWE) problem assures the security of modern lattice-based cryptosystems. It can be reduced to classical lattice problems such as the shortest vector problem (SVP) and the closest vector problem (CVP). In particular, the search-LWE problem is reduced to a particular case of SVP by Kannan's embedding technique mials f would strengthen our confidence in the hardness of MP-LWE. A first strategy towards this goal would be to design a reduction from search PLWE(f) to decision PLWE(f) for larger classes of f's than cur-rentlyhandled(thereductionfrom[LPR13]requiresftobecyclotomic). ThisreductioncouldthenbecombinedwiththeonefromApproxSVP(f The proof of security is by reduction to the decision version of LWE: an algorithm for distinguishing between encryptions (with above parameters) of and can be used to distinguish between , and the uniform distribution ove That is, search reduces to decision for SAT. This implies that if Sat is in P then its search version is solvable in polynomial time. (Don't say that the search problem is \in P. That is not meaningful since P only contains languages, meaning decision problems.) So we can concentrate on the decision version, which is simpler. Th

Talk at crypto 2011. Authors: Daniele Micciancio, Petros Mol. See http://www.iacr.org/cryptodb/data/paper.php?pubkey=2359 Article Pseudorandom Knapsacks and the Sample Complexity of LWE Search-to-Decision Reductions Detailed information of the J-GLOBAL is a service based on the concept of Linking, Expanding, and Sparking, linking science and technology information which hitherto stood alone to support the generation of ideas. By linking the information entered, we provide opportunities to make unexpected. More precisely: we prove that the (decision/search) dual to primal reduction from Lyubashevsky et al. [EUROCRYPT~2010] and Peikert [SCN~2016] can be implemented with a small error rate growth for all rings (the resulting reduction is non-uniform polynomial time); we extend it to polynomial-time reductions between (decision/search) primal RLWE and PLWE that work for a family of polynomials f that is exponentially large as a function of deg f (the resulting reduction is also non. Bibliographic details on Pseudorandom Knapsacks and the Sample Complexity of LWE Search-to-Decision Reductions We distinguish self-reducibility of a language L with the question of whether search reduces to decision for L. Results include: (i) If NE ≠ E, then there exists a set t in NP - P such that search reduces to decision for L, search does not nonadaptively reduce to decision for L and L is not self-reducible, (ii) If UE ≠ E, then there exists a language L ∈ UP - P such that search.

This is the best known algorithm for the LWE problem. Our main theorem shows that for certain choices of p and ´, a solution to LWEp;´ implies a quantum solution to worst-case lattice problems. Theorem 1.1 (Informal) Let n;p be integers and fi 2 (0;1) be such that fip > 2 p n. If there exists an efficient algorithm that solves LWEp; This publication has not been reviewed yet. rating distribution. average user rating 0.0 out of 5.0 based on 0 review

Search by expertise, name or affiliation. Solving the Search-LWE Problem by Lattice Reduction over Projected Bases. Satoshi Nakamura, Nariaki Tateiwa, Koha Kinjo, Yasuhiko Ikematsu, Masaya Yasuda, Katsuki Fujisawa. Laboratory of Mathematical Design for Advanced Cryptography CiteSeerX - Scientific articles matching the query: Pseudorandom Knapsacks and the Sample Complexity of LWE Search-to-Decision Reductions

LWE, the worst-case lattices are restricted to classes of very special lattices known as ideal lattices. Our reductions are based on a new tool, which we call structural lattice reduction, an A longstanding open question is whether there is an equivalence between the computational task of determining the minimum size of any circuit computing a given function and the task of producing a minimum-sized circuit for a given function. While it is widely conjectured that both tasks require perebor, or brute-force search, researchers have not yet ruled out the possibility that the search. The ring learning with errors (RLWE) problem is built on the arithmetic of polynomials with coefficients from a finite field. A typical polynomial. a ( x ) {\textstyle a (x)} is expressed as: a ( x ) = a 0 + a 1 x + a 2 x 2 + + a n − 2 x n − 2 + a n − 1 x n − 1 {\displaystyle a (x)=a_ {0}+a_ {1}x+a_ {2}x^ {2}+\ldots +a_ {n-2}x^ {n-2}+a_ {n-1}x^. reducing a decision problem to a local search problemHelpful? Please support me on Patreon: https://www.patreon.com/roelvandepaarWith thanks & praise to God.. In computability theory and computational complexity theory, a reduction is an algorithm for transforming one problem into another problem. A sufficiently efficient reduction from one problem to another may be used to show that the second problem is at least as difficult as the first

