https://www.nature.com/articles/s41580-024-00756-6
- Comment
- Published:
Harnessing the deep learning power of foundation models in single-cell omics
Nature Reviews Molecular Cell Biology 25, 593–594 (2024)
Tuesday, December 31, 2024
Sunday, December 29, 2024
Aphasia is a language disorder t
ChatGPT
Aphasia is a language disorder that affects a person's ability to communicate. It typically occurs when the areas of the brain responsible for language (often in the left hemisphere) are damaged, usually due to a stroke, traumatic brain injury, brain tumor, or other neurological conditions.
Key Features of Aphasia
Impairments in Language:
- Speaking: Difficulty finding words, forming sentences, or articulating speech.
- Understanding: Problems comprehending spoken or written language.
- Reading and Writing: Challenges in reading comprehension and writing.
- Gestural Communication: Even non-verbal communication like gestures may be affected.
Preserved Abilities:
- Intellectual and cognitive functions outside language (e.g., memory, reasoning) are often intact.
- Basic social skills and emotional expressions are usually preserved.
Types of Aphasia
Aphasia varies in severity and type, depending on the brain areas affected:
Broca's Aphasia (Non-Fluent Aphasia):
- Difficulty producing speech; sentences are short and effortful.
- Comprehension remains relatively intact.
- Example: "Want... coffee... shop..."
Wernicke's Aphasia (Fluent Aphasia):
- Speech is fluent but often nonsensical or filled with made-up words.
- Comprehension is impaired.
- Example: "The fish are on the cupboard with delight."
Global Aphasia:
- Severe impairment in both understanding and producing language.
- Often occurs after extensive brain damage.
Anomic Aphasia:
- Difficulty finding specific words, especially nouns and verbs.
- Speech is grammatically correct but lacks specific terms.
Primary Progressive Aphasia (PPA):
- A form of dementia where language abilities decline gradually over time.
- Not caused by acute events like a stroke.
Causes of Aphasia
- Stroke: The most common cause.
- Traumatic Brain Injury (TBI).
- Brain Tumors.
- Neurodegenerative Diseases: Such as Alzheimer's disease or other dementias.
- Infections: Rarely, brain infections can cause aphasia.
Diagnosis and Treatment
- Diagnosis:
- Medical imaging (MRI or CT scans) to locate brain damage.
- Language assessments to evaluate speaking, listening, reading, and writing skills.
- Treatment:
- Speech and Language Therapy: Tailored exercises to rebuild language skills and find alternative ways of communicating.
- Technology Aids: Apps or devices that assist in communication.
- Family Support and Education: Teaching family members effective ways to communicate.
Prognosis
- Recovery depends on the cause and extent of brain damage, as well as the individual's age, overall health, and rehabilitation efforts.
- Some people experience significant improvement, while others may have long-term challenges.
Patients with aphasia often have difficulty producing correct phonemes, but the nature and extent of this difficulty depend on the type and severity of their aphasia. Here's a detailed breakdown:
Phoneme-Related Issues in Aphasia
Broca’s Aphasia (Non-Fluent Aphasia):
- Phoneme Production: Patients often struggle with motor planning for speech (a condition called apraxia of speech, which can co-occur with Broca’s aphasia). This leads to distorted or incorrect phoneme articulation.
- Characteristics:
- Speech is effortful and halting.
- Frequent phoneme omissions, substitutions, or distortions.
- Example: "spoon" might be pronounced as "poon" or "foon."
Wernicke’s Aphasia (Fluent Aphasia):
- Phoneme Selection: Errors are more related to selecting the correct phonemes rather than articulating them.
- Phonemic Paraphasias: Substitutions, additions, or omissions of phonemes within words.
- Example: "table" might be pronounced as "fable" or "gable."
- Speech remains fluent but may be filled with neologisms (made-up words) and phonemic errors.
Global Aphasia:
- Severe impairments across all language domains, including phoneme production.
- Patients may have limited speech output and struggle to produce recognizable phonemes.
Anomic Aphasia:
- Phoneme production is usually intact, but patients struggle to retrieve the right words.
- Phoneme errors are rare unless the person is also under stress or experiencing secondary language difficulties.
Conduction Aphasia:
- Marked by phonemic paraphasias (errors in the arrangement of phonemes) during speech, especially when attempting to repeat phrases.
- Example: "butterfly" might be pronounced as "flutterby."
Underlying Causes of Phoneme Errors
Motor Speech Disorders:
- Co-occurring apraxia of speech or dysarthria can impair phoneme articulation.
