Miracle-dz/UniFAR-FTUs huggingface dataset:
Identiﬁcation of Geriatric Depression and Anxiety Using Activity Tracking Data and Minimal Geriatric Assessment Scales

: The identiﬁcation of geriatric depression and anxiety is important because such conditions are the most common comorbid mood problems that occur in older adults. The goal of this study was to build a machine learning framework that identiﬁes geriatric mood disorders of depression and anxiety using low-cost activity trackers and minimal geriatric assessment scales. We collected activity tracking data from 352 mild cognitive impairment patients, from 60 to 90 in age, by having them wear activity trackers on their wrist for more than a month. We then extracted the features of 24-h activity rhythms and sleep patterns from the time-series activity tracking data. To increase the accuracy, we designed a novel method to incorporate additional features from questionnaire-based assessments of the geriatric depression scale and geriatric anxiety inventory into the activity tracking features. In the multi-label classiﬁcation, we applied the binary relevance method to develop two single-label classiﬁers for depression and anxiety. The best hyper-parameters of classiﬁcation algorithms for each label were selected by comparing the classiﬁcation performance. We ﬁnally selected the combination of classiﬁers for depression and anxiety with the lowest Hamming loss as a multi-label classiﬁer. This study successfully demonstrated the possibility of identifying geriatric depression and anxiety using low-cost activity trackers and minimal geriatric assessment scales for use in the real ﬁelds.
Computers & Industrial Engineering

Consolidation problems in cloud computing (CC) encompass server consolidation, virtual machine (VM) consolidation, and task consolidation. These problems have become increasingly challenging for resource allocation in distributed systems. Group technology (GT) has been e ﬀ ectively used to manage resource allocation problems by reducing manufacturing costs and increasing system productivity. We propose a discrete cuckoo optimization algorithm (DCOA) based on GT for consolidation in CC. The proposed model is designed to control manufacturing costs (i.e., energy, penalty, VM creating, and task migration). The DCOA developed in this study contains several new adjustments that allow it to solve large-sized discrete problems, including a grouping strategy based on the Jaccard similarity coe ﬃ cient, as well as modi ﬁ ed egg laying and immigration processes. A numerical example is used to demonstrate the applicability of the proposed model and exhibit the e ﬃ cacy of the DCOA. The results illustrate the quality superiority of the DCOA over the ﬁ rst ﬁ t (FF) and round robin (RR) algorithms, and the e ﬃ ciency and e ﬀ ectiveness superiority of the DCOA over the genetic algorithm (GA).
a Computational

The purpose of this work is to investigate the process of grammatical acquisition from data. We are using a computational learning sys-tem that is composed of a Universal Grammar with associated parameters, and a learning al-gorithm, following the Principles and Parameters Theory. The Universal Grammar is implemented as a Uni(cid:12)cation-Based Generalised Cat-egorial Grammar, embedded in a default inheritance network of lexical types. The learning al-gorithm receives input from a corpus annotated with logical forms and sets the parameters based on this input. This framework is used as basis to investigate several aspects of language acquisition. In this paper we are concentrating on the acquisition of word order for di(cid:11)erent learners. The results obtained show the di(cid:11)erent learners having a similar performance and converging towards the target grammar given the input data available, regardless of their starting points. It also shows how the amount of noise present in the input data a(cid:11)ects the speed of convergence of the learners towards the target.
OpenEQA: Embodied Question Answering in the Era of Foundation Models

We present a modern formulation of Embodied Question Answering (EQA) as the task of understanding an environment well enough to answer questions about it in natural language. An agent can achieve such an understanding by either drawing upon episodic memory, exemplified by agents on smart glasses, or by actively exploring the environment, as in the case of mobile robots. We accompany our formulation with OpenEQA – the first open-vocabulary benchmark dataset for EQA supporting both episodic memory and active exploration use cases. OpenEQA contains over 1600 high-quality human generated questions drawn from over 180 real-world environments. In addition to the dataset, we also provide an automatic LLM-powered evaluation protocol that has excellent correlation with human judgement. Using this dataset and evaluation protocol, we evaluate several state-of-the-art foundation models including GPT-4V, and find that they significantly lag behind human-level performance. Consequently, OpenEQA stands out as a straightforward, measurable, and practically relevant benchmark that poses a considerable challenge to current generation of foundation models. We hope this inspires and stimulates future research at the intersection of Embodied AI, conversational agents, and world models.
Ready or Not, AI Comes: An Interview Study of Organizational AI Readiness Factors

