Publication date: January 2025 Source: Research in Autism Spectrum Disorders, Volume 119 Author(s): W.C. Su, S. Srinivasan, A.N. Bhat Read the full article ›

The post Effects of creative movement, general movement, or seated play interventions on motor performance in children with autism spectrum disorder: A pilot randomized controlled trial was curated by information for practice.

​​This done, see that you take no longer days, But send the midwife presently to me. -Aaron Titus Andronicus        Act IV, scene ii      Line 166 Let's be clear here. The 'this done' that the speaker is referring to is the burying of the body of the nurse he just killed. And the reason he...

You can upgrade RAM on your computer at home without the need of a specialist. Take a look at these easy steps to upgrade RAM on your PC.

Will's pick this week is Random String Utility by Dan Couture. This is an oldie but a goodie. This submission is from 2012 but still works just fine in R2022b (another testament to the... read more >>

A new mathematical proof helps show whether a sequence of numbers is “pseudorandom”

Want to create more randomized effects in your JavaScript code? The `Math.random()` method alone, with its limitations, won’t cut it for generating unique random numbers. Amejimaobari Ollornwi explains how to generate a series of unique random numbers using the `Set` object, how to use these random numbers as indexes for arrays, and explores some practical applications of randomization.

Tags: random, uncertainty

Debbie Nicholson, Web Support Officer, University of Essex will show how putting quality measures in place can prevent getting a brief for a Web design job that reads "not blue, a bit random, and not too Scottish". Don't laugh, this actually happened! This hands on session will get participants thinking about how they can introduce quality assurance procedures within the web design process. It will cover establishing a 'quality loop', creating measurable standards and will introduce ways to enable clients to be better informed about what they want from their new Web site.

Location: Electronic Resource- 

We’ve got several subfolders for the Inbox in a Shared Mailbox which we want to sort in such a way that the most frequently folders are at the top. The rest can remain ordered alphabetically.

We have no issues changing the sorting order at first. However, the sorting order seems to randomly get reset again and the folders end up in their original location.

We’ve tried renaming the folders with special prefixes as well, but that ended up in an even bigger mess.

Is there really no way to manually sort the folders of a Shared Mailbox and make them stay in their location?

A Random Walk Down Wall Street (paperback)

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Lost in Random

ends in 13 days

Marcelo Campos, Matthew Jenssen, Marcus Michelen and Julian Sahasrabudhe
J. Amer. Math. Soc. 38 (), 179-224.
Abstract, references and article information

Ran Gu and Bing Gao
Math. Comp. 93 (), 2837-2860.
Abstract, references and article information

Marko Voutilainen, Lauri Viitasaari and Pauliina Ilmonen
Theor. Probability and Math. Statist. 111 (), 181-197.
Abstract, references and article information

Ta Cong Son, Tran Manh Cuong, Le Quang Dung and Le Van Dung
Theor. Probability and Math. Statist. 111 (), 153-165.
Abstract, references and article information

Bero Roos
Theor. Probability and Math. Statist. 111 (), 137-151.
Abstract, references and article information

Abdessamad Dehaj and Mohamed Guessous
Theor. Probability and Math. Statist. 111 (), 1-8.
Abstract, references and article information

R. Greg Stacey
Nov 4, 2020; 0:RA120.002275v1-mcp.RA120.002275
Research

Mendelian randomization (MR) of lipid traits in coronary artery disease (CAD) has provided evidence for causal associations of low-density lipoprotein cholesterol (LDL-C) and triglycerides (TG) in CAD, but many lipid trait genetic variants have pleiotropic effects on other cardiovascular risk factors that may bias MR associations. The goal of this study was to evaluate pleiotropic effects of lipid trait genetic variants and to account for these effects in MR of lipid traits in CAD. We performed multivariable MR using inverse variance-weighted (IVW) and MR-Egger methods in large (n ≥ 300,000) GWAS datasets. We found that 30% of lipid trait genetic variants have effects on metabolic syndrome traits, including body mass index (BMI), type 2 diabetes (T2D), and systolic blood pressure (SBP). Nonetheless, in multivariable MR analysis, LDL-C, high-density lipoprotein cholesterol (HDL-C), TG, BMI, T2D, and SBP are independently associated with CAD, and each of these associations is robust to adjustment for directional pleiotropy. MR at loci linked to direct effects on HDL-C and TG suggests locus- and mechanism-specific causal effects of these factors on CAD.

