Miracle-dz/UniFAR-FTUs huggingface dataset:
Continuous flow analysis of the Dye 3 ice core: New data from old ice

The Dye 3 ice core was drilled to bedrock in Southern Greenland (65°11’N, 43°50’W) in 1979-1981. The southern Greenland location is characterized by relatively warm temperatures and high accumulation rates. Since its drilling, numerous analyses of the core have been performed, and the ice has since been in freezer storage both in the USA and in Denmark. In October and November 2019, remaining Dye 3 ice, was melted during a continuous flow analysis (CFA) campaign at the Physics of Ice, Climate, and Earth (PICE) section of the Niels Bohr Institute at the University of
A NOVEL MULTILAYER EBG STRUCTURE TO RECONFIGURE THE BAND-NOTCH OF UWB MONOPOLE PRINTED ANTENNA

In high speed indoor communication, ultra-wideband (UWB) plays a crucial role. UWB contains several other narrow band systems, which give interference. In order to reject these narrow bands present in UWB system, a novel multilayer step via electromagnetic band gap (MS-EBG) structure to vary the band-notch of UWB monopole printed antenna is presented in this work. The proposed EBG consists of grooved substrate with step via arrangement. These grooved substrate allow for the deposition of the liquids with different dielectric constants to achieve the variations in band gap center frequency of EBG. The microstrip line based model with equivalent circuit diagram of MS-EBG is developed with experimental results using suspended microstrip line (SML) method, with different liquids like kerosene, sea water, mineral oil, without grooved substrate, etc. The simulated and experimental results show liquid sensing ability of the proposed MS-EBG structure. The application of MS-EBG to vary the band notch in UWB hexagonal monopole antenna (HMA) is also demonstrated. Simulated and experimental results show noticeable variation in the band notch center frequency with different liquids deposited in the grooved substrate. The proposed method required only liquid change arrangement to get desired band notch in UWB monopole antenna. Compared to electrical and mechanical method to get band notch in UWB monopole antenna, the proposed method works without any power supply, active devices, and additional complex arrangement.
Diffusion of light adsorbates on transition metal surfaces

Helium-3 surface spin echo spectroscopy (HeSE) has been used to measure the diffusive dynamics of adsorbates on close-packed metal surfaces, namely hydrogen on Cu(111), Pd(111) and Ru(0001), carbon and oxygen on Ru(0001), and oxygen on Cu(111). Chapter 2 reviews the HeSE technique and describes the relevant dynamical models and statistical methods used to interpret data in later chapters. The performance of the ionizing detector is analysed, with a focus on the signal-to-noise ratio. In Chapter 3 expressions for the classical intermediate scattering function (ISF) are introduced for open and closed systems. The effects of corrugation and surfaceperpendicular motion on the amplitude of different components in the ISF are modelled analytically and compared with simulation. The exact ISF for a particle on a flat surface, obeying the Generalized Langevin Equation with exponential memory friction, is calculated analytically. In Chapter 4 the analytical ISF is calculated for quantum Brownian motion and for coherent tunneling dynamics in a tight binding system. The bounce method for calculating quantum mechanical hopping rates in dissipative systems is applied to model diffusion of hydrogen on Ru(0001). Chapter 5 presents the first HeSE measurements of carbon and oxygen diffusion. C/Ru(0001) diffusion is assigned to a small carbon cluster. The jump rate has an activation energy EA = 292± 7 meV in the temperature range 550 K ≤ T ≤ 1300 K. Oxygen diffusion is significantly slower. By comparison of literature data with the new HeSE results, the activation energy for oxygen diffusion at low coverage is estimated as 650± 10meV. Oxygen measurements at high coverage θ ≈ 0.22 ML are consistent with strong mutual O-O interactions. Surface diffusion is also observed after exposing Cu(111) to oxygen. Chapter 6 presents low-coverage measurements of protium (H) and deuterium (D) diffusion on Ru(0001), Pd(111) and Cu(111). In the quantum activated regime there is evidence for multiple jumps in all three systems, suggesting a low dynamical friction. The measurements on Ru(0001) indicate that the deep tunneling rate is much slower for D than for H.
Speech Emotion Recognition Using Auditory Spectrogram and Cepstral Features

