nils-herrmann/clean_scirepeval huggingface dataset:
The In fl uence of Chomsky on the Neuroscience of Language

What is the state of the neuroscience of language – and cognitive neuroscience more broadly – in light of the linguistic research, the arguments, and the theories advanced in the context of the program developed over the past 60 years by Noam Chomsky? There are, presumably, three possible outcomes: neuroscience of language is better off, worse off, or untouched by this intellectual tradition. In some sense, all three outcomes are true. The field has made remarkable progress, in no small part because the questions were so carefully and provocatively defined by the generative research program. But insights into neuroscience and language have also been stymied because of many parochial battles that have led to little light beyond rhetorical fireworks. Finally, a disturbing amount of neuroscience research has progressed as if the significant advances beginning in the 1950s and 1960s had not been made. This work remains puzzling because it builds on ideas known to be dodgy or outright false. In sum, when it comes to the neurobiology of language, the past sixty years have been fabulous, terrible, and puzzling. Chomsky has not helped matters by being so relentlessly undidactic in his exposition of ideas germane to the neurobiological enterprise. The present moment is a good one to assess the current state, because there are energetic thrusts of research that pursue an overtly anti-Chomskyan stance. I have in mind here current research that focuses on big (brain) data, relying on no more than the principle of association, often with implicit anti-mentalist sentiments, typically skeptical of the tenets of the computational theory of mind, associated with relentless enthusiasm for embodied cognition, the ubiquitous role of context, and so on. A large proportion of current research on the neuroscience of language has embraced these ideas, and it is fair to ask why – and whether – this approach is more likely to yield substantive progress. It is also fair to say that the traditional four (and now five) leading questions that have always formed the basis for the generative research program as
Lossy Image Compression with Compressive Autoencoders

We propose a new approach to the problem of optimizing autoencoders for lossy image compression. New media formats, changing hardware technology, as well as diverse requirements and content types create a need for compression algorithms which are more flexible than existing codecs. Autoencoders have the potential to address this need, but are difficult to optimize directly due to the inherent non-differentiabilty of the compression loss. We here show that minimal changes to the loss are sufficient to train deep autoencoders competitive with JPEG 2000 and outperforming recently proposed approaches based on RNNs. Our network is furthermore computationally efficient thanks to a sub-pixel architecture, which makes it suitable for high-resolution images. This is in contrast to previous work on autoencoders for compression using coarser approximations, shallower architectures, computationally expensive methods, or focusing on small images.
Cacao domestication I: the origin of the cacao cultivated by the Mayas

Criollo cacao (Theobroma cacao ssp. cacao) was cultivated by the Mayas over 1500 years ago. It has been suggested that Criollo cacao originated in Central America and that it evolved independently from the cacao populations in the Amazon basin. Cacao populations from the Amazon basin are included in the second morphogeographic group: Forastero, and assigned to T. cacao ssp. sphaerocarpum. To gain further insight into the origin and genetic basis of Criollo cacao from Central America, RFLP and microsatellite analyses were performed on a sample that avoided mixing pure Criollo individuals with individuals classified as Criollo but which might have been introgressed with Forastero genes. We distinguished these two types of individuals as Ancient and Modern Criollo. In contrast to previous studies, Ancient Criollo individuals formerly classified as ‘wild’, were found to form a closely related group together with Ancient Criollo individuals from South America. The Ancient Criollo trees were also closer to Colombian-Ecuadorian Forastero individuals than these Colombian-Ecuadorian trees were to other South American Forastero individuals. RFLP and microsatellite analyses revealed a high level of homozygosity and significantly low genetic diversity within the Ancient Criollo group. The results suggest that the Ancient Criollo individuals represent the original Criollo group. The results also implies that this group does not represent a separate subspecies and that it probably originated from a few individuals in South America that may have been spread by man within Central America.
Show, Reward and Tell: Automatic Generation of Narrative Paragraph From Photo Stream by Adversarial Training