Linguistic Processing Errors:
- Difficulty selecting or sequencing phonemes in the brain's language-processing regions.
Auditory Feedback Issues:
- Difficulty monitoring and correcting phoneme production in real time, particularly in Wernicke’s and conduction aphasia.
Treatment and Rehabilitation
Speech Therapy:
- Techniques like phoneme repetition, minimal pair contrast drills (e.g., "bat" vs. "pat"), and syllable shaping exercises.
Motor Training:
- For apraxia, focus on motor planning and sequencing exercises.
Auditory-Phonological Training:
- Use of tools and exercises to improve phoneme selection and self-monitoring.
Technology-Aided Interventions:
- Speech-generating devices and software to aid communication.
Prognosis
- With consistent therapy, many patients can improve their phoneme production, though the degree of recovery depends on the type and severity of the aphasia, as well as associated conditions like apraxia.
Saturday, December 28, 2024
overleaf to IEEE PDF express
download overleaf working directory in zip file
expand the zip file and remove unnecssary files, and then create a new file. When working on github folders, make sure that hidden .git folder is removed.
"Create a new title" on IEEE PDF Express
upload the new zip file. (this sometime stuck. make sure that session has been log-out.)
wait for pdf to be generated. (This sometime fail frequently).
compare two pdf files for IEEE PDF Express conference submission
chatgpt: To compare two PDF files for differences, you have several options:
1. Using a PDF Comparison Tool
There are tools specifically designed for PDF comparison, such as:
- Adobe Acrobat Pro DC: It has a built-in "Compare Files" feature.
- DiffPDF: A lightweight application to compare two PDFs visually or textually.
- Beyond Compare: A general comparison tool with PDF support via add-ons.
- PDF-XChange Editor: Offers PDF comparison as part of its suite.
2. Online PDF Comparison Tools
There are free and paid online services for comparing PDFs:
- Draftable: Offers a detailed side-by-side view of differences.
- PDF24 Tools: A straightforward online PDF comparison tool.
3. Python for PDF Comparison
If you'd like to script the comparison, you can use Python libraries like PyPDF2, pdfplumber, or pdfdiff. Here's an example:
Example Python Code
from PyPDF2 import PdfReader
# Function to extract text from a PDF file
def extract_text_from_pdf(pdf_path):
reader = PdfReader(pdf_path)
text = ""
for page in reader.pages:
text += page.extract_text()
return text
# Compare two PDFs
pdf1_text = extract_text_from_pdf("file1.pdf")
pdf2_text = extract_text_from_pdf("file2.pdf")
if pdf1_text == pdf2_text:
print("The PDF files are identical.")
else:
print("The PDF files have differences.")
4. Using Command-Line Tools
- diffpdf: A command-line tool to compare PDFs.
- pdftotext: Convert both PDFs to text and then use a standard text comparison tool like
difforvimdiff.
hqin@Hong-MBP2 Downloads % diff-pdf -v file1.pdf file2.pdf
page 0 has 3756 pixels that differ
page 0 differs
page 1 has 369491 pixels that differ
page 1 differs
page 2 has 0 pixels that differ
page 3 has 0 pixels that differ
page 4 has 0 pixels that differ
page 5 has 0 pixels that differ
page 6 has 0 pixels that differ
page 7 has 0 pixels that differ
page 8 has 0 pixels that differ
2 of 9 pages differ.
Visual comparison shows that IEEE PDF Express put an extract figure name in Figure 3. This png file name has extra space. So, I renamed this png file by replace white space with '_'s. This seems to fix the error.
hqin@Hong-MBP2 Downloads % diff-pdf -v overleaf-MABEL_neuralnetwork_SHAP_IEEE2024Fall.pdf /Users/hqin/Downloads/2024402761.pdf
page 0 has 3756 pixels that differ
page 0 differs
page 1 has 0 pixels that differ
page 2 has 0 pixels that differ
page 3 has 0 pixels that differ
page 4 has 0 pixels that differ
page 5 has 0 pixels that differ
page 6 has 0 pixels that differ
page 7 has 0 pixels that differ
page 8 has 0 pixels that differ
1 of 9 pages differ.
Visual check the first page to find they are consistent.
