Deep Neural Networks are vulnerable toTrojan (or backdoor) attacks.

In recent years, large language models (LLM) have emerged as powerful tools for diverse natural language processing tasks. However, their potential for recommender systems under the generative recommendation paradigm remains relatively unexplored. This paper presents an innovative approach to recommendation systems using large language models (LLMs) based on text data. In this paper, we present a novel LLM for generative recommendation (GenRec) that utilized the expressive power of LLM to directly generate the target item to recommend, rather than calculating ranking score for each candidate item one by one as in traditional discriminative recommendation. GenRec uses LLM's understanding ability to interpret context, learn user preferences, and generate relevant recommendation. Our proposed approach leverages the vast knowledge encoded in large language models to accomplish recommendation tasks. We first we formulate specialized prompts to enhance the ability of LLM to comprehend recommendation tasks. Subsequently, we use these prompts to fine-tune the LLaMA backbone LLM on a dataset of user-item interactions, represented by textual data, to capture user preferences and item characteristics. Our research underscores the potential of LLM-based generative recommendation in revolutionizing the domain of recommendation systems and offers a foundational framework for future explorations in this field. We conduct extensive experiments on benchmark datasets, and the experiments shows that our GenRec has significant better results on large dataset.

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The public queues for hours at the Musée du Louvre in Paris to catch a glimpse of this much studied portrait. But, according to AI, it's not much to look at really. At least that is the outcome from a project being undertaken at the Art and Artificial Intelligence Lab at Rutgers University. The team at Rutgers has been using AI to analyze and create art for the past five years, studying around 80,000 different paintings by over 1,100 artists. One of the first outcomes from the research was the replication of known painting styles.

Background and Overview The founding of a new College at Rutgers in 1969 became the occasion for building a strong Computer Scicncc presence in the University. Livingston College thus provided the home for the newly organized Department of Computer Science (DCS) and for the beginning of Computer Science research at Rutgers. 1 came to chair the depart,- ment after ten years at RCA Labs in Princeton, where 1 headed the Computer Theory Research group. My own work in the Labs concentrated mainly in Al. In the early 196Os, 1 became interested in problems of representation in problem solving, and in computer methods for building models and solving formation problems. As 1 continued working on these problems at, Rutgers, their central significance for Al became increasingly clear to me; so was their difficulty.

The fall of 2002 marked the passing of Ray Reiter, for whom a memorial article by Jack Minker appears in this issue. As the issue was going to press, AI lost Saul Amarel, Norm Nielsen, and Charles Rosen. We thank Tom Mitchell and Casimir Kulikowski for their memorial to Saul Amarel, Ray Perrault for his remembrance of Norm Nielsen, and Peter Hart and Nils Nilsson for their tribute to Charles Rosen. The AI community mourns our lost colleagues and gratefully remembers their contributions, which meant so much to so many and to the advancement of artificial intelligence as a whole. The foundation of Charlie's creativity was his broad knowledge.

In the last few years, interest has grown in exploring AI approaches to design problems, both because of the enormous potential impact on productivity of improved design tools and because of the interesting basic AI issues that these problems raise. In particular, a number of ship designers and AI researchers recently became interested in applying AI to the hydrodynamic design of ship hulls. A typical problem here is to design the shape of a ship's hull in response to desired hydrodynamic properties such as drag and stability, taking into consideration a variety of design constraints, such as total hull volume. This problem differs in a number of ways from most previous work in AI and design. For instance, unlike circuit or program design, hull design involves designing a shape rather than a structure of discrete primitives.

Dr. Saul Amarel, who helped develop the field of artificial intelligence and founded the computer science department at Rutgers University, died on Wednesday in Princeton, N.J., where he lived. The cause was complications of cancer, according to Rutgers. At Rutgers, Dr. Amarel developed computer time-sharing, and his laboratory became an early node on Arpanet, the precursor to the Internet. He took a leave in the 1980's to spend a few years directing a computer science program at the Pentagon, and returned to Rutgers in 1988. Among his peers, Dr. Amarel was perhaps best known for a paper he wrote in 1968, which put him at the vanguard of the artificial intelligence movement.

I keep posting ads for postdocs with other people but this is actually to work with little old me! The Department of Electrical and Computer Engineering (ECE) at Rutgers University is seeking a dynamic and motivated Postdoctoral Fellow to work on developing distributed machine learning algorithms that work on complex neuroimaging data. This work is in collaboration with the Mind Research Network in Albuquerque, New Mexico under NIH Grant 1R01DA040487-01A1. Candidates with a Ph.D. in Electrical Engineering, Computer Science, Statistics or related areas with experience in one of The Fellow will receive valuable experience in translational research as well as career mentoring, opportunities to collaborate with others outside the project within the ECE Department, DIMACS, and other institutions.The initial appointment is for 1 year but can be renewed subject to approval. Salary and compensation will be commensurate with the standard NIH scale for postdocs.

Rutgers is taking a leading role in an IBM-sponsored World Community Grid project that will use supercomputing power to identify potential drug candidates to cure the Zika virus. The project, known as OpenZika, employs a global team of scientists who will perform "virtual" experiments in a search of treatments for the fast-spreading virus that the World Health Organization has declared a global public health emergency. OpenZika will screen current drugs and millions of drug-like compounds from existing databases against models of Zika protein structures (and also against structures of proteins from related viruses, including West Nile Virus and Dengue). These computational results will be shared quickly with the research community and general public, with compounds showing the most promise then tested in laboratory settings. "Instead of having to wait a number of years, even decades potentially, to test all these compounds in order to find a few that could form the basis of antiviral drugs to cure Zika, we will perform these initial tests in a matter of months, just by using idle computing power that would otherwise go to waste," says Alex Perryman, a research teaching specialist at Rutgers' New Jersey Medical School, in Professor Joel Freundlich's lab.