Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncertainty Quantification for a Microstrip Self-Biased Ku-band Circulator

This paper focuses on uncertainty quantification for a Y-junction circulator. In the process of transitioning from prototyping to mass-production it is very important to understand how the uncertainties affecting input design parameters impact crucial features of the device behavior such as bandwidth or resonant frequency. This paper demonstrates the construction of a polynomial chaos expansion metamodel and its use in performing sensitivity analysis. The impact of the various sources of uncertainty affecting the circulator’s behavior is quantified and the results are discussed.
A 60-GHz 27.8-dBm GaN-based Doherty Power Amplifier for V-band Applications

This paper presents a fully integrated Doherty power amplifier (PA) targeting V-band applications. With the implementation in the standard 100-nm GaN/SiC high-electron mobility transistor (HEMT) technology, the fabricated monolithic-microwave integrated circuit (MMIC) Doherty PA achieves a small-signal gain of 17.9 dB, a saturated output power $\left({\mathrm {P}}_{{\mathrm {sat}}}\right)$ of 27.8 dBm, and a peak power-added-efficiency (PAE) of 28.9% at 60 GHz, respectively. Characterized using a 250 MHz 200 MHz 64-quadrature-amplitude-modulation (QAM) single carrier modulated signal at 60 GHz, a corresponding error-vector-magnitude (EVM) of −25.4 dB was obtained while delivering an average output power of 23.4 dBm.
Efficient Approach Towards Detection and Classification for Image Forgery by Using Machine Learning Technique

Copy-move forgery (CMF) is one of the most malicious forgery attacks often manipulated to spread misinformation and influence security. In the paper, utilized Machine Learning techniques specifically, a Multiclass Support Vector Machine (MSVM)-based Particle Swarm Optimization (PSO) algorithm was utilized for image forgery. PSO is an optimization algorithm in which a group of particles representing individual solutions moves through a search space to find the best solution. The objective function that the PSO algorithm seeks to minimize or maximize is typically related to the MSVM's performance on a validation dataset. The MICC-F200 dataset was used to verify the uniqueness of CMF and evaluate the model's image classification performance. It's important to note that MSVM based PSO can become computationally expensive and memory-intensive when dealing with a large number of classes. They are also sensitive to noisy or mislabeled data points, which can potentially lead to suboptimal performance. The results obtained from this study reveal that the MSVM-based PSO model achieves a high accuracy of 0.9936 on the MICC-F200 dataset, outperforming other existing models such as VGG16 and Mobile Net, thereby ensuring accurate image classification.
TrustDroid: Enhancing Trust and Privacy for Android Mobile Phones by Preventing Riskware and SMS Malware

Android mobile phones are widely targeted by SMS Malware and Riskware attacks for stealing users' private information like user credentials, pictures, videos, banking credentials etc. Android users are not aware of the incoming link or message and unintentionally allow attackers to intrude on their mobile devices. The objective of this research paper is to enhance user confidence and privacy on Android smartphones. The CICMalDroid 2020 dataset provides the foundation for this research work by having a dataset on traffic patterns of riskware and SMS malware attacks on Android smartphones. In this research work, the methodology is proposed for early detection of riskware and SMS malware attacks by integrating a machine learning-trained chip at the hardware level. The dataset is trained with multiple machine learning algorithms to explore the effectiveness of appropriate algorithms for devising solutions for the detection of Android malware attacks. The results prove the efficiency of the random forest technique for the CICMalDroid dataset. This classifier has the best accuracy, recall, and $F1$ score in comparison with other classifiers on the taken dataset. The Naive Bayes approach, on the other hand, is dependable and reduces the incidence of false positives. These results not only highlight the need to reinforce cybersecurity and regularly update software, but they also highlight the importance of machine learning in terms of Android device security.
Keyword-Based Information Retrieval Model for the Dark Web

Due to its encryption networks and websites that cannot be found via traditional search engines, searching for specific information on the dark web poses unique challenges. The lack of an indexing mechanism requires an improvised searching mechanism on the dark web. This paper presents retrieving information on the dark web. The proposed method utilizes a keyword-driven ranking system to show relevant dark web content. Within a crawled dataset of dark web websites, a given set of websites is ranked using keyword analysis, cosine similarity, and GloVe embeddings. Initially, keywords are selected to ensure that they pertain to the content contained within the dataset. Choosing appropriate keywords determines how accurately the crawled dataset represents the various topics. A comprehensive keyword analysis is then performed on each website's content, scoring each keyword based on importance. A retrieval model combines these scores to create composite scores, which indicate whether a website is relevant to a search query. The websites are sorted in descending order based on their combined keyword scores. Using user keywords, the presented work identifies the most relevant results from dark web onion services.
Complete Genome Sequence of Klebsiella pneumoniae Myophage Mineola

Klebsiella pneumoniae is an important human pathogen due to the wide range of infections it can cause and its emerging drug resistance. Isolation and characterization of phage infecting K. pneumoniae could be important for future therapeutic applications. ABSTRACT Klebsiella pneumoniae is an important human pathogen due to the wide range of infections it can cause and its emerging drug resistance. Isolation and characterization of phage infecting K. pneumoniae could be important for future therapeutic applications. Here, we report the complete genome sequence of the T4-like Klebisella pneumoniae myophage Mineola.
Comparison of the complete blood count-derived inflammatory markers in bipolar patients with manic and mixed episodes.