Friday, December 27, 2024
Schmidt Sciences
Schmidt Sciences offers several funding opportunities across various scientific disciplines. Here are some of their current programs:
【{"image_fetch": "AI2050 Fellowship Program"}】
AI2050 Fellowship Program
Supports researchers working on artificial intelligence projects aimed at benefiting humanity by 2050. The program funds both senior and early-career scholars for two years to address a wide range of global challenges in AI. citeturn0search0
【{"image_fetch": "Humanities and AI Virtual Institute"}】
Humanities and AI Virtual Institute (HAVI)
Launching on January 15, 2025, this program will support research at the intersection of artificial intelligence and the humanities. It aims to advance humanities scholarship through AI-based technology. Expressions of interest are due by March 15, 2025. citeturn0search4
【{"image_fetch": "Safety Science Program"}】
Safety Science Program
Focuses on advancing the science of AI safety by developing methods for testing and evaluating large language models. The program seeks to deepen understanding of safety properties in AI systems. citeturn0search5
【{"image_fetch": "Schmidt Science Polymath Program"}】
Schmidt Science Polymath Program
Empowers early-to-mid-career researchers to explore new research domains and methodologies. Each award provides $500,000 per year for up to five years to support innovative, cross-disciplinary research. citeturn0search7
【{"image_fetch": "BioFutures Program"}】
BioFutures Program
Aims to accelerate the translation of bioeconomy research into public and economic benefits. The program focuses on repurposing sustainable waste biomass, overcoming engineering constraints, and mobilizing talent for bioeconomy-related federal agencies. citeturn0search8
Please note that Schmidt Sciences often operates through proactive grantmaking and does not always accept unsolicited proposals. For the most accurate and up-to-date information on funding opportunities and application processes, it's advisable to visit their official website or contact them directly. citeturn0search2
FedRAMP (Federal Risk and Authorization Management Program)
FedRAMP (Federal Risk and Authorization Management Program) is a US government program that standardizes security assessment and authorization for cloud computing services used by federal agencies.
Key Components:
- Security standards based on NIST SP 800-53
- Three impact levels: Low, Moderate, High
- Third-party assessment organizations (3PAOs) conduct evaluations
- Continuous monitoring requirements
- "Do once, use many times" approach
Authorization Process:
1. Security controls implementation
2. 3PAO assessment
3. Agency review
4. Authorization decision
5. Continuous monitoring
Benefits:
- Standardized security approach
- Cost savings through reuse
- Improved cloud adoption
- Risk management consistency
Providers must maintain compliance through continuous monitoring and annual assessments to retain authorization.
Thursday, December 26, 2024
antibiotics
https://ourworldindata.org/golden-age-antibiotics?utm_source=tldrnewsletter
What was the Golden Age of Antibiotics, and how can we spark a new one?
Many antibiotics were developed during the “Golden Age of Antibiotics”. How did it happen, why has antibiotic development slowed down since then, and what can we do to reignite it?
generative AI?
Wednesday, December 25, 2024
字节跳动大模型训练被实习生攻击，涉事者已被辞退
字节跳动大模型训练被实习生攻击，涉事者已被辞退
https://news.qq.com/rain/a/20241018A0822700?devid=2FE562BF-3FE6-474F-B040-27C1FD35A8F2&qimei=a0fc08047fbcf074727889e700001d714c0a&uid=100086206015&QIMEI36=fake2fe562bf3fe6474fb04027c1fd35a8f2&suid=&media_id=
10月18日，多个微信群流传一则消息：“某头部大厂的大模型训练被实习生入侵，注入了破坏代码，导致其训练成果不可靠，可能需要重新训练。据称遭到入侵的代码注入了8000多张卡，带来的损失可能超过千万美元。”
界面新闻从知情人士处获悉，该头部大厂为字节跳动。此事发生在今年6月，起因是某高校的博士在字节跳动商业化技术团队实习，因对团队资源分配不满，使用攻击代码破坏团队的模型训练任务。
界面新闻向字节跳动方面求证此事，截至目前，官方未进行回应。
传闻显示，该田姓实习生利用了HF（huggingface）的漏洞，在公司的共享模型里写入破坏代码，导致模型的训练效果忽高忽低，无法产生预期的训练效果，而且AML团队无法核查原因。但传闻曝出之后，该实习生还在某微信群里辟谣称，自己发完论文后就从字节跳动离职，这个当口有另一个人钻漏洞改模型代码，把锅扣到自己头上。
界面新闻从知情人士处了解到，字节跳动内部已经调查明确此事为田姓实习生所为。目前，该实习生已被辞退，字节跳动同时把此事同步给大模型行业联盟和该实习生所在的学校。但这名实习生被辞退后到处“辟谣”甩锅，称是其他人所为。
但该实习生攻击的并不是豆包大模型，而是商业化技术团队的模型训练任务，影响了该技术团队的一些业务进展，但整体对公司造成的损失并没有传闻中的超过千万美元那么大。
一位技术安全专家告诉界面新闻，HF漏洞产生肯定是单一性集中训练，此事暴露了字节跳动技术训练存在安全管理问题。公司既没做权限隔离，也没有做好共用代码的审计。正常情况下，每次大的代码变动必须做审计，无论谁做操作都有痕迹记录，一个人想动代码是不可能的。
上述技术安全专家表示，目前行业通用的做法是做三级隔离导向性部署，每一级只有镜像，每次先扫描。调用按次算、必须密钥。每一级完成后，才能进入下一级，而上一级自动删除，进行分级训练。
how address serialization vulnerabilities in R and Python
Perperlexity: To address serialization vulnerabilities in R and Python:
### **R Programming (CVE-2024-27322)**
1. **Upgrade R**: Update to version 4.4.0 or later, which includes patches for the vulnerability[1][4][8].
