LIVIVO - Search results -

Search results

Result 1 - 10 of total 9594

Search options

Book ; Online: Blending Is All You Need

Lu, Xiaoding / Liusie, Adian / Raina, Vyas / Zhang, Yuwen / Beauchamp, William

Cheaper, Better Alternative to Trillion-Parameters LLM

2024

Abstract: In conversational AI research, there's a noticeable trend towards developing models with a larger number of parameters, exemplified by models like ChatGPT. While these expansive models tend to generate increasingly better chat responses, they demand ... ...

Abstract	In conversational AI research, there's a noticeable trend towards developing models with a larger number of parameters, exemplified by models like ChatGPT. While these expansive models tend to generate increasingly better chat responses, they demand significant computational resources and memory. This study explores a pertinent question: Can a combination of smaller models collaboratively achieve comparable or enhanced performance relative to a singular large model? We introduce an approach termed "blending", a straightforward yet effective method of integrating multiple chat AIs. Our empirical evidence suggests that when specific smaller models are synergistically blended, they can potentially outperform or match the capabilities of much larger counterparts. For instance, integrating just three models of moderate size (6B/13B paramaeters) can rival or even surpass the performance metrics of a substantially larger model like ChatGPT (175B+ paramaters). This hypothesis is rigorously tested using A/B testing methodologies with a large user base on the Chai research platform over a span of thirty days. The findings underscore the potential of the "blending" strategy as a viable approach for enhancing chat AI efficacy without a corresponding surge in computational demands.
Keywords	Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
Publishing date	2024-01-04
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Book ; Online: NightRain

Lin, Beibei / Jin, Yeying / Yan, Wending / Ye, Wei / Yuan, Yuan / Zhang, Shunli / Tan, Robby

Nighttime Video Deraining via Adaptive-Rain-Removal and Adaptive-Correction

2024

Abstract: ... hampering synthetic-trained models in removing rain streaks properly and leading to over-saturation and ... color shifts. Motivated by this, we introduce NightRain, a novel nighttime video deraining method ... with adaptive-rain-removal and adaptive-correction. Our adaptive-rain-removal uses unlabeled rain videos ...

Abstract	Existing deep-learning-based methods for nighttime video deraining rely on synthetic data due to the absence of real-world paired data. However, the intricacies of the real world, particularly with the presence of light effects and low-light regions affected by noise, create significant domain gaps, hampering synthetic-trained models in removing rain streaks properly and leading to over-saturation and color shifts. Motivated by this, we introduce NightRain, a novel nighttime video deraining method with adaptive-rain-removal and adaptive-correction. Our adaptive-rain-removal uses unlabeled rain videos to enable our model to derain real-world rain videos, particularly in regions affected by complex light effects. The idea is to allow our model to obtain rain-free regions based on the confidence scores. Once rain-free regions and the corresponding regions from our input are obtained, we can have region-based paired real data. These paired data are used to train our model using a teacher-student framework, allowing the model to iteratively learn from less challenging regions to more challenging regions. Our adaptive-correction aims to rectify errors in our model's predictions, such as over-saturation and color shifts. The idea is to learn from clear night input training videos based on the differences or distance between those input videos and their corresponding predictions. Our model learns from these differences, compelling our model to correct the errors. From extensive experiments, our method demonstrates state-of-the-art performance. It achieves a PSNR of 26.73dB, surpassing existing nighttime video deraining methods by a substantial margin of 13.7%. Comment: Accepted by AAAI24
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	551
Publishing date	2024-01-01
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Targeting of REST with rationally-designed small molecule compounds exhibits synergetic therapeutic potential in human glioblastoma cells.

Panina, Svetlana B / Schweer, Joshua V / Zhang, Qian / Raina, Gaurav / Hardtke, Haley A / Kim, Seungjin / Yang, Wanjie / Siegel, Dionicio / Zhang, Y Jessie

BMC biology

2024 Volume 22, Issue 1, Page(s) 83

Abstract: Background: Glioblastoma (GBM) is an aggressive brain cancer associated with poor prognosis, intrinsic heterogeneity, plasticity, and therapy resistance. In some GBMs, cell proliferation is fueled by a transcriptional regulator, repressor element-1 ... ...

