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Being able to recognize emotional intensity is a desirable feature for a facial emotional recognition (FER) system. However, the development of such a feature is hindered by the paucity of intensity-labeled data for model training. To ameliorate the situation, the present study proposes using face morphing as a way of data augmentation to synthesize faces that express different degrees of a designated emotion. Such an approach has been successfully validated on humans and machines. Specifically, humans indeed perceived different levels of intensified emotions in these parametrically synthesized faces, and FER systems based on neural networks indeed showed improved sensitivities to intensities of different emotions when additionally trained on the synthesized faces. Overall, the proposed data augmentation method is not only simple and effective but also useful for building FER systems that recognize facial expressions of mixed emotions.

Background: Mild cognitive impairment (MCI), which is common in older adults, is a risk factor for dementia. Rapidly growing health care demand associated with global population aging has spurred the development of new digital tools for the assessment of cognitive performance in older adults. Objective: To overcome methodological drawbacks of previous studies (e.g., use of potentially imprecise screening tools that fail to include patients with MCI), this study investigated the feasibility of assessing multiple cognitive functions in older adults with and without MCI by using a social robot. Methods: This study included 33 older adults with or without MCI and 33 healthy young adults. We examined the utility of five robotic cognitive tests focused on language, episodic memory, prospective memory, and aspects of executive function to classify age-associated cognitive changes versus MCI. Standardized neuropsychological tests were collected to validate robotic test performance. Results: The assessment was well received by all participants. Robotic tests assessing delayed episodic memory, prospective memory, and aspects of executive function were optimal for differentiating between older adults with and without MCI, whereas the global cognitive test (i.e., Mini-Mental State Examination) failed to capture such subtle cognitive differences among older adults. Furthermore, robot-administered tests demonstrated sound ability to predict the results of standardized cognitive tests, even after adjustment for demographic variables and global cognitive status. Conclusion: Overall, our results suggest the human–robot interaction approach is feasible for MCI identification. Incorporating additional cognitive test measures might improve the stability and reliability of such robot-assisted MCI diagnoses.

We provide two methodologies in the area of computation theory to solve optimal strategies for games such as Watermelon chess and Go. From experimental results, we find relevance to graph theory, group representation, and mathematical consciousness. We prove that the decision strategy of movement for Watermelon chess and Chinese checker games belongs to a matrix that is a noncommutative ring or an abelian group over set Y={-1,0,1}. Additionally, the movement for any chess game with two players belongs to a noncommutative ring or an abelian group from Occam’s razor principle. We derive the closed form of the transition matrix for any chess game with two players and discover that the element of the transition matrix belongs to a rational number. We propose a different methodology based on abstract algebra to analyze the complexity of chess games in their entirety, instead of being limited solely to endgame results. It is probable that similar decision processes of people may also belong to a noncommutative ring or an abelian group.

Reading comprehension requires the semantic integration of words across space and time. However, it remains unclear whether comprehension requires visual awareness for such semantic integration. Compared to earlier studies that investigated semantic integration indirectly from its priming effect, we used functional magnetic resonance imaging (fMRI) to directly examine the processes of semantic integration with or without visual awareness. Specifically, we manipulated participants' visual awareness by continuous flash suppression (CFS) while they viewed a meaningful sequence of four Chinese words (i.e., an idiom) or its meaningless counterpart (i.e., a random sequence). Behaviorally, participants had better recognition memory for idioms than random sequences only when their visual awareness was interfered rather than blocked by CFS. Neurally, semantics-processing areas, such as the superior temporal gyrus and inferior frontal gyrus, were significantly activated only when participants were aware of word sequences, be they meaningful or meaningless. By contrast, orthography-processing areas, such as the fusiform gyrus and inferior occipital gyrus, were significantly activated regardless of visual awareness or word sequence. Taken together, these results suggest that visual awareness modules the functioning of the semantic neural network in the brain and facilitates reading comprehension.

Psychological variables of a person (e.g., cognitive abilities, personality traits, emotional states, and preferences) are valuable information that can be utilized by social robots to offer personalized human–robot interaction. These variables are often latent and inferred indirectly from a third-person perspective based on an individual’s behavioral manifestations (e.g., facial emotion expressions), and hence the true values of inferred psychological variables remain unknown to a robot observer. Although earlier studies have employed robot-administered psychological tests to infer psychological variables based on an individual’s first-person responses, these tests were formally presented and could be tedious to some users. To leverage the validity and reliability of well-established psychological tests for user profiling with ease, the present study examined the possibility of asynchronously embedding psychological test questions into casual human–robot conversations. In our experiment using a big-five personality inventory, the verbal responses from users to these asynchronous test questions were then compared with the written responses to the same personality test. The personality measures estimated from the two approaches correlated strongly in a young adult population but only moderately in an older population. These findings demonstrate the validity of the proposed asynchronous method for psychological testing in human–agent interactions and suggest some caveats when this testing method is applied to older adults or other special populations.