2. **Validate Data**: Avoid loading untrusted RDS files. Validate input data against expected schemas[3][6].
3. **Restrict Package Sources**: Use only trusted repositories like CRAN and limit third-party packages[1][8].
### **Python**
1. **Avoid `pickle`**: Use safer libraries like JSON or YAML with `SafeLoader`[2][5].
2. **Integrity Checks**: Implement HMAC or digital signatures to ensure data integrity[3][7].
3. **Sandboxing**: Deserialize untrusted data in restricted environments with limited privileges[7].
Citations:
[1] https://www.securityweek.com/vulnerability-in-r-programming-language-enables-supply-chain-attacks/
[2] https://www.reddit.com/r/Python/comments/iahmc/python_safe_serialization/
[3] https://learn.snyk.io/lesson/insecure-deserialization/
[4] https://thehackernews.com/2024/04/new-r-programming-vulnerability-exposes.html
[5] https://snyk.io/blog/python-security-best-practices-cheat-sheet/
[6] https://cyberint.com/blog/research/new-vulnerability-in-rs-deserialization-discovered/
[7] https://avatao.com/blog-common-issues-and-best-practices-in-python/
[8] https://security.berkeley.edu/news/cve-2024-27322-vulnerability-r-programming-language
Friday, December 20, 2024
2026 NIAID Omnibus Broad Agency Announcement HHS-NIH-NIAID-BAA2025-1
2026 NIAID Omnibus Broad Agency Announcement HHS-NIH-NIAID-BAA2025-1 Now Available
Notice Number:
NOT-AI-25-017
Key Dates
Release Date:
December 19, 2024
Related Announcements
None
Issued by
National Institute of Allergy and Infectious Diseases (NIAID)
Purpose
The National Institute of Allergy and Infectious Diseases (NIAID), one of 27 institutes of the National Institutes of Health, an agency within the Department of Health and Human Services (DHHS), conducts and supports research to understand, treat, and ultimately prevent the myriad infectious, immunologic, and allergic diseases that threaten millions of human lives. Through a variety of research grants and contracts, NIAID’s Division of Microbiology and Infectious Diseases (DMID) specifically supports extramural research to develop new medical countermeasures (MCMs) against potential agents of bioterrorism, drug-resistant pathogens, and emerging and re-emerging infectious diseases. This Broad Agency Announcement (BAA) is soliciting proposals to advance the research and development of promising candidate therapeutics, vaccines, and diagnostics for biodefense and emerging infectious diseases.
The Omnibus BAA is governed by Federal Acquisition Regulation (FAR) 6.102(d)(2) and FAR 35.016, as well as the NIH Policy Manual, Manual Chapter 6035, Broad Agency Announcements. A BAA may be used as a solicitation mechanism for basic and applied research directed toward advancing the state-of-the-art or increasing knowledge or understanding and that part of development not related to the development of a specific system or hardware procurement. BAAs are general in nature, identifying areas of research interest, and shall only be used when meaningful proposals with varying technical/scientific approaches can be reasonably anticipated.
Offers submitted in response to this BAA will be required to submit separate detailed technical and business proposals designed to meet the Technical Objectives described for each Research Area and/or Topic proposed. The Statement of Work (SOW), including the specific technical requirements and performance specifications, shall be developed and proposed by the Offeror, not the Government.
