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Math Lab
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Math Lab Rooms located in the Main Library in rooms 300X and 300Y
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Located at the first floor of the LSU Main Library.
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Does the library offer a book delivery service?
Yes, LSU Library offers book deliveries to faculty and staff only . We will deliver LSU Library books and interlibrary loan books to your departmental mailbox. Reserve and Government Document materials will not be included. To use the service, request the item through your interlibrary loan account. On the request form under "Delivery Options" select "Deliver to Departmental Mailbox." The request will arrive 1-2 business days from the date we receive the item. Heres how to get your materials delivered:
- You will need an ILLiad account to make the request.
- Once logged in, you will need to select "Deliver to Departmental Mailbox" for your Delivery Method. Books will be delivered to the departmental mailbox of the requestors within 1-2 business days. A second attempt will be made if the delivery is not successful the first time, but if the department office is inaccessible again, patrons will receive an e-mail stating the problem and asking them to come pick up their materials.
- The materials will be placed on hold and kept at the checkout desk for 7 days. If they are not picked up in this time frame, they will be returned to the collection. Be aware of the following:
- Users are responsible for returning materials back to the library.
- Materials will be checked out to the patrons library account prior to being delivered.
- The user is responsible for the items requested through this service.
- Any LSU Library books not returned or renewed within 40 days of the due date will be considered lost and the usual penalties will apply.
- A limit of eight physical material format items can be requested per day. LSU Libraries | Book Delivery Answered by: Access Services Staff

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2062205
Orthogonally Functionalizable Redox-Responsive Polymer Brushes: Catch and Release Platform for Proteins and Cells
Polymer brushes engineered to “specifically capture” and “release on demand” analytes such as dyes, proteins, and cells find biomedical applications ranging from protein immobilization to cell death. Utilizing a disulfide-linker-containing monomer as a building block enables the fabrication of a redox-responsive polymer brush platform with the “catch and release” attribute. Herein, thiol-reactive redox-responsive polymer brushes are fabricated using a pyridyl disulfide-based monomer, and their postpolymerization functionalization is demonstrated via thiol–disulfide exchange reaction with thiol-containing dyes, (bio)­molecules, and cell adhesive ligands. After establishing reversible conjugation using a fluorescent dye and other model compounds, copolymer brushes postmodified with thiol-containing mannose demonstrated selective immobilization of concanavalin A in the presence of peanut agglutinin. In addition, a thiolated RGD peptide was conjugated to the side chain of polymer brushes to facilitate cell adhesion, followed by on-demand harvesting. To enable localized drug delivery to surface-adhered cells, orthogonal chain end and side chain functionalization using the thiol-Michael addition and thiol–disulfide exchange reaction, respectively, was used to conjugate the cell adhesive RGD peptide and the anticancer drug doxorubicin (DOX). On-demand DOX release and internalization by surface-bound cancer cells were demonstrated via cleavage of disulfide linkages in the presence of a reducing agent. This approach may provide an attractive methodology to deliver therapeutic agents precisely to specific cells.
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning
Vision-language models (VLMs) have shown remarkable advancements in multimodal reasoning tasks. However, they still often generate inaccurate or irrelevant responses due to issues like hallucinated image understandings or unrefined reasoning paths. To address these challenges, we introduce Critic-V, a novel framework inspired by the Actor-Critic paradigm to boost the reasoning capability of VLMs. This framework decouples the reasoning process and critic process by integrating two independent components: the Reasoner, which generates reasoning paths based on visual and textual inputs, and the Critic, which provides constructive critique to refine these paths. In this approach, the Reasoner generates reasoning responses according to text prompts, which can evolve iteratively as a policy based on feedback from the Critic. This interaction process was theoretically driven by a reinforcement learning framework where the Critic offers natural language critiques instead of scalar rewards, enabling more nuanced feedback to boost the Reasoner’s capability on complex reasoning tasks. The Critic model is trained using Direct Preference Optimization (DPO), leveraging a preference dataset of critiques ranked by Rule-based Reward (RBR) to enhance its critic capabilities. Evaluation results show that the Critic-V framework significantly outperforms existing methods, including GPT-4V, on 5 out of 8 benchmarks, especially regarding reasoning accuracy and efficiency. Combining a dynamic text-based policy for the Reasoner and constructive feedback from the preference-optimized Critic enables a more reliable and context-sensitive multimodal reasoning process. Our approach provides a promising solution to enhance the reliability of VLMs, improving their performance in real-world reasoning-heavy multimodal applications such as autonomous driving and embodied intelligence. Our data and code are released at https://github.com/kyrieLei/Critic-V.