Impressive image captioning results (i.e., an objective description for an image) are achieved with plenty of training pairs. In this paper, we take one step further to investigate the creation of narrative paragraph for a photo stream. This task is even more challenging due to the difficulty in modeling an ordered photo sequence and in generating a relevant paragraph with expressive language style for storytelling. The difficulty can even be exacerbated by the limited training data, so that existing approaches almost focus on search-based solutions. To deal with these challenges, we propose a sequenceto-sequence modeling approach with reinforcement learning and adversarial training. First, to model the ordered photo stream, we propose a hierarchical recurrent neural network as story generator, which is optimized by reinforcement learning with rewards. Second, to generate relevant and story-style paragraphs, we design the rewards with two critic networks, including a multi-modal and a language-style discriminator. Third, we further consider the story generator and reward critics as adversaries. The generator aims to create indistinguishable paragraphs to human-level stories, whereas the critics aim at distinguishing them and further improving the generator by policy gradient. Experiments on three widely-used datasets show the effectiveness, against state-of-the-art methods with relative increase of 20.2% by METEOR. We also show the subjective preference for the proposed approach over the baselines through a user study with 30 human subjects.
Energy efficient IoT-based smart home

Smart Home technology is the future of residential related technology which is designed to deliver and distribute number of services inside and outside the house via networked devices in which all the different applications & the intelligence behind them are integrated and interconnected. These smart devices have the potential to share information with each other given the permanent availability to access the broadband internet connection. Hence, Smart Home Technology has become part of IoT (Internet of Things). In this work, a home model is analyzed to demonstrate an energy efficient IoT based smart home. Several Multiphysics simulations were carried out focusing on the kitchen of the home model. A motion sensor with a surveillance camera was used as part of the home security system. Coupled with the home light and HVAC control systems, the smart system can remotely control the lighting and heating or cooling when an occupant enters or leaves the kitchen.
Technological Pedagogical Content Knowledge : A Framework for Teacher Knowledge

Research in the area of educational technology has often been critiqued for a lack of theoretical grounding. In this article we propose a conceptual framework for educational technology by building on Shulman’s formulation of ‘‘pedagogical content knowledge’’ and extend it to the phenomenon of teachers integrating technology into their pedagogy. This framework is the result of 5 years of work on a program of research focused on teacher professional development and faculty development in higher education. It attempts to capture some of the essential qualities of teacher knowledge required for technology integration in teaching, while addressing the complex, multifaceted, and situated nature of this knowledge. We argue, briefly, that thoughtful pedagogical uses of technology require the development of a complex, situated form of knowledge that we call Technological Pedagogical Content Knowledge (TPCK). In doing so, we posit the complex roles of, and interplay among, three main components of learning environments: content, pedagogy, and technology. We argue that this model has much to offer to discussions of technology integration at multiple levels: theoretical, pedagogical, and methodological. In this article, we describe the theory behind our framework, provide examples of our teaching approach based upon the framework, and illustrate the methodological contributions that have resulted from this work.
Efficient Machine Learning for Big Data: A Review

With the emerging technologies and all associated devices, it is predicted that massive amount of data will be created in the next few years – in fact, as much as 90% of current data were created in the last couple of years – a trend that will continue for the foreseeable future. Sustainable computing studies the process by which computer engineer/scientist designs computers and associated subsystems efficiently and effectively with minimal impact on the environment. However, current intelligent machine-learning systems are performance driven – the focus is on the predictive/classification accuracy, based on known properties learned from the training samples. For instance, most machine-learning-based nonparametric models are known to require high computational cost in order to find the global optima. With the learning task in a large dataset, the number of hidden nodes within the network will therefore increase significantly, which eventually leads to an exponential rise in computational complexity. This paper thus reviews the theoretical and experimental data-modeling literature, in large-scale data-intensive fields, relating to: (1) model efficiency, including computational requirements in learning, and data-intensive areas’ structure and design, and introduces (2) new algorithmic approaches with the least memory requirements and processing to minimize computational cost, while maintaining/improving its predictive/classification accuracy and stability.
Psychological Flexibility and Resilience in Parentally Bereaved College Students.

Losing a parent prior to age 18 years can have life-long implications. The challenges of emerging adulthood may be even more difficult for parentally bereaved college students, and studying their coping responses is crucial for designing campus services and therapy interventions. This study examined the relationships between bereavement-related distress, experiential avoidance (EA), values, and resilience. Findings indicated that EA and low importance of values were correlated with bereavement difficulties, with EA accounting for 26% of the variance in the bereavement distress measure. In addition, reports of behaving consistently with values accounted for 20% of the variance in the resiliency measure. Contrary to hypotheses and previous literature, there were no significant relationships between the measures of EA and values. The results, limitations, and directions for future research are discussed.
Removable partial denture design: a need to focus on hygienic principles?

The purpose of this study was to critically analyze important hygienic/secondary prophylactic and biomechanical aspects of removable partial denture (RPD) design.



The literature related to traditional biomechanical design and open/hygienic design of RPDs was discussed by the authors at a 2.5-day workshop. The written report was circulated among the authors until a consensus was reached.



