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1. The Socioeconomic Attainments of Second-Generation Nigerian and Other Black Americans: Evidence from the Current Population Survey, 2009 to 2019

   https://doi.org/10.1177/23780231211001971  

   Sakamoto, Arthur; Amaral, Ernesto_F_L; Wang, Sharron_Xuanren; Nelson, Courtney (March 2021, Socius: Sociological Research for a Dynamic World)

   Second-generation black Americans have been inadequately studied in prior quantitative research. The authors seek to ameliorate this research gap by using the Current Population Survey to investigate education and wages among second-generation black Americans with a focus on Nigerian Americans. The latter group has been identified in some qualitative studies as having particularly notable socioeconomic attainments. The results indicate that the educational attainment of second-generation Nigerian Americans exceeds other second-generation black Americans, third- and higher generation African Americans, third- and higher generation whites, second-generation whites, and second-generation Asian Americans. Controlling for age, education, and disability, the wages of second-generation Nigerian Americans have reached parity with those of third- and higher generation whites. The educational attainment of other second-generation black Americans exceeds that of third- and higher generation African Americans but has reached parity with that of third- and higher generation whites only among women. These results indicate significant socioeconomic variation within the African American/black category by gender, ethnicity, and generational status that merits further research. 

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2. Speaking up for the invisible minority: First‐generation students in higher education

   https://doi.org/10.1002/jcp.31158  

   Kamalumpundi, Vijayvardhan; Silvers, Sophielle; Franklin, Latisha; Neikirk, Kit; Spencer, Elsie; Beasley, Heather K.; Wanajalla, Celestine N.; Vue, Zer; Crabtree, Amber; Kirabo, Annet; et al (January 2024, Journal of Cellular Physiology)

   Abstract A first‐generation college student is typically defined as a student whose biological parent(s) or guardian(s) never attended college or who started but did not finish college. However, “first‐generation” can represent diverse family education situations. The first‐generation student community is a multifaceted, and intersectional group of individuals who frequently lack educational/financial resources to succeed and, consequently, require supportive environments with rigorous mentorship. However, first‐generation students often do not make their identity as first‐generation students known to others due to several psychosocial and academic factors. Therefore, they are often “invisible minorities” in higher education. In this paper, we describe the diverse family situations of first‐generation students, further define “first‐generation,” and suggest five actions that first‐generation trainees at the undergraduate/graduate stages can engage in to succeed in an academic climate. We also provide suggestions for mentors to accommodate first‐generation students' unique experiences and equip them with tools to deliver intentional mentoring practices. We hope that this paper will help promote first‐generation student success throughout the academic pipeline. 

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3. Data Center Waste Heat Reuse: An Investment Analysis

   https://doi.org/10.1115/1.4067444  

   Khalid, Rehan; Wemhoff, Aaron P; Khuc, Mai; Nayar, Ajay J; Schon, Steven (February 2025, ASME Journal of Engineering for Sustainable Buildings and Cities)

   Abstract A thermo-economic analysis (TEA) of a novel cooling and enhanced heat recovery (CEHR) system for data centers (DCs) is presented. Three financial metrics (net present value—NPV, return on investment—ROI, and payback period—PP) are calculated for hot and chilled water generation. Hot water generation uses vapor recompression to produce water at approximately 75 °C. Chilled water generation builds upon the hot water generation scenario by feeding the hot water stream into an absorption chiller. Without considering the additional costs for connecting the infrastructure with the customer, a payback period shorter than 2 years is found for a hot water generation system for a base case assuming a 10-MW data center (DC) in Philadelphia, PA, when carbon (reduction) credits are included. Chilled water generation is found to be economically unfavorable in this location. Sensitivities of economics to data center power, hot water versus chilled water generation, geographic region, and carbon credits are evaluated for five additional global locations in Europe and Asia. The economies of scale enable favorable payback periods for integrating hot water generation for facilities beyond 7 MW. Hot water generation is especially favorable in Singapore when replacing natural gas-based heating or hot water heat pumps. 

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4. Efficient and consistent zero-shot video generation with diffusion models

   https://doi.org/10.1117/12.3013575  

   Frakes, Ethan; Khalid, Umar; Chen, Chen (June 2024, SPIE)

   Kehtarnavaz, Nasser; Shirvaikar, Mukul V (Ed.)

   Recent diffusion-based generative models employ methods such as one-shot fine-tuning an image diffusion model for video generation. However, this leads to long video generation times and suboptimal efficiency. To resolve this long generation time, zero-shot text-to-video models eliminate the fine-tuning method entirely and can generate novel videos from a text prompt alone. While the zero-shot generation method greatly reduces generation time, many models rely on inefficient cross-frame attention processors, hindering the diffusion model’s utilization for real-time video generation. We address this issue by introducing more efficient attention processors to a video diffusion model. Specifically, we use attention processors (i.e. xFormers, FlashAttention, and HyperAttention) that are highly optimized for efficiency and hardware parallelization. We then apply these processors to a video generator and test with both older diffusion models such as Stable Diffusion 1.5 and newer, high-quality models such as Stable Diffusion XL. Our results show that using efficient attention processors alone can reduce generation time by around 25%, while not resulting in any change in video quality. Combined with the use of higher quality models, this use of efficient attention processors in zero-shot generation presents a substantial efficiency and quality increase, greatly expanding the video diffusion model’s application to real-time video generation. 

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5. From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models

   Welleck, Sean; Bertsch, Amanda; Finlayson, Matthew; Schoelkopf, Hailey; Xie, Alex; Neubig, Graham; Kulikov, Ilia; Harchaoui, Zaid (November 2024, https://doi.org/10.48550/arXiv.2406.16838 Focus to learn more)

   One of the most striking findings in modern research on large language models (LLMs) is that scaling up compute during training leads to better results. However, less attention has been given to the benefits of scaling compute during inference. This survey focuses on these inference-time approaches. We explore three areas under a unified mathematical formalism: token-level generation algorithms, meta-generation algorithms, and efficient generation. Token-level generation algorithms, often called decoding algorithms, operate by sampling a single token at a time or constructing a token-level search space and then selecting an output. These methods typically assume access to a language model's logits, next-token distributions, or probability scores. Meta-generation algorithms work on partial or full sequences, incorporating domain knowledge, enabling backtracking, and integrating external information. Efficient generation methods aim to reduce token costs and improve the speed of generation. Our survey unifies perspectives from three research communities: traditional natural language processing, modern LLMs, and machine learning systems. 

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