Miracle-dz/UniFAR-FTUs huggingface dataset:
Decoding Music-Evoked Valence and Arousal: Unraveling the Neural Correlates of Naturalistic Music Characteristics through fMRI

Music can convey basic emotions, such as joy and sadness, and more complex ones, such as tenderness or nostalgia. Its effects on emotion regulation and reward have attracted much attention in cognitive and affective neuroscience. Understanding the underlying neural mechanisms of music-evoked emotions could guide the development of novel technological and individually-tuned neurorehabilitation music-based therapies. This study aims to unravel the relationship between the classification of music excerpts regarding perceived affective states and their associated neural correlates, as measured by fMRI. We used valence and arousal to classify both the stimuli and the affective states perceived by the participants. We acquired fMRI data from 20 participants while listening to 96 musical excerpts a priori classified into four quadrants, considering the valence-arousal model. We first characterized the neural correlates resulting from a GLM analysis of the quadrants defined by valence (positive, negative) and arousal (high, low). Our results highlight the role of neocortical regions, most noticeably the music-specific sub-regions of the auditory cortex and thalamus, as well as regions from the reward network such as the amygdala. Using multivoxel activity patterns corresponding to the four quadrants representation of core affect, we were able to create a computational model that decodes the quadrant corresponding to the music with significant accuracy, well above a stringent chance level. We further analyzed a set of musical features using regression analysis and explored how they relate to brain activity in valence-, arousal-, reward-, and auditory-related ROIs. The results emphasize the role of expressive features in emotion-related networks. These results contribute to the definition of a relation between music and the neural substrate of music listening and emotions, which is key to developing novel music-based neurorehabilitation strategies.
Secondary structure of the SARS-CoV-2 genome is predictive of nucleotide substitution frequency

Accurate estimation of the effects of mutations on SARS-CoV-2 viral fitness can inform public-health responses such as vaccine development and predicting the impact of a new variant; it can also illuminate biological mechanisms including those underlying the emergence of variants of concern1. Recently, Lan et al reported a high-quality model of SARS-CoV-2 secondary structure and its underlying dimethyl sulfate (DMS) reactivity data2. I investigated whether secondary structure can explain some variability in the frequency of observing different nucleotide substitutions across millions of patient sequences in the SARS-CoV-2 phylogenetic tree3. Nucleotide basepairing was compared to the estimated “mutational fitness” of substitutions, a measurement of the difference between a substitution’s observed and expected frequency that is correlated with other estimates of viral fitness4. This comparison revealed that secondary structure is often predictive of substitution frequency, with significant decreases in substitution frequencies at basepaired positions. Focusing on the mutational fitness of C→T, the most common type of substitution, I describe C→T substitutions at basepaired positions that characterize major SARS-CoV-2 variants; such mutations may have a greater impact on fitness than appreciated when considering substitution frequency alone.
Tissue-specific and endogenous protein labeling with split fluorescent proteins

The ability to label proteins by fusion with genetically encoded fluorescent proteins is a powerful tool for understanding dynamic biological processes. However, current approaches for expressing fluorescent protein fusions possess drawbacks, especially at the whole organism level. Expression by transgenesis risks potential overexpression artifacts while fluorescent protein insertion at endogenous loci is technically difficult and, more importantly, does not allow for tissue-specific study of broadly expressed proteins. To overcome these limitations, we have adopted the split fluorescent protein system mNeonGreen21-10/11 (split-mNG2) to achieve tissue-specific and endogenous protein labeling in zebrafish. In our approach, mNG21-10 is expressed under a tissue-specific promoter using standard transgenesis while mNG211 is inserted into protein-coding genes of interest using CRISPR/Cas-directed gene editing. Each mNG2 fragment on its own is not fluorescent, but when co-expressed the fragments self-assemble into a fluorescent complex. Here, we report successful use of split-mNG2 to achieve differential labeling of the cytoskeleton genes tubb4b and krt8 in various tissues. We also demonstrate that by anchoring the mNG21-10 component to specific cellular compartments, the split-mNG2 system can be used to manipulate protein function. Our approach should be broadly useful for a wide range of applications.
Emo-FilM: A multimodal dataset for affective neuroscience using naturalistic stimuli

