استكشف المجموعة الواسعة من العناوين المتاحة.

Models that represent meaning as high-dimensional numerical vectors—such as latent semantic analysis (LSA), hyperspace analogue to language (HAL), bound encoding of the aggregate language environment (BEAGLE), topic models, global vectors (GloVe), and word2vec—have been introduced as extremely powerful machine-learning proxies for human semantic representations and have seen an explosive rise in popularity over the past 2 decades. However, despite their considerable advancements and spread in the cognitive sciences, one can observe problems associated with the adequate presentation and understanding of some of their features. Indeed, when these models are examined from a cognitive perspective, a number of unfounded arguments tend to appear in the psychological literature. In this article, we review the most common of these arguments and discuss (a) what exactly these models represent at the implementational level and their plausibility as a cognitive theory, (b) how they deal with various aspects of meaning such as polysemy or compositionality, and (c) how they relate to the debate on embodied and grounded cognition. We identify common misconceptions that arise as a result of incomplete descriptions, outdated arguments, and unclear distinctions between theory and implementation of the models. We clarify and amend these points to provide a theoretical basis for future research and discussions on vector models of semantic representation.

Drug discovery is a cost and time-intensive process that is often assisted by computational methods, such as virtual screening, to speed up and guide the design of new compounds. For many years, machine learning methods have been successfully applied in the context of computer-aided drug discovery. Recently, thanks to the rise of novel technologies as well as the increasing amount of available chemical and bioactivity data, deep learning has gained a tremendous impact in rational active compound discovery. Herein, recent applications and developments of machine learning, with a focus on deep learning, in virtual screening for active compound design are reviewed. This includes introducing different compound and protein encodings, deep learning techniques as well as frequently used bioactivity and benchmark data sets for model training and testing. Finally, the present state-of-the-art, including the current challenges and emerging problems, are examined and discussed.

While influential across a wide variety of subfields, cultural analysis in sociology continues to be hampered by coarse-grained conceptualizations of the different modes in which culture becomes personal, as well as the process via which persons acquire and use different forms of culture. In this article, I argue that persons acquire and use culture in two analytically and empirically distinct forms, which I label declarative and nondeclarative. The mode of cultural acquisition depends on the dynamics of exposure and encoding, and modulates the process of cultural accessibility, activation, and use. Cultural knowledge about one domain may be redundantly represented in both declarative and nondeclarative forms, each linked via analytically separable pathways to corresponding public cultural forms and ultimately to substantive outcomes. I outline how the new theoretical vocabulary, theoretical model, and analytic distinctions that I propose can be used to resolve contradictions and improve our understanding of outstanding substantive issues in empirically oriented subfields that have recently incorporated cultural processes as a core explanatory resource.

Understanding the neural basis of speech perception requires that we study the human brain both at the scale of the fundamental computational unit of neurons and in their organization across the depth of cortex. Here we used high-density Neuropixels arrays 1 – 3 to record from 685 neurons across cortical layers at nine sites in a high-level auditory region that is critical for speech, the superior temporal gyrus 4 , 5 , while participants listened to spoken sentences. Single neurons encoded a wide range of speech sound cues, including features of consonants and vowels, relative vocal pitch, onsets, amplitude envelope and sequence statistics. Neurons at each cross-laminar recording exhibited dominant tuning to a primary speech feature while also containing a substantial proportion of neurons that encoded other features contributing to heterogeneous selectivity. Spatially, neurons at similar cortical depths tended to encode similar speech features. Activity across all cortical layers was predictive of high-frequency field potentials (electrocorticography), providing a neuronal origin for macroelectrode recordings from the cortical surface. Together, these results establish single-neuron tuning across the cortical laminae as an important dimension of speech encoding in human superior temporal gyrus. High-density single-neuron recordings show diverse tuning for acoustic and phonetic features across layers in human auditory speech cortex.

We present the enhanced feature quantum autoencoder, or EF-QAE, a variational quantum algorithm capable of compressing quantum states of different models with higher fidelity. The key idea of the algorithm is to define a parameterized quantum circuit that depends upon adjustable parameters and a feature vector that characterizes such a model. We assess the validity of the method in simulations by compressing ground states of the Ising model and classical handwritten digits. The results show that EF-QAE improves the performance compared to the standard quantum autoencoder using the same amount of quantum resources, but at the expense of additional classical optimization. Therefore, EF-QAE makes the task of compressing quantum information better suited to be implemented in near-term quantum devices.