OBJECTIVES We aimed to compare altered inflammatory status between patients with bipolar manic and mixed episodes through neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR) levels. BACKGROUND NLR, PLR, and MLR are systemic inflammation biomarkers that have recently studied in bipolar disorder (BD) manic and depressive episodes. Immunological biomarker signature of mixed episodes and MLR levels in BD have less been studied. DESIGN AND SETTING Our study included 48 bipolar patients in a mood episode (28 manic, 20 mixed) and 32 controls. Study-specific form including sociodemographic and clinical variables with laboratory findings were filled for all participants. METHODS Red cell distribution width (RDW), mean platelet volume (MPV) neutrophil, lymphocyte and platelet count, NLR, PLR, and MLR were recorded. RESULTS PLR and MLR were found significantly higher in bipolar patients compared to controls while NLR and MLR were significantly higher in manic patients than in mixed patients. RDW was found higher in mixed episode compared to controls. CONCLUSIONS One can interpret these findings as MLR would be considered as a novel state biomarker for bipolar mood episodes and greater inflammatory activation may be involved in mania rather than mixed episode (Tab. 2, Fig. 1, Ref. 35).
Harnessing Character-level Dynamics and Word-level Semantics: A New Perspective on Twitter Sentiment Analysis

In recent years, Sentiment Analysis has emerged as an indispensable component of social media analysis. It not only allows understanding public opinion on various issues, but also aids in monitoring real-time trends and sentiments, critical for domains such as brand management, market research, political campaign management, and crisis management. Central to the effectiveness of sentiment analysis is the extraction of meaningful features from the text data, with sentence embeddings being a vital component in capturing semantic relationships and sentence structures. This paper presents a novel approach to Twitter sentiment analysis that exploits sentence embeddings derived from Dynamic Mode Decomposition (DMD). DMD, a dimensionality reduction technique, enables us to capture the underlying dynamics of character transitions in tweets and to construct expressive sentence embeddings. The advantage of DMD lies in its ability to represent complex patterns and dependencies within the tweet text, thereby improving the richness of the derived sentence embeddings. Additionally, we investigate the use of Graph Neural Networks (GNNs) for sentiment classification, harnessing the power of relational modeling in the context of Twitter data. We also delve into word-level analysis with different emotion-based embeddings, assessing their impact on the sentiment classification performance. Our methodology integrates character-level analysis, graph-based modeling, and word-level semantics, providing a comprehensive, multi-layered approach to sentiment analysis on Twitter. Our experiments yield insightful results, highlighting the potential of DMD-based embeddings and the importance of high-quality, context-rich word embeddings in sentiment analysis tasks. These findings contribute to our understanding of sentiment analysis on social media platforms and suggest promising directions for future research in this field.
An Electrothermal Micromirror Array Integrated with Thermal Convection-Based Mirror Position Sensors

A novel electrothermal micromirror array integrated with thermal convection-based mirror position sensors is proposed, in which a heater is integrated on the mirror plate to generate a temperature distribution that will be changed by the movement of the mirror plate. The temperature change is then picked up by the thermistors integrated on the substrate. Both the displacement and the tip-tilt angle can be detected. A 3×3 micromirror array has been successfully fabricated. Experiments show an average sensitivity value of 8.6 mV/μm in the displacement range of 50 μm to 150 μm, and an average sensitivity of 95.5 mV/° in the angular scan range of 2° to 10°.
Daily Life Activity Routine Discovery in Hemiparetic Rehabilitation Patients Using Topic Models

Julia Seiter, Wearable Computing Lab. , ETH Zurich, Switzerland Adrian Derungs, ACTLab, University of Passau, Germany Corina Schuster-Amft, Research Department, Reha Rheinfelden, Switzerland Oliver Amft, ACTLab, University of Passau, Germany Gerhard Tröster, Wearable Computing Lab. , ETH Zurich, Switzerland Correspondence: Julia Seiter, ETH Zurich, Gloriastrasse 35, 8092 Zurich, Switzerland, Phone: +41 44 632 27 44, Fax: +41 44 632 12 10, E-mail: julia.seiter@ife.ee. ethz.ch
Diffused Lattice Vibration and Ultralow Thermal Conductivity in the Binary Ln–Nb–O Oxide System

In the pursuit of low thermal conductivity materials for thermal management, one always tries to increase the material entropy by increasing the number of components in the materials to scatter heat‐carrying phonons. However, it also drastically increases the technological complexity to synthesize materials with the target complex composition. Here, a material family is presented with simple composition Ln3NbO7, which only contains binary oxides of Ln2O3 (Ln = Dy, Er, Y, Yb) and Nb2O5. The thermal conductivities approach the theoretical minimum limit, where the large chemical inhomogeneity due to the charge disorder and fluctuation of bonding length in Ln3NbO7 plays a major role. Despite the simple composition, Ln3NbO7 demonstrates an unprecedentedly high scattering rate of vibration states, as confirmed by the highest elastic constant/thermal conductivity ratio, as well as the diffused wavevector‐frequency dispersion. In contrast to the conventional wisdom that low thermal conductivity materials should be explored in the pool of “complex” multiple‐component materials, this work points out an avenue to look into materials with simple composition but large internal chemical inhomogeneity, which would be of both scientific and technological significance in the fields of thermal barrier coating, thermoelectric materials, etc.
Feature-Level and Model-Level Audiovisual Fusion for Emotion Recognition in the Wild

