Miracle-dz/UniFAR-FTUs huggingface dataset:
Why Does Surprisal From Smaller GPT-2 Models Provide Better Fit to Human Reading Times?

This work presents an in-depth analysis of 001 an observation that contradicts the findings of 002 recent work in computational psycholinguis-003 tics, namely that smaller GPT-2 models that 004 show higher test perplexity nonetheless gen-005 erate surprisal estimates that are more predic-006 tive of human reading times. Analysis of the 007 surprisal values shows that rare proper nouns, 008 which are typically tokenized into multiple sub-009 word tokens, are systematically assigned lower 010 surprisal values by the larger GPT-2 models. 011 A comparison of residual errors from regres-012 sion models fit to reading times reveals that re-013 gression models with surprisal predictors from 014 smaller GPT-2 models have significantly lower 015 mean absolute errors on words that are tok-016 enized into multiple tokens, while this trend 017 is not observed on words that are kept intact. 018 These results indicate that the ability of larger 019 GPT-2 models to predict internal pieces of rare 020 words more accurately makes their surprisal 021 estimates deviate from humanlike expectations 022 that manifest in self-paced reading times and 023 eye-gaze durations. 024
High-field magnetotransmission investigation of natural graphite

Magneto-transmission measurements in magnetic fields in the range B=20-60T have been performed to probe the H and K-point Landau level transitions in natural graphite. At the H-point, two series of transitions, whose energy evolves as $\sqrt{B}$ are observed. A reduced Slonczewski, Weiss and McClure (SWM) model with only two parameters to describe the intra-layer (gamma0) and inter-layer (gamma1) coupling correctly describes all observed transitions. Polarization resolved measurements confirm that the observed apparent splitting of the H-point transitions at high magnetic field cannot be attributed to an asymmetry of the Dirac cone.
Dermal Delivery of Diclofenac Sodium—In Vitro and In Vivo Studies

Previously, we reported the use of confocal Raman spectroscopy (CRS) as a novel non-invasive approach to determine drug disposition in the skin in vivo. Results obtained by CRS were found to correlate with data from the well-established in vitro permeation test (IVPT) model using human epidermis. However, these studies used simple vehicles comprising single solvents and binary or ternary solvent mixtures; to date, the utility of CRS for monitoring dermal absorption following application of complex marketed formulations has not been examined. In the present work, skin delivery of diclofenac sodium (DFNa) from two topical dermatological drug products, namely Diclac® Lipogel 10 mg/g and Primofenac® Emulsion gel 1%, was determined by IVPT and in vivo by both CRS and tape stripping (TS) methodologies under similar experimental conditions. The in vivo data were evaluated against the in vitro findings, and a direct comparison between CRS and TS was performed. Results from all methodologies showed that Diclac promoted significantly greater DFNa delivery to the skin (p < 0.05). The cumulative amounts of DFNa which permeated at 24 h in vitro for Diclac (86.5 ± 9.4 µg/cm2) were 3.6-fold greater than the corresponding amounts found for Primofenac (24.4 ± 2.7 µg/cm2). Additionally, total skin uptake of DFNa in vivo, estimated by the area under the depth profiles curves (AUC), or the signal intensity of the drug detected in the upper stratum corneum (SC) (4 µm) ranged from 3.5 to 3.6-fold greater for Diclac than for Primofenac. The shape of the distribution profiles and the depth of DFNa penetration to the SC estimated by CRS and TS were similar for the two methods. However, TS data indicated a 4.7-fold greater efficacy of Diclac relative to Primofenac, with corresponding total amounts of drug penetrated, 94.1 ± 22.6 µg and 20.2 ± 7.0 µg. The findings demonstrate that CRS is a methodology that is capable of distinguishing skin delivery of DFNa from different formulations. The results support the use of this approach for non-invasive evaluation of topical products in vivo. Future studies will examine additional formulations with more complex compositions and will use a wider range of drugs with different physicochemical properties. The non-invasive nature of CRS coupled with the ability to monitor drug permeation in real time offer significant advantages for testing and development of topical dermatological products.
Effect of Solar Radiation on Freshwater Fish Puntius bimaculatus (Bleeker) (Cypriniformes: Cyprinidae) International Journal of Zoological Investigations

