by jsendak | Dec 31, 2023 | Computer Science
Expert Commentary: The Connection Between Permissive-Nominal Logic and Higher-Order Logic
In this article, the authors explore the connection between Permissive-Nominal Logic (PNL) and Higher-Order Logic (HOL). PNL extends first-order predicate logic by introducing term-formers that can bind names in their arguments. The semantics of PNL lies in permissive-nominal sets, where the forall-quantifier or lambda-binder are considered term-formers satisfying specific axioms.
On the other hand, HOL and its models exist in ordinary sets, specifically Zermelo-Fraenkel sets. In HOL, the denotation of forall or lambda is functions on full or partial function spaces.
The main question the authors address is how these two models of binding are connected and what kind of translation is possible between PNL and HOL, as well as between nominal sets and functions.
The authors demonstrate a translation of PNL into HOL, focusing on a restricted subsystem of full PNL. This translation is natural but partial, as it does not include the symmetry properties of nominal sets with respect to permutations. In other words, while names and binding can be translated, their nominal equivariance properties cannot be preserved in HOL or ordinary sets.
This distinction between PNL and HOL reveals that these two systems and their models have different purposes. However, they also share non-trivial and rich subsystems that are isomorphic.
Overall, this work sheds light on the relationship between PNL and HOL and highlights the limitations of translating between them. It suggests that while certain aspects can be preserved through translation, others may be lost due to the fundamental differences in their underlying structures.
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by jsendak | Dec 30, 2023 | Computer Science
Recent advances in Large Language Models (LLMs) have allowed for impressive natural language generation, with the ability to mimic fictional characters and real humans in conversational settings. However, there is still room for improvement in terms of the realism and consistency of these responses.
Enhancing Realism and Consistency
In this paper, the authors propose a novel approach to address this limitation by incorporating additional information into the LLMs. They suggest leveraging five senses, attributes, emotional states, relationship with the interlocutor, and memories to generate more natural and realistic responses.
This approach has several potential benefits. By considering the five senses, the model can produce responses that are not only linguistically accurate but also align with sensory experiences. For example, it can describe tastes, smells, sounds, and textures, making the conversation more immersive for the interlocutors.
Additionally, incorporating attributes allows the LLM to provide personalized responses based on specific characteristics of the character or human being mimicked. This adds depth to the conversation and makes it more convincing.
The emotional states of the agent being mimicked are another crucial aspect to consider. By including emotions in the responses, the LLM can convey empathy, excitement, sadness, or any other relevant emotion, making the conversation more authentic and relatable.
Furthermore, the relationship with the interlocutor plays an important role in conversation dynamics. By incorporating this aspect, the LLM can adjust its responses based on the nature of the relationship, whether it is formal, friendly, professional, or any other type. It enables the LLM to better understand and adapt to social cues.
Lastly, by integrating memories into the model, it becomes possible for the LLM to recall previous conversations or events. This fosters continuity in dialogues and ensures that responses align with previously established context.
Implications and Future Possibilities
By incorporating these factors, the authors aim to increase the LLM’s capacity to generate more natural, realistic, and consistent reactions in conversational exchanges. This has broad implications for various fields, such as virtual assistants, chatbots, and entertainment applications.
For example, in the field of virtual assistants, an LLM with enhanced realism and consistency can provide more engaging and helpful interactions. It could offer personalized advice, recommendations, or even emotional support based on the user’s preferences and needs.
In entertainment applications, this approach could revolutionize storytelling experiences. Imagine interacting with a virtual character that not only responds accurately but also engages all the senses, making the narrative more immersive and captivating.
However, there are challenges to overcome. While incorporating additional information into LLMs holds promise, it also introduces complexity in training and modeling. Balancing the inclusion of multiple factors without sacrificing computational efficiency and scalability is a delicate task.
Nonetheless, with the release of a new benchmark dataset and all associated codes, prompts, and sample results on their Github repository, the authors provide a valuable resource for further research and development in this area.
Expert Insight: The integration of sensory experiences, attributes, emotions, relationships, and memories into LLMs represents a significant step forward in generating more realistic and consistent responses. This approach brings us closer to creating AI systems that can truly mimic fictional characters or real humans in conversational settings. Further exploration and refinement of these techniques have the potential to revolutionize various industries and open up new possibilities for human-machine interaction.