Artificial intelligence (AI) offers organizations much potential. Considering the manifold application areas, AI’s inherent complexity, and new organizational necess ities, companies encounter pitfalls when adopting AI. An informed decision regarding an organization’s readiness increases the probability of successful AI adoption and is important to successfully leverage AI’s business value . Thus, companies need to assess whether their assets, capabilities, and commitment are ready for the individual AI adoption purpose. Research on AI readiness and AI adoption is still in its infancy. Consequently, researchers and practitioners lack guidance on the adoption of AI. This paper presents five categories of AI readiness factors and their illustrative actionable indicators. The AI readiness factors are deduced from an in-depth interview study with 25 AI experts and triangulated with both scientific and practitioner literature. Thus, this paper provides a sound set of organizational AI readiness factors, derives corresponding indicators for AI readiness assessments, and discusses the general implications for AI adoption. This is a first step toward conceptualizing relevant organizational AI readiness factors and guiding purposeful decisions in the entire AI adoption process for both research and practice.
OEIS Cross-Reference Network Visualization and PageRank Analysis Interactive Visualization, Spring 2020

We set out to visualize the On-Line Encyclopedia of Integer Sequences as a network using the cross-references between sequence pages. The visualization extends previous visualization work by computing the PageRank of each sequence as a measure of “im-portance”. As part of this paper, we discuss the results of our PageRank analysis as well as feedback received on the visualization. We hope that this work may serve as a starting point for an interesting way to navigate the OEIS, or that it may be integrated into existing work.
HN Dundee Discussion Papers in Economics Option Value , Policy Uncertainty and the Foreign Direct Investment Decision

In this paper we analyse the impact of policy uncertainty on foreign direct investment strategies. The paper follows the real options approach, which allows to investigate the value to a firm of waiting to invest and/or disinvest, when payoffs are stochastic due to political uncertainty and investments are partially reversible. Across the board we find that political uncertainty can be very detrimental to FDI decisions.
Time’s up for the Myo? The smartwatch as a ubiquitous alternative for audio-gestural analyses.

The utility of gestural technologies in broadening analytical-and expressive-interface possibilities has been documented extensively; both within the sphere of NIME and beyond. Wearable gestural sensors have proved integral components of many past NIMEs. Previous implementations have typically made use of specialist, IMU and EMG based gestural technologies. Few have proved, singularly, as popular as the Myo armband. An informal review of the NIME archives found that the Myo has featured in 21 NIME publications, since an initial declaration of the Myo’s promise as “a new standard controller in the NIME community”by Ny-omen et al. in 2015 [10]. Ten of those found were published after the Myo’s discontinuation in 2018, including three as recently as 2022 [7, 12, 15]. This paper details an assessment of smartwatch-based IMU and audio logging as a ubiquitous, accessible alternative to the IMU capabilities of the Myo armband. Six violinists were recorded performing a number of exercises using VioLogger; a purpose-built application for the Apple Watch. Participants were simultaneously recorded us-ing a Myo armband and a freestanding microphone. Initial testing upon this pilot dataset indicated promising results for the purposes of audio-gestural analysis; both implementations demonstrated similar efficacy for the purposes of MLP-based bow-stroke classification.
The effect of seasonal variation on the antibacterial activity of Tithonia diversifolia (Hemsl.) A. Gray leaf extracts against pathogenic bacterial strains

The study investigates the antibacterial activity of Tithonia diversifolia leaves, including methanol, ethanol and water extracts, against Pseudomonas aeruginosa, Klebsiella pneumoniae, Serratia marcescens, Proteus vulgaris, Micrococcus luteus, Staphylococcus aureus, Bacillus subtilis, Clostridium sporogenes using the agar well diffusion technique. Minimum inhibitory concentrations (MIC) and minimum bactericidal concentrations (MBC) were performed on bacteria by a modi�cation of the standard agar dilution method. Qualitative phytochemical analysis of leaf extract was determined by standard analytic methods. The study found that Klebsiella pneumoniae and Bacillus subtilis showed less susceptibility to water extracts, while Pseudomonas aeruginosa and Proteus vulgaris were sensitive to all water extract samples with zone of inhibition of 20.5 mm and 20.0 mm, respectively. However, all methanol extracts were susceptible to Pseudomonas aeruginosa, Proteus vulgaris, and Staphylococcus aureus with zones of inhibition of 18.0, 14.0, and 15.0 mm, respectively. Serratia marcescens showed less susceptibility to most methanol extracts, while Clostridium sporogenes showed susceptibility to all ethanol extracts. MICs and MBC values against bacterial strains ranged from 12 - 50 mgmL -1 and 25 - 100 mgmL -1 respectively. The study concluded that the leaves of Tithonia diversifolia contain phytochemicals with broad-spectrum antimicrobial effects, with ethanol extracts showing high sensitivity. The concentration of these phytochemicals varies by season, with higher levels observed during rainy seasons. The study highlights the potential of Tithonia diversifolia leaves for antimicrobial properties.
An ultra-fast method for scanning tunneling spectroscopy