: Environmental and physical stressors such as sunlight, atmospheric temperature, photoperiod, and UV radiation have a significant impact on all living organisms in the aquatic ecosystem, particularly on rocky shores, shallow water bodies, lakes, and ponds. In this study, the freshwater fish Punitus bimaculatus (Bleeker) (Cypriniformes: Cyprinidae) was exposed to solar radiation for 1h to 2h duration during the summer season from March to May (water temperature 40 ºC to 42 ºC). The stress caused by increased water temperature (40 o C to 42 o C) significantly reduced the physiological activities and alter the metabolism of the fish, particularly carbohydrate, protein, and lipid levels in the experimental fish, and also damaged the muscle tissue. The muscle carbohydrate, protein and lipid content of Punitus bimaculatus reduced when exposed at 40 ºC and 42 ºC . This could be due to the stress-induced on the fish to utilize the stored energy in the body. Punitus bimaculatus muscle tissue showed marked degeneration of the epithelium, disappearance of striations, and thickened septal wall. The continual degradation of primary metabolites is vital for tissue maintenance but is also important in allowing animals to adapt to changing environmental conditions, replace denatured or damaged proteins, mobilize amino acids, and allow metabolic regulation.
Looking for structure in the cobordism conjecture conjecture and global symmetry: review

The cobordism conjecture of the swampland program states that the bordism group of quantum gravity must be trivial. We investigate this statement in several directions, on both the mathematical and physical side. We consider the Whitehead tower construction as a possible organising principle for the topological structures entering the formulation of the conjecture. We discuss why and how to include geometric structures in bordism groups, such as higher U(1)-bundles with connection. The inclusion of magnetic defects is also addressed in some detail. We further elaborate on how the conjecture could predict Kaluza–Klein monopoles, and we study the gravity decoupling limit in the cobordism conjecture, with a few observations on NSNS string backgrounds. We end with comments in relation to T-duality, as well as the ﬁniteness conjecture.
Joint Graph-Sequence Learning for Molecular Property Prediction

Molecular property prediction has achieved promising improvement for accelerating drug development with machine learning models. The emergence of graph neural networks especially benefits the discriminative representation learning of molecular graph data, which has become the key challenge of molecular property prediction. However, most of the existing works extract either graph features or sequence features of molecules, while the significant information from both graph and sequence representations is not well discovered and utilized. In this paper, we propose a Joint Graph-Sequence framework, JointGS, for learning high-quality molecular property representations by jointly capturing information from both graph and sequence molecular data. Specifically, JointGS contains a sequence encoder and a graph encoder working in parallel for learning sequence-level and graph-level molecular embeddings, respectively. Moreover, an attentional feature fusion mechanism is presented to better aggregate graph-sequence molecular embeddings. Extensive experiments demonstrate that the proposed JointGS can achieve superior molecular property prediction performance, especially in the SARS-CoV dataset for screening potential molecules benefiting the COVID-19 drug development.
A Study on Development of Evaluation Indicators for Diagnosis of Digital Transformation Capability: Focus on the Financial Industry

Convergence between industries based on digital technology and reorganization of the existing industrial order result in changes in business sources, business methods, and business expansion structure. In the financial sector, new customer-centered financial services such as strengthening customer experience and customized services are being provided, and the Big-Blur phenomenon is accelerating, where the boundaries between the high expectations of customers and the information and communication technology industry are fading. As a result, financial institutions are focusing on customer service differentiation using new technologies and are promoting Digital Transformation, which is various strategic changes in business models, and ways of working, to actively respond to changes in the digital environment. However, while the importance of digital transformation capabilities increases, there is a lack of research on specific evaluation indicators that can identify the current status or diagnose the level to promote them. Hence, in this study, the current status was identified by presenting a digital transformation competency diagnosis evaluation index suitable for the actual situation in Korea. In addition, for the purpose of setting goals, a Digital Transformation evaluation model was developed through the Delphi technique, literature survey, and previous research review, and a survey was conducted on executives and employees related to the financial industry to conduct statistical verification.
Analysis of Workloads for Cloud Services

Capturing best quality datasets for a study is the first evidence for better outcomes of research. If the analysis are based on such datasets, then the metrics, the characteristics and few factors determines proof point for well proven theories. Hence it is obvious that we rely on the best possible ways to arrive at such data acquiring sources. It can be either based on historical techniques or from the innovations in application of it to industry. This paper introduces a mapping framework for analyzing, and characterizing data previously used by research community and how they are made to fit for Cloud systems, i.e. using “workloads” and “datasets” as the “refined definitions”. It was contributed in the past two decades within the scientific community setting their own workflow analysis mechanisms. The framework thus is validated by acquiring a sample workload per layer of cloud. The sources are form the literature that are available from existing scientific theories. These workloads are then experimented against the three tiers of the cloud computing ie. , IaaS(Infrastructure as a Service), PaaS(Platform as a Service), & SaaS(Software as a Service). The selected data is analyzed by the authors for an offline model presented here based on the Machine Learning tool-kits. There are future studies planned for and to be experimented in a cloud auto scaled environment with online model as well.
Topic Model on Microblog with Dual-Streams Graph Convolution Networks

