The Tensor Brain: A Unified Theory of Perception, Memory and Semantic Decoding

We present a unified computational theory of an agent's perception and memory. In our model, perception, episodic memory, and semantic memory are realized by different operational modes of the oscillating interactions between a symbolic index layer and a subsymbolic representation layer. The two layers form a bilayer tensor network (BTN). Although memory appears to be about the past, its main purpose is to support the agent in the present and the future. Recent episodic memory provides the agent with a sense of the here and now. Remote episodic memory retrieves relevant past experiences to provide information about possible future scenarios. This aids the agent in decision-making. "Future" episodic memory, based on expected future events, guides planning and action. Semantic memory retrieves specific information, which is not delivered by current perception, and defines priors for future observations. We argue that it is important for the agent to encode individual entities, not just classes and attributes. We demonstrate that a form of self-supervised learning can acquire new concepts and refine existing ones. We test our model on a standard benchmark data set, which we expanded to contain richer representations for attributes, classes, and individuals. Our key hypothesis is that obtaining a better understanding of perception and memory is a crucial prerequisite to comprehending human-level intelligence.Comment: Accepted for publication at Neural Computatio

Near-band-gap photo-induced nuclear spin dynamics in semi-insulating GaAs: Hyperfine- and quadrupolar-driven relaxation

Understanding and manipulating spin polarization and transport in the vicinity of semiconductor-hosted defects is a problem of present technological and fundamental importance. Here, we use high-field magnetic resonance to monitor the relaxation dynamics of spin-3/2 nuclei in semi-insulating GaAs. Our experiments benefit from the conditions created in the limit of low illumination intensities, where intermittent occupation of the defect site by photo-excited electrons leads to electric field gradient fluctuations and concomitant spin relaxation of the neighboring quadrupolar nuclei. We find indication of a heterogeneous distribution of polarization, governed by different classes of defects activated by either weak or strong laser excitation. Upon application of a train of light pulses of variable repetition rate and on/off ratio, we uncover an intriguing regime of mesoscale nuclear spin diffusion restricted by long-range, non-uniform electric field gradients. Given the slow time scale governing nuclear spin evolution, such optically-induced polarization patterns could be exploited as a contrast mechanism to expose dark lattice defects or localized charges with nanoscale resolution

Microwave absorbing alkaline catalyst for biodiesel production via MIL-100(Fe): Catalytic optimization, characterizations, kinetics, and distillation simulation

Microwave heating (MW) is known for its efficacy in promoting transesterification for biodiesel production. However, the microwave-induced catalysis, linked to catalyst absorbing capability, remains poorly understood. Herein, a class of alkaline catalysts with strong microwave absorption were synthesized, validating their positive impact on transesterification. Various methods were used to reveal the relationship between microwave absorbing capacity and physicochemical properties of the synthesized catalyst (KF/Mg-MIL). Results indicated the previously recognized basicity’s role for KF/Mg-MIL was surpassed by microwave absorbing capability (permittivity and permeability) in MW (2.45 GHz). KF/Mg-MIL, with εr = 4.94′-j1.09″ and μr = 1.03′-j0.024″, efficiently transformed microwave into thermal energy via the dielectric loss and magnetic loss, saving 50 % energy consumption and reducing 1051.61 kg CO2 for per ton biodiesel compared to water bath heating (WB). Notably, “non-thermal” effect was observed with KF/Mg-MIL in MW, which reduced activation energy by 2.49 kJ/mol and increased the frequency factor by 793.32 min−1 in comparison to WB

Near-Field Beam Management for Extremely Large-Scale Array Communications

Extremely large-scale arrays (XL-arrays) have emerged as a promising technology to achieve super-high spectral efficiency and spatial resolution in future wireless systems. The large aperture of XL-arrays means that spherical rather than planar wavefronts must be considered, and a paradigm shift from far-field to near-field communications is necessary. Unlike existing works that have mainly considered far-field beam management, we study the new near-field beam management for XL-arrays. We first provide an overview of near-field communications and introduce various applications of XL-arrays in both outdoor and indoor scenarios. Then, three typical near-field beam management methods for XL-arrays are discussed: near-field beam training, beam tracking, and beam scheduling. We point out their main design issues and propose promising solutions to address them. Moreover, other important directions in near-field communications are also highlighted to motivate future research.Comment: We studied the new near-field beam management for XL-arrays. This paper has been submitted to IEEE for possible publicatio