Abstract	Background: Glioblastoma (GBM) is an aggressive brain cancer associated with poor prognosis, intrinsic heterogeneity, plasticity, and therapy resistance. In some GBMs, cell proliferation is fueled by a transcriptional regulator, repressor element-1 silencing transcription factor (REST). Results: Using CRISPR/Cas9, we identified GBM cell lines dependent on REST activity. We developed new small molecule inhibitory compounds targeting small C-terminal domain phosphatase 1 (SCP1) to reduce REST protein level and transcriptional activity in glioblastoma cells. Top leads of the series like GR-28 exhibit potent cytotoxicity, reduce REST protein level, and suppress its transcriptional activity. Upon the loss of REST protein, GBM cells can potentially compensate by rewiring fatty acid metabolism, enabling continued proliferation. Combining REST inhibition with the blockade of this compensatory adaptation using long-chain acyl-CoA synthetase inhibitor Triacsin C demonstrated substantial synergetic potential without inducing hepatotoxicity. Conclusions: Our results highlight the efficacy and selectivity of targeting REST alone or in combination as a therapeutic strategy to combat high-REST GBM.
MeSH term(s)	Humans ; Transcription Factors ; Glioblastoma/drug therapy ; Gene Expression Regulation ; Brain ; Aggression
Chemical Substances	Transcription Factors
Language	English
Publishing date	2024-04-12
Publishing country	England
Document type	Journal Article
ZDB-ID	2133020-7
ISSN	1741-7007 ; 1741-7007
ISSN (online)	1741-7007
ISSN	1741-7007
DOI	10.1186/s12915-024-01879-0
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

Article ; Online: In inpatients with COVID-19, none of remdesivir, hydroxychloroquine, lopinavir, or interferon β-1a differed from standard care for in-hospital mortality.

Zhang, Raina / Mylonakis, Eleftherios

Annals of internal medicine

2021 Volume 174, Issue 2, Page(s) JC17

Abstract: Source citation: Pan H, Peto R, Henao-Restrepo AM, et al. ...

Abstract	Source citation: Pan H, Peto R, Henao-Restrepo AM, et al.
MeSH term(s)	Adenosine Monophosphate/analogs & derivatives ; Adenosine Monophosphate/therapeutic use ; Aged ; Alanine/analogs & derivatives ; Alanine/therapeutic use ; Antiviral Agents/therapeutic use ; COVID-19/drug therapy ; COVID-19/mortality ; Drug Therapy, Combination ; Female ; Hospital Mortality ; Hospitalization ; Humans ; Hydroxychloroquine/therapeutic use ; Intention to Treat Analysis ; Interferon beta-1a/therapeutic use ; Kaplan-Meier Estimate ; Length of Stay ; Lopinavir/therapeutic use ; Male ; Middle Aged ; Multicenter Studies as Topic ; Randomized Controlled Trials as Topic ; Respiration, Artificial ; Treatment Failure ; World Health Organization
Chemical Substances	Antiviral Agents ; Lopinavir (2494G1JF75) ; remdesivir (3QKI37EEHE) ; Adenosine Monophosphate (415SHH325A) ; Hydroxychloroquine (4QWG6N8QKH) ; Alanine (OF5P57N2ZX) ; Interferon beta-1a (XRO4566Q4R)
Language	English
Publishing date	2021-02-02
Publishing country	United States
Document type	Journal Article
ZDB-ID	336-0
ISSN	1539-3704 ; 0003-4819
ISSN (online)	1539-3704
ISSN	0003-4819
DOI	10.7326/ACPJ202102160-017
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Ua VI Zs.178/2: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Book ; Online: RAIN

Li, Yuhui / Wei, Fangyun / Zhao, Jinjing / Zhang, Chao / Zhang, Hongyang

Your Language Models Can Align Themselves without Finetuning

2023

Abstract: ... Rewindable Auto-regressive INference (RAIN), that allows pre-trained LLMs to evaluate their own generation ... and use the evaluation results to guide rewind and generation for AI safety. Notably, RAIN operates ... parameter updates. Experimental results evaluated by GPT-4 and humans demonstrate the effectiveness of RAIN ...