A systematic comparison on the impact of environmental noises on key acoustic features is critical in order to transfer speech emotion recognition (SER) systems into real world applications. In this study, we investigate the noise-tolerance of different acoustic features in distinguishing various emotions by comparing the SER classification performance on clean speech signals and noisy speech signals. We extract the spectrum and cepstral parameters based on human auditory characteristics and develop machine learning algorithms to classify four types of emotions using these features. Experimental results across the clean and noisy data show that compared to cepstral features, the auditory spectrogram-based features can achieve higher recognition accuracy for low signal-to-noise ratios (SNRs), but lower accuracy for high SNRs. Gammatone filter cepstral coefficients (GFCCs) outperformed all the extracted features on the Berlin database of emotional speech (EmoDB), under all four kinds of tested noise conditions. These results show compensation relationships between auditory spectrogram-based features and cepstral features for SER with better noise robustness in real-world applications.
Analysis of Mutational Profile of Hypopharyngeal and Laryngeal Head and Neck Cancers Identifies KMT2C as a Potential Tumor Suppressor

Hypopharyngeal cancer represents one of worst types of head and neck tumors with only few studies focusing on the genomic profile of this type of cancer. Using targeted next-generation sequencing we analyzed 48 HPV-negative tumor samples including 23 originating from hypopharynx, and 25 from larynx. Among genes previously described as significantly mutated, TP53, FAT1, NOTCH1, KMT2C and CDKN2A were found to be most frequently mutated. We also found that more than three-fourths of our patients harbored candidate ac-tionable or prognostic alterations in genes belonging to RTK/ERK/PI3K, cell-cycle and DNA-damage repair pathways. Using previously published data we compared 67 hypopharyngeal cancers to 595 head and neck cancers from other sites and found no prominent differences in mutational frequency except for CASP8 and HRAS genes. Since we observed relatively frequent mutations of KTM2C (MLL3) in our dataset, we analyzed their role in vitro by generating KMT2C-mutant hypopharyngeal cancer cell line FaDu with CRISPR-Cas9. We demonstrate that KMT2C loss-of-function results in increased colony formation and proliferation, in concordance with its previously published results. In summary, our results show that mutational profile of hypopharyngeal cancers might be similar to the one observed for other head and neck cancers with respect to minor differences and includes multiple candidate actionable and prognostic genetic alterations. We also demonstrate for the first time that KMT2C gene may play a role of tumor suppressor in head and neck cancer, which opens new possibilities in the search for new targeted treatment approaches.
Effects of humic acid on pentachlorophenol biodegrading microorganisms elucidated by stable isotope probing and high‐throughput sequencing approaches

Humic substances (HSs) are ubiquitous in soil and can be reversibly oxidized and reduced by acting as redox mediators. They participate in microbial metabolism and directly affect the biodegradation of organic pollutants. However, the effects of HSs on specific microbial taxa that are responsible for the degradation of organic pollutants remain unclear. In this research, the effects of humic acids (HAs), extracted from three types of soil, forest (CBHA), paddy (PSHA) and peat (YNHA) on the microbial community involved in anaerobic mineralization of pentachlorophenol (PCP) were investigated with high‐throughput sequencing and stable isotope probing (SIP) approaches. The results showed that all HA samples accelerated the biotransformation processes of PCP; the largest rate was obtained with YNHA. Illumina sequencing revealed that Desulfovibrio and Clostridium were the dominant functional bacteria for the dechlorination of PCP. During the subsequent PCP degradation and mineralization processes, HAs affected the diversity and abundance of microbial communities, and several taxa were enriched in the 13C‐heavy fractions compared with 12C‐heavy fractions. In the absence of HA, Methanobacterium and Spartobacteria showed a significant increase in 13C‐heavy fractions. Methanosarcina and OP11 were the dominant PCP degraders in microcosms when amended with CBHA, whereas Burkholderia and Methanobacterium were the key PCP degraders in PSHA‐ and YNHA‐amended experimental microcosms. These results improve our knowledge of the diversity and ubiquitous nature of HS‐utilizing microorganisms involved in PCP degradation, providing scientific support for the development of in situ bioremediation technologies for HS‐rich soil contaminated by PCP.
Privacy-preserving Collaborative Filtering by Distributed Mediation