This paper aims to address some methodological issues related to case-based research in the philosophy of contemporary sciences. We focus on the selection processes by which philosophers pick or generate a particular set of papers to conduct their case-based research. We illustrate how to use various quantitative and qualitative methods to improve the epistemic features of the selection processes, and help generate some potential case-based hypotheses for further philosophical investigation.

Accumulating evidence suggests AKT1 and DRD2-AKT-GSK3 signaling involvement in schizophrenia. AKT1 activity is also required for lithium, a GSK3 inhibitor, to modulate mood-related behaviors. Notably, GSK3 inhibitor significantly alleviates behavioral deficits in Akt1−/− female mice, whereas typical/atypical antipsychotics have no effect. In agreement with adjunctive therapy with lithium in treating schizophrenia, our data mining indicated that the average utilization rates of lithium in the Taiwan National Health Insurance Research Database from 2002 to 2013 are 10.9% and 6.63% in inpatients and outpatients with schizophrenia, respectively. Given that lithium is commonly used in clinical practice, it is of great interest to evaluate the effect of lithium on alleviating Akt1-related deficits. Taking advantage of Akt1+/− mice to mimic genetic deficiency in patients, behavioral impairments were replicated in female Akt1+/− mice but were alleviated by subchronic lithium treatment for 13 days. Lithium also effectively alleviated the observed reduction in phosphorylated GSK3α/β expression in the brains of Akt1+/− mice. Furthermore, inhibition of Akt expression using an Akt1/2 inhibitor significantly reduced neurite length in P19 cells and primary hippocampal cell cultures, which was also ameliorated by lithium. Collectively, our findings implied the therapeutic potential of lithium and the importance of the AKT1-GSK3 signaling pathway.

To assess cutting-tools degradation, mounted accelerometers and current clamp probes were used to acquire machining vibration for both the band-saw machine and the drilling machine, of which a structural-steel machining center is composed. Significant features were first extracted through spectral analysis, and tool degradation assessment was conducted through using a supervised learning scheme, self-organizing map. The results reveal that 10% remaining useful life can be predicted before the band-saws and drills wore out.

Human social interactions are laden with behavioral preferences that stem from hidden social network representations. In this study, we applied an artificial neural network with machine theory of mind (ToMnet+) to learn and predict social preferences based on implicit information from the way agents and social targets interact behaviorally. Our findings have implications for machine applications that seek to infer hidden information structures solely from third-person observation of behaviors. We consider that social machines with such an ability would have an enhanced potential for more naturalistic human-machine interactions.

Several experiments, notably one done by Bruner and Potter (1964), have demonstrated delayed object recognition when viewing a blurred image gradually come into focus. Bruner and Potter (1964) suggested that the wrong answer is held until there is an obvious contradiction. Others have hypothesized that “competitive activation” is responsible for delayed recognition. The results of the experiments reported in this paper are consistent with a third hypothesis, that delayed recognition is due to an initial organization of image elements that is incompatible with correct recognition and that the initial grouping and figure-ground perception, among other aspects of organization, drive subsequent perception via top-down cortical pathways. A total of 7 experiments using 3 forms of degradation supported this hypothesis. Images degraded by low-pass filtering produced significant delay in recognition, while degradation by fragmentation did not, and a third form of degradation similar to fragmentation mitigated the effect. The experiments also demonstrate that if images are low-pass filtered delayed recognition occurs with presentations of as little as 100 ms and early presentations lead to delayed recognition over long intertrial intervals, at least 105 seconds. Further, support for the hypothesis that top-down cortical influence is key to this phenomenon came from an experiment showing that masking eliminates delayed recognition for short presentations. Taken together these results support a hypothesis that delayed recognition is due to errors in perceptual organization that lead to incorrect responses and these errors are fostered by low spatial frequencies.