The extensive Emo-FilM dataset stands for Emotion research using Films and fMRI in healthy participants. This dataset includes detailed emotion annotations by 44 raters for 14 short films with a combined duration of over 2½ hours, as well as recordings of respiration, heart rate, and functional magnetic resonance imaging (fMRI) from a different sample of 30 individuals watching the same films. The detailed annotations of experienced emotion evaluated 50 items including ratings of discrete emotions and emotion components from the domains of appraisal, motivation, motor expression, physiological response, and feeling. Quality assessment for the behavioural data shows a mean inter-rater agreement of 0.38. The parallel fMRI data was acquired at 3 Tesla in four sessions, accompanied with a high-resolution structural (T1) and resting state fMRI scans for each participant. Physiological recordings during fMRI included heart rate, respiration, and electrodermal activity (EDA). Quality assessment indicators confirm acceptable quality of the MRI data. This dataset is designed, but not limited, to studying the dynamic neural processes involved in emotion experience. A particular strength of this data is the high temporal resolution of behavioural annotations, as well as the inclusion of a validation study in the fMRI sample. This high-quality behavioural data in combination with continuous physiological and MRI measurements makes this dataset a treasure trove for researching human emotion in response to naturalistic stimulation in a multimodal framework.
Identification of teleost tnnc1a enhancers for specific pan-cardiac transcription

Troponin C regulates muscle contraction by forming the troponin complex with troponin I and troponin T. Different muscle types express different troponin C genes. The mechanisms of such differential transcription are not fully understood. The Zebrafish tnnc1a gene is restrictively expressed in cardiac muscles. We here identify the enhancers and promoters of the zebrafish and medaka tnnc1a genes, including intronic enhancers in zebrafish and medaka and an upstream enhancer in the medaka. The intronic and upstream enhancers are likely functionally redundant. The GFP transgenic reporter driven by these enhancers is expressed more strongly in the ventricle than in the atrium, recapitulating the expression pattern of the endogenous zebrafish tnnc1a gene. Our study identifies a new set of enhancers for cardiac-specific transgenic expression in zebrafish. These enhancers can serve as tools for future identification of transcription factor networks that drive cardiac-specific gene transcription.
A Decade of Progress: Insights of Open Data Practices in Biosciences at the University of Edinburgh

The evolution of research practices emphasizes the role of Open Data in fostering transparency and collaboration. This study evaluates the adoption of Open Data practices in the School of Biological Sciences at a research-intensive university in the United Kingdom. Our analysis of research data sharing from 2014 to 2022 manually reviewed 193 journal articles against criteria for Openness and FAIRness, including the Completeness of data shared relative to data generated. The findings reveal an uptick in data completeness and reusability, with a clear influence of data type, and genomic data being shared more frequently than image data. Data availability statements (DAS) and preprint sharing show a strong correlation with higher Openness and FAIRness scores. Since 2016, when the FAIR guidelines were published, data Reusability increased along with the inclusion of Data Availability Statements. On the other hand, since the COVID-19 pandemic, we have found a substantial increase in preprint sharing and significant improvements in Completeness, Reusability, and Accessibility scores. This paper documents a local research institute’s journey towards Open Data, addressing the changes and advocating for best practices to nurture this progression.
A Holistic UX Methodological Framework for Measuring the Aspects of How Dynamic, Adaptive and Intelligent a Software Solution is and Make Recommendations for Improvement

The aim of this paper is to present PhD research that proposes a methodological assessment framework based upon criteria / variables which have been grouped into three core aspects namely how Dynamic, Adaptive and Intelligent the user experience is. The framework aims to enhance the user experience of an application by analyzing these aspects and providing recommendations to a developer to produce a more enhanced Dynamic Adaptive Intelligent User Interface. Research into this field has identified that not all current applications are aware of, or capable of measuring all the aspects. The framework is based upon a three phased approach: phase one will measure the variables within each aspect and produce a score which will indicate to a developer the degree to which their current application fits within each aspect of the framework; phase two highlights the areas of growth within each aspect and provide recommendations that would enhance the application’s user experience; and phase three validates the framework by highlighting the application’s user experience progress from previous measurements. All three phases are combined to produce a robustly proven tool that aids the developer with validated advice in order to enhance their products
High Speed Rate Compatible Ldpc Codes For Long Distance Transmission with Fec

: In this paper, we propose a rate-compatible FEC scheme based on LDPC codes together with its soft-ware reconfigurable unified FPGA architecture. By FPGA emulation, we demonstrate that the proposed class of rate-compatible LDPC codes based on puncturing and general-ized LDPC coding with an overhead from 25% to 46% provides a coding gain ranging from 12.67 dB to 13.8 dB at a post-FEC BER of 10-15. As a result, the proposed rate-compatible codes represent one of the strong FEC candidates of soft-decision FEC for both short-haul and long-haul optical transmission systems.
Showcase @ CMU