Topic detection in social media is a challenging task due to the large-scale short and informal nature of messages. Existing methods choose to integrate structure or contexts among posts to alleviate the data sparsity of short text, however they ignore the spread characteristics of topics. A topic can spread in depth-wise and width-wise way on social networks. Specifically, wide dispersion gives priority to the paths that will spread to the low-order neighborhood, while deep propagation may spread to the more distant users along the propagation chains. Therefore, we propose a Dual-streams Graph convolution networks based Topic Model (DGTM) for microblog conversations. It aims to learn the contexts with full spread mode feature to alleviate data sparsity of microblog posts. Firstly, a user-level social network with posts information is built by the user interactions. Then, we design a dual-streams graph convolution networks module to learn the context-richer node embeddings by modeling spread characteristics. Lastly, the variational auto-encoder (VAE) is used to fuse the features of two spread modes and generate the more coherent topics. The experiments on three real-world datasets validate the effectiveness of our proposed model.
Evaluation of Expressive Motions based on the Framework of Laban Effort Features for Social Attributes of Robots*

In today’s world, it is not uncommon to see robots adopted in various domains and environments. Robots take over several roles and tasks from manufacturing facilities to households and offices. It is crucial to measure people’s judgment of robots' social attributes since the findings can shape the future design for social robots. Using only a simple and mono-functional robotic vacuum cleaner, this paper investigates the impact of expressive motions on how people perceive the social attributes of the robot. The Laban Effort Features, a framework for movement analysis that emerged from dance, was modified to design expressive motions for a simple cleaning task. Participants were asked to rate the social attributes of the robot under several treatment conditions using a video-based online survey. The results indicated that velocity influenced people’s ratings of the robot’s warmth and competence, while path planning behavior influenced people’s ratings of the robot’s competence and discomfort. Limitations of this study include the kinematic constraints of the robot, potential issues with survey design, and technical constraints related to the open interface provided by the robot’s developer. The findings should be considered when incorporating expressive motions into domestic service robots operating in social settings.
A Performance Analysis of Machine Learning Models for Attack Prediction using Different Feature Selection Techniques

The cyber assault has emerged as a serious digital threat. Day after day, hackers inflict severe financial harm on nations and people alike. As a result, threat detection has become increasingly critical for computer system security going forward. This research focused on seeing if it can recognize and anticipate dangers based on complicated instances by using machine learning. This can help identify and prevent attacks before they happen. Many well-known threat classification methods are tested in this work. It is centered around six of the most prominent machine learning (ML) techniques used in intrusion detection research: Random Forest, Naive Bayes, Artificial Neural Networks, K-nearest neighbor algorithm, Support Vector Machines, and bagging. In this study, a machine learning-based strategy for reliably predicting dos, R2L, U2R, probe, and overall attacks was established by assessing the accuracy of several ML tactics. Finding effective machine learning algorithms, as well as determining critical qualities that can provide the best outcomes, is a massive task for academics. This paper also examines the benefits and drawbacks of each method before applying it to the NSL-KDD dataset to evaluate how well they perform. The novelty in this research work lies in the fact that it utilizes various feature selection methods to find the set of features that have the greatest impact on classification performance. Analysis of the results using different performance metrics revealed improved accuracy. For the NSL-KDD dataset’s intrusion detection and attack type classification tasks, the highest detection rate was obtained with 99.86%.
Joint Modeling of Document and Label with Clause Interaction Hypergraph for ICD Medical Code Assignment

Automatic medical code assignment for clinical records is the fundamental problem of medical statistical research and informatization. Due to the high dimension and sparse distribution of label space, it is necessary to make full use of the description information of the labels. However, most of the current work is based on similarity matching at the level of token or n-gram, and ignores information fusion with richer semantic structure and representation. In this paper, we propose a Clause Interaction HyperGraph (CIHG) to jointly model documents and label descriptions, which construct a richer semantic interaction at the level of clause. The CIHG models the high-order co-occurrence relationship between document and labels based on hypergraph, and uses the semantic structure of the document to constrain the encoding of labels. Experiments on widely used medical code assignment datasets show that our method successfully constrains the embedding of labels and significantly improves predictive precision11The code is available at https://github.com/CKRE/CIHG.
Enhancing Affective Robotics via Human Internal State Monitoring