Cryptology ePrint Archive: Report 2011/521 - Pseudorandom

  1. Serious Violence Reduction SVROs give us a chance to look again at stop and search and what more can be done in the courts to reduce offending. We welcome the Government's decision to.
  2. Design reduction is essential to creating engaging, user-centered design but only works when coupled with rigorous user research that contributes to informed design decisions. Conclusion Because research, decisions, and the design process go together, the focus of this article has been to identify the risks of rushing user research and design
  3. Computer Science: Reduce set partition search to decision?Helpful? Please support me on Patreon: https://www.patreon.com/roelvandepaarWith thanks & praise t..
  4. But, if the decision variable is a continuous type variable, then we usually use another impurity metric 'standard deviation reduction'. But, whatever metric you use, depending on your algorithm (i.e. ID3, C4.5, etc) you actually find an attribute that will be used for splitting

Information Search: The second of five stages that comprise the Consumer Decision Process. It can be categorized as internal or external research. External Research : When a person has no prior knowledge about a product, which then leads them to seek information from personal or public sources In the case of lattice-based cryptography, currently existing quantum attacks are mainly classical attacks, carried out with quantum basis reduction as subroutine. In this work, we propose a new quantum attack on the learning with errors (LWE) problem, whose hardness is the foundation for many modern lattice-based cryptographic constructions

Solving the Search-LWE Problem by Lattice Reduction over

We obtain several results that distinguish self-reducibility of a language L with the question of whether search reduces to decision for L. We prove that if NE intersects co-NE ≠ E, then there exists a set L in NP - P such that search reduces to decision for L, search does not nonadaptively reduce to decision for L, and L is not self-reducible Find out about education and training opportunities offered by the institutions participating in ATHENE. more INF Several results that distinguish self-reducibility of a language L with the question of whether search reduces to decision for L are obtained. It is proved that if NE intersection co-NE not=E, then there exists a set L in NP-P such that search reduces to decision for L, search does not nonadaptively reduce to decision for L, and L is not self-reducible

Learning with errors - Wikipedi

Pruning is a data compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and redundant to classify instances. Pruning reduces the complexity of the final classifier, and hence improves predictive accuracy by the reduction of overfitting.. One of the questions that arises in a decision tree. Decision Tree Splitting Method #1: Reduction in Variance Reduction in Variance is a method for splitting the node used when the target variable is continuous, i.e., regression problems. It is so-called because it uses variance as a measure for deciding the feature on which node is split into child nodes

Decision tree learning or induction of decision trees is one of the predictive modelling approaches used in statistics, data mining and machine learning.It uses a decision tree (as a predictive model) to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves).). Tree models where the target variable can take a. The results were dramatic: The global company reduced the review cycle to just one person owning the process, which took a single month. Prior, it took three people and six months, so that's an. Hyper-parameters of Decision Tree model. 2. So this recipe is a short example of how to use Grid Search and get the best set of hyperparameters. (PCA) which will reduce the dimension of features by creating new features which have most of the varience of the original data

