ContributorsPublishersAdvertisers

#Sequential

Jessica Simpson Officially Buys Back Her Billion-Dollar Empire and Teases New Categories

Jessica Simpson and her mother Tina Simpson have officially re-acquired full ownership of The Jessica Simpson Lifestyle Brand, the brand announced Wednesday. Sequential bought the majority share from Camuto Group in 2015 but filed for Chapter 11 bankruptcy protection in August. In September, the singer opened up to FN about buying back her business as she and Tina were in the midst of negotiations with Sequential Brands Group Inc. At the time they currently owned 37.5% of the brand they founded in 2005. Now, the deal has been sealed. “I am humbled to reclaim 100% of my brand and my name. I am...
RETAIL
Picture for Jessica Simpson Officially Buys Back Her Billion-Dollar Empire and Teases New Categories
buzinessbytes.com

Domestic air passenger traffic grew 15-16% sequentially in Nov: ICRA

New Delhi, Dec 7 (IANS) Domestic air passenger traffic grew 15-16 per cent at around 104-105 lakh in November 2021, compared to 89.85 lakh in October 2021, said ratings agency ICRA. The same trend reflected on a year-on-year basis with a growth of 64 per cent. Besides, the ratings agency...
ECONOMY
Picture for Domestic air passenger traffic grew 15-16% sequentially in Nov: ICRA

Winning numbers drawn in ‘Fantasy 5’ game

SACRAMENTO (AP) _ The winning numbers in Tuesday evening’s drawing of the California Lottery’s “Fantasy 5” game were:. (one, twenty-one, twenty-three, thirty-three, thirty-seven) Estimated jackpot: $317,000. ¶ The numbers are listed in sequential order, but any combination wins.
SACRAMENTO, CA
TRENDING TOPICS
Synthtopia

Karplus-Strong Physical Modeling Synthesis On The Sequential Pro 3

Here’s a demonstration, via ToyKeeper, of a Karplus-Strong synthesis patch on the Sequential Pro 3. Karplus–Strong string synthesis is based on using very short filtered delays to create sounds similar to plucked strings or hammered percussion. The video is not intended to be a performance or a tutorial, but simply...
COMPUTERS
motionarray.com

4 Ways to Work with Motion Array’s Premiere Pro Transition Templates

In this easy-to-follow Premiere Pro video tutorial, we will show you how easy it is to work with our Premiere Pro transition templates. Make a boring edit feel great with professional templates that enhance your edit. In this video, we will show you how to replace media on placeholders to...
COMPUTERS
arxiv.org

Quantum error correction in the NISQ regime for sequential quantum computing

We use density matrix simulations to study the performance of three distance three quantum error correcting codes in the context of the rare-earth-ion-doped crystal (RE) platform for quantum computing. We analyze pseudothresholds for these codes when parallel operations are not available, and examine the behavior both with and without resting errors. In RE systems, resting errors can be mitigated by extending the system's ground state coherence time. For the codes we study, we find that if the ground state coherence time is roughly 100 times larger than the excited state coherence time, resting errors become small enough to be negligible compared to other error sources. This leads us to the conclusion that beneficial QEC could be achieved in the RE system with the expected gate fidelities available in the NISQ regime. However, for codes using more qubits and operations, a factor of more than 100 would be required. Furthermore, we investigate how often QEC should be performed in a circuit. We find that for early experiments in RE systems, the minimal $[\![5,1,3]\!]$ would be most suitable as it has a high threshold error and uses few qubits. However, when more qubits are available the $[\![9,1,3]\!]$ surface code might be a better option due to its higher circuit performance. Our findings are important for steering experiments to an efficient path for realizing beneficial quantum error correcting codes in early RE systems where resources are limited.
COMPUTERS
arxiv.org