Episodic memory describes the conscious reimagining of our memories and is often considered to be a uniquely human ability. As these phenomenological components are embedded within its definition, major issues arise when investigating the presence of episodic memory in non-human animals. Importantly, however, when we as humans recall a specific experience, we may remember details from that experience that were inconsequential to our needs, thoughts, or desires at that time. This 'incidental' information is nevertheless encoded automatically as part of the memory and is subsequently recalled within a holistic representation of the event. The incidental encoding and unexpected question paradigm represents this characteristic feature of human episodic memory and can be employed to investigate memory recall in non-human animals. However, without evidence for the associated phenomenology during recall, this type of memory is termed 'episodic-like memory'. Using this approach, we tested seven Eurasian jays (Garrulus glandarius) on their ability to use incidental visual information (associated with observed experimenter made 'caches') to solve an unexpected memory test. The birds performed above chance levels, suggesting that Eurasian jays can encode, retain, recall, and access incidental visual information within a remembered event, which is an ability indicative of episodic memory in humans.

Speech consists of a continuously-varying acoustic signal. Yet human listeners experience it as sequences of discrete speech sounds, which are used to recognise discrete words. To examine how the human brain appropriately sequences the speech signal, we recorded two-hour magnetoencephalograms from 21 participants listening to short narratives. Our analyses show that the brain continuously encodes the three most recently heard speech sounds in parallel, and maintains this information long past its dissipation from the sensory input. Each speech sound representation evolves over time, jointly encoding both its phonetic features and the amount of time elapsed since onset. As a result, this dynamic neural pattern encodes both the relative order and phonetic content of the speech sequence. These representations are active earlier when phonemes are more predictable, and are sustained longer when lexical identity is uncertain. Our results show how phonetic sequences in natural speech are represented at the level of populations of neurons, providing insight into what intermediary representations exist between the sensory input and sub-lexical units. The flexibility in the dynamics of these representations paves the way for further understanding of how such sequences may be used to interface with higher order structure such as lexical identity. Speech unfolds faster than the brain completes processing of speech sounds. Here, the authors show that brain activity moves systematically within neural populations of auditory cortex, allowing accurate representation of a speech sound’s identity and its position in the sound sequence.

Insider threats are malicious acts that can be carried out by an authorized employee within an organization. Insider threats represent a major cybersecurity challenge for private and public organizations, as an insider attack can cause extensive damage to organization assets much more than external attacks. Most existing approaches in the field of insider threat focused on detecting general insider attack scenarios. However, insider attacks can be carried out in different ways, and the most dangerous one is a data leakage attack that can be executed by a malicious insider before his/her leaving an organization. This paper proposes a machine learning-based model for detecting such serious insider threat incidents. The proposed model addresses the possible bias of detection results that can occur due to an inappropriate encoding process by employing the feature scaling and one-hot encoding techniques. Furthermore, the imbalance issue of the utilized dataset is also addressed utilizing the synthetic minority oversampling technique (SMOTE). Well known machine learning algorithms are employed to detect the most accurate classifier that can detect data leakage events executed by malicious insiders during the sensitive period before they leave an organization. We provide a proof of concept for our model by applying it on CMU-CERT Insider Threat Dataset and comparing its performance with the ground truth. The experimental results show that our model detects insider data leakage events with an AUC-ROC value of 0.99, outperforming the existing approaches that are validated on the same dataset. The proposed model provides effective methods to address possible bias and class imbalance issues for the aim of devising an effective insider data leakage detection system.

Since most machine learning (ML) algorithms are designed for numerical inputs, efficiently encoding categorical variables is a crucial aspect in data analysis. A common problem are high cardinality features, i.e. unordered categorical predictor variables with a high number of levels. We study techniques that yield numeric representations of categorical variables which can then be used in subsequent ML applications. We focus on the impact of these techniques on a subsequent algorithm’s predictive performance, and—if possible—derive best practices on when to use which technique. We conducted a large-scale benchmark experiment, where we compared different encoding strategies together with five ML algorithms (lasso, random forest, gradient boosting, k-nearest neighbors, support vector machine) using datasets from regression, binary- and multiclass–classification settings. In our study, regularized versions of target encoding (i.e. using target predictions based on the feature levels in the training set as a new numerical feature) consistently provided the best results. Traditionally widely used encodings that make unreasonable assumptions to map levels to integers (e.g. integer encoding) or to reduce the number of levels (possibly based on target information, e.g. leaf encoding) before creating binary indicator variables (one-hot or dummy encoding) were not as effective in comparison.