Scanning tunneling microscope combines unique capabilities in imaging and spectroscopy with atomic precision and it can obtain energy-resolved spectroscopic data with atomic resolution. In this paper we utilize a recently proposed modiﬁcation to the STM feedback control loop to acquire high quality d 2 I / dV 2 images. We have developed a constant differential conductance imaging method by closing the STM feedback loop with a high precision dI / dV measurement. In this mode, the tip’s vertical position is adjusted so as to keep the differential conductance constant during raster scanning of the surface. Based on this imaging mode, we propose a new technique to acquire fast and reliable scanning tunneling spectroscopy data simultaneously with the imaging.
Using Artiﬁcial Intelligence for Space Challenges: A Survey

: Artiﬁcial intelligence is applied to many ﬁelds and contributes to many important applications and research areas, such as intelligent data processing, natural language processing, autonomous vehicles, and robots. The adoption of artiﬁcial intelligence in several ﬁelds has been the subject of many research papers. Still, recently, the space sector is a ﬁeld where artiﬁcial intelligence is receiving signiﬁcant attention. This paper aims to survey the most relevant problems in the ﬁeld of space applications solved by artiﬁcial intelligence techniques. We focus on applications related to mission design, space exploration, and Earth observation, and we provide a taxonomy of the current challenges. Moreover, we present and discuss current solutions proposed for each challenge to allow researchers to identify and compare the state of the art in this context.
Attenuation of electromagnetic energy in soil from Yuma Proving Ground

Attenuation rates of the amplitude of electromagnetic waves were measured for desert soils collected from four locations at Yuma Proving Ground, the primary desert test facility of the U.S. Army. Frequencies from 250 MHz to 4 GHz were selected to support military interest in detection of buried targets using ground-penetrating radar. Also measured were moisture content, percentages of different grain size fractions, salinity, and mineralogy, because these parameters have a high potential to affect attenuation. For example, one-way attenuation rates at 1 GHz ranged from 20 to 60 dB/m at different soil sampling locations for a given soil moisture content. For a given location, the attenuation rate is higher for soils with higher moisture content. For soils with similar moisture contents, variability in attenuation is attributed to the varying amount of soluble salt present at the source locations. DISCLAIMER: The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents. DESTROY THIS REPORT WHEN NO LONGER NEEDED. DO NOT RETURN IT TO THE ORIGINATOR.
Cross-jurisdictional disaster preparedness: A Nigeria-USA data-analytical approach

Disasters pose significant challenges globally, demanding effective preparedness strategies to mitigate their impact. This study proposes a novel approach to disaster preparedness through cross-jurisdictional collaboration between Nigeria and the United States, leveraging data analytics for enhanced readiness. Nigeria, with its vulnerability to various disasters, and the United States, known for its advanced disaster management systems, offer a unique opportunity for mutual learning and collaboration. This research aims to bridge the gap in disaster preparedness between these two countries by employing a data-driven analytical framework. Through the collection, analysis, and comparison of disaster-related data from both nations, this approach seeks to identify commonalities, differences, and best practices in disaster preparedness strategies. By leveraging the strengths of both countries, such as Nigeria's community resilience and the USA's technological advancements, this approach aims to develop comprehensive disaster preparedness models tailored to the specific needs and contexts of each jurisdiction. Key components of this approach include data collection from various sources, including historical disaster records, socio-economic indicators, infrastructure data, and community resilience metrics. Advanced analytical techniques, such as machine learning algorithms and spatial analysis, will be employed to identify patterns, assess vulnerabilities, and predict potential disaster scenarios. The findings will inform the development of targeted preparedness strategies, including early warning systems, evacuation plans, resource allocation, and capacity building initiatives. Furthermore, this research emphasizes the importance of multi-stakeholder collaboration and knowledge exchange in disaster preparedness. By involving government agencies, non-governmental organizations, academic institutions, and local communities from both Nigeria and the USA, this approach aims to foster a holistic and inclusive preparedness framework. Ultimately, this study seeks to contribute to the global discourse on disaster risk reduction by demonstrating the effectiveness of cross-jurisdictional collaboration and data-driven approaches in enhancing disaster preparedness. By leveraging the strengths and experiences of Nigeria and the USA, this research aims to develop scalable and adaptable models that can be applied in diverse contexts to build resilience and mitigate the impact of disasters.
Stability of Quasiperiodic Systems