Abstract	Large language models (LLMs) often demonstrate inconsistencies with human preferences. Previous research typically gathered human preference data and then aligned the pre-trained models using reinforcement learning or instruction tuning, a.k.a. the finetuning step. In contrast, aligning frozen LLMs without requiring alignment data is more appealing. This work explores the potential of the latter setting. We discover that by integrating self-evaluation and rewind mechanisms, unaligned LLMs can directly produce responses consistent with human preferences via self-boosting. We introduce a novel inference method, Rewindable Auto-regressive INference (RAIN), that allows pre-trained LLMs to evaluate their own generation and use the evaluation results to guide rewind and generation for AI safety. Notably, RAIN operates without the need of extra data for model alignment and abstains from any training, gradient computation, or parameter updates. Experimental results evaluated by GPT-4 and humans demonstrate the effectiveness of RAIN: on the HH dataset, RAIN improves the harmlessness rate of LLaMA 30B from 82% of vanilla inference to 97%, while maintaining the helpfulness rate. On the TruthfulQA dataset, RAIN improves the truthfulness of the already-well-aligned LLaMA-2-chat 13B model by 5%.
Keywords	Computer Science - Computation and Language
Publishing date	2023-09-13
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: A machine learning-based universal outbreak risk prediction tool.

Zhang, Tianyu / Rabhi, Fethi / Chen, Xin / Paik, Hye-Young / MacIntyre, Chandini Raina

Computers in biology and medicine

2023 Volume 169, Page(s) 107876

Abstract: In order to prevent and control the increasing number of serious epidemics, the ability to predict the risk caused by emerging outbreaks is essential. However, most current risk prediction tools, except EPIRISK, are limited by being designed for ... ...

Abstract	In order to prevent and control the increasing number of serious epidemics, the ability to predict the risk caused by emerging outbreaks is essential. However, most current risk prediction tools, except EPIRISK, are limited by being designed for targeting only one specific disease and one country. Differences between countries and diseases (e.g., different economic conditions, different modes of transmission, etc.) pose challenges for building models with cross-country and cross-disease prediction capabilities. The limitation of universality affects domestic and international efforts to control and prevent pandemic outbreaks. To address this problem, we used outbreak data from 43 diseases in 206 countries to develop a universal risk prediction system that can be used across countries and diseases. This system used five machine learning models (including Neural Network XGBoost, Logistic Boost, Random Forest and Kernel SVM) to predict and vote together to make ensemble predictions. It can make predictions with around 80%-90 % accuracy from economic, cultural, social, and epidemiological factors. Three different datasets were designed to test the performance of ML models under different realistic situations. This prediction system has strong predictive ability, adaptability, and generality. It can give universal outbreak risk assessment that are not limited by border or disease type, facilitate rapid response to pandemic outbreaks, government decision-making and international cooperation.
MeSH term(s)	Disease Outbreaks ; Neural Networks, Computer ; Machine Learning ; Pandemics ; Support Vector Machine
Language	English
Publishing date	2023-12-24
Publishing country	United States
Document type	Journal Article
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2023.107876
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 653: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Article ; Online: Non-pharmaceutical interventions during the roll out of covid-19 vaccines.

Zhang, Yi / Quigley, Ashley / Wang, Quanyi / MacIntyre, C Raina

BMJ (Clinical research ed.)

2021 Volume 375, Page(s) n2314

MeSH term(s)	COVID-19/epidemiology ; COVID-19/prevention & control ; COVID-19/transmission ; COVID-19 Vaccines/immunology ; China/epidemiology ; Humans ; Masks ; Pandemics ; Physical Distancing ; Quarantine ; SARS-CoV-2
Chemical Substances	COVID-19 Vaccines
Language	English
Publishing date	2021-12-01
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1362901-3
ISSN	1756-1833 ; 0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
ISSN (online)	1756-1833
ISSN	0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
DOI	10.1136/bmj.n2314
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Ua VI Zs.10: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Article ; Online: Dual-function AzuCR RNA modulates carbon metabolism.

Raina, Medha / Aoyama, Jordan J / Bhatt, Shantanu / Paul, Brian J / Zhang, Aixia / Updegrove, Taylor B / Miranda-Ríos, Juan / Storz, Gisela

Proceedings of the National Academy of Sciences of the United States of America

2022 Volume 119, Issue 10, Page(s) e2117930119

Abstract: SignificanceWhile most small, regulatory RNAs are thought to be "noncoding," a few have been found to also encode a small protein. Here we describe a 164-nucleotide RNA that encodes a 28-amino acid, amphipathic protein, which interacts with aerobic ... ...