Proposals received in response to this BAA are NOT evaluated against each other since they are not submitted in accordance with a common SOW issued by the Government. Instead, Research and Technical Objectives will be provided in the BAA that describe individual Research Areas in which the Government is interested. Proposals received in response to the BAA will be evaluated in accordance with the Evaluation Factors for Award specified in the announcement. The Government reserves the right to conduct discussions with all, some, one, or none of the proposals received in response to this BAA. If discussions are conducted, the Government reserves the right to suggest modifying, adding or deleting milestones, decision points, research plans, processes, schedules, budget or product. The Government also reserves the right to make awards without discussions. Additionally, the Government reserves the right to accept proposals in their entirety or to select only portions of proposals for award. Multiple awards are anticipated. Selection for award under this BAA will be based upon the evaluation factors, importance to the agency programs, and the availability of funds.
The Research Areas included in this NIAID OMNIBUS BROAD AGENCY ANNOUNCEMENT No. HHS-NIH-NIAID-BAA2025-1, as well as the projected amounts of available funding, are discussed below. Dates for receipt of proposals are identified separately for EACH Research Area within the solicitation.
Description:
Research Area 001 – Development of Candidate Therapeutics, Vaccines, and In Vitro Diagnostics for Antimicrobial-Resistant (AMR) Bacterial or Fungal Pathogens
For Research Area 001, there are three (3) separate Topics – A, B, and C. Offerors may submit a proposal in response to Topics A, B, and/or C. If proposing to multiple Topics, Offerors must submit separate technical and business proposals for each Topic.
Topic A: Therapeutics for AMR Bacterial or Fungal Pathogens
The objective of Topic A is to develop new therapeutic products against severe infections and/or drug-resistant strains of the following bacterial and fungal pathogens:
a. Pseudomonas aeruginosa, and/or Acinetobacter baumannii; OR
b. Candida auris, Cryptococcus spp., Aspergillus fumigatus, and/or Mucorales.
For the purpose of this Topic, “therapeutic” activity refers to the cure of disease, by elimination or substantial reduction of infective pathogens, by administration of a pharmaceutical agent after symptoms of disease are clinically observable. An antimicrobial therapeutic candidate refers to an advanced lead series, optimized leads, or product candidate, that is a new chemical entity and either a small molecule (e.g., natural products, nucleosides, or peptides of </= 40 amino acids), monoclonal antibody or a nanobody conjugate/fusion product, or a bacteriophage product. The following are not included: proteins, other biological entities, and conjugates of such entities (except monoclonal antibodies, nanobodies and bacteriophages).
This Topic will support lead optimization, pre-clinical Investigational New Drug (IND) enabling studies, and clinical Phase I trials of lead candidates with demonstrated therapeutic activities. For some pathogens, the development of a therapeutic product under the U.S. Food and Drug Administration’s (FDA) Animal Rule will be supported.
Topic B: Vaccines for AMR Bacterial Pathogens
The objective of Topic B is to protect human health and well-being by advancing vaccine candidates for the following ESKAPE bacterial pathogens: Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species.
For the purpose of this Topic, the definition of a lead vaccine candidate is a candidate in which the antigen(s), adjuvant (if applicable), vaccine platform (e.g., mRNA, viral vector, subunit, etc.), and delivery route have been selected and are clinically relevant (i.e., intended for the final clinical product), for which proof-of-concept immunogenicity in relevant animal model(s) has already been demonstrated.
This Topic will support the advancement of a promising lead candidate from pre-clinical testing through IND submission to the FDA, as well as Phase I clinical trial conduct.
Topic C: In Vitro Diagnostics for AMR Fungal Pathogens
The objective of Topic C is to develop innovative platform technologies to speed the identification of infection from among a broad panel of fungi and to profile the phenotypic antifungal susceptibility. This emphasis aligns with NIAID’s goal of addressing persistent challenges in adequate clinical management associated with mycological infections and alleviating the burden of antifungal resistance.
The diagnostic test system must detect analytes from at least one, and preferably several, of the following agents and markers:
- Candida spp. and associated resistance markers
- Aspergillus fumigatus and associated resistance markers
- Coccidioides spp.
- Mucorales
Funding for Research Area 001: NIAID estimates that one to two awards may be issued for this Research Area for a total cost of up to $8.5 million for the non-severable base period across all contracts (direct and indirect costs combined). The total duration of a proposed contract should be consistent with the nature and complexity of the offeror’s proposed research. The total performance period comprised of the base and any options proposed by an Offeror should not exceed five (5) years.