During the last years, many solutions have been proposed to achieve a natural Human-Robot Interaction (HRI) and Communication paving the way to new paradigms of under-standing and adaptation based on mutual affective perception. Especially in human-robot social interaction, it is helpful not only that people can understand the robot’s behavioral state, but also robots possess the ability to detect, interpret and adaptively react to human affective responses. Typical approaches are able to assess humans’ affective responses from the observation of overt behavior. However, there are cases in which the overt observable behaviors could not match with the internal states (e.g., people with diseases compromising normal emotional responses). In such cases, having an objective measure of the users’ state from ‘inside’ is of paramount importance. This work presents an affect detection model able to provide a measure of the human affective state, with particular focus on the stress state, from the analysis of EEG users’ activity during the interaction with a social humanoid robot endowed with diverse affective elicitation behaviors. We argue that monitoring the stress state of a human during HRI is necessary to adapt the robot behavior in a way to avoid possible counterproductive effects of its use.
Open-Set Intrusion Detection with MinMax Autoencoder and Pseudo Extreme Value Machine

The constant emergence of previously unseen applications and network attacks on the ever-changing Internet poses a serious challenge to traditional intrusion detection solutions. This paper aims to develop an intrusion detection system capable of distinguishing between past seen malicious and benign behaviors and inferring unseen ones. We formulate the problem of seen/unseen intrusion detection as an openset detection problem for known malicious, known benign and unknown behaviors, where unknown ones include both unseen malicious and benign behaviors. Subsequently, we propose an open-set intrusion detection system, OpenIDS, which addresses the problem through three modules: the MinMax autoencoder, the classifier, and the pseudo extreme value machine. The MinMax autoencoder maximizes the difference between the known malicious traffic and benign traffic and places unknown traffic in between. The deep classifier takes the basic traffic features and the discriminative traffic features extracted by the MinMax autoencoder as input to achieve the known intrusion detection. The completely modeled pseudo extreme value machine gets used to calibrate the penultimate layer activation of the classifier and then infers the unknown traffic by thresholding the calibrated scores. Experiments performed on datasets USTC-TFC2016 & CSE-CIC-IDS2018+ show that the proposed scheme is better than previously baseline models and can be effectively applied to realistic network environments.
Parameterization of Vector Symbolic Approach for Sequence Encoding Based Visual Place Recognition

Sequence-based methods for visual place recognition (VPR) have great importance due to their ability of additional information capture through the sequences compared to single image comparison. Vector symbolic architecture (VSA) started to gain attention within these methods due to the unique capabilities for representing variable-length sequences using single high-dimensional vectors. But the effect of different sequence parameters for the visual place recognition task is yet to be explored. In this work, we explore the parametrization of sequence encoding with VSA in the SeqNet variant of sequence-based visual place recognition and introduce a new hierarchical VPR method, which utilizes the proposed parametrization. We show that with our parametrization the VSA realization of sequence-based visual place recognition achieves on par results to conventional algorithms, while featuring the capability of being implemented on novel neuromorphic hardware for efficient execution.
A hybrid paradigm based on steady-state visual evoked potentials and motor imagery

Hybrid brain-computer interfaces have the advantage of higher accuracy and more commands. This paper presents a hybrid brain-computer interface (BCI) paradigm based on steady-state visual evoked potentials (SSVEP) and motor imagery(MI). The subject started the first SSVEP task, and 2 seconds later the subject was asked to perform the second MI task and keep both tasks running simultaneously. The moment when the second task was joined was defined as the moment of switching. The performance of SSVEP was improved after the switch. Five healthy subjects participated in the experiment. The experimental results demonstrated the improvement of SSVEP classification accuracy, in addition to demonstrating that the performance of most of the subject MI was not affected in the paradigm.
Attributed Graph Clustering with Double Contrastive Projector

Attributed graph clustering is a fundamental yet challenging task for graph analysis, which is of significance in the real world. Recently, graph contrastive learning have demonstrated excellent performance on clustering task. However, we observe that existing methods have the following three defects: a) an end-to-end graph clustering framework based on contrastive clustering has not yet been implemented, b) they require a pretext task to obtain initial labels, c) and most methods lack a specific loss function for clustering processes. To address above issues, we innovatively propose Attributed Graph Contrastive Clustering (AGCC), which is a novel self-supervised attributed graph clustering framework. AGCC can directly produce a clustering assignment matrix without calculating target distribution matrix. Specifically, in our method, an effective module based on contrastive graph representation learning is being leveraged to extract node embeddings by node features and topological structure. In the phase of data augmentation, the module can adaptively mask node features and drops edges. Meanwhile, another reliable contrastive clustering module generates clustering results through contrasting the representation of each cluster. Comprehensive empirical analysis, compared with various types of classic and deep methods, demonstrates the efficacy and the scalability of the proposed method.