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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 We identify common misconceptions that arise as a result of incomplete descriptions, outdated arguments, and unclear distinctions between theory and implementation of the models.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.

Background Recombinant adeno‐associated viruses (AAVs) capable of crossing the blood‐brain barrier (e.g. AAV.PHP.eB) and encoding antibodies against amyloid beta peptides (Aβ) have potential to evaluate brain‐wide gene immunotherapies in Alzheimer's disease (AD). Furthermore, leveraging astrocytic reactivity in response to Aβ pathology, the glial fibrillary acidic protein (GFAP) promoter could serve as a regulator of gene immunotherapy. Hypothesis Reactive astrocytes can regulate the expression of the recombinant anti‐Aβ antibody (rSol) under the control of a GFAP promoter in the TgCRND8 (Tg) mouse model of amyloidosis. Method To study GFAP expression in Tg mice, GFAP mRNA levels were quantified using qPCR in the hippocampal formation at 3, 5, and 6 months (n = 6 per group). Next, AAV.PHP.eB.GFAP.rSol‐myc‐tag and AAV.PHP.eB.GFAP.GFP were co‐injected intravenously in Tg mice while non‐Tg littermates and C57BL/6J mice served as controls. One‐month post‐injection, brain sections were processed for immunohistochemistry and RNAscope. Result GFAP mRNA levels doubled in 6‐month‐old compared to 3‐month‐old Tg mice. Brain‐wide GFP expression in astrocytes confirmed efficacy of the GFAP promoter. Notably, brain cell transduction varied across Tg mice, peaking in the C57BL/6J line. Ly6A, a protein previously shown to facilitate AAV.PHP.eB entry into the brain, may explain this variability in transduction levels. We are currently examining Ly6A expression in our transgenic mouse line to determine the Tg background that will deliver the most efficient viral transduction. Conclusion These results suggest that the GFAP promoter could control the production of therapeutics, such as rSol, in response to amyloid‐induced astrocytic reactivity. Long‐term studies will assess whether rSol prevents Aβ pathology progression in Tg‐Aβ mice.

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.

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.

External distractions often occur when information must be retained in visual working memory (VWM)—a crucial element in cognitive processing and everyday activities. However, the distraction effects can differ if they occur during the encoding rather than the delay stages. Previous research on these effects used simple stimuli (e.g., color and orientation) rather than considering distractions caused by real-world stimuli on VWM. In the present study, participants performed a facial VWM task under different distraction conditions across the encoding and delay stages to elucidate the mechanisms of distraction resistance in the context of complex real-world stimuli. VWM performance was significantly impaired by delay-stage but not encoding-stage distractors (Experiment 1). In addition, the delay distraction effect arose primarily due to the absence of distractor process at the encoding stage rather than the presence of a distractor during the delay stage (Experiment 2). Finally, the impairment in the delay-distraction condition was not due to the abrupt appearance of distractors (Experiment 3). Taken together, these findings indicate that the processing mechanisms previously established for resisting distractions in VWM using simple stimuli can be extended to more complex real-world stimuli, such as faces.

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.

Optical vortex is a promising candidate for capacity scaling in next-generation optical communications. The generation of multi-vortex beams is of great importance for vortex-based optical communications. Traditional approaches for generating multi-vortex beams are passive, unscalable and cumbersome. Here, we propose and demonstrate a multi-vortex laser, an active approach for creating multi-vortex beams directly at the source. By printing a specially-designed concentric-rings pattern on the cavity mirror, multi-vortex beams are generated directly from the laser. Spatially, the generated multi-vortex beams are decomposable and coaxial. Temporally, the multi-vortex beams can be simultaneously self-mode-locked, and each vortex component carries pulses with GHz-level repetition rate. Utilizing these distinct spatial-temporal characteristics, we demonstrate that the multi-vortex laser can be spatially and temporally encoded for data transmission, showing the potential of the developed multi-vortex laser in optical communications. The demonstrations may open up new perspectives for diverse applications enabled by the multi-vortex laser.