Sustained attention is essential for older adults to maintain an active lifestyle, and the deficiency of this function is often associated with health-related risks such as falling and frailty. The present study examined whether the well-established age-effect on reducing mind-wandering, the drift to internal thoughts that are seen to be detrimental to attentional control, could be replicated by using a robotic experimenter for older adults who are not as familiar with online technologies. A total of 28 younger and 22 older adults performed a Sustained Attention to Response Task (SART) by answering thought probes regarding their attention states and providing confidence ratings for their own task performances. The indices from the modified SART suggested a well-documented conservative response strategy endorsed by older adults, which were represented by slower responses and increased omission errors. Moreover, the slower responses and increased omissions were found to be associated with less self-reported mind-wandering, thus showing consistency with their higher subjective ratings of attentional control. Overall, this study demonstrates the potential of constructing age-related cognitive profiles with attention evaluation instruction based on a social companion robot for older adults at home.

Mental health is as crucial as physical health, but it is underappreciated by mainstream biomedical research and the public. Compared to the use of AI or robots in physical healthcare, the use of AI or robots in mental healthcare is much more limited in number and scope. To date, psychological resilience—the ability to cope with a crisis and quickly return to the pre-crisis state—has been identified as an important predictor of psychological well-being but has not been commonly considered by AI systems (e.g., smart wearable devices) or social robots to personalize services such as emotion coaching. To address the dearth of investigations, the present study explores the possibility of estimating personal resilience using physiological and speech signals measured during human–robot conversations. Specifically, the physiological and speech signals of 32 research participants were recorded while the participants answered a humanoid social robot’s questions about their positive and negative memories about three periods of their lives. The results from machine learning models showed that heart rate variability and paralinguistic features were the overall best predictors of personal resilience. Such predictability of personal resilience can be leveraged by AI and social robots to improve user understanding and has great potential for various mental healthcare applications in the future.

Current theorizing of person–environment fit rests predominantly on investigations performed according to psychological methods. The current study advances fit research by exploring the meaning and...

Are all religions essentially the same? Are believers of different religions heading in the same mental direction? To answer these questions from a sociopsychological perspective, we compared social sensitivity and causal attribution styles between Mahayana Buddhists, who practice unbiased love and compassion toward every being, and Christians, who pursue a union with God. Despite a similar cultural background, sex ratio, age distribution, socioeconomic status, and fluid intelligence level, these two religious groups in Taiwan showed opposite tendencies when inferring the mental states of others - as religiosity increased, the theory of mind ability increased in Mahayana Buddhists but decreased in Christians. Furthermore, these two religious groups showed opposite tendencies of attributional style - as religiosity increased, self-serving bias decreased in Buddhists but increased in Christians. These marked religiosity-dependent, sociopsychological effects suggest that different religions may shape or attract their followers who are moving in quite distinct mental directions.

Early fMRI studies suggested that brain areas processing self-related and other-related information were highly overlapping. Hypothesising functional localisation of the cortex, researchers have tried to locate “self-specific” and “other-specific” regions within these overlapping areas by subtracting suspected confounding signals in task-based fMRI experiments. Inspired by recent advances in whole-brain dynamic modelling, we instead explored an alternative hypothesis that similar spatial activation patterns could be associated with different processing modes in the form of different synchronisation patterns. Combining an automated synthesis of fMRI data with a presumption-free diffusion spectrum image (DSI) fibre-tracking algorithm, we isolated a network putatively composed of brain areas and white matter tracts involved in self-other processing. We sampled synchronisation patterns from the dynamical systems of this network using various combinations of physiological parameters. Our results showed that the self-other processing network, with simulated gamma-band activity, tended to stabilise at a number of distinct synchronisation patterns. This phenomenon, termed “multistability,” could serve as an alternative model in theorising the mechanism of processing self-other information.

We address strategic cognitive sequencing, the “outer loop” of human cognition: how the brain decides what cognitive process to apply at a given moment to solve complex, multistep cognitive tasks. We argue that this topic has been neglected relative to its importance for systematic reasons but that recent work on how individual brain systems accomplish their computations has set the stage for productively addressing how brain regions coordinate over time to accomplish our most impressive thinking. We present four preliminary neural network models. The first addresses how the prefrontal cortex (PFC) and basal ganglia (BG) cooperate to perform trial-and-error learning of short sequences; the next, how several areas of PFC learn to make predictions of likely reward, and how this contributes to the BG making decisions at the level of strategies. The third models address how PFC, BG, parietal cortex, and hippocampus can work together to memorize sequences of cognitive actions from instruction (or “self-instruction”). The last shows how a constraint satisfaction process can find useful plans. The PFC maintains current and goal states and associates from both of these to find a “bridging” state, an abstract plan. We discuss how these processes could work together to produce strategic cognitive sequencing and discuss future directions in this area.