Recommender systems have become very influential in our everyday decision making, e.g., helping us choose a movie from a content platform, or offering us suitable products on e-commerce websites. While most vendors who utilize recommender systems rely exclusively on training data consisting of past transactions that took place through them, it would be beneficial to base recommendations on the rating data of more than one vendor. However, enlarging the training data by means of sharing information between different vendors may jeopardize the privacy of users. We devise here secure multi-party protocols that enable the practice of Collaborative Filtering (CF) in a manner that preserves the privacy of the vendors and users. Shmueli and Tassa [38] introduced privacy-preserving protocols of CF that involved a mediator; namely, an external entity that assists in performing the computations. They demonstrated the significant advantages of mediation in that context. We take here the mediation approach into the next level by using several independent mediators. Such distributed mediation maintains all of the advantages that were identified by Shmueli and Tassa, and offers additional ones, in comparison with the single-mediator protocols: stronger security and dramatically shorter runtimes. In addition, while all prior art assumed limited and unrealistic settings, in which each user can purchase any given item through only one vendor, we consider here a general and more realistic setting, which encompasses all previously considered settings, where users can choose between different competing vendors. We demonstrate the appealing performance of our protocols through extensive experimentation.
Factors Influencing Tax Income: A Panel Data Analysis

This study analyzes the factors that are thought to affect tax revenues. The data used in this study is panel data involving seven ASEAN countries for 2009 2015. The Hausman test was carried out to determine the estimation model correctly, and the results revealed that the fixed-effects model was preferred. Based on the fixed-effects model's estimation results, this study finds that per capita income, the industrial sector's contribution in GDP, and trade liberalization have a significant and positive effect on tax revenue. In contrast, inflation worsens the flow of tax revenues significantly. Based on the results of this study, we recommend several policies that can be taken to increase the productivity of tax revenues by focusing on those four macroeconomic factors.
CAMBRIDGE WORKING PAPERS IN ECONOMICS JANEWAY INSTITUTE WORKING PAPERS Conditional Heteroskedasticity in the Volatility of Asset Returns

We propose a new class of conditional heteroskedasticity in the volatility (CHV) models which allows for time-varying volatility of volatility in the volatility of asset returns. This class nests a variety of GARCH-type models and the SHARV model of Ding (2021). CH-V models can be seen as a special case of the stochastic volatility of volatility model. We then introduce two examples of CH-V in which we specify a GJR-GARCH and an E-GARCH processes for the volatility of volatility, respectively. We also show a novel way of introducing the leverage effect of negative returns on the volatility through the volatility of volatility process. Empirical study confirms that CH-V models have better goodness-of-fit and out-of-sample volatility and Value-at-Risk forecasts than common GARCH-type models. Reference Details 2179 Cambridge Working Papers in Economics 2021/11 Janeway Institute Working Paper Series Published 08 November 2021
DESIGN OF LOW-PROFILE AND SAFE LOW SAR TRI-BAND TEXTILE EBG-BASED ANTENNA FOR IOT APPLICATIONS

A coplanar tri-band wearable antenna combined with an electromagnetic bandgap (EBG) structure is described for sub-6GHz 5G and wireless local area network (WLAN) applications. The proposed antenna is fully implemented in textile materials thus offering a robust, compact, and discreet solution to meet the requirements of wearable applications. The addition of the EBG structure increases the textile antenna performance in terms of radiation patterns in the presence of the human body. The experimental results show that the proposed design exhibits tolerance to various bending conditions as well as loading by body tissues. In addition, to ensure the safety of the design for human health, the values of the specific absorption rate (SAR) have been reduced by more than 95%, which complies with the international standard. This design could thus be considered as a good candidate for IoT applications compared to the current state of the art while having a tri-band behavior and smaller volume.
Adaptive and Efficient Models for Intent Recognition

