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1. Machine Learning Classification of Event-Related Brain Potentials during a Visual Go/NoGo Task

   https://doi.org/10.3390/e26030220  

   Bryniarska, Anna; Ramos, José A.; Fernández, Mercedes (March 2024, Entropy)

   Machine learning (ML) methods are increasingly being applied to analyze biological signals. For example, ML methods have been successfully applied to the human electroencephalogram (EEG) to classify neural signals as pathological or non-pathological and to predict working memory performance in healthy and psychiatric patients. ML approaches can quickly process large volumes of data to reveal patterns that may be missed by humans. This study investigated the accuracy of ML methods at classifying the brain’s electrical activity to cognitive events, i.e., event-related brain potentials (ERPs). ERPs are extracted from the ongoing EEG and represent electrical potentials in response to specific events. ERPs were evoked during a visual Go/NoGo task. The Go/NoGo task requires a button press on Go trials and response withholding on NoGo trials. NoGo trials elicit neural activity associated with inhibitory control processes. We compared the accuracy of six ML algorithms at classifying the ERPs associated with each trial type. The raw electrical signals were fed to all ML algorithms to build predictive models. The same raw data were then truncated in length and fitted to multiple dynamic state space models of order nx using a continuous-time subspace-based system identification algorithm. The 4nx numerator and denominator parameters of the transfer function of the state space model were then used as substitutes for the data. Dimensionality reduction simplifies classification, reduces noise, and may ultimately improve the predictive power of ML models. Our findings revealed that all ML methods correctly classified the electrical signal associated with each trial type with a high degree of accuracy, and accuracy remained high after parameterization was applied. We discuss the models and the usefulness of the parameterization. 

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2. Task difficulty modulates brain-behavior correlations in language production and cognitive control: Behavioral and fMRI evidence from a phonological go/no-go picture-naming paradigm

   https://doi.org/10.3758/s13415-018-0616-2  

   Zhang, Haoyun; Eppes, Anna; Beatty-Martínez, Anne; Navarro-Torres, Christian; Diaz, Michele T. (October 2018, Cognitive, Affective, & Behavioral Neuroscience)
3. Go Go Gadget Hammer: Flipping Nested Pointers for Arbitrary Data Leakage

   Tobah, Youssef; Kwong, Andrew; Kang, Ingab; Genkin, Daniel; Shin, Kang G (August 2024, Usenix Security)

   Rowhammer is an increasingly threatening vulnerability that grants an attacker the ability to flip bits in memory without directly accessing them. Despite efforts to mitigate Rowhammer via software and defenses built directly into DRAM modules, more recent generations of DRAM are actually more susceptible to malicious bit-flips than their predecessors. This phenomenon has spawned numerous exploits, showing how Rowhammer acts as the basis for various vulnerabilities that target sensitive structures, such as Page Table Entries (PTEs) or opcodes, to grant control over a victim machine. However, in this paper, we consider Rowhammer as a more general vulnerability, presenting a novel exploit vector for Rowhammer that targets particular code patterns. We show that if victim code is designed to return benign data to an unprivileged user, and uses nested pointer dereferences, Rowhammer can flip these pointers to gain arbitrary read access in the victim's address space. Furthermore, we identify gadgets present in the Linux kernel, and demonstrate an end-to-end attack that precisely flips a targeted pointer. To do so we developed a number of improved Rowhammer primitives, including kernel memory massaging, Rowhammer synchronization, and testing for kernel flips, which may be of broader interest to the Rowhammer community. Compared to prior works' leakage rate of .3 bits/s, we show that such gadgets can be used to read out kernel data at a rate of 82.6 bits/s. By targeting code gadgets, this work expands the scope and attack surface exposed by Rowhammer. It is no longer sufficient for software defenses to selectively pad previously exploited memory structures in flip-safe memory, as any victim code that follows the pattern in question must be protected. 

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4. Topological Materials Go Meta

   https://doi.org/10.1021/acs.chemrev.4c00439  

   Tempelaar, Roel (June 2024, Chemical Reviews)
5. Go or No Go: Learning to Persuade in an Early- Stage Student Entrepreneurship Program

   Spinuzzi, C. Altunian (January 2019, IEEE International Professional Communication Conference ProComm 2019;)