There is little scientific support for most of the traditional design principles of RPDs, nor has patient satisfaction shown any correlation with design factors. However, there is evidence that an open/hygienic design is more important than biomechanical aspects for long-term oral health. The biomechanical importance of some components is questioned, e.g., indirect retention and guiding planes. Alternative connector designs that reduce risks of tissue injury are described. Direct retainers and pontics are discussed in relation to the possibilities they offer for gingival relief.



Greater attention should be paid to RPD design principles that minimize the risks of tissue injury and plaque accumulation in accordance with modern concepts of preventive dentistry.
LOGAN: Membership Inference Attacks Against Generative Models

Generative models estimate the underlying distribution of a dataset to generate realistic samples according to that distribution. In this paper, we present the first membership inference attacks against generative models: given a data point, the adversary determines whether or not it was used to train the model. Our attacks leverage Generative Adversarial Networks (GANs), which combine a discriminative and a generative model, to detect overfitting and recognize inputs that were part of training datasets, using the discriminator’s capacity to learn statistical differences in distributions. We present attacks based on both white-box and black-box access to the target model, against several state-of-the-art generative models, over datasets of complex representations of faces (LFW), objects (CIFAR-10), and medical images (Diabetic Retinopathy). We also discuss the sensitivity of the attacks to different training parameters, and their robustness against mitigation strategies, finding that defenses are either ineffective or lead to significantly worse performances of the generative models in terms of training stability and/or sample quality.
Theorizing celebrity cultures : thickenings of celebrity cultures and the role of cultural ( working ) memory

The concept of celebrity culture remains remarkably undertheorized in the literature, and it is precisely this gap that this article aims to begin filling in. Starting with media culture definitions, celebrity culture is conceptualized as collections of sense-making practices whose main resources of meaning are celebrity. Consequently, celebrity cultures are necessarily plural. This approach enables us to focus on the spatial differentiation between (sub)national celebrity cultures, for which the Flemish case is taken as a central example. We gain a better understanding of this differentiation by adopting a translocal frame on culture and by focusing on the construction of celebrity cultures through the ‘us and them’ binary and communities. Finally, it is also suggested that what is termed cultural working memory improves our understanding of the remembering and forgetting of actual celebrities, as opposed to more historical figures captured by concepts such as cultural memory.
Securing vehicular ad hoc networks

Vehicular networks are very likely to be deployed in the coming years and thus become the most relevant form of mobile ad hoc networks. In this paper, we address the security of these networks. We provide a detailed threat analysis and devise an appropriate security architecture. We also describe some major design decisions still to be made, which in some cases have more than mere technical implications. We provide a set of security protocols, we show that they protect privacy and we analyze their robustness and efficiency.
The Nature of Phonological Processing and Its Causal Role in the Acquisition of Reading Skills

Three bodies of research that have developed in relative isolation center on each of three kinds of phonological processing: phonological awareness, awareness of the sound structure of language; phonological receding in lexical access, receding written symbols into a sound-based representational system to get from the written word to its lexical referent; and phonetic receding in working memory, recoding written symbols into a sound-based representational system to maintain them efficiently in working memory. In this review we integrate these bodies of research and address the interdependent issues of the nature of phonological abilities and their causal roles in the acquisition of reading skills. Phonological ability seems to be general across tasks that purport to measure the three kinds of phonological processing, and this generality apparently is independent of general cognitive ability. However, the generality of phonological ability is not complete, and there is an empirical basis for distinguishing phonological awareness and phonetic recoding in working memory. Our review supports a causal role for phonological awareness in learning to read, and suggests the possibility of similar causal roles for phonological recoding in lexical access and phonetic recoding in working memory. Most researchers have neglected the probable causal role of learning to read in the development of phonological skills. It is no longer enough to ask whether phonological skills play a causal role in the acquisition of reading skills. The question now is which aspects of phonological processing (e.g., awareness, recoding in lexical access, recoding in working memory) are causally related to which aspects of reading (e.g., word recognition, word analysis, sentence comprehension), at which point in their codevelopment, and what are the directions of these causal relations?
Cellular basis of working memory