Pseudorandom Knapsacks and the Sample Complexity of LWE

Map > Data Science > Predicting the Future > Modeling > Classification > Decision Tree: Decision Tree - Classification: Decision tree builds classification or regression models in the form of a tree structure. It breaks down a dataset into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed How to Deal with Overfitting? From previous section, we know the behind-scene reason why a decision tree overfits. To prevent overfitting, there are two ways: 1. we stop splitting the tree at some point; 2. we generate a complete tree first, and then get rid of some branches English Dictionary and Translation Search with 1,000,000,000 example sentences from human translators. Languages: English, German, French, Spanish, and Portugues Decisions are unique actions a landed character who is at least Count can take. Most decisions are unavailable if the character has the Incapable trait. Decisions are not shown unless the character meets one of the more specific requirements

We will see what is decision tree learning Then decision tree representation ID3 learning algorithm Concepts like - Entropy, information gain which help to improve the inductive bias of the decision trees. Overfitting problem which can occur in the decision trees and ways to solve the problem. Decision Tree learning is a method of approximating discrete-values functions that is robust to. Summary. AI holds the greatest promise for eliminating bias in hiring for two primary reasons. It can eliminate unconscious human bias, and it can assess the entire pipeline of candidates rather. The decision hinges on what size the market for the product will be. Possibly demand will be The claimed advantages of the system will be a reduction in labor cost and an improved product yield Algorithms for the soft-decision decoding of linear block codes are presented. These algorithms perform a reduced complexity search through a trellis derived from the parity check matrix of an (n, k) linear block code Decision problems can be ordered according to many-one reducibility and related to feasible reductions such as polynomial-time reductions.A decision problem P is said to be complete for a set of decision problems S if P is a member of S and every problem in S can be reduced to P.Complete decision problems are used in computational complexity theory to characterize complexity classes of.

This is 19 How To Reduce Decisions 2 by iStudy on Vimeo, the home for high quality videos and the people who love them Step one: Don't be ashamed of your fear. Summary. No one can reduce mistakes to zero, but you can learn to harness your drive to prevent them and channel it into better decision making Decision to reduce PUP 'premature and unfair' - SF. rte.ie - Tommy Meskill • 4h. The Sinn Féin leader described the reductions in the Pandemic Unemployment Payment from September as premature and deeply unfair. The PUP. Here's a five-step process you can use to get started with data-driven decisions. 1. Look at your objectives and prioritize. Any decision you make needs to start with your business' goals at the core. So, In fact, companies that use data at the core of their decision-making reduce costs and increase profit Interventions that aim to facilitate shared decision making reduce antibiotic prescribing in primary care in the short term. Effects on longer-term rates of prescribing are uncertain and more evidence is needed to determine how any sustained reduction in antibiotic prescribing affects hospital admis

For example, consider an AI that is able, with a good degree of accuracy, to detect a decline in a person's health, say through spending patterns (doctor's co-pays), internet searches (cancer. This is 4 How To Reduce Decisions 2 copy by iStudy on Vimeo, the home for high quality videos and the people who love them

Cryptology ePrint Archive: Report 2018/170 - On the Ring

So taking a look at our fall data we find that the starting impurity score is 0.3648, if we split at 1.5 shoe slipperiness then we get a score of 0.2747 (0.0901 reduction) and if we were to split at 2.5 for floor slipperiness we get a score of 0.288 (0.0768) making the shoe cut the best Shared decision making is a joint process. It allows a health and care professional support people reach a decision about their care. Health and care professionals work together with people who use health and care services, their families, and carers If you are using the Decision Tree Tool for In-DB processing on a SQL Server, a third rpackage, RevoScaleR is used to construct the decision tree. The rpart (recursive partitioning) package implements the functionality from the book Classification and Regression Trees by Breiman, Freidman, Olshen and Stone, published in 1984 Search. Home. All India We've Been Judge Questions Narada Case Decisions . We have been reduced to a mockery, Justice Arindam Sinha writes in the letter that has stunned the judiciary Reduction in European supplementary search fee Decision of the Administrative Council of 12 December 2019 amending Articles 2 and 7 of the Rules relating to Fees ( CA/D12/19 ) (OJ EPO 2020, A3) see article

Search this website Search. (i.e. a projected decrease of 0.112 million stunted children). SoSuRe - protecting the most vulnerable in Malawi 7665 final COMMISSION DECISION of 22.11.2016 on the Annual Action Programme 2016 in favour of the Republic of Malawi to be financed from the 11 th European Development Fund EN 2 EN. Shared decision making to improve care and reduce costs. Shared decision making to improve care and reduce costs N Engl J Med. 2013 Jan 3;368(1):6-8. doi: 10.1056/NEJMp1209500. Authors Emily Oshima Lee 1 , Ezekiel J Emanuel. Affiliation 1 Center for American.