Assistive robots should have the ability to understand the intent of humans, predict their behavior, and plan to provide anticipatory assistance in complex real-life environments. In this thesis, we present adaptive and efficient algorithms for recognizing human intent. We develop adaptive models for human intent recognition in a simulated search and rescue scenario. Humans vary widely in their behavior style due to different preferences, initial beliefs, internal world models, and planning mechanisms. A generic (non-adaptive) prediction model, therefore, has limited utility in this setting. Our adaptive model can recognize a rescuer’s behavior patterns online and make better predictions. We show that adaptive models trained on a wide variety of simulated planning-based agents can transfer to humans and outperform generic models trained on limited human data. We also present an efficient inverse reinforcement learning algorithm, called f-IRL, which directly optimizes a parameterized reward function to match the demonstrator’s state distribution. We show that f-IRL can efficiently learn the demonstrator’s intent it can learn to imitate control policies from just a single demonstration. In addition, we show that the learned reward can be used to transfer policies to different dynamics.
Abstractified Multi-instance Learning (AMIL) for Biomedical Relation Extraction

Relation extraction in the biomedical domain is a challenging task due to a lack of labeled data and a long-tail distribution of fact triples. Many works leverage distant supervision which automatically generates labeled data by pairing a knowledge graph with raw textual data. Distant supervision produces noisy labels and requires additional techniques, such as multi-instance learning (MIL), to denoise the training signal. However, MIL requires multiple instances of data and struggles with very long-tail datasets such as those found in the biomedical domain. In this work, we propose a novel reformulation of MIL for biomedical relation extraction that abstractifies biomedical entities into their corresponding semantic types. By grouping entities by types, we are better able to take advantage of the benefits of MIL and further denoise the training signal. We show this reformulation, which we refer to as abstractified multi-instance learning (AMIL), improves performance in biomedical relationship extraction. We also propose a novel relationship embedding architecture that further improves model performance.
Polycentric Structure and Firms’ Environmental Performance: Evidence from China

Urban spatial structure has a crucial impact on the sustainable development of urban areas. Based on a unique firm-level panel data in China, a fixed-effect model is used to empirically study the influence of multi-center spatial structure on firm environmental performance. The research of this article draws three conclusions. First, the polycentric spatial structure has a significant negative effect on firms' COD emissions, which still holds after addressing the endogeneity issue and conducting robustness tests. Second, the effect of polycentric structure on reducing firms' COD emissions is weakened when the distance between cities in the regional network is too large. However, the development of high-speed rail and Internet will strengthen the role of polycentric structure for firms' COD emission reduction. The results of the intermediary effect test indicate that the polycentric structure indirectly controls firms' COD emissions through the partial intermediary effect of promoting market integration and factor mobility. Third, the role of polycentric structure on firms' COD emissions varies depending on the cities and positions where the firms are located. The polycentric structure can reduce firms' COD emissions more effectively when firms are located in higher density cities and lower development level cities. In contrast, the influence of polycentric structure on firms' COD emission reduction fails in western regions and administrative boundary counties. These findings can provide some empirical support for Chinese policy makers to promote polycentric development strategies.
The predictive influence of gender, onset of deafness and academic self-efficacy on the attitudes of deaf learners towards Biology

In the study reported on here we examined the attitudes of deaf learners towards biology in Ibadan, Nigeria. Forty-one deaf learners (28 males; 13 females) in the Secondary School II science classes from 3 government-funded secondary schools for the deaf were purposively selected for the study. In the study we adopted a descriptive survey research design of the correlational type. Four research questions were raised and answered. A structured questionnaire, “Deaf Learners and Biology Questionnaire” with a reliability coefficient of 0.81, was used for data collection. Data were analysed with descriptive statistics and inferential statistics of Pearson product moment correlation, multiple regression and t-test at 0.05 level of significance. There was a positively significant relationship between academic self-efficacy and the attitudes of deaf learners towards biology (r = 0.511; p < 0.05); a significant but negative relationship between gender and the attitudes of deaf learners towards biology (r = 0.15; p < 0.05). No significant relationship between the onset of deafness and the attitudes of deaf learners towards biology (r = 0.810; p > 0.05) was observed. A total of 58.1% of the variance of the independent variables accounted for the attitudes towards biology among deaf learners who participated in the study. We conclude that academic self-efficacy has a great effect on the attitude of deaf learners towards biology. Gender contributes to and is inversely proportional to learners’ disposition towards biology. In light of the findings, appropriate recommendations are made.
ADAPTIVE SELF PACED DEEP CLUSTERING WITH DATA AUGUMENTION

