Children with low working memory and children with ADHD: same or different?

The purpose of this study was to compare working memory (WM), executive function, academic ability and problem classroom behaviors in children aged 8 to 11 years who were either identified via routine screening as having low WM, or had been diagnosed with ADHD. Standardised assessments of WM, executive function and reading and mathematics were administered to 83 children with ADHD, 50 children with low WM and 50 typically developing children. Teachers rated problem behaviors on checklists measuring attention, hyperactivity/impulsivity, oppositional behavior, and difficulties associated with executive function in the classroom. The ADHD and low WM groups had highly similar WM and executive function profiles, but were distinguished in two key respects: children with ADHD had higher levels of rated and observed impulsive behavior, and children low WM had slower response times. Possible mechanisms for these common and distinct deficits are discussed

From Parallel Sequence Representations to Calligraphic Control: A Conspiracy of Neural Circuits

Calligraphic writing presents a rich set of challenges to the human movement control system. These challenges include: initial learning, and recall from memory, of prescribed stroke sequences; critical timing of stroke onsets and durations; fine control of grip and contact forces; and letter-form invariance under voluntary size scaling, which entails fine control of stroke direction and amplitude during recruitment and derecruitment of musculoskeletal degrees of freedom. Experimental and computational studies in behavioral neuroscience have made rapid progress toward explaining the learning, planning and contTOl exercised in tasks that share features with calligraphic writing and drawing. This article summarizes computational neuroscience models and related neurobiological data that reveal critical operations spanning from parallel sequence representations to fine force control. Part one addresses stroke sequencing. It treats competitive queuing (CQ) models of sequence representation, performance, learning, and recall. Part two addresses letter size scaling and motor equivalence. It treats cursive handwriting models together with models in which sensory-motor tmnsformations are performed by circuits that learn inverse differential kinematic mappings. Part three addresses fine-grained control of timing and transient forces, by treating circuit models that learn to solve inverse dynamics problems.National Institutes of Health (R01 DC02852

SAFE: Self-Attentive Function Embeddings for Binary Similarity

The binary similarity problem consists in determining if two functions are similar by only considering their compiled form. Advanced techniques for binary similarity recently gained momentum as they can be applied in several fields, such as copyright disputes, malware analysis, vulnerability detection, etc., and thus have an immediate practical impact. Current solutions compare functions by first transforming their binary code in multi-dimensional vector representations (embeddings), and then comparing vectors through simple and efficient geometric operations. However, embeddings are usually derived from binary code using manual feature extraction, that may fail in considering important function characteristics, or may consider features that are not important for the binary similarity problem. In this paper we propose SAFE, a novel architecture for the embedding of functions based on a self-attentive neural network. SAFE works directly on disassembled binary functions, does not require manual feature extraction, is computationally more efficient than existing solutions (i.e., it does not incur in the computational overhead of building or manipulating control flow graphs), and is more general as it works on stripped binaries and on multiple architectures. We report the results from a quantitative and qualitative analysis that show how SAFE provides a noticeable performance improvement with respect to previous solutions. Furthermore, we show how clusters of our embedding vectors are closely related to the semantic of the implemented algorithms, paving the way for further interesting applications (e.g. semantic-based binary function search).Comment: Published in International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment (DIMVA) 201

Ghost story. II. The midpoint ghost vertex

We construct the ghost number 9 three strings vertex for OSFT in the natural normal ordering. We find two versions, one with a ghost insertion at z=i and a twist-conjugate one with insertion at z=-i. For this reason we call them midpoint vertices. We show that the relevant Neumann matrices commute among themselves and with the matrix $G$ representing the operator K1. We analyze the spectrum of the latter and find that beside a continuous spectrum there is a (so far ignored) discrete one. We are able to write spectral formulas for all the Neumann matrices involved and clarify the important role of the integration contour over the continuous spectrum. We then pass to examine the (ghost) wedge states. We compute the discrete and continuous eigenvalues of the corresponding Neumann matrices and show that they satisfy the appropriate recursion relations. Using these results we show that the formulas for our vertices correctly define the star product in that, starting from the data of two ghost number 0 wedge states, they allow us to reconstruct a ghost number 3 state which is the expected wedge state with the ghost insertion at the midpoint, according to the star recursion relation.Comment: 60 pages. v2: typos and minor improvements, ref added. To appear in JHE

Memory in autism spectrum disorder: a meta-analysis of experimental studies

To address inconsistencies in the literature on memory in Autism Spectrum Disorder (ASD), we report the first ever meta-analysis of short-term (STM) and episodic long-term (LTM) memory in ASD, evaluating the effects of type of material, type of retrieval and the role of inter-item relations. Analysis of 64 studies comparing individuals with ASD and typical development (TD) showed greater difficulties in ASD compared to TD individuals in STM (Hedges’ g=-0.53 [95%CI -0.90; -0.16], p=.005, I²=96%) compared to LTM (g=-0.30 [95%CI -0.42; -0.17], p<.00001, I²=24%), a small difficulty in verbal LTM (g=-0.21, p=.01), contrasting with a medium difficulty for visual LTM (g= -0.41, p=.0002) in ASD compared to TD individuals. We also found a general diminution in free recall compared to cued recall and recognition (LTM, free recall: g=-0.38, p<.00001, cued recall: g=-0.08, p=.58, recognition: g=-0.15, p=.16; STM, free recall: g=-0.59, p=.004, recognition: g=-0.33, p=.07). We discuss these results in terms of their relation to semantic memory. The limited diminution in verbal LTM and preserved overall recognition and cued recall (supported retrieval) may result from a greater overlap of these tasks with semantic long-term representations which are overall preserved in ASD. By contrast, difficulties in STM or free recall may result from less overlap with the semantic system or may involve additional cognitive operations and executive demands. These findings highlight the need to support STM functioning in ASD and acknowledge the potential benefit of using verbal materials at encoding and broader forms of memory support at retrieval to enhance performance