Previous infant studies on the other-race effect have favored the perceptual narrowing view, or declined sensitivities to rarely exposed other-race faces. Here we wish to provide an alternative possibility, perceptual learning, manifested by improved sensitivity for frequently exposed own-race faces in the first year of life. Using the familiarization/visual-paired comparison paradigm, we presented 4-, 6-, and 9-month-old Taiwanese infants with oval-cropped Taiwanese, Caucasian, Filipino faces, and each with three different manipulations of increasing task difficulty (i.e., change identity, change eyes, and widen eye spacing). An adult experiment was first conducted to verify the task difficulty. Our results showed that, with oval-cropped faces, the 4 month-old infants could only discriminate Taiwanese “change identity” condition and not any others, suggesting an early own-race advantage at 4 months. The 6 month-old infants demonstrated novelty preferences in both Taiwanese and Caucasian “change identity” conditions, and proceeded to the Taiwanese “change eyes” condition. The 9-month-old infants demonstrated novelty preferences in the “change identity” condition of all three ethnic faces. They also passed the Taiwanese “change eyes” condition but could not extend this refined ability of detecting a change in the eyes for the Caucasian or Philippine faces. Taken together, we interpret the pattern of results as evidence supporting perceptual learning during the first year: the ability to discriminate own-race faces emerges at 4 months and continues to refine, while the ability to discriminate other-race faces emerges between 6 and 9 months and retains at 9 months. Additionally, the discrepancies in the face stimuli and methods between studies advocating the narrowing view and those supporting the learning view were discussed.

Mobile advertising refers to communication in which mobile phones are used as a medium to efficiently attract potential customers. Among mobile advertising applications, barcodes are becoming a very powerful mobile commerce tool. By capturing a barcode with a camera scanner, people can easily access a wealth of information online. Barcodes have thus converted hard copies of newspapers, wallpapers, and magazines into crucial platforms for mobile commerce. However, although barcodes are frequently used for embedding information in printed matter, they have unsightly overt patterns. Concealing data in visually meaningful image barcodes (such as trademarks) instead of using extra barcode areas has the advantage of increasing the added value of using conventional barcode patterns, and thus it is desirable for future mobile advertising. This paper presents a novel data-hiding method for halftone images. Data-hiding and halftoning algorithms are integrated into the method to prevent extreme bi-level quantization in the halftoning process, for printing purposes. We first applied regular ordered dithering to divide the input continuous-tone image into discrete-parameter halftone cells. The hidden data message was embedded in the halftone cells by using a screen column-shift method. A modified direct binary search (DBS) optimization framework was then used to enhance the output image quality. Simulation results demonstrated that the proposed method outperformed a state-of-the-art method in terms of image quality and data capacity.

Sleep is beneficial for various types of learning and memory, including a finger-tapping motor-sequence task. However, methodological issues hinder clarification of the crucial cortical regions for sleep-dependent consolidation in motor-sequence learning. Here, to investigate the core cortical region for sleep-dependent consolidation of finger-tapping motor-sequence learning, while human subjects were asleep, we measured spontaneous cortical oscillations by magnetoencephalography together with polysomnography, and source-localized the origins of oscillations using individual anatomical brain information from MRI. First, we confirmed that performance of the task at a retest session after sleep significantly increased compared with performance at the training session before sleep. Second, spontaneous δ and fast-σ oscillations significantly increased in the supplementary motor area (SMA) during post-training compared with pretraining sleep, showing significant and high correlation with the performance increase. Third, the increased spontaneous oscillations in the SMA correlated with performance improvement were specific to slow-wave sleep. We also found that correlations of δ oscillation between the SMA and the prefrontal and between the SMA and the parietal regions tended to decrease after training. These results suggest that a core brain region for sleep-dependent consolidation of the finger-tapping motor-sequence learning resides in the SMA contralateral to the trained hand and is mediated by spontaneous δ and fast-σ oscillations, especially during slow-wave sleep. The consolidation may arise along with possible reorganization of a larger-scale cortical network that involves the SMA and cortical regions outside the motor regions, including prefrontal and parietal regions.

We can learn from the wisdom of others to maximize success. However, it is unclear how humans take advice to flexibly adapt behavior. On the basis of data from neuroanatomy, neurophysiology, and neuroimaging, a biologically plausible model is developed to illustrate the neural mechanisms of learning from instructions. The model consists of two complementary learning pathways. The slow-learning parietal pathway carries out simple or habitual stimulus–response (S-R) mappings, whereas the fast-learning hippocampal pathway implements novel S-R rules. Specifically, the hippocampus can rapidly encode arbitrary S-R associations, and stimulus-cued responses are later recalled into the basal ganglia-gated pFC to bias response selection in the premotor and motor cortices. The interactions between the two model learning pathways explain how instructions can override habits and how automaticity can be achieved through motor consolidation.