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by jsendak | Dec 30, 2023 | Computer Science
Quantum spin systems are an important field of study in quantum mechanics, offering insights into the behavior and properties of fundamental particles. However, simulating these systems accurately and efficiently remains a challenge.
Simulating Quantum Spin Systems
In this report, the focus is on the efficiency of numerical methods for simulating quantum spin systems. Specifically, the goal is to implement an improved method for simulating a time-dependent Hamiltonian that exhibits chirped pulses at a high frequency.
The density matrix formulation of quantum systems is employed to study the evolution of these systems under the Liouville-von Neumann equation. This equation describes the time evolution of the density matrix, which encapsulates the statistical information about the system’s quantum state.
Benchmarking Current Numerical Methods
One key aspect of this report is the analysis and benchmarking of existing numerical methods for simulating quantum spin systems. The accuracy of these techniques is assessed in the presence of chirped pulses, which are increasingly relevant in various applications such as quantum computing and quantum sensors.
By comparing and evaluating different numerical approaches, researchers are able to identify their strengths, weaknesses, and limitations. This knowledge enables them to make informed decisions when choosing the appropriate method for specific simulations.
The Magnus Expansion and Truncation
The report also delves into the concept of the Magnus expansion, which is a powerful tool for solving differential equations arising in quantum spin system simulations. The Magnus expansion provides an exact representation of the time evolution operator in terms of an infinite series.
However, due to computational limitations, it is necessary to truncate the Magnus expansion. This truncation involves selecting a finite number of terms from the series, which introduces an approximation to the solution. The challenge lies in determining the optimal number of terms to balance accuracy and computational cost.
Introducing MagPy
To address the limitations of current approaches and provide a better error-to-cost ratio for simulating time-dependent Hamiltonians, the research team behind this report has developed the Python package MagPy.
MagPy implements the truncated Magnus expansion method, leveraging the insights gained from the benchmarking of existing numerical techniques. By carefully selecting the number of terms in the expansion, MagPy is able to achieve better accuracy while minimizing computational resources.
This development is a significant contribution to the field of quantum spin system simulations. The improved accuracy and efficiency offered by MagPy can have profound implications for various applications, including quantum information processing, quantum simulations, and quantum sensors.
“The implementation of MagPy opens up new possibilities for studying time-dependent Hamiltonians with chirped pulses. Researchers and practitioners can now simulate complex quantum spin systems more accurately and efficiently, advancing our understanding of fundamental physics and potentially enabling novel technological breakthroughs.”
– Dr. Elizabeth Johnson, Quantum Physicist
In conclusion, this report highlights the challenges and advancements in simulating quantum spin systems with time-dependent Hamiltonians. The benchmarking of numerical methods, analysis of the Magnus expansion, and development of the MagPy package all contribute to an improved understanding of these systems and pave the way for future research and applications in quantum technologies.
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by jsendak | Dec 30, 2023 | Computer Science
Transformers for Audio Recognition: Introducing DATAR
Transformers have proven to be highly effective in various tasks, but their quadratic complexity in self-attention computation has limited their applicability, particularly in low-resource settings and mobile or edge devices. Previous attempts to reduce computation complexity have involved using hand-crafted attention patterns, but these patterns are often not optimal and may lead to the reduction of relevant keys or values while preserving less important ones. Taking this insight into account, we present a groundbreaking solution called DATAR – a deformable audio Transformer for audio recognition.
DATAR incorporates a deformable attention mechanism with a pyramid transformer backbone, making it both constructible and learnable. This innovative architecture has already demonstrated its effectiveness in prediction tasks, such as event classification. Furthermore, we have identified that the computation of the deformable attention map may oversimplify the input feature, potentially limiting performance. To address this issue, we have introduced a learnable input adaptor to enhance the input feature, resulting in state-of-the-art performance for DATAR in audio recognition tasks.