In this notebook paper we describe the technical details of the submissions to TRECVID 2006 from CMU Informedia team. We participated in the high-level feature extraction and the search (automatic and interactive) tasks. Our emphasis is on various techniques used for the search task, where our interactive runs won the first place in the interactive track and our automatic runs are also among the top performers in the automatic track
E-Government in Singapore — A Swot and Pest Analysis

This paper aims to review and evaluate the vision, the objectives and the strategic framework of e-Government in Singapore. Rapidity, Reliability, Efficiency, Cost-effectiveness, Customer-orientation and Accessibility are the main guidelines for the development of egovernment in Singapore in order to provide quality services to users in the digital economy. There are five thrusts and six programs of egovernance in Singapore. The development of e-Government involves three main relationships: Government to Citizen (G2C), Government to Business (G2B) and Government to Employees/Public Servants (G2E). This paper employs the SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis and PEST (Political, Economic, Social and Technological) determinants to evaluate the current state of e-Government in Singapore and its preparedness.
Hybrid-SD (HSD) : A new hybrid evaluation metric for automatic speech recognition tasks

Many studies have examined the shortcomings of word error rate (WER) as an evaluation metric for automatic speech recognition (ASR) systems, particularly when used for spoken language understanding tasks such as intent recognition and dialogue systems. In this paper, we propose Hybrid-SD (H SD ), a new hybrid evaluation metric for ASR systems that takes into account both semantic correctness and error rate. To generate sentence dissimilarity scores (SD), we built a fast and lightweight SNanoBERT model using distillation tech-niques. Our experiments show that the SNanoBERT model is 25.9x smaller and 38.8x faster than SRoBERTa while achiev-ing comparable results on well-known benchmarks. Hence, making it suitable for deploying with ASR models on edge devices. We also show that H SD correlates more strongly with downstream tasks such as intent recognition and named-entity recognition (NER).
UNIVERSITY OF CAGLIARI

Various elements associated with communication theories, emotions, and behaviors including semantic and pragmatic knowledge constitute psychotherapeutic conversations. Among the key aspects that need to explore this knowledge are the different topics driving the conversation. In this work, we propose a framework to detect and track topics in real-life psychotherapeutic conversations based on Partially Labeled Dirichlet Allocation. Topics detection summarizes the semantic themes in therapeutic conversations and predicts a speciﬁc topic for each talk-turn, converting, a sequence of talk to a distribution of ongoing topics. Topics tracking has the aims to explore the dynamics of how these topics propagate throughout the conversation and to offers insights into the underlying conversation logic and strategy. We present an alternative way to look at face-to-face conversations in conjunction with a new approach that combines topic models and transitions matrices to elicit valuable knowledge.
Food Image Classification with Convolutional Neural Networks

Food images dominate across social media platforms and drive restaurant selection and travel, but are still fairly unorganized due to the sheer volume of images. Utilized correctly, food image classification can improve food experiences across the board, such as to recommend dishes and new eateries, improve cuisine lookup, and help people make the right food choices for their diets. In this paper, we explore the problem of food image classification through training convolutional neural networks, both from scratch and with pre-trained weights learned on a larger image dataset (transfer learning), achieving an accuracy of 61.4% and top-5 accuracy of 85.2%.
Motion Primitive-Based Graph Planning for Mobile Manipulation with Closed-Chain Systems GRASP Laboratory Technical Report MS-CIS-1206

Motion primitive-based (lattice-based) graphs have been used extensively in navigation, but application to high-dimensional state-spaces has remained limited by computational complexity. In this work, we show how these graphs can be applied to mobile manipulation. The formation of closed chains in tasks that involve contacts with the environment may reduce the number of available degrees of freedom but add complexity in terms of constraints in the high-dimensional state space. We propose a novel method to reduce dimensionality by abstracting away the constraints associated with closed-chain systems. Proofs are introduced for the application to graph-search and its theoretical guarantees of optimality. The dimensionality-reduction is done in a manner that enables finding optimal solutions to low-dimensional problems which map to correspondingly optimal full-dimensional solutions. We demonstrate the usefulness of our method with simulation results; we apply our approach to moving an object in 2D using a mobile manipulation platform with a planar arm. Disciplines Engineering Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-12-06. This technical report is available at ScholarlyCommons: http://repository.upenn. edu/cis_reports/968
A GVoS : A General System for Near-Duplicate Video Related Applications on Storm