How do humans use target-predictive contextual information to facilitate visual search? How are consistently paired scenic objects and positions learned and used to more efficiently guide search in familiar scenes? For example, humans can learn that a certain combination of objects may define a context for a kitchen and trigger a more efficient search for a typical object, such as a sink, in that context. The ARTSCENE Search model is developed to illustrate the neural mechanisms of such memory-based context learning and guidance and to explain challenging behavioral data on positive-negative, spatial― object, and local-distant cueing effects during visual search, as well as related neuroanatomical, neurophysiological, and neuroimaging data. The model proposes how global scene layout at a first glance rapidly forms a hypothesis about the target location. This hypothesis is then incrementally refined as a scene is scanned with saccadic eye movements. The model simulates the interactive dynamics of object and spatial contextual cueing and attention in the cortical What and Where streams starting from early visual areas through medial temporal lobe to prefrontal cortex. After learning, model dorsolateral prefrontal cortex (area 46) primes possible target locations in posterior parietal cortex based on goal-modulated percepts of spatial scene gist that are represented in parahippocampal cortex. Model ventral prefrontal cortex (area 47/12) primes possible target identities in inferior temporal cortex based on the history of viewed objects represented in perirhinal cortex.

How do humans rapidly recognize a scene? How can neural models capture this biological competence to achieve state-of-the-art scene classification? The ARTSCENE neural system classifies natural scene photographs by using multiple spatial scales to efficiently accumulate evidence for gist and texture. ARTSCENE embodies a coarse-to-fine Texture Size Ranking Principle whereby spatial attention processes multiple scales of scenic information, from global gist to local textures, to learn and recognize scenic properties. The model can incrementally learn and rapidly predict scene identity by gist information alone, and then accumulate learned evidence from scenic textures to refine this hypothesis. The model shows how texture-fitting allocations of spatial attention, called attentional shrouds, can facilitate scene recognition, particularly when they include a border of adjacent textures. Using grid gist plus three shroud textures on a benchmark photograph dataset, ARTSCENE discriminates 4 landscape scene categories (coast, forest, mountain, and countryside) with up to 91.85% correct on a test set, outperforms alternative models in the literature which use biologically implausible computations, and outperforms component systems that use either gist or texture information alone.

We discuss the salient features of Zolotarev optimal rational approximation for the inverse square root function, in particular, for its applications in lattice QCD with overlap Dirac quark. The theoretical error bound for the matrix-vector multiplication $ H_w (H_w^2)^{-1/2}Y $ is derived. We check that the error bound is always satisfied amply, for any QCD gauge configurations we have tested. An empirical formula for the error bound is determined, together with its numerical values (by evaluating elliptic functions) listed in Table 2 as well as plotted in Figure 3. Our results suggest that with Zolotarev approximation to $ (H_w^2)^{-1/2} $, one can practically preserve the exact chiral symmetry of the overlap Dirac operator to very high precision, for any gauge configurations on a finite lattice., 23 pages, 5 eps figures, v2:minor clarifications, and references added, to appear in Phys. Rev. D

A computational system for lattice QCD with exact chiral symmetry is described. The platform is a home-made Linux PC cluster, built with off-the-shelf components. At present this system constitutes of 64 nodes, with each node consisting of one Pentium 4 processor (1.6/2.0/2.5 GHz), one Gbyte of PC800/PC1066 RDRAM, one 40/80/120 Gbyte hard disk, and a network card. The computationally intensive parts of our program are written in SSE2 codes. The speed of this system is estimated to be 70 Gflops, and its price/performance is better than $1.0/Mflops for 64-bit (double precision) computations in quenched QCD. We discuss how to optimize its hardware and software for computing quark propagators via the overlap Dirac operator., Comment: 24 pages, LaTeX, 2 eps figures, v2:a note and references added, the version published in Int. J. Mod. Phys. C

We outline the essential features of a Linux PC cluster which is now being developed at National Taiwan University, and discuss how to optimize its hardware and software for lattice QCD with overlap Dirac quarks. At present, the cluster constitutes of 30 nodes, with each node consisting of one Pentium 4 processor (1.6/2.0 GHz), one Gbyte of PC800 RDRAM, one 40/80 Gbyte hard disk, and a network card. The speed of this system is estimated to be 30 Gflops, and its price/performance ratio is better than $1.0/Mflops for 64-bit (double precision) computations in quenched lattice QCD with overlap Dirac quarks., Comment: 3 pages, Lattice 2002(machine)