Humane Living

Summer Chillin’ Languid lizards, sluggish squirrels, overheated orioles? Help backyard wildlife beat the heat with DIY dipping spots and shade zones. In the Limelight Classical music goes to the dogs with A Grateful Tail; book explores canine smarts; documentary highlights orphaned orca’s plight

The Advocate

Public Interest Resource Center Holds First Annual Dinner; FSSF Auction Proceeds Matched by Dean; Coffee and Bias Talk at the South African Consulatehttps://ir.lawnet.fordham.edu/student_the_advocate/1098/thumbnail.jp

Enhancing e-Infrastructures with Advanced Technical Computing: Parallel MATLAB® on the Grid

MATLAB® is widely used within the engineering and scientific fields as the language and environment for technical computing, while collaborative Grid computing on e-Infrastructures is used by scientific communities to deliver a faster time to solution. MATLAB allows users to express parallelism in their applications, and then execute code on multiprocessor environments such as large-scale e-Infrastructures. This paper demonstrates the integration of MATLAB and Grid technology with a representative implementation that uses gLite middleware to run parallel programs. Experimental results highlight the increases in productivity and performance that users obtain with MATLAB parallel computing on Grids

Return to the idea of homely city

Homeliness is associated with something friendly and well-known. The idea of homeliness in architecture and urban planning does not mean only people-friendly space. First of all, it means the city that is familiar to the citizens, what cannot be realized without specific conditions - creation of the feeling of being part of the community, possession and identity. The antithesis of the city - agricultural landscape - seems to be the perfect basis for them. Can the idea of homeliness be created based on the relationship between the town and its opposition? How can the "lost" agricultural landscape" work as a catalyst of the idea of homeliness and community integration? In the article, there will be the attempt of answer to these questions given. New ideas, such Agrarian Urbanism or Urban Horticulture will be presented and discussed. Throughout the world, research is on-going to develop techniques for assimilating agriculture into an urbanism acceptable to the expectations of modern life. The ability to grow food has implications for communities on multiple levels: from food security and health issues, to ensuring a local economy and to the social benefits of a productive activity in which all members of a community can engage. In Agrarian Urbanism a whole society is involved with the growing of food: people can have gardens instead of yards, or community gardens and even window boxes if they live in an apartment. Can these ideas create new ways of thinking about the contemporary city

Investigating the Existence of "Secret Language'' in Language Models

In this paper, we study the problem of secret language in NLP, where current language models (LMs) seem to have a hidden vocabulary that allows them to interpret absurd inputs as meaningful concepts. We investigate two research questions: ``Does the secret language phenomenon exist in different language models?'' and ``Does secret language depend on specific context?'' To answer these questions, we introduce a novel method named \textit{SecretFinding}, a gradient-based approach that can automatically discover secret languages in LMs. We conduct experiments on five representative models (Electra, ALBERT, Roberta, DistillBERT, and CLIP) finetuned on four NLP benchmarks (SST-2, MRPC, SNLI, and SQuAD) and a language-grounding benchmark (MSCOCO). Our experimental results show that even when we replace the most important words with others that are semantically dissimilar to the original words in a sentence, LMs do not consider the new sentence semantically dissimilar to the original, as the output does not change with a high probability. This phenomenon holds true across the five models and five tasks and gives a positive answer to the first research question. As for the second research question, we find that the secret language discovered by \textit{SecretFinding} is quite general and could even be transferred to other models in the black-box settings, such as GPT-3 and ChatGPT. Finally, we discuss the causes of secret language, how to eliminate it, the potential connection to memorization, and ethical implications. Examples of secret language found by SecretFinding are available on https://huggingface.co/spaces/anonymousauthors/ACL23_SecretLanguage