Deep clustering is one of the most prominent clustering techniques compared with superior clustering techniques by jointly performing feature learning and cluster assignment. Although numerous deep clustering algorithms have emerged in various applications, most of them fail to learn robust cluster-oriented features which in turn hurt the final clustering performance. In order to overcome all these problems, we try to propose a two-stage deep clustering algorithm by incorporating data augmentation and self-paced learning. In the first stage, we try to train the system with all the inputs which are required for identifying the post with category name and rank. In the next stage we just want to identify the test condition in which user try to recommend the post to others and combine a post based on category.
Hierarchical Multi-Agent Deep Reinforcement Learning for SFC Placement on Multiple Domains

Service Function Chaining (SFC) is the process of decomposing a network service into multiple functions that successively process packets to deliver the end-to-end service. In a multi-domain context, SFC placement is a challenging problem due to limited knowledge of the infrastructure of the local domains, which complicates the process of finding the optimal placement solutions. On the other hand, Reinforcement Learning has gained momentum as a tool for decision-making, allowing agents to construct and improve policies using feedback from the environment. In this paper, we leverage Deep Reinforcement Learning (DRL) to perform SFC placement on multiple domains. We devise a hierarchical architecture where the local domain agents and the multi-domain agent are trained using different DRL models to perform SFC and sub-SFC placement while satisfying the SLA requirements.
Optimization of hydrodynamic vortex separator for removal of sand particles from storm water by computational fluid dynamics

Storm water treatment has been gradually acknowledged for the removal of pollutants from urban areas using the hydro cyclone separation technique. The separation efficiency of the hydrodynamic vortex separator (HDVS) is a complex phenomenon. With the aim enhance the separation potency of HDVS for storm runoff to get rid of sand particles, the HDVS with different structural configurations was studied by computational fluid dynamics. A steady-state model was created to simulate the HDVS, where the simulation of solid–liquid part flow within the HDVS was conducted by 3-D Eulerian–Eulerian multiphase model and Reynolds stress model combined with the kinetic theory of granular flow. The rate contours, recess, outlet volume fraction of solid phase and particle removal efficiency were specially analyzed. The simulation results showed that the separation efficiency of HDVS had a positive relation to different structural configurations. The optimal design parameters of HDVS were obtained. The great agreement of flow rate and alter the law of separation potency proves that the mentioned multiphase is in-line accustomed to optimize the separation efficiency of hydrodynamic vortex separator.
DUAL-ELEMENT MULTIPLE-INPUT-MULTIPLE-OUTPUT SYSTEM FOR SUB-6 GHZ (5G) AND WLAN APPLICATIONS WITH ENHANCED ISOLATION

A dual-band two-port MIMO antenna with very high isolation is proposed for 5G/WLAN application. The overall size of the MIMO antenna is (18 × 44 × 0.8) mm3. The unequal arm of the Inverted-F Antenna (IFA) is the reason for the dual bands. Bending and extending one of the arms with the staircase shape is responsible for the proposed dual-bands having resonant frequency at 3.45 GHz (3.3 GHz–3.65 GHz) and 5.1 GHz (4.8 GHz–5.5 GHz) respectively with percentage impedance bandwidth of 10% and 13.6%, respectively. The proposed antenna uses a simple decoupling structure based on a wide inverted T-shaped slot to achieve good isolation (better than 18 dB and 34 dB respectively for the dual-bands) between the ports. The envelope correlation coefficient (ECC) and channel capacity loss (CCL) are within the acceptable limits.