Proposals Due Date and Time: February 21, 2025, 3:00PM Eastern Time
Research Area 002 – Development of Direct Acting Antivirals (DAA) for Viral Families of Pandemic Potential
This Research Area aims to develop safe and effective antivirals to combat viruses of pandemic potential, as well as to build sustainable platforms for targeted drug discovery and the development of a robust pipeline of candidates. Proposals MUST focus on antivirals that:
- Directly modify viral target function (not through the modulation of the host responses); AND
- Act by reducing viral burden in early stages of disease; AND
- Act against viruses of pandemic potential (i.e., Bunyaviridae, Coronaviridae, Filoviridae, Flaviviridae, Orthopoxviridae, Paramyxoviridae, Picornaviridae, and Togaviridae); AND
- Are new chemical entities limited to small molecules (e.g., natural products, nucleosides, or peptides of </= 40 amino acids) and nanobody conjugates/fusion products that are directly acting on viral targets and functions (not through the modulation of the host responses); AND
- Have safety profiles and suitable routes of administration for broad outpatient use.
For the purpose of this Topic, “therapeutic” activity refers to the elimination or substantial reduction of infective pathogens by administration of a pharmaceutical agent after viral challenge. A “therapeutic” candidate refers to an advanced lead series, optimized leads, or product candidate, that is a new chemical entity and either a small molecule (e.g., natural products, nucleosides, or peptides of </= 40 amino acids) or nanobody conjugate/fusion product. The following are not included: proteins, monoclonal antibodies, other biological entities, and conjugates of such entities.
Research Area 002 will support lead optimization, pre-clinical (IND enabling) studies, and/or Phase I clinical trials. Proposed products are not required to be narrow-spectrum and may include other pathogens in their spectrum of activity, provided one of the listed pathogens is in the primary indication of the proposed Target Product Profile (TPP). Product development under the FDA’s Animal Rule (21 CFR 314 subpart I) will be supported if appropriate to the proposed pathogen target.
Funding for Research Area 002: NIAID estimates that three to four awards may be issued for this Research Area for a total cost of up to $20 million for the non-severable base period across all contracts (direct and indirect costs combined). The total duration of a proposed contract should be consistent with the nature and complexity of the offeror’s proposed research. The total performance period comprised of the base and any options proposed by an Offeror should not exceed five (5) years.
Proposals Due Date and Time: January 21, 2025, 3:00PM Eastern Time
Any responsible offeror may submit a proposal which shall be considered by the Agency. This BAA can be accessed through Sam.Gov: https://sam.gov/opp/e1e43a392c2449e6805b9300906222a2/view. This notice does not commit the Government to award a contract.
For this solicitation, the NIAID requires proposals to be submitted online via the NIAID electronic Contract Proposal Submission (eCPS) website. Submission of proposals by facsimile or e-mail is not acceptable. For directions on using eCPS, go to the website: https://ecps.nih.gov and then click on "How to Submit."
Inquiries
Please direct all inquiries to:
Swee L. Teo
Contracting Officer
National Institute of Allergy and Infectious Diseases (NIAID)
Telephone: 240-669-5173
Email: teosl@niaid.nih.gov
Department of Health
and Human Services (HHS)
and Human Services (HHS)
Wednesday, December 18, 2024
DASC new courses
- DASC 728/828, “Deep Learning Fundamentals and Applications” (Deep Learning Fund & App) (frank)
“This course covers key components of deep learning framework, including loss functions, regularization, training and batch normalization. The course also covers several fundamental deep learning architectures such as multilayer perceptrons, convolutional neural network, recurrent neural network and transformers, as well as some advanced topics such as graph neural network and deep reinforcement learning. The class activities include traditional lectures, paper reading and presentation, and projects.”
Prerequisites: be: CS 422 or CS 522 or CS 480 or CS 580 or CS 722 or CS 822 or CS 733 or CS 833 or CS 620, or other equivalent courses at the discretion of the instructor. - DASC 605, “Statistical Inference and Experimental Design for Data Science” (Stat Inf & Exp Design for Data Sci) (Trent)
“description”
Prerequisites: STAT 603 and instructor approval - DASC 715/815 Generative AI (3 credits)
- · Course Description: This course provides a deep dive into the foundations and current advancements in generative AI. It covers key concepts such as transformer models, GANs, VAEs, LLMs, and their applications across various fields, emphasizing both theory and hands-on learning, including ethical considerations such as fairness and bias mitigation. Students will develop a comprehensive understanding of generative AI and gain practical experience.