Biological functions emerge from complex and dynamic networks of protein-protein interactions. Because these protein-protein interaction networks, or interactomes, represent pairwise connections within a hierarchically organized system, it is often useful to identify higher-order associations embedded within them, such as multi-member protein complexes. Graph-based clustering techniques are widely used to accomplish this goal, and dozens of field-specific and general clustering algorithms exist. However, interactomes can be prone to errors, especially when inferred from high-throughput biochemical assays. Therefore, robustness to network-level noise is an important criterion for any clustering algorithm that aims to generate robust, reproducible clusters. Here, we tested the robustness of a range of graph-based clustering algorithms in the presence of noise, including algorithms common across domains and those specific to protein networks. Strikingly, we found that all of the clustering algorithms tested here markedly amplified noise within the underlying protein interaction network. Randomly rewiring only 1% of network edges yielded more than a 50% change in clustering results, indicating that clustering markedly amplified network-level noise. Moreover, we found the impact of network noise on individual clusters was not uniform: some clusters were consistently robust to injected noise while others were not. To assist in assessing this, we developed the clust.perturb R package and Shiny web application to measure the reproducibility of clusters by randomly perturbing the network. We show that clust.perturb results are predictive of real-world cluster stability: poorly reproducible clusters as identified by clust.perturb are significantly less likely to be reclustered across experiments. We conclude that graph-based clustering amplifies noise in protein interaction networks, but quantifying the robustness of a cluster to network noise can separate stable protein complexes from spurious associations.

While testing the "infinite monkey theorem," mathematicians found that the odds of a chimpanzee typing even a short phrase like "I chimp, therefore I am" before the death of the universe are 1 in 10 million billion billion

Learn how to fit a random forest and use your model to score new data. In Part 6 and Part 7 of this series, we fit a logistic regression and decision tree to the Home Equity data we saved in Part 4. In this post we will fit a Random [...]

Getting Started with Python Integration to SAS Viya for Predictive Modeling - Fitting a Random Forest was published on SAS Users.

Optimize Regression Suite, Accelerate Coverage Closure, and Increase hit count of rare bins using Xcelium Machine Learning. It is easy to use and has no learning curve for existing Xcelium customers. Xcelium Machine Learning Technology helps you discover hidden bugs when used early in your design verification cycle.(read more)

