View-Invariant Object Category Learning, Recognition, and Search: How Spatial and Object Attention Are Coordinated Using Surface-Based Attentional Shrouds

Air Force Office of Scientific Research (F49620-01-1-0397); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Introducing Robust Design in Product Development Learning from an initiative at Volvo

Robust design (RD) has a large potential to contribute to product and process improvements providing increased customer value. However, it has shown to be difficult to obtain these benefits in practice. This study aims to evaluate and learn from an initial approach to introducing RD within the Volvo Group. It is based on three pilot cases within the product development organisation of a business unit. Data were collected through formal interviews and informal dialogues with pilot participants, supplemented by existing documentation of the pilot cases. The main finding was that a RD initiative, characterised by ‘tool-pushing’ and with a predefined solution introduced by an external consultant, faced many obstacles and could not create a sustainable result. Instead, it was found that there is a need to involve engineers and create a learning culture in which RD principles can become a natural part of work practices. This study identified six obstacles to the success of the initiative, which were perceived as learning points for a broader application of RD at the company. This underscores that RD initiatives can also be hampered by similar types of obstacles that have been identified in research on other change processes

Categorization of indoor places by combining local binary pattern histograms of range and reflectance data from laser range finders

This paper presents an approach to categorize typical places in indoor environments using 3D scans provided by a laser range finder. Examples of such places are offices, laboratories, or kitchens. In our method, we combine the range and reflectance data from the laser scan for the final categorization of places. Range and reflectance images are transformed into histograms of local binary patterns and combined into a single feature vector. This vector is later classified using support vector machines. The results of the presented experiments demonstrate the capability of our technique to categorize indoor places with high accuracy. We also show that the combination of range and reflectance information improves the final categorization results in comparison with a single modality

An Ethnobotanical study of Medicinal Plants in high mountainous region of Chail valley (District Swat- Pakistan)

BACKGROUND: This paper represents the first ethnobotanical study in Chail valley of district Swat-Pakistan and provides significant information on medicinal plants use among the tribal people of the area. The aim of this study was to document the medicinal uses of local plants and to develop an ethnobotanical inventory of the species diversity. METHODS: In present study, semi-structured interviews with 142 inhabitants (age range between 31–75 years) were conducted. Ethnobotanical data was analyzed using relative frequency of citation (RFC) to determine the well-known and most useful species in the area. RESULTS: Current research work reports total of 50 plant species belonging to 48 genera of 35 families from Chail valley. Origanum vulgare, Geranium wallichianum and Skimmia laureola have the highest values of relative frequency of citation (RFC) and are widely known by the inhabitants of the valley. The majority of the documented plants were herbs (58%) followed by shrubs (28%), trees (12%) and then climbers (2%). The part of the plant most frequently used was the leaves (33%) followed by roots (17%), fruits (14%), whole plant (12%), rhizomes (9%), stems (6%), barks (5%) and seeds (4%). Decoction was the most common preparation method use in herbal recipes. The most frequently treated diseases in the valley were urinary disorders, skin infections, digestive disorders, asthma, jaundice, angina, chronic dysentery and diarrhea. CONCLUSION: This study contributes an ethnobotanical inventory of medicinal plants with their frequency of citations together with the part used, disease treated and methods of application among the tribal communities of Chail valley. The present survey has documented from this valley considerable indigenous knowledge about the local medicinal plants for treating number of common diseases that is ready to be further investigated for biological, pharmacological and toxicological screening. This study also provides some socio-economic aspects which are associated to the local tribal communities

New Numerical Insights into Electromagnetic Mass in Spherically Symmetric Configurations

