Reliable and Energy Efficient MLC STT-RAM Buffer for CNN Accelerators

We propose a lightweight scheme where the formation of a data block is changed in such a way that it can tolerate soft errors significantly better than the baseline. The key insight behind our work is that CNN weights are normalized between -1 and 1 after each convolutional layer, and this leaves one bit unused in half-precision floating-point representation. By taking advantage of the unused bit, we create a backup for the most significant bit to protect it against the soft errors. Also, considering the fact that in MLC STT-RAMs the cost of memory operations (read and write), and reliability of a cell are content-dependent (some patterns take larger current and longer time, while they are more susceptible to soft error), we rearrange the data block to minimize the number of costly bit patterns. Combining these two techniques provides the same level of accuracy compared to an error-free baseline while improving the read and write energy by 9% and 6%, respectively

Divisible load scheduling of image processing applications on the heterogeneous star and tree networks using a new genetic algorithm

The divisible load scheduling of image processing applications on the heterogeneous star and multi-level tree networks is addressed in this paper. In our platforms, processors and network links have different speeds. In addition, computation and communication overheads are considered. A new genetic algorithm for minimizing the processing time of low-level image applications using divisible load theory is introduced. The closed-form solution for the processing time, the image fractions that should be allocated to each processor, the optimum number of participating processors, and the optimal sequence for load distribution are derived. The new concept of equivalent processor in tree network is introduced and the effect of different image and kernel sizes on processing time and speed up are investigated. Finally, to indicate the efficiency of our algorithm, several numerical experiments are presented

Eye Tracking Algorithm Based on Multi Model Kalman Filter

One of the most important pieces of Human Machine Interface (HMI) equipment is an eye tracking system that is used for many different applications. This paper aims to present an algorithm in order to improve the efficiency of eye tracking in the image by means of a multi-model Kalman filter. In the classical Kalman filter, one model is used for estimation of the object, but in the multi-model Kalman filter, several models are used for estimating the object. The important features of the multiple-model Kalman filter are improving the efficiency and reducing its estimating errors relative to the classical Kalman filter. The proposed algorithm consists of two parts. The first step is recognizing the initial position of the eye, and Support Vector Machine (SVM) has been used in this part. In the second part, the position of the eye is predicted in the next frame by using a multi-model Kalman filter, which applies constant speed and acceleration models based on the normal human eye. Doi: 10.28991/HIJ-2022-03-01-02 Full Text: PD

A Search-Based Testing Approach for Deep Reinforcement Learning Agents

Deep Reinforcement Learning (DRL) algorithms have been increasingly employed during the last decade to solve various decision-making problems such as autonomous driving and robotics. However, these algorithms have faced great challenges when deployed in safety-critical environments since they often exhibit erroneous behaviors that can lead to potentially critical errors. One way to assess the safety of DRL agents is to test them to detect possible faults leading to critical failures during their execution. This raises the question of how we can efficiently test DRL policies to ensure their correctness and adherence to safety requirements. Most existing works on testing DRL agents use adversarial attacks that perturb states or actions of the agent. However, such attacks often lead to unrealistic states of the environment. Their main goal is to test the robustness of DRL agents rather than testing the compliance of agents' policies with respect to requirements. Due to the huge state space of DRL environments, the high cost of test execution, and the black-box nature of DRL algorithms, the exhaustive testing of DRL agents is impossible. In this paper, we propose a Search-based Testing Approach of Reinforcement Learning Agents (STARLA) to test the policy of a DRL agent by effectively searching for failing executions of the agent within a limited testing budget. We use machine learning models and a dedicated genetic algorithm to narrow the search towards faulty episodes. We apply STARLA on Deep-Q-Learning agents which are widely used as benchmarks and show that it significantly outperforms Random Testing by detecting more faults related to the agent's policy. We also investigate how to extract rules that characterize faulty episodes of the DRL agent using our search results. Such rules can be used to understand the conditions under which the agent fails and thus assess its deployment risks

Bioaccumulation of copper nanoparticle in gill, liver, intestine and muscle of Siberian sturgeon (Acipenser baerii) juvenile

Copper (Cu) is an essential element required by all living organisms, since at least 30 enzymes are known to use Cu as a cofactor. Cu is also toxic in excess and liver and gills are known to be target organs for it. In the present study, 240 Siberian sturgeon juvenile (with initial weight 29.2 ± 3.1 g and initial length 21.8 ± 1.4 cm) were randomly distributed in 12 fiberglass tanks at 4 different copper nanoparticle (Cu-NPs) treatments with 3 replicates. Treatments included control (T0 = no added Cu-NPs), 50 (T50), 100 (T100), 200 (T200) µg.l -1 Cu-NPs (mean primary particle size of 2 - 6 nm) in a semi-static waterborne exposure regime. Water exchanged were 20% daily with redosing after each change. The experimental period lasted 28 days, 14 days exposure to Cu-NPs and 14 days as recovery time. Fish liver, gill, intestine and muscle were sampled at days 0, 7, 14, 21 and 28. Samples were weighed, dried (100 ◦C for 48 h) then digested in concentrated nitric acid in a water bath, cooled, and analyzed for Cu concentration in the tissues with graphite furnace atomic absorption spectroscope. Most of the Cu-NPs were accumulated in the intestine, gill, liver and muscle. The accumulation of NPs in tissues was increased in all treatments from day 7 through 14. In the recovery period, Cu-NPs in tissues decreased but it was still higher than the control treatment. The current findings indicate that preventing the entry of Cu-NPs into the aquatic environment would seem to be essential

The impact of emotional facial expressions on reflexive attention depends on the aim of dynamic gaze changes: An ERP study

