Features of software development using arrays in an object-oriented environment

The features of software development using static and dynamic arrays in the C ++ Builder object-oriented environment are considered. The syntax of various options for creating static and dynamic arrays in the C ++ Builder language is considered in detail. Examples of working with static and dynamic arrays in C ++ Builder developed by the authors and the corresponding algorithms are presented in the form of block diagrams, program codes and program interfaces. Examples of program development are given using one-dimensional and multidimensional arrays. Examples of memory allocation are given for dynamic arrays. The choice of the required method for solving the problem is substantiated, taking into account the available input data and taking into account the expected results, as well as the peculiarities of their obtaining and processing. The external specification and the main features of the solution of the assigned tasks are considered. The development of algorithms and programs for solving problems using arrays in the C ++ Builder environment is the basis for solving engineering and technical problems using software on a computer. The proposed approaches can be used in practice, since the algorithms outlined in the work will serve as a complex example in solving the set engineering and technical problems.

RustHorn: CHC-based Verification for Rust Programs

Reduction to satisfiability of constrained Horn clauses (CHCs) is a widely studied approach to automated program verification. Current CHC-based methods, however, do not work very well for pointer-manipulating programs, especially those with dynamic memory allocation. This article presents a novel reduction of pointer-manipulating Rust programs into CHCs, which clears away pointers and memory states by leveraging Rust’s guarantees on permission. We formalize our reduction for a simplified core of Rust and prove its soundness and completeness. We have implemented a prototype verifier for a subset of Rust and confirmed the effectiveness of our method.

Performance characterization of containerization for HPC workloads on InfiniBand clusters: an empirical study

Containerization technology offers an appealing alternative for encapsulating and operating applications (and all their dependencies) without being constrained by the performance penalties of using Virtual Machines and, as a result, has got the interest of the High-Performance Computing (HPC) community to obtain fast, customized, portable, flexible, and reproducible deployments of their workloads. Previous work on this area has demonstrated that containerized HPC applications can exploit InfiniBand networks, but has ignored the potential of multi-container deployments which partition the processes that belong to each application into multiple containers in each host. Partitioning HPC applications has demonstrated to be useful when using virtual machines by constraining them to a single NUMA (Non-Uniform Memory Access) domain. This paper conducts a systematical study on the performance of multi-container deployments with different network fabrics and protocols, focusing especially on Infiniband networks. We analyze the impact of container granularity and its potential to exploit processor and memory affinity to improve applications’ performance. Our results show that default Singularity can achieve near bare-metal performance but does not support fine-grain multi-container deployments. Docker and Singularity-instance have similar behavior in terms of the performance of deployment schemes with different container granularity and affinity. This behavior differs for the several network fabrics and protocols, and depends as well on the application communication patterns and the message size. Moreover, deployments on Infiniband are also more impacted by the computation and memory allocation, and because of that, they can exploit the affinity better.

A retrograde mechanism coordinates memory allocation across brain regions

Memories engage ensembles of neurons across different brain regions within a memory system. However, it is unclear whether the allocation of a memory to these ensembles is coordinated across brain regions. To address this question, we used CREB expression to bias memory allocation in one brain region, and rabies retrograde tracing to test memory allocation in connected presynaptic neurons in the other brain regions. We find that biasing allocation of CTA memory in the basolateral amygdala (BLA) also biases memory allocation in presynaptic neurons of the insular cortex (IC). By manipulating the allocation of CTA memory to specific neurons in both BLA and IC, we found that we increased their connectivity and enhanced CTA memory performance. These results, which are corroborated by mathematical simulations and by studies with auditory fear conditioning, demonstrate that a retrograde mechanism coordinates the allocation of memories across different brain regions.

CCR5 closes the temporal window for memory linking

Real world memories are formed in a particular context and are not acquired or recalled in isolation. Time is a key variable in the organization of memories, since events experienced close in time are more likely to be meaningfully associated, while those experienced with a longer interval are not. How does the brain segregate events that are temporally distinct? Here, we report that a delayed (12-24h) increase in the expression of the C-C chemokine receptor type 5 (CCR5), an immune receptor well known as a co-receptor for HIV infection, following the formation of a contextual memory, determines the duration of the temporal window for associating or linking that memory with subsequent memories. This delayed CCR5 expression in mouse dorsal CA1 (dCA1) neurons results in a decrease in neuronal excitability, which in turn negatively regulates neuronal memory allocation, thus reducing the overlap between dCA1 memory ensembles. Lowering this overlap affects the ability of one memory to trigger the recall of the other, thus closing the temporal window for memory linking. Remarkably, our findings also show that an age-related increase in CCL5/CCR5 expression leads to impairments in memory linking in aged mice, which could be reversed with a CCR5 knockout and an FDA approved drug that inhibits this receptor, a result with significant clinical implications. All together the findings reported here provide the first insights into the molecular and cellular mechanisms that shape the temporal window for memory linking.

Incremental Learning in Multiple Limb Positions for Electromyography-Based Gesture Recognition using Hyperdimensional Computing

<div>Prosthetic control for rehabilitation, among many other applications, can leverage in-sensor hand gesture recognition in which lightweight machine learning models for classifying electromyogram (EMG) signals are embedded on miniature, low-power devices. While research efforts have demonstrated high accuracy in controlled settings, these methods have yet to make a significant commercial or clinical impact due to the wide variety of scenarios and situational contexts that are faced during everyday use. Typical static models suffer from the effects of EMG signal variation caused by changing contexts in which they are deployed. Here, we propose an incremental learning algorithm using hyperdimensional (HD) computing that can efficiently learn gesture patterns performed in new limb positions, a context-change which normally significantly degrades classification accuracy. A prototype-based learning algorithm, HD computing enables memory- and computation-efficient incorporation of new training examples into the model, while preserving information about already learned contexts. We present various configurations of the incremental HD classifier, allowing system designers to trade classification performance for implementation efficiency as measured through memory footprint. Incremental learning experiments with data from 5 subjects show that HD computing can achieve similar accuracies as incrementally trained SVM and LDA classifiers while requiring a fraction of the memory allocation. </div>

Incremental Learning in Multiple Limb Positions for Electromyography-Based Gesture Recognition using Hyperdimensional Computing

<div>Prosthetic control for rehabilitation, among many other applications, can leverage in-sensor hand gesture recognition in which lightweight machine learning models for classifying electromyogram (EMG) signals are embedded on miniature, low-power devices. While research efforts have demonstrated high accuracy in controlled settings, these methods have yet to make a significant commercial or clinical impact due to the wide variety of scenarios and situational contexts that are faced during everyday use. Typical static models suffer from the effects of EMG signal variation caused by changing contexts in which they are deployed. Here, we propose an incremental learning algorithm using hyperdimensional (HD) computing that can efficiently learn gesture patterns performed in new limb positions, a context-change which normally significantly degrades classification accuracy. A prototype-based learning algorithm, HD computing enables memory- and computation-efficient incorporation of new training examples into the model, while preserving information about already learned contexts. We present various configurations of the incremental HD classifier, allowing system designers to trade classification performance for implementation efficiency as measured through memory footprint. Incremental learning experiments with data from 5 subjects show that HD computing can achieve similar accuracies as incrementally trained SVM and LDA classifiers while requiring a fraction of the memory allocation. </div>