Peripheral Component Interconnect Express (PCIe) is a high-speed interface standard widely used for connecting processors, memory, and peripherals. With the increasing reliance on PCIe to handle sensitive data and critical high-speed data transfer, ensuring data integrity and encryption during verification is the most essential goal. As we know, in the field of verification, randomization is a key technique that drives robust PCIe verification. It introduces unpredictability to simulate real-world conditions and uncover hidden bugs from the design. This blog examines the significance of randomization in PCIe IDE verification, focusing on how it ensures data integrity and encryption reliability, while also highlighting the unique challenges it presents. For more relevant details and understanding on PCIe IDE you can refer to Introducing PCIe's Integrity and Data Encryption Feature . The Importance of Data Integrity and Data Encryption in PCIe Devices Data Integrity : Ensures that the transmitted data arrives unchanged from source to destination. Even minor corruption in data packets can compromise system reliability, making integrity a critical aspect of PCIe verification. Data Encryption : Protects sensitive data from unauthorized access during transmission. Encryption in PCIe follows a standard to secure information while operating at high speeds. Maintaining both data integrity and data encryption at PCIe’s high-speed data transfer rate of 64GT/s in PCIe 6.0 and 128GT/s in PCIe 7.0 is essential for all end point devices. However, validating these mechanisms requires comprehensive testing and verification methodologies, which is where randomization plays a very crucial role. You can refer to Why IDE Security Technology for PCIe and CXL? for more details on this. Randomization in PCIe Verification Randomization refers to the generation of test scenarios with unpredictable inputs and conditions to expose corner cases. In PCIe verification, this technique helps us to ensure that all possible behaviors are tested, including rare or unexpected situations that could cause data corruption or encryption failures that may cause serious hindrances later. So, for PCIe IDE verification, we are considering the randomization that helps us verify behavior more efficiently. Randomization for Data Integrity Verification Here are some approaches of randomized verifications that mimic real-world traffic conditions, uncovering subtle integrity issues that might not surface in normal verification methods. 1. Randomized Packet Injection: This technique randomized data packets and injected into the communication stream between devices. Here we Inject random, malformed, or out-of-sequence packets into the PCIe link and mix valid and invalid IDE-encrypted packets to check the system’s ability to detect and reject unauthorized or invalid packets. Checking if encryption/decryption occurs correctly across packets. On verifying, we check if the system logs proper errors or alerts when encountering invalid packets. It ensures coverage of different data paths and robust protocol check. This technique helps assess the resilience of the IDE feature in PCIe in below terms: (i) Data corruption: Detecting if the system can maintain data integrity. (ii) Encryption failures: Testing the robustness of the encryption under random data injection. (iii) Packet ordering errors: Ensuring reordering does not affect data delivery. 2. Random Errors and Fault Injection: It involves simulating random bit flips, PCRC errors, or protocol violations to help validate the robustness of error detection and correction mechanisms of PCIe. These techniques help assess how well the PCIe IDE implementation: (i) Detects and responds to unexpected errors. (ii) Maintains secure communication under stress. (iii) Follows the PCIe error recovery and reporting mechanisms (AER – Advanced Error Reporting). (iv) Ensures encryption and decryption states stay synchronized across endpoints. 3. Traffic Pattern Randomization: Randomizing the sequence, size, and timing of data packets helps test how the device maintains data integrity under heavy, unpredictable traffic loads. Randomization for Data Encryption Verification Encryption adds complexity to verification, as encrypted data streams are not readable for traditional checks. Randomization becomes essential to test how encryption behaves under different scenarios. Randomization in data encryption verification ensures that vulnerabilities, such as key reuse or predictable patterns, are identified and mitigated. 1. Random Encryption Keys and Payloads: Randomly varying keys and payloads help validate the correctness of encryption without hardcoding assumptions. This ensures that encryption logic behaves correctly across all possible inputs. 2. Randomized Initialization Vectors (IVs): Many encryption protocols require a unique IV for each transaction. Randomized IVs ensure that encryption does not repeat patterns. To understand the IDE Key management flow, we can follow the below diagram that illustrates a detailed example key programming flow using the IDE_KM protocol. Figure 1: IDE_KM Example As Figure 1 shows, the functionality of the IDE_KM protocol involves Start of IDE_KM Session, Device Capability Discovery, Key Request from the Host, Key Programming to PCIe Device, and Key Acknowledgment. First, the Host starts the IDE_KM session by detecting the presence of the PCIe devices; if the device supports the IDE protocol, the system continues with the key programming process. Then a query occurs to discover the device’s encryption capabilities; it ensures whether the device supports dynamic key updates or static keys. Then the host sends a request to the Key Management Entity to obtain a key suitable for the devices. Once the key is obtained, the host programs the key into the IDE Controller on the PCIe endpoint. Both the host and the device now share the same key to encrypt and authenticate traffic. The device acknowledges that it has received and successfully installed the encryption key and the acknowledgment message is sent back to the host. Once both the host and the PCIe endpoint are configured with the key, a secure communication channel is established. From this point, all data transmitted over the PCIe link is encrypted to maintain confidentiality and integrity. IDE_KM plays a crucial role in distributing keys in a secure manner and maintaining encryption and integrity for PCIe transactions. This key programming flow ensures that a secure communication channel is established between the host and the PCIe device. Hence, the Randomized key approach ensures that the encryption does not repeat patterns. 3. Randomization PHE: Partial Header Encryption (PHE) is an additional mechanism added to Integrity and Data Encryption (IDE) in PCIe 6.0. PHE validation using a variety of traffic; incorporating randomization in APIs provided for validating PHE feature can add more robust Encryption to the data. Partial Header Encryption in Integrity and Data Encryption for PCIe has more detailed information on this. Figure 2: High-Level Flow for Partial Header Encryption 4. Randomization on IDE Address Association Register values: IDE Address Association Register 1/2/3 are supposed to be configured considering the memory address range of IDE partner ports. The fields of IDE address registers are split multiple values such as Memory Base Lower, Memory Limit Lower, Memory Base Upper, and Memory Limit Upper. IDE implementation can have multiple register blocks considering addresses with 32 or 64, different registers sizes, 0-255 selective streams, 0-15 address blocks, etc. This Randomization verification can help verify all the corner cases. Please refer to Figure 2. Figure 3: IDE Address Association Register 5. Random Faults During Encryption: Injecting random faults (e.g., dropped packets or timing mismatches) ensures the system can handle disruptions and prevent data leakage. Challenges of IDE Randomization and its Solution Randomization introduces a vast number of scenarios, making it computationally intensive to simulate every possibility. Constrained randomization limits random inputs to valid ranges while still covering edge cases. Again, using coverage-driven verification to ensure critical scenarios are tested without excessive redundancy. Verifying encrypted data with random inputs increases complexity. Encryption masks data, making it hard to verify outputs without compromising security. Here we can implement various IDE checks on the IDE callback to analyze encrypted traffic without decrypting it. Randomization can trigger unexpected failures, which are often difficult to reproduce. By using seed-based randomization, a specific seed generates a repeatable random sequence. This helps in reproducing and analyzing the behavior more precisely. Conclusion Randomization is a powerful technique in PCIe verification, ensuring robust validation of both data integrity and data encryption. It helps us to uncover subtle bugs and edge cases that a non-randomized testing might miss. In Cadence PCIe VIP, we support full-fledged IDE Verification with rigorous randomized verification that ensures data integrity. Robust and reliable encryption mechanisms ensure secure and efficient data communication. However, randomization also brings various challenges, and to overcome them we adopt a combination of constrained randomization, seed-based testing, and coverage-driven verification. As PCIe continues to evolve with higher speeds and focuses on high security demands, our Cadence PCIe VIP ensures it is in line with industry demand and verify high-performance systems that safeguard data in real-world environments with excellence. For more information, you can refer to Verification of Integrity and Data Encryption(IDE) for PCIe Devices and Industry's First Adopted VIP for PCIe 7.0 . More Information: For more info on how Cadence PCIe Verification IP and TripleCheck VIP enables users to confidently verify IDE, see our VIP for PCI Express , VIP for Compute Express Link for and TripleCheck for PCI Express For more information on PCIe in general, and on the various PCI standards, see the PCI-SIG website .