Learning Reinforced Dynamic Representations for Sequential Recommendation

Recently, sequential recommendation systems are important in solving the information overload in many online services. Current methods in sequential recommendation focus on learning a fixed number of representations for each user at any time, with a single representation or multi-interest representations for the user. However, when a user is exploring items on an e-commerce recommendation system, the number of this user's interests may change overtime (e.g. increase/reduce one interest), affected by the user's evolving self needs. Moreover, different users may have various number of interests. In this paper, we argue that it is meaningful to explore a personalized dynamic number of user interests, and learn a dynamic group of user interest representations accordingly. We propose a Reinforced sequential model with dynamic number of interest representations for recommendation systems (RDRSR). Specifically, RDRSR is composed of a dynamic interest discriminator (DID) module and a dynamic interest allocator (DIA) module. The DID module explores the number of a user's interests by learning the overall sequential characteristics with bi-directional self-attention and Gumbel-Softmax. The DIA module allocates the historical clicked items into a group of sub-sequences and constructs user's dynamic interest representations. We formalize the allocation problem in the form of Markov Decision Process(MDP), and sample an action from policy pi for each item to determine which sub-sequence it belongs to. Additionally, experiments on the real-world datasets demonstrates our model's effectiveness.
INTERNET
marktechpost.com

Google Research Release Reinforcement Learning Datasets For Sequential Decision Making

Most reinforcement learning (RL) and sequential decision-making agents generate training data through a high number of interactions with their environment. While this is done to achieve optimal performance, it is inefficient, especially when the interactions are difficult to generate, such as when gathering data with a real robot or communicating with a human expert.
SOFTWARE
dweb.news

BUSINESS: SEQUENTIAL ALERT: Bragar Eagel & Squire, P.C. Is Investigating Sequential Brands Group, Inc. On Behalf Of Long-Term Stockholders And Encourages Investors To Contact The Firm

NEW YORK–(BUSINESS WIRE)–Bragar Eagel & Squire, P.C., a nationally recognized shareholder rights law firm, is investigating potential claims against Sequential Brands Group, Inc. (NASDAQ: SQBG) on behalf of long-term stockholders following a class action complaint that was filed against Sequential on March 16, 2021. Our investigation concerns whether the board of directors of Sequential have breached their fiduciary duties to the company.
BUSINESS
complianceweek.com

Sequential Brands avoids fine in SEC goodwill impairment case

Sequential Brands Group has settled with the Securities and Exchange Commission (SEC) over charges it violated accounting principles in securities law when it did not acknowledge goodwill impairment that eventually landed on its balance sheet as a $304 million write-down. The New York City-based company, whose brands included Jessica Simpson’s...
BUSINESS
arxiv.org

Robust Sequential Online Prediction with Dynamic Ensemble of Multiple Models: A Concise Introduction

In this paper, I give a concise introduction to a generic theoretical framework termed Bayesian Dynamic Ensemble of Multiple Models (BDEMM) that is used for robust sequential online prediction. This framework has three major features: (1) it employs a model pool, rather than a single model, to capture possible statistical regularities underlying the data; (2) the model pool consists of multiple weighted candidate models, wherein the model weights are adapted online to capture possible temporal evolutions of the data; (3) the adaptation for the model weights follows Bayesian formalism. These features together define BDEMM. To make this introduction comprehensive, I describe BDEMM from four perspectives, namely the related theories, the different forms of its algorithmic implementations, its classical applications, related open resources, followed by a discussion of open problems that are worth further research.
COMPUTERS
docoh.com

ZYMERGEN ALERT: Bragar Eagel & Squire, P.C. Is Investigating Zymergen Inc. on Behalf of Long-Term Stockholders and Encourages Investors to Contact the Firm

Bragar Eagel & Squire, P.C., a nationally recognized shareholder rights law firm, is investigating potential claims against Zymergen Inc. (NASDAQ:ZY) on behalf of long-term stockholders following a class action complaint that was filed against Sequential on August 4, 2021. Our investigation concerns whether the board of directors of Sequential have breached their fiduciary duties to the company.
BUSINESS
YOU MAY ALSO LIKE