In this paper, we present some observations on the stability of a special class of quasiperiodic systems. In quasiperiodic system, the periodicity of parametric excitation is incommensurate with the periodicity of the certain terms multiplying state vector. We present a Lyapunov type approach and the Lyapunov-Floquet (LF) transformation to derive stability conditions. This approach can be utilized to investigate the robustness, stability margin and design controller for the system
In Vitro Models for Anti-Aging Efﬁcacy Assessment: A Critical Update in Dermocosmetic Research

: The skin is the human body’s largest organ and serves as a crucial boundary between the body and the external environment. As a natural process, skin aging cannot be avoided, and it causes changes in the skin’s strength, structure, elasticity, and integrity. Many approaches have been developed over the years to study the skin, including in vivo and in vitro methods. Nevertheless, animal assays have ethical issues and a lack of reproducibility. Hence, in vitro skin models have been increasingly developed and used. For the assessment of the potential of the anti-aging activity of compounds of different origins, the most commonly used in vitro assays are the ones evaluating antioxidant, anti-collagenase, anti-elastase, anti-hyaluronidase, anti-tyrosinase, anti-inﬂammatory, antiglycation, or moisturizing activity, and the induction of skin cell proliferation/anti-senescence effects or the inhibition of matrix metalloproteinase production. The purpose of this review is to summarize the most commonly used in vitro models for the evaluation of skin aging and cometic products’ anti-aging efﬁcacy, providing a useful guide for researchers in the ﬁeld. Overall, these assays provide important data on the safety and efﬁcacy of anti-aging compounds, and a foundation for research on and the eventual introduction of formulations into the cosmetics market.
Exploring Antifragility in Urban Road Networks: Anticipating Disturbances with Reinforcement Learning

Transport networks in the real world are often susceptible to disturbances, while the established control methods based on control theory focus rather on optimizing the exact timing to implement control, they lack the adaptiveness to cope with disturbances well. Hence, transport systems call for solutions that are more antifragile. Antifragility is a property of systems in which they can increase in performance to thrive as a result of stressors, shocks and volatility. In this work, to exploit and strengthen the learning capability of Reinforcement Learning (RL) and achieve antifragility, we propose a deep RL algorithm to regulate perimeter control in a two-region urban network. Based on the principle of first and second order derivatives, we investigate the impact of various additional state and reward terms on the algorithm to learn from disturbances. In comparison with other methods, the proposed RL algorithm has proven its antifragility and effectiveness.
Variance decomposition of firm productivity: evidence from the Peruvian economic context

Academics have performed several studies on variance decomposition of several types of performance in context of developed economies. Nevertheless, empirical research of variance decomposition in emerging economies is more limited than that of developed economies. This study analyzes the variance decomposition of Peruvian enterprises in the period between 2010 and 2014, employing multidimensional performance measures, and thus two objectives are present. Firstly; determine the financial performance variance composition and compare this with developed economies. Secondly; determine the variance composition of productivity and compare it with the variance structure of financial performance. This empirical research is based on an original longitudinal dataset which contains financial, production and labour market information at firm level and it uses the Hierarchical Lineal Model (HLM) which provides conceptual and robust statistics for analyzing the nested structure of the data. Regarding the first objective, the empirical results demonstrate that the industry effect account for 34% and the firm effect account for 66% of the variance explained. This differs with previous studies such as Makino et al. (2004) who presented an industry effect of 18% of the variance explained in a developed economy. Regarding the second objective, three measures of performance are used to capture the multidimensional nature of performance. The dimensions are financial (measured as ROA and ROS) and operational (measured as productivity). The findings show that operational productivity model explain 65% of their variability; whereas the ROA and ROS explain 55% and 61% respectively. This study also confirms that the variance structure impacts the performance of each aggregate sector separately. For manufacturing, the result show that the percentage of industry effect is stronger than that of other aggregate sectors. For service, the industry effect (when it uses profitability measures) show similar values with some studies in the Latin American context. Finally; for wholesale & retail trade, the results confirm that the industry effect is lower than the other aggregate sector, and shows values around 4%, and therefore, firm effect ranges between 40% and 70% of the total variance.