P-selective sets and reducing search to decision vs self

That's because many may provide side benefits such as reduced air pollution, which could make them attractive even if they entail high carbon emission-reduction costs. Moreover, in the longer term, their resulting emission reductions and cost per ton reduced may look very different, owing to spillovers from induced technological change Applying this to decision trees, it means that that we can construct a high number of trees which will have high variance and low bias. Then, we can average their predictions to reduce the variance to improve the performance of the decision trees Shell ordered to cut emissions by 45% by 2030 in 'historic' court decision. Anglo-Dutch oil supermajor Royal Dutch Shell has been ordered by a Dutch court to reduce its carbon emissions by 45% by 2030, which campaigners describe as a historic decision

Sources of Information Top 10 Activities of Adult Internet Users E-Mail 91% Use search engine to find information 84 Search for a map or driving directions 84 Do an Internet search to answer a specific question 80 Research a product/service before buying it 78 Check the weather 78 Look for informtion on a habbyor interest 77 Get travel information 73 Get news 72 Buy a Product 67 Number. CiteSeerX - Scientific articles matching the query: P-Selective Sets, and Reducing Search to Decision vs. Self-Reducability

INTRODUCTION: Administration of human serum albumin (HSA) solutions for the resuscitation of critically ill patients remains controversial. The objective of this study was to assess the effect of continuing medical education (CME) on health care professionals' clinical decision making with regard to HSA administration and the costs of quality (COQ) Apple on Wednesday announced the App Store Small Business Program, which will allow eligible developers to earn an extra 15 percent of revenue so long as they earn less than $1 million in sales. Unconscious Bias. Unconscious bias is different from cognitive bias. Also known as implicit bias, it refers to unconscious forms of discrimination and stereotyping based on race, gender, sexuality, ethnicity, ability, age, and so on [6].Despite cognitive biases sometimes leading to discriminatory thinking and feeling patterns, these are two separate and distinct concepts Apple's iPhone 12 won't come with earbuds or a wall charger in the box. The company says that will cut down greenhouse gas emissions, but the environmental effects of its decision might be.

CiteSeerX — Search Results — Pseudorandom Knapsacks and

  1. g.
  2. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We obtain several results that distinguish self-reducibility of a language L with the question of whether search reduces to decision for L. These include: (i) If NE 6= E, then there exists a set L in NP \Gamma P such that search reduces to decision for L, search does not nonadaptively reduces to decision for L, and L.
  3. Decision trees are the most susceptible out of all the machine learning algorithms to overfitting and effective pruning can reduce this likelihood. This post will go over two techniques to help with overfitting - pre-pruning or early stopping and post-pruning with examples

DROPS - Connecting Perebor Conjectures: Towards a Search

Search to Selection: Reducing Decision Fatigue on your eCommerce site. Posted by Elizabeth Kottoor November 12, 2020. As the world continues to go digital-first, customers have what seems like unlimited choices when it comes to finding a product they are looking for Background: Within the EU-funded project PRIMA-eDS (Polypharmacy in chronic diseases: Reduction of Inappropriate Medication and Adverse drug events in older populations by electronic Decision Support) an electronic decision support tool (the PRIMA-eDS-tool) was developed for general practitioners (GPs) to reduce inappropriate medication in their older polypharmacy patients Introduction to Decision Tree Algorithm. Decision Tree algorithm belongs to the family of supervised learning algorithms.Unlike other supervised learning algorithms, decision tree algorithm can be used for solving regression and classification problems too.. The general motive of using Decision Tree is to create a training model which can use to predict class or value of target variables by.