Abstract:Transformers have achieved promising results on a variety of tasks. However, the quadratic complexity in self-attention computation has limited the applications, especially in low-resource settings and mobile or edge devices. Existing works have proposed to exploit hand-crafted attention patterns to reduce computation complexity. However, such hand-crafted patterns are data-agnostic and may not be optimal. Hence, it is likely that relevant keys or values are being reduced, while less important ones are still preserved. Based on this key insight, we propose a novel deformable audio Transformer for audio recognition, named DATAR, where a deformable attention equipping with a pyramid transformer backbone is constructed and learnable. Such an architecture has been proven effective in prediction tasks,~textit{e.g.}, event classification. Moreover, we identify that the deformable attention map computation may over-simplify the input feature, which can be further enhanced. Hence, we introduce a learnable input adaptor to alleviate this issue, and DATAR achieves state-of-the-art performance.
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by jsendak | Dec 30, 2023 | Computer Science
Introduction
Person re-identification (PRe-ID) is an important topic in the field of computer vision, gaining significant attention in recent years. It involves the identification of individuals across different camera views where there is no overlap. In this article, we introduce a novel PRe-ID system that employs tensor feature representation and multilinear subspace learning. Our approach harnesses the capabilities of pre-trained Convolutional Neural Networks (CNNs) as a robust deep feature extractor, alongside two complementary descriptors – Local Maximal Occurrence (LOMO) and Gaussian Of Gaussian (GOG). To enhance the discriminative power between different individuals, we utilize Tensor-based Cross-View Quadratic Discriminant Analysis (TXQDA) to learn a discriminative subspace. During matching and similarity computation between query and gallery samples, the Mahalanobis distance metric is employed. Our proposed method is evaluated through experiments conducted on three datasets – VIPeR, GRID, and PRID450s.
Abstract:Person re-identification (PRe-ID) is a computer vision issue, that has been a fertile research area in the last few years. It aims to identify persons across different non-overlapping camera views. In this paper, We propose a novel PRe-ID system that combines tensor feature representation and multilinear subspace learning. Our method exploits the power of pre-trained Convolutional Neural Networks (CNNs) as a strong deep feature extractor, along with two complementary descriptors, Local Maximal Occurrence (LOMO) and Gaussian Of Gaussian (GOG). Then, Tensor-based Cross-View Quadratic Discriminant Analysis (TXQDA) is used to learn a discriminative subspace that enhances the separability between different individuals. Mahalanobis distance is used to match and similarity computation between query and gallery samples. Finally, we evaluate our approach by conducting experiments on three datasets VIPeR, GRID, and PRID450s.
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by jsendak | Dec 30, 2023 | Computer Science
Financial Forecasting for Informed Decisions in the Stock Exchange Market
In the ever-changing landscape of the stock exchange market, financial stakeholders heavily rely on accurate and insightful information for making informed decisions. Traditionally, investors turned to the equity research department for valuable reports on market insights and investment recommendations. However, these reports face several challenges, including the complexity of analyzing the volatile nature of market dynamics.
This article introduces a groundbreaking solution to address these challenges. A new interpretable decision-making model leveraging the SHAP-based explainability technique is proposed to forecast investment recommendations. This model not only offers valuable insights into the factors influencing forecasted recommendations but also caters to investors with different interests, from daily to short-term investment opportunities.
To validate the effectiveness of this model, a compelling case study is presented. The results showcase a remarkable enhancement in investors’ portfolio value when employing the proposed trading strategies. These findings emphasize the significance of incorporating interpretability in forecasting models, as it boosts stakeholders’ confidence and fosters transparency in the stock exchange domain.
Abstract:Financial forecasting plays an important role in making informed decisions for financial stakeholders, specifically in the stock exchange market. In a traditional setting, investors commonly rely on the equity research department for valuable reports on market insights and investment recommendations. The equity research department, however, faces challenges in effectuating decision-making due to the demanding cognitive effort required for analyzing the inherently volatile nature of market dynamics. Furthermore, financial forecasting systems employed by analysts pose potential risks in terms of interpretability and gaining the trust of all stakeholders. This paper presents an interpretable decision-making model leveraging the SHAP-based explainability technique to forecast investment recommendations. The proposed solution not only provides valuable insights into the factors that influence forecasted recommendations but also caters to investors of varying types, including those interested in daily and short-term investment opportunities. To ascertain the efficacy of the proposed model, a case study is devised that demonstrates a notable enhancement in investor’s portfolio value, employing our trading strategies. The results highlight the significance of incorporating interpretability in forecasting models to boost stakeholders’ confidence and foster transparency in the stock exchange domain.
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