Assessing and Understanding Creativity in Large Language Models

In the field of natural language processing, the rapid development of large language model (LLM) has attracted more and more attention. LLMs have shown a high level of creativity in various tasks, but the methods for assessing such creativity are inadequate. The assessment of LLM creativity needs to consider differences from humans, requiring multi-dimensional measurement while balancing accuracy and efficiency. This paper aims to establish an efficient framework for assessing the level of creativity in LLMs. By adapting the modified Torrance Tests of Creative Thinking, the research evaluates the creative performance of various LLMs across 7 tasks, emphasizing 4 criteria including Fluency, Flexibility, Originality, and Elaboration. In this context, we develop a comprehensive dataset of 700 questions for testing and an LLM-based evaluation method. In addition, this study presents a novel analysis of LLMs' responses to diverse prompts and role-play situations. We found that the creativity of LLMs primarily falls short in originality, while excelling in elaboration. Besides, the use of prompts and the role-play settings of the model significantly influence creativity. Additionally, the experimental results also indicate that collaboration among multiple LLMs can enhance originality. Notably, our findings reveal a consensus between human evaluations and LLMs regarding the personality traits that influence creativity. The findings underscore the significant impact of LLM design on creativity and bridges artificial intelligence and human creativity, offering insights into LLMs' creativity and potential applications

ForecastTKGQuestions: A Benchmark for Temporal Question Answering and Forecasting over Temporal Knowledge Graphs

Question answering over temporal knowledge graphs (TKGQA) has recently found increasing interest. TKGQA requires temporal reasoning techniques to extract the relevant information from temporal knowledge bases. The only existing TKGQA dataset, i.e., CronQuestions, consists of temporal questions based on the facts from a fixed time period, where a temporal knowledge graph (TKG) spanning the same period can be fully used for answer inference, allowing the TKGQA models to use even the future knowledge to answer the questions based on the past facts. In real-world scenarios, however, it is also common that given the knowledge until now, we wish the TKGQA systems to answer the questions asking about the future. As humans constantly seek plans for the future, building TKGQA systems for answering such forecasting questions is important. Nevertheless, this has still been unexplored in previous research. In this paper, we propose a novel task: forecasting question answering over temporal knowledge graphs. We also propose a large-scale TKGQA benchmark dataset, i.e., ForecastTKGQuestions, for this task. It includes three types of questions, i.e., entity prediction, yes-no, and fact reasoning questions. For every forecasting question in our dataset, QA models can only have access to the TKG information before the timestamp annotated in the given question for answer inference. We find that the state-of-the-art TKGQA methods perform poorly on forecasting questions, and they are unable to answer yes-no questions and fact reasoning questions. To this end, we propose ForecastTKGQA, a TKGQA model that employs a TKG forecasting module for future inference, to answer all three types of questions. Experimental results show that ForecastTKGQA outperforms recent TKGQA methods on the entity prediction questions, and it also shows great effectiveness in answering the other two types of questions.Comment: Accepted to ISWC 202

Microwave‐Assisted Pyrolysis of Biomass for Bio‐Oil Production

Microwave‐assisted pyrolysis (MAP) is a new thermochemical process that converts biomass to bio‐oil. Compared with the conventional electrical heating pyrolysis, MAP is more rapid, efficient, selective, controllable, and flexible. This chapter provides an up‐to‐date knowledge of bio‐oil production from microwave‐assisted pyrolysis of biomass. The chemical, physical, and energy properties of bio‐oils obtained from microwave‐assisted pyrolysis of biomass are described in comparison with those from conventional pyrolysis, the characteristics of microwave‐assisted pyrolysis as affected by biomass feedstock properties, microwave heating operations, use of exogenous microwave absorbents, and catalysts are discussed. With the advantages it offers and the further research and development recommended, microwave‐assisted pyrolysis has a bright future in production of bio‐oils that can effectively narrow the energy gap and reduce negative environmental impacts of our energy production and application practice

Manufacturing Process Improvement for Automotive Supplier

Tato diplomová práce je zaměřena na zlepšení výrobního procesu v automobilovém průmyslu ve společnosti Brose CZ, spol. s r. o. V teoretické části jsou vysvětleny základní pojmy a metody z oblasti kvality, následně je provedena analýza vybraného procesu. Na základě poznatků jsou navržena řešení sloužící k eliminaci případně k odstranění nedostatků.This thesis is focused on „Manufacturing Process Improvement for Automotive Supplier“ in company Brose CZ s. r. o. Theoretical part covers the basic term, methods from the field of quality. Next part explores the analysis of the selected process. There are proposed corrective and preventive action based on the discovered findings.152 - Katedra podnikohospodářskávýborn