Ring learning with errors - Wikipedi

  1. Decision Tree Classification Algorithm. Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems. It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome
  2. Laboratory test overutilization increases health care costs, leads to unwarranted investigations, and may have a negative impact on health outcomes. The American Society of Clinical Pathology, in its Choosing Wisely Campaign, advocates that inflammation be investigated with C-reactive protein (CRP) instead of Erythrocyte Sedimentation Rate (ESR). London Health Sciences Centre (LHSC), a.
  3. What is Decision Making? In its simplest sense, decision-making is the act of choosing between two or more courses of action. In the wider process of problem-solving, decision-making involves choosing between possible solutions to a problem.Decisions can be made through either an intuitive or reasoned process, or a combination of the two.. Intuitio

Objective: We created a system using a triad of change management, electronic surveillance, and algorithms to detect sepsis and deliver highly sensitive and specific decision support to the point of care using a mobile application. The investigators hypothesized that this system would result in a reduction in sepsis mortality. Methods: A before-and-after model was used to study the impact of. P-Selective Sets, and Reducing Search to Decision vs. Self-Reducability Ashish V. Naik , Mitsunori Ogiwara , Alan L. Selman . In Structure in Complexity Theory Conference

reducing a decision problem to a local search problem

  1. Because people will be scrutinizing buying decisions more, you need to be more intentional, direct, Hard ROI is usually defined by the money saved in terms of reducing or avoiding a cost
  2. Patient decision aids that aim to reduce the use of care considered low value are not catalogued in the Cochrane review on decision aids because this objective renders them ineligible.3 However, through our research in this field, consultation with colleagues and searches of the published literature, we have become aware of six such decision aids
  3. Decrease Font Size Font Decrease. and work continues on making the text of all decisions available in the PERB Decision Search Engine. Word searches can be run against the text of those PERB decisions which have full text available on the web site

Reduction (complexity) - Wikipedi

So taking a look at our fall data we find that the starting impurity score is 0.3648, if we split at 1.5 shoe slipperiness then we get a score of 0.2747 (0.0901 reduction) and if we were to split at 2.5 for floor slipperiness we get a score of 0.288 (0.0768) making the shoe cut the best Section 3506 of the Affordable Care Act encourages use of shared decision making in health care, but progress on this front has been slow. The first step is for the Centers for Medicare and Medicai.. You understand how a decision tree either by itself or in a tree based ensemble decides on the best order of features to split on and decides when to stop when it trains itself on given data. If you every have to explain the intricacies of how decision trees work to someone, hopefully you won't do too bad Here's a quick look at the history of decision trees: 1963: The Department of Statistics at the University of Wisconsin-Madison writes that the first regression tree was invented in 1963 (AID project, Morgan and Sonquist) P-selective sets and reducing search to decision vs self-reducibility Edith Hemaspaandra, Ashish V. Naik, Mitsunori Ogihara , Alan L. Selman Research output : Contribution to journal › Articl

New stop and search powers for convicted knife criminals

CiteSeerX - Scientific articles matching the query: Algorithms for Reduced Ordered Binary Decision Diagrams Based on the reduced searches for pregnancy-related terms, the authors of that study forecast a reduction of births on the order of 15 percent, an even larger drop than what we forecasted The U.S. Food and Drug Administration today announced a new comprehensive plan for tobacco and nicotine regulation that will serve as a multi-year roadmap to better protect kids and significantly. Table 1 y Case Scenarios Presented to Subjects* - Research Paper: Can Cognitive Biases during Consumer Health Information Searches Be Reduced to Improve Decision Making Reduce set partition search to decision? 1. How do I reduce subset sum to another problem in NP? 2. How to prove the NP-completeness of MOD-PARTITION. Hot Network Questions Is there any realistic explanation for Earth-like climate on a Sun-sized planet