Our study aims to evolve artificial intelligence that emerges from natural processes and be able to predict nonlinearities in the lane Emden-Fowler (LEF) equation. More precisely, the feedforward artificial neural network model is an adaptive one that leads to accurate solutions of the LEF equation. This dataset concerns a neural network whose parameters have been made adjustable so that initial predictions can be based on a given model quite easily. The energy reduction objective function for the specific limitations of the LEF equations is based upon contextual nuances introduced in previous optimization process. To begin with, this proposed methodology has been tested through experiments and initial conditions affecting the initial value of any such problem for LEF. We consider three cases in detail which show how our method can solve the LEF equation effectively. Our combined method (PSO-GWO-IPA) with Particle Swarm Optimizer (PSO) and Grey Wolf Optimizer (GWO) achieves very good convergence speed when compared to PS, IPA, PSO, PS-IPA, HPM and OHM. Through statistical tests, we will verify reliability and validity of our approach mentioned above. Our empirical results are in perfect agreement with the mathematical model, demonstrating the wisdom of the proposed method

A Review of the Application of Metal-Organic Frameworks in the Absorption, Storage and Release of Methane

Natural gas, which mainly consists of methane, is a good fuel for vehicles. Metal-organic frameworks (MOF) have attracted much attention as a new group of adsorbent materials in natural gas storage. MOF structures form various networks by connecting secondary structural units composed of metal ions and organic binders. These regular materials have high porosity and have high design capabilities. This feature has made MOFs suitable for special applications in trapping and absorbing various materials. The investigation of these materials has focused on the absorption of pure methane, although natural gas contains a small amount of larger hydrocarbons such as ethane and propane, which have greater absorption than methane. This Manuscript presents an overview of the current state of the metal-organic framework for methane storage

DeepDecipher: Accessing and Investigating Neuron Activation in Large Language Models

As large language models (LLMs) become more capable, there is an urgent need for interpretable and transparent tools. Current methods are difficult to implement, and accessible tools to analyze model internals are lacking. To bridge this gap, we present DeepDecipher - an API and interface for probing neurons in transformer models' MLP layers. DeepDecipher makes the outputs of advanced interpretability techniques for LLMs readily available. The easy-to-use interface also makes inspecting these complex models more intuitive. This paper outlines DeepDecipher's design and capabilities. We demonstrate how to analyze neurons, compare models, and gain insights into model behavior. For example, we contrast DeepDecipher's functionality with similar tools like Neuroscope and OpenAI's Neuron Explainer. DeepDecipher enables efficient, scalable analysis of LLMs. By granting access to state-of-the-art interpretability methods, DeepDecipher makes LLMs more transparent, trustworthy, and safe. Researchers, engineers, and developers can quickly diagnose issues, audit systems, and advance the field.Comment: 5 pages (9 total), 1 figure, submitted to NeurIPS 2023 Workshop XAI

Towards interpretable sequence continuation: analyzing shared circuits in large language models

While transformer models exhibit strong capabilities on linguistic tasks, their complex architectures make them difficult to interpret. Recent work has aimed to reverse engineer transformer models into human-readable representations called circuits that implement algorithmic functions. We extend this research by analyzing and comparing circuits for similar sequence continuation tasks, which include increasing sequences of Arabic numerals, number words, and months. By applying circuit interpretability analysis, we identify a key sub-circuit in both GPT-2 Small and Llama-2-7B responsible for detecting sequence members and for predicting the next member in a sequence. Our analysis reveals that semantically related sequences rely on shared circuit subgraphs with analogous roles. Additionally, we show that this sub-circuit has effects on various math-related prompts, such as on intervaled circuits, Spanish number word and months continuation, and natural language word problems. This mechanistic understanding of transformers is a critical step towards building more robust, aligned, and interpretable language models

Interpreting Context Look-ups in Transformers: Investigating Attention-MLP Interactions

In this paper, we investigate the interplay between attention heads and specialized "next-token" neurons in the Multilayer Perceptron that predict specific tokens. By prompting an LLM like GPT-4 to explain these model internals, we can elucidate attention mechanisms that activate certain next-token neurons. Our analysis identifies attention heads that recognize contexts relevant to predicting a particular token, activating the associated neuron through the residual connection. We focus specifically on heads in earlier layers consistently activating the same next-token neuron across similar prompts. Exploring these differential activation patterns reveals that heads that specialize for distinct linguistic contexts are tied to generating certain tokens. Overall, our method combines neural explanations and probing isolated components to illuminate how attention enables context-dependent, specialized processing in LLMs.Comment: 15 pages, 11 figure