The emotional expression and gaze direction of a face are important cues for human social interactions. However, the interplay of these factors and their neural correlates are only partially understood. In the current study, we investigated ERP correlates of gaze and emotion processing following the initial presentation of faces with different emotional expressions (happy, neutral, angry) and an averted or direct gaze direction as well as following a subsequent change in gaze direction that occurred in half of the trials. We focused on the time course and scalp topography of the N170 and EPN components. The N170 amplitude was larger to averted than direct gaze for the initial face presentation and larger to gaze changes from direct to averted than from averted to direct in response to the gaze change. For the EPN component in response to the initial face presentation, we replicate classic effects of emotion, which did not interact with gaze direction. As a major new finding, changes from direct to averted gaze elicited an EPN-like effect when the face showed a happy expression. No such effect was seen for angry expressions. We conclude that happy faces reflexively attract attention when they look at the observer rather than away from the observer. These results for happy expressions are in line with the shared signal hypothesis that posits a better processing of expressions if their approach or avoidance tendency is consistent with gaze direction. However, the shared signal hypothesis is not supported by the present results for angry faces

Deliberate control over facial expressions in motherhood. Evidence from a Stroop-like task.

The deliberate control of facial expressions is an important ability in human interactions, in particular for mothers with prelinguistic infants. Because research on this topic is still scarce, we investigated the control over facial expressions in a Stroop-like paradigm. Mothers of 2-6 months old infants and nullipara women produced smiles and frowns in response to verbal commands written on distractor faces of adults or infants showing expressions of happiness or anger/distress. Analyses of video recordings with a machine classifier for facial expression revealed pronounced effects of congruency between the expressions required by the participants and those displayed by the face stimuli on the onset latencies of the deliberate facial expressions. With adult distractor faces this Stroop effect was similar whether participants smiled or frowned. With infant distractor faces mothers and non-mothers showed indistinguishable Stroop effects on smile responses; however, for frown responses, the Stroop effect in mothers was smaller than in non-mothers. We suggest that for frown responses in mothers when facing infants, the effect of mimicry or stimulus response compatibility, leading to the Stroop effect, is offset by a caregiving response or empathy

The Actor–Partner Effects of Parenting Stress on Quality of Life Among Parents of Children with ASD: The Mediating Role of Mental Quality of Life

The present study investigated the actor–partner effects of parenting stress (PS) on quality of life (QoL) among parents (96 couples) of children with autism spectrum disorder (ASD). Data were collected using the QoL Scale and the PS Index. Structural equation modeling was also utilized to test the hypothesis. The results revealed the effects of PS in each parent on mental QoL of that parent. Maternal PS further shaped physical QoL in mothers. However, PS in one parent did not influence QoL of his or her partner. Accordingly, mental QoL had a mediating role between PS and physical QoL. It was ultimately suggested to take account of QoL among parents in addition to the treatment of children with ASD

Electrospun ZnO/Poly(Vinylidene fluoride-trifluoroethylene) scaffolds for lung tissue engineering

Due to the morbidity and lethality of pulmonary diseases, new biomaterials and scaffolds are needed to support the regeneration of lung tissues, while ideally providing protective effects against inflammation and microbial aggression. In this study, we investigated the potential of nanocomposites of poly(vinylidene fluoride-co-trifluoroethylene) [P(VDF-TrFE)] incorporating zinc oxide (ZnO), in the form of electrospun fiber meshes for lung tissue engineering. We focused on their anti-inflammatory, antimicrobial, and mechanoelectrical character according to different fiber mesh textures (i.e., collected at 500 and 4000 rpm) and compositions: (0/100) and (20/80) w/w% ZnO/P(VDF-TrFE), plain and composite, respectively. The scaffolds were characterized in terms of morphological, physicochemical, mechanical, and piezoelectric properties, as well as biological response of A549 alveolar epithelial cells in presence of lung-infecting bacteria. By virtue of ZnO, the composite scaffolds showed a strong anti-inflammatory response in A549 cells, as demonstrated by a significant decrease of interleukin (IL) IL-1a, IL-6, and IL-8 expression in 6 h. In all the scaffold types, but remarkably in the aligned composite ones, transforming growth factor b (TGF-b) and the antimicrobial peptide human b defensin-2 (HBD-2) were significantly increased. The ZnO/P(VDF-TrFE) electrospun fiber meshes hindered the biofilm formation by Staphylococcus aureus and Pseudomonas aeruginosa and the cell/scaffold constructs were able to impede S. aureus adhesion and S. aureus and P. aeruginosa invasiveness, independent of the scaffold type. The data obtained suggested that the composite scaffolds showed potential for tunable mechanical properties, in the range of alveolar walls and fibers. Finally, we also showed good piezoelectricity, which is a feature found in elastic and collagen fibers, the main extracellular matrix molecules in lungs. The combination of all these properties makes ZnO/P(VDF-TrFE) fiber meshes promising for lung repair and regeneration

Business-to-business open innovation: COVID-19 lessons for small and medium-sized enterprises from emerging markets

Small and medium-sized enterprises (SMEs) from emerging markets are the most vulnerable types of firms, especially in times of crisis due to time and resource constraints. Thus, this paper aims to help SMEs from emerging markets in choosing the right business partners with whom to cooperate to develop relevant innovations in crisis periods in general, and during the COVID-19 pandemic in particular. To obtain relevant insights, qualitative data from SMEs in Bosnia and Herzegovina were collected in March-April 2020. The findings show that SMEs have embraced new collaborations with business customers and competitors, and developed a collaborative mindset opposed to the traditionally competitive way of doing business in emerging markets. Based on the findings, this paper presents a set of recommendations for managers, and suggests several future research opportunities around the management of openness in the context of SMEs from emerging markets