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Understanding the impossibility of machine learning fairness with data examples

Pillutla et al. | Sep 04, 2026

Understanding the impossibility of machine learning fairness with data examples

Machine learning systems are often expected to make fair decisions, yet the most widely used fairness criteria—independence, separation, and sufficiency—cannot generally be satisfied at the same time. In this study, students tested these criteria using a logistic regression model on a real-world student performance dataset and found that each criterion was met only at different prediction thresholds, with no threshold satisfying all three simultaneously. These results illustrate the inherent trade-offs in algorithmic fairness and highlight why achieving perfectly fair machine learning models is often impossible in practice.

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Evaluating need for adversarial training data given algorithmic defense methods against adversarial attacks

Yian et al. | Jul 05, 2026

Evaluating need for adversarial training data given algorithmic defense methods against adversarial attacks

The purpose of this study was to determine the necessity of previous non-algorithmic attacks (Adversarial Training) in light of algorithmic defense methods (Gradient Masking and Defensive Distillation) against FGSM attacks. We found a significant increase in image classification accuracy from defense methods with the non-algorithmic defense method compared to ones without. By analyzing the significance with a McNemar test, we determined that the inclusion of non-algorithmic defense methods is still necessary in light of new algorithmic defense methods.

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The Effect of Radiant Energy on Radish Seed Germination

Simon et al. | Jul 06, 2018

The Effect of Radiant Energy on Radish Seed Germination

Simon and colleagues test how exposure to microwaves affect radish seed germination, either microwaving seeds for ninety seconds or four minutes prior to planting. Surprisingly, the authors found that seeds microwaved for four minutes exhibited 150% increased germination as compared to controls. The authors hypothesize that breakdown of the radish seed coat when exposed to heat may allow seedlings to sprout more efficiently.

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Locating sources of a high energy cosmic ray extensive air shower using HiSPARC data

Aziz et al. | Oct 24, 2023

Locating sources of a high energy cosmic ray extensive air shower using HiSPARC data

Using the data provided by the University of Twente High School Project on Astrophysics Research with Cosmics (HiSPARC), an analysis of locations for possible high-energy cosmic ray air showers was conducted. An example includes an analysis conducted of the high-energy rain shower recorded in January 2014 and the use of Stellarium™ to discern its location.

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Ribosome distribution affects stalling in amino-acid starved cancer cells

Deng et al. | Jan 07, 2022

Ribosome distribution affects stalling in amino-acid starved cancer cells

In this article, the authors analyzed ribosome profiling data from amino acid-starved pancreatic cancer cells to explore whether the pattern of ribosome distribution along transcripts under normal conditions can predict the degree of ribosome stalling under stress. The authors found that ribosomes in amino acid-deprived cells stalled more along elongation-limited transcripts. By contrast, they observed no relationship between read density near start and stop and disparities between mRNA sequencing reads and ribosome profiling reads. This research identifies an important relationship between read distribution and propensity for ribosomes to stall, although more work is needed to fully understand the patterns of ribosome distribution along transcripts in ribosome profiling data.

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Large Language Models are Good Translators

Zeng et al. | Oct 16, 2024

Large Language Models are Good Translators

Machine translation remains a challenging area in artificial intelligence, with neural machine translation (NMT) making significant strides over the past decade but still facing hurdles, particularly in translation quality due to the reliance on expensive bilingual training data. This study explores whether large language models (LLMs), like GPT-4, can be effectively adapted for translation tasks and outperform traditional NMT systems.

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Analysis of Patterns in the Harmonics of a String with Artificially Enforced Nodes

Jain et al. | Jan 28, 2021

Analysis of Patterns in the Harmonics of a String with Artificially Enforced Nodes

This study examines the higher harmonics in an oscillating string by analyzing the sound produced by a guitar with a spectrum analyzer. The authors mathematically hypothesized that the higher harmonics in the series of the directly excited 2nd harmonic contain the alternate frequencies of the fundamental series, the higher harmonics of the directly excited 3rd harmonic series contain every third frequency of fundamental series, and so on. To test the hypotheses, they enforced artificial nodes to excite the 2nd, 3rd, and 4th harmonics directly, and analyzed the resulting spectrum to verify the mathematical hypothesis. The data analysis corroborates both hypotheses.

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