Abstract	SignificanceWhile most small, regulatory RNAs are thought to be "noncoding," a few have been found to also encode a small protein. Here we describe a 164-nucleotide RNA that encodes a 28-amino acid, amphipathic protein, which interacts with aerobic glycerol-3-phosphate dehydrogenase and increases dehydrogenase activity but also base pairs with two mRNAs to reduce expression. The coding and base-pairing sequences overlap, and the two regulatory functions compete.
MeSH term(s)	Carbon/metabolism ; Culture Media ; Escherichia coli/growth & development ; Escherichia coli/metabolism ; Escherichia coli Proteins/chemistry ; Escherichia coli Proteins/metabolism ; Galactose/metabolism ; Glycerol/metabolism ; Glycerolphosphate Dehydrogenase/metabolism ; Membrane Proteins/chemistry ; Membrane Proteins/metabolism ; Protein Biosynthesis ; RNA, Bacterial/chemistry ; RNA, Bacterial/metabolism ; RNA, Bacterial/physiology ; RNA, Messenger/metabolism
Chemical Substances	Culture Media ; Escherichia coli Proteins ; Membrane Proteins ; RNA, Bacterial ; RNA, Messenger ; Carbon (7440-44-0) ; Glycerolphosphate Dehydrogenase (EC 1.1.-) ; Glycerol (PDC6A3C0OX) ; Galactose (X2RN3Q8DNE)
Language	English
Publishing date	2022-03-03
Publishing country	United States
Document type	Journal Article
ZDB-ID	209104-5
ISSN	1091-6490 ; 0027-8424
ISSN (online)	1091-6490
ISSN	0027-8424
DOI	10.1073/pnas.2117930119
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 1148: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Article ; Online: A compute-in-memory chip based on resistive random-access memory.

Wan, Weier / Kubendran, Rajkumar / Schaefer, Clemens / Eryilmaz, Sukru Burc / Zhang, Wenqiang / Wu, Dabin / Deiss, Stephen / Raina, Priyanka / Qian, He / Gao, Bin / Joshi, Siddharth / Wu, Huaqiang / Wong, H-S Philip / Cauwenberghs, Gert

Nature

2022 Volume 608, Issue 7923, Page(s) 504–512

Abstract: Realizing increasingly complex artificial intelligence (AI) functionalities directly on edge devices calls for unprecedented energy efficiency of edge hardware. Compute-in-memory (CIM) based on resistive random-access memory (RRAM) ...

Abstract	Realizing increasingly complex artificial intelligence (AI) functionalities directly on edge devices calls for unprecedented energy efficiency of edge hardware. Compute-in-memory (CIM) based on resistive random-access memory (RRAM)
Language	English
Publishing date	2022-08-17
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	120714-3
ISSN	1476-4687 ; 0028-0836
ISSN (online)	1476-4687
ISSN	0028-0836
DOI	10.1038/s41586-022-04992-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 26: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MG 9: Show issues
Zs.MO 244: Show issues

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Book ; Online: Canal

Melchert, Jackson / Zhang, Keyi / Mei, Yuchen / Horowitz, Mark / Torng, Christopher / Raina, Priyanka

A Flexible Interconnect Generator for Coarse-Grained Reconfigurable Arrays

2022

Abstract: The architecture of a coarse-grained reconfigurable array (CGRA) interconnect has a significant effect on not only the flexibility of the resulting accelerator, but also its power, performance, and area. Design decisions that have complex trade-offs need ...

Abstract	The architecture of a coarse-grained reconfigurable array (CGRA) interconnect has a significant effect on not only the flexibility of the resulting accelerator, but also its power, performance, and area. Design decisions that have complex trade-offs need to be explored to maintain efficiency and performance across a variety of evolving applications. This paper presents Canal, a Python-embedded domain-specific language (eDSL) and compiler for specifying and generating reconfigurable interconnects for CGRAs. Canal uses a graph-based intermediate representation (IR) that allows for easy hardware generation and tight integration with place and route tools. We evaluate Canal by constructing both a fully static interconnect and a hybrid interconnect with ready-valid signaling, and by conducting design space exploration of the interconnect architecture by modifying the switch box topology, the number of routing tracks, and the interconnect tile connections. Through the use of a graph-based IR for CGRA interconnects, the eDSL, and the interconnect generation system, Canal enables fast design space exploration and creation of CGRA interconnects. Comment: Preprint version
Keywords	Computer Science - Hardware Architecture
Publishing date	2022-11-30
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

To top

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

Full text online

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

Full text online

More links

Kategorien

Inter-library loan at ZB MED