A set of mathematical equations can help turn apparently random observations into a clock – and then measure its accuracy

The infinite monkey theorem states that illiterate primates could write great literature with enough time, but the amount of time needed is much longer than the lifespan of the universe

Title: People Underestimate Impact of Random Acts of Kindness
Category: Health News
Created: 8/24/2022 12:00:00 AM
Last Editorial Review: 8/25/2022 12:00:00 AM

BACKGROUND AND PURPOSE:

Recently, artificial intelligence tools have been deployed with increasing speed in educational and clinical settings. However, the use of artificial intelligence by trainees across different levels of experience has not been well-studied. This study investigates the impact of artificial intelligence assistance on the diagnostic accuracy for intracranial hemorrhage and large-vessel occlusion by medical students and resident trainees.

PURPOSE

We undertook a trial to test the efficacy of a technology-assisted health coaching intervention for weight management, called Goals for Eating and Moving (GEM), within primary care.

Stratosphere is taking Vegas back from the pricey and pretentious. From the casino to the top of the Tower, Stratosphere offers great fun and real values backed up by an unforgettable experience.

The infinite monkey theorem states that illiterate primates could write great literature with enough time, but the amount of time needed is much longer than the lifespan of the universe

Penguin Random House, the trade publisher, is adding language to the copyright pages of its books to prohibit the use of those books to train AI. The Bookseller reports that new books and reprints of older titles from the publisher will now include the statement, “No part of this book may be used or reproduced […]

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Cambridge, MA. : National Bureau of Economic Research, 2023

MGNREGS has created assets that have made farming viable. But funds cutback and rural vested interests can erode its impact. A BusinessLine analysis

National Bureau of Economic Research

National Bureau of Economic Research