In the presence of normal sensory and motor capacity, intelligent behavior is widely acknowledged to develop from the interaction of short-and long-term memory. While the behavioral, cellular, and molecular underpinnings of the long-term memory process have long been associated with the hippocampal formation, and this structure has become a major model system for the study of memory, the neural substrates of specific short-term memory functions have more and more become identified with prefrontal cortical areas (Goldman-Rakic, 1987; Fuster, 1989). The special nature of working memory was first identified in studies of human cognition and modern neuro-biological methods have identified a specific population of neurons, patterns of their intrinsic and extrinsic circuitry, and signaling molecules that are engaged in this process in animals. In this article, I will first define key features of working memory and then descdbe its biological basis in primates. Distinctive Features of a Working Memory System Working memory is the term applied to the type of memory that is active and relevant only for a short period of time, usually on the scale of seconds. A common example of working memory is keeping in mind a newly read phone number until it is dialed and then immediately forgotten. This process has been captu red by the analogy to a mental sketch pad (Baddeley, 1986) an~l is clearly different from the permanent inscription on neuronal circuitry due to learning. The criterion-useful or relevant only transiently distinguishes working memory from the processes that have been variously termed semantic (Tulving, 1972) or procedural (Squire and Cohen, 1984) memory, processes that can be considered associative in the traditional sense, i.e., information acquired by the repeated contiguity between stimuli and responses and/or consequences. If semantic and procedural memory are the processes by which stimuli and events acquire archival permanence , working memory is the process for the retrieval and proper utilization of this acquired knowledge. In this context, the contents of working memory are as much on the output side of long-term storage sites as they are an important source of input to those sites. Considerable evidence is now at hand to demonstrate that the brain obeys the distinction between working and other forms of memory , and that the prefrontal cortex has a preeminent role mainly in the former (Goldman.Rakic, 1987). However, memory-guided behavior obviously reflects the operation of a widely distributed system of brain structures and psychological functions, and understanding …
Computer-aided generation of mandala thangka patterns

The mandala thangka, as a religious art in Tibetan Buddhism, is an invaluable cultural and artistic heritage. However, drawing a mandala is both time and effort consuming and requires mastery skills due to its intricate details. Retaining and digitizing this heritage is an unresolved research challenge to date. In this paper, we propose a computer-aided generation approach of mandala thangka patterns to address this issue. Specifically, we construct parameterized models of three stylistic patterns used in the interior mandalas of Nyingma school in Tibetan Buddhism according to their geometric features, namely the star, crescent and lotus flower patterns. Varieties of interior mandalas are successfully generated using these proposed patterns based on the hierarchical structures observed from hand drawn mandalas. The experimental results show that our approach can efficiently generate beautifully-layered colorful interior mandalas, which significantly reduces the time and efforts in manual production and, more importantly, contributes to the digitization of this great heritage.
How far did we get in face spoofing detection?

The growing use of control access systems based on face recognition shed light over the need for even more accurate systems to detect face spoofing attacks. In this paper, an extensive analysis on face spoofing detection works published in the last decade is presented. The analyzed works are categorized by their fundamental parts, i.e., descriptors and classifiers. This structured survey also brings a comparative performance analysis of the works considering the most important public data sets in the field. The methodology followed in this work is particularly relevant to observe temporal evolution of the field, trends in the existing approaches,  Luciano Oliveira, tel.  luiz.otavio@ufba.br (Luiz Souza), lrebouca@ufba.br (Luciano Oliveira), mauricio@dcc.ufba.br (Mauricio Pamplona), papa@fc.unesp.br (Joao Papa) to discuss still opened issues, and to propose new perspectives for the future of face spoofing detection.
A Deep Convolutional Generative Adversarial Networks (DCGANs)-Based Semi-Supervised Method for Object Recognition in Synthetic Aperture Radar (SAR) Images

Synthetic aperture radar automatic target recognition (SAR-ATR) has made great progress in recent years. Most of the established recognition methods are supervised, which have strong dependence on image labels. However, obtaining the labels of radar images is expensive and time-consuming. In this paper, we present a semi-supervised learning method that is based on the standard deep convolutional generative adversarial networks (DCGANs). We double the discriminator that is used in DCGANs and utilize the two discriminators for joint training. In this process, we introduce a noisy data learning theory to reduce the negative impact of the incorrectly labeled samples on the performance of the networks. We replace the last layer of the classic discriminators with the standard softmax function to output a vector of class probabilities so that we can recognize multiple objects. We subsequently modify the loss function in order to adapt to the revised network structure. In our model, the two discriminators share the same generator, and we take the average value of them when computing the loss function of the generator, which can improve the training stability of DCGANs to some extent. We also utilize images of higher quality from the generated images for training in order to improve the performance of the networks. Our method has achieved state-of-the-art results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, and we have proved that using the generated images to train the networks can improve the recognition accuracy with a small number of labeled samples.