- · Grading: Normal/Letter, Pass/Fail, Audit allowed.
- · Prerequisite courses: Prior programming experience are expected.
- DASC 717/817 AI for Health Sciences (3 credits)
- · Course Description: This course explores the application of AI in health sciences, focusing on machine learning, NLP, computer vision, generative AI techniques for diagnostics, treatment planning, patient monitoring, and biomedical research. It covers precision medicine, ethical AI, and the integration of AI into practice. Students will gain a deep understanding and practical skills to develop innovative AI solutions that address real-world challenges in health sciences.
- · Grading: Normal/Letter, Pass/Fail, Audit allowed.
- · Prerequisite courses: Prior programming experience are expected.
- DASC 7xx/8xx, “Data-Driven Computational Imaging” (Dushan)
“please update course number, title and description after coordination with CS” - DASC 600 (Sampath)
“please update title and description” - DASC 699 Thesis Research (3 Credit Hours)
Prerequisites: Departmental permission required - DASC 697 Independent Study in Data Science (1-3 Credit Hours)
Independent study under the direction of an instructor.
Prerequisites: permission of the instructor - DASC 668 Internship (1-3 credits) (P/F only)
Requirements will be established by the School of Data Science and Career Development Services and will vary with the amount of credit desired. Allows students an opportunity to gain a short duration career-related experience.
Actually submitted
CS 781 AI for Health Science,
Cross-listed and/orEquivalent Courses | CS 881, DASC 781, DASC 881 |
CS 782 Generative AI , cross listed with CS 882, DASC 782, DASC 882
Monday, December 16, 2024
Alzheimer’s Disease Sequencing Project (ADSP)
The Alzheimer’s Disease Sequencing Project (ADSP) is a comprehensive, multi-phase national consortium aimed at understanding the genetic basis of Alzheimer’s disease and related dementias. Here are the key aspects of the ADSP:
https://www.nia.nih.gov/research/dn/alzheimers-disease-sequencing-project-consortia
NO gene expression?!
## Genomic Data
- The ADSP involves whole-genome sequencing (WGS) and whole exome sequencing (WES) of samples from various cohorts.
- **Discovery Phase**: Includes WGS for 584 samples from 113 multiplex families, WES for 5,096 AD cases and 4,965 controls, and WES of an enriched sample set comprising 853 AD cases from multiply affected families and 171 Hispanic controls[2][5][6].
- **Follow-Up Study Phases**: The project has progressed through several phases, including the Discovery Extension Phase, Follow-Up Study Phase, and Follow-Up Study 2.0 Diversity Initiative Phase, which focus on expanding the genetic data to include more diverse populations, such as African Americans, Hispanics, and Asians[1][5].
## Phenotypic Data
- While the primary focus of the ADSP is on genomic data, it also incorporates rich phenotypic data.
- **Clinical and Cognitive Data**: The project includes clinical cognitive data such as memory, language, and executive function scores. However, it does not directly collect neuroimaging data like T1 MRI, Amyloid-beta, or tau PET scans as part of its core sequencing efforts. Instead, these data are often integrated from other studies and consortia[1][3][6].
- **Longitudinal and Autopsy-Confirmed Data**: The project emphasizes the use of well-phenotyped participants with autopsy-confirmed diagnoses and longitudinal data[2][5].
## Harmonized Data
- The ADSP Phenotype Harmonization Consortium (ADSP-PHC) plays a crucial role in harmonizing phenotypic data across different cohorts.
- **ADSP-PHC**: Established to harmonize endophenotype data, including cognitive, imaging, longitudinal clinical, neuropathological, cardiovascular risk, and biomarker data. This harmonization enables modern genomic analyses and generates a perpetually curated and shared legacy dataset[3][6].
## Study Design and Objectives
- The ADSP uses both case-control and family-based study designs.
- **Objectives**: The overarching goals include identifying new genes involved in Alzheimer’s disease, identifying gene alleles contributing to increased risk or protection against the disease, understanding why individuals with known risk factor genes do not develop AD, and identifying potential therapeutic approaches and prevention strategies[1][4][5].
## Diversity and Global Collaboration
- The ADSP places a high priority on racial/ethnic diversity, recognizing that most genetic studies have been conducted in non-Hispanic white populations.