Reducing borderline content and harmful misinformation Content that comes close to — but doesn't quite cross the line of — violating our Community Guidelines is a fraction of 1% of what's watched on YouTube in the U.S Rethink decision making. For 2021, especially, it will be critical to get buy-in from entire leadership teams on high-level strategic objectives, projected economic scenarios, cost-saving targets, resource-reallocation targets, and financial plans. CFOs will need to explain why the standard budgeting process is, in many respects, moot and how, for 2021 and beyond, finance teams will be focused. How To Simplify Decision-making With Design. Psychologist Barry Schwartz gave a TED talk on this exact problem and explained how it doesn't just lead to abandoned purchases, but to less satisfying purchases.. Essentially, Schwartz argues that too many choices cause consumers to Reducing administrative costs by 10%, or 20%, or 30% is a daunting task, but take heart. If you start now and stick with it, you will find enough ideas—and the right ones—to reach your goal Optus' Critical Information Summaries (CIS) contain detailed information about our personal products and services in plain language that is easy to understand

Reducing Design Risk — Smashing Magazin

Hi @data2,. 1. The Number of folds to use in cross-validation to prune the tree option under the model tab is related to a procedure for pruning the decision tree trained by the tool, and the Number of cross-validation folds option under the cross-validation tab is used for performing a cross-validation routine to evaluate the decision tree model Rather, we must look to see if in fact the mental illness (or the cognitive impairment) is undermining decision-making capacity for a particular individual and in relation to a particular decision. Clinicians look for the same features that are supposedly relevant for ordinary patients: understanding, appreciation, and reasoning, as described above (Whiting 2015) The U.S. Court of Appeals for the District of Columbia Circuit vacated a Trump administration rule on carbon dioxide emissions, effectively restoring President Barack Obama's Clean Power Plan. SAS decision trees with multiple targets are not suitable for this because the split search looks only at one of the targets. In order to use multiple targets, you set the Use Multiple Targets property to Yes In recent years, the theory of decision-theoretic rough set and its applications have been studied, including the attribute reduction problem. However, most researchers only focus on decision cost instead of test cost. In this paper, we study the attribute reduction problem with both type

Computer Science: Reduce set partition search to decision

Full title: OJ EPO SE 4/2016, p460 - Decision of the Administrative Council of 21 October 2008 concerning the reduction of the fees for the international search and the international preliminary examination carried out on international applications in favour of nationals of certain states (CA/D 7/08 OSTI.GOV Journal Article: Look and listen before making costly decisions on noise reduction Title: Look and listen before making costly decisions on noise reduction Full Recor AOL Customer Support Number ⋘ +1888 ≡ 210 ≡ 2883 ⋙ Contact AOL Email Customer Support Toll Free Helpline services LWE Search Board decisions by year since 1974 in appeals from DAB ALJ decisions and disputes in HHS programs, including grant disallowances and terminations. DAB Administrative Law Judge (ALJ) Decisions Search DAB ALJ decisions by year since 1985, for CMS, SSA, HHS IG, and FDA actions, including civil money penalties, Medicare enrollment/revocation, and exclusions Search. Portal sign in menu. Portal sign in Expand Portal sign in sub menu. myplace participant portal Substantially reduced functional capacity to undertake relevant activities. Requesting further information or reports to inform the access decision. Clients of specified new south wales disability programs

Introduction The early postattempt period is considered to be one of the most at-risk time windows for suicide reattempt or completion. Among the postcrisis prevention programmes developed to compensate for this risk, brief contact interventions (BCIs) have been proven to be efficient but not equally for each subpopulation of attempters. VigilanS is a region-wide programme that relies on an. Reducing urban traffic congestion due to localized routing decisions Bo Li, David Saad, and Andrey Y. Lokhov Phys. Rev. Research 2, 032059(R) - Published 2 September 202 Power lines are seen on Feb. 16 in Houston. The Public Utility Commission of Texas has declined to reverse $16 billion in charges from the worst of February's winter storm

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