The exponential increase of online videos greatly enriches the life of users but also brings huge numbers of near-duplicate videos (NDVs) that seriously challenge the video websites. The video websites entail NDV-related applications such as detection of copyright violation, video monitoring, video re-ranking, and video recommendation. Since these applications adopt different features and different processing procedures due to diverse scenarios, constructing separate and specialpurpose systems for them incurs considerable costs on design, implementation, and maintenance. In this paper, we propose GVoS — a general NDV system built on Storm, a popular distributed real-time stream processing platform, to simultaneously support a wide variety of video applications. The generality of GVoS is achieved in two aspects. First, we extract the reusable components from various applications. Second, we conduct the communication between components via a mechanism called Stream Shared Message (SSM) that contains the video-related data. Furthermore, we present an algorithm to reduce the size of SSM in order to avoid the data explosion and decrease the network latency. The experimental results demonstrate that GVoS can achieve performance almost the same as the customized systems. Meanwhile, GVoS accomplishes remarkably higher systematic versatility and efficiently facilitates the development of various NDV-related applications. CCS Concepts: rInformation systems→Multimedia and multimodal retrieval; Distributed retrieval; Multimedia information systems; Near-duplicate and plagiarism detection;
Managing the Energy-Saving of College Students: Based on an Analysis of Comprehensive Information of the Energy-Saving Behaviors

Gaining a comprehensive understanding of the energy-saving behaviors (ESB) of college students, who represent the backbone of the future society, is imperative for advancing energy conservation initiatives. This paper employs a PLS-SEM model with 2786 valid responses to investigate the determinants of college students' ESB within the comprehensive action determination model (CADM). The results show that (1) the applicability of CADM, including the theory of planned behavior, norm activation model, and ipsative theory of behavior, was confirmed in the context of college students' ESB. (2) The cultivation of energy-saving habits significantly contributes to the promotion of ESB. (3) The availability of energy-saving access has a positive impact on ESB. (4) Media's energy-saving promotional activities positively influence the energy-saving intention. (5) Energy-saving policy incentives exert a positive moderating influence on the transition from energy-saving intentions to ESB. Based on these empirical findings, this paper provides specific and target policy recommendations.
CFI2P: Coarse-to-Fine Cross-Modal Correspondence Learning for Image-to-Point Cloud Registration

In the context of image-to-point cloud registration, acquiring point-to-pixel correspondences presents a challenging task since the similarity between individual points and pixels is ambiguous due to the visual differences in data modalities. Nevertheless, the same object present in the two data formats can be readily identified from the local perspective of point sets and pixel patches. Motivated by this intuition, we propose a coarse-to-fine framework that emphasizes the establishment of correspondences between local point sets and pixel patches, followed by the refinement of results at both the point and pixel levels. On a coarse scale, we mimic the classic Visual Transformer to translate both image and point cloud into two sequences of local representations, namely point and pixel proxies, and employ attention to capture global and cross-modal contexts. To supervise the coarse matching, we propose a novel projected point proportion loss, which guides to match point sets with pixel patches where more points can be projected into. On a finer scale, point-to-pixel correspondences are then refined from a smaller search space (i.e., the coarsely matched sets and patches) via well-designed sampling, attentional learning and fine matching, where sampling masks are embedded in the last two steps to mitigate the negative effect of sampling. With the high-quality correspondences, the registration problem is then resolved by EPnP algorithm within RANSAC. Experimental results on largescale outdoor benchmarks demonstrate our superiority over existing methods.
Citizen-Centric E-Government

Electronic government or e-government in this chapter can be defined as the delivery of government information and services to citizens through the Internet 24 hours a day, seven days per week. This definition has been used in other empirical studies of e-government adoption (Moon and Norris, 2005a). This chapter adds to this definition Grant and Chau’s (2005) interpretation of e-government as a broad-based transformative initiative, which is consistent with creating more citizen-centric government. Gronlund (2005) reviews the various definitions of e-government and has found they share a common theme of the need for organizational transformation through technological implementation. Citizen-centric e-government is the delivery of government services continuously to citizens, businesses, and other government agencies through the Internet (Seifert and Relyea, 2004). Citizen-centric government through e-government acts as a transformational tool that provides a new government model based on being citizen focused (Schelin, 2003). Some scholars have argued that for e-government to fully realize its capabilities, it must transform government from agency-centric to citizen-centric (Seifert and Relyea, 2004). The term e-government emerged in the late 1990s. It was born out of the Internet boom. The literature on information technology (IT) use within government is different from e-government because it more often focuses on external use, such as services to citizens’ and organizational change (Gronlund and Horan, 2004). Definitions of e-government that focus exclusively on service delivery components fail to capture the more complex aspects of government transformation because of IT (Grant and Chau, 2005). The purpose of this chapter is to provide an overview of the e-government literature with a focus on how it influences citizens. In order to understand some of the issues associated with Homeland Security