- **Diverse Population Initiative**: The Follow-Up Study 2.0 phase aims to conduct whole-genome sequencing on 18,500 AD cases and 18,500 controls from African American, Hispanic, and Asian populations, ensuring a more diverse sample set[1][2][5].
The ADSP is a collaborative effort involving over 350 investigators from global institutions, funded under several cooperative agreements and research grant awards, and is part of the NIA Alzheimer’s Disease Genetics Portfolio.
Citations:
[1] https://www.nia.nih.gov/research/dn/alzheimers-disease-sequencing-project-consortia
[2] https://dss.niagads.org/studies/sa000001/
[3] https://www.vumc.org/cnt/harmonization-initiative
[4] https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000572.v1.p1
[5] https://adsp.niagads.org/about/adsp-phases/
[6] https://adsp.niagads.org/funded-programs/phenotype-harmonization/
[7] https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/alz.13705
[8] https://adsp.niagads.org/adsp-and-affiliates-whole-genome-sequencing-report/
Sunday, December 15, 2024
free tools that can detect AI-generated content
Here are some free tools that can detect AI-generated content with a limit of up to 5,000 words:
1. **SEO.ai**:
- **Word Limit**: 5,000 characters.
- **Features**: Provides a probability score indicating whether the content is AI-generated. It uses an ensemble model for more stable results, making it a reliable choice for detecting AI content[2].
2. **Smodin**:
- **Word Limit**: No specific character limit mentioned, but it supports document uploads (PDF, DOC, DOCX).
- **Features**: Offers a simple interface for pasting text or uploading documents. It can handle multiple languages and provides a probability score for AI detection[1][3].
3. **QuillBot**:
- **Word Limit**: No strict limit mentioned; however, it typically processes smaller texts effectively.
- **Features**: Offers an overall percentage likelihood of AI generation and categorizes the text into different classifications (AI-generated, human-written, etc.). It does not require sign-up for use[1][4].
4. **GPTZero**:
- **Word Limit**: Up to 5,000 characters.
- **Features**: Allows users to input text directly or upload documents. It analyzes the text quickly and provides insights on whether the content is likely human or AI-generated[3][7].
5. **Leap AI**:
- **Word Limit**: Not specified, but allows document uploads.
- **Features**: Provides a percentage score estimating AI involvement and highlights sentences with high scores for AI generation[1].
These tools vary in their specific capabilities and user interfaces, but they all provide free options for detecting potential AI-generated content effectively.
Citations:
[1] https://surferseo.com/blog/best-ai-content-detection-tools/
[2] https://seo.ai/blog/free-ai-content-detectors
[3] https://zapier.com/blog/ai-content-detector/
[4] https://www.scribbr.com/ai-tools/best-ai-detector/
[5] https://originality.ai/blog/best-ai-content-detection-tools-reviewed
[6] https://contentdetector.ai
[7] https://www.twixify.com/post/best-ai-content-detectors
Friday, December 13, 2024
Everything you never wanted to know about the R vulnerability ...but shouldn't be afraid to ask
Everything you never wanted to know about the R vulnerability
...but shouldn't be afraid to ask
https://aitap.github.io/2024/05/02/unserialize.html
R-bitrary Code Execution: Vulnerability in R’s Deserialization
R-bitrary Code Execution: Vulnerability in R’s Deserialization
https://hiddenlayer.com/innovation-hub/r-bitrary-code-execution/
CWE-502: Deserialization of Untrusted Data
CWE-502: Deserialization of Untrusted Data
https://cwe.mitre.org/data/definitions/502.html
Thursday, December 12, 2024
New R programming vulnerability exposes projects to supply chain attacks:
https://thehackernews.com/2024/04/new-r-programming-vulnerability-exposes.html
How about citing the following R risk issue before R.4.3.1.
New R programming vulnerability exposes projects to supply chain attacks:
https://thehackernews.com/2024/04/new-r-programming-vulnerability-exposes.html
A critical security vulnerability, CVE-2024-27322, has been identified in R versions 1.4.0 through 4.3.1. This flaw allows attackers to execute arbitrary code by exploiting the deserialization process of untrusted data, particularly through maliciously crafted RDS (R Data Serialization) files or R packages. The issue stems from R's handling of promise objects and lazy evaluation, enabling an attacker to embed arbitrary R code within an RDS file that executes upon loading and accessing the associated object. This vulnerability poses significant risks in environments where R packages are shared, potentially leading to widespread supply chain attacks.
This issue was fixed in R4.4.0.
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