Emotion recognition plays an important role in human-computer interaction (HCI) and has been extensively studied for decades. Although tremendous improvements have been achieved for posed expressions, recognizing human emotions in "close-to-real-world" environments remains a challenge. In this paper, we proposed two strategies to fuse information extracted from different modalities, i.e., audio and visual. Specifically, we utilized LBP-TOP, an ensemble of CNNs, and a bi-directional LSTM (BLSTM) to extract features from the visual channel and the OpenSmile toolkit to extract features from the audio channel, respectively. Two kinds of fusion methods, i, e. , feature-level fusion and model-level fusion, were developed to utilize the information extracted from the two channels. Experimental results on the EmotiW2018 AFEW dataset have shown that the proposed fusion methods outperform the baseline methods significantly and achieve comparable performance compared with the state-of-the-art methods, where the model-level fusion performs better when one of the channels totally fails.
Cardamonin Induces Cell Cycle Arrest, Apoptosis and Alters Apoptosis Associated Gene Expression in WEHI-3 Mouse Leukemia Cells.

Cardamonin, the chalcone class, is one of the natural components from the spicy herbaceous plant (Alpinia conchigera Griff) and has anticancer activities in many human cancer cell lines. There is, however, no information to show that cardamonin induces cell apoptosis and alters apoptosis associated gene expressions in mouse leukemia cells. Thus, we investigated the effects of cardamonin on the apoptotic cell death and associated gene expression in mouse leukemia WEHI-3 cells in vitro. Results indicated that cardamonin decreased total viable cell number via induced cell morphological changes and apoptotic cell death in WEHI-3 cells that were assay by contrast-phase microscopy and flow cytometry examinations, respectively. The flow cytometry assay indicated that cardamonin increased reactive oxygen species (ROS) and Ca 2+ production, decreased the levels of mitochondrial membrane potential ( ΔΨm) and increased caspase-3, -8 and -9 activities in WEHI-3 cells. Western blotting was performed to analyze expression of relevant pro- and anti-apoptotic proteins and results showed that cardamonin decreased anti-apoptotic protein of Bcl-2 but increased pro-apoptotic protein of Bax in WEHI-3 cells. Furthermore, cardamonin increased cytochrome c, AIF and Endo G release, increased GRP78, caspase-12 that were associated with ER stress and increased Fas, Fas-Ligand and FADD expression. Furthermore, cardamonin increased the gene expressions of DAP (death-associated protein), TMBIM4 transmembrane (BAX inhibitor motif containing 4), ATG5 (autophagy related 5) but decreased the gene expression of DDIT3 (DNA-damage inducible transcript 3), DDIT4 (DNA-damage-inducible transcript 4), BAG6 (BCL2-associated athanogene 6), BCL2L13 [BCL2-like 13 (apoptosis facilitator)] and BRAT1 (BRCA1-associated ATM activator 1) that are associated with apoptosis pathways. Based on those findings, we may suggest cardamonin induced apoptotic cell death through Fas and Fas-Ligand-, caspase- and mitochondria-dependently pathways and also affects the apoptotic gene expression in WEHI-3 cells in vitro.
A ALEA : A Fine-grained Energy Profiling Tool

Energy efficiency is becoming increasingly important, yet few developers understand how source code changes affect the energy and power consumption of their programs. To enable them to achieve energy savings, we must associate energy consumption with software structures, especially at the fine-grained level of functions and loops. Most research in the field relies on direct power/energy measurements taken from on-board sensors or performance counters. However, this coarse granularity does not directly provide the needed fine-grained measurements. This article presents ALEA, a novel fine-grained energy profiling tool based on probabilistic analysis for fine-grained energy accounting. ALEA overcomes the limitations of coarse-grained power-sensing instruments to associate energy information effectively with source code at a fine-grained level. We demonstrate and validate that ALEA can perform accurate energy profiling at various granularity levels on two different architectures: Intel Sandy Bridge and ARM big.LITTLE. ALEA achieves a worst case error of only 2% for coarse-grained code structures and 6% for fine-grained ones, with less than 1% runtime overhead. Our use cases demonstrate that ALEA supports energy optimizations, with energy savings of up to 2.87 times for a latency-critical option pricing workload under a given power budget. CCS Concepts: rHardware → Power and energy; rSoftware and its engineering → Software notations and tools;
Parametric Optimization of Trochoidal Step on Surface Roughness and Dish Angle in End Milling of AISID3 Steel Using Precise Measurements

Tool steel play a vital role in modern manufacturing industries due to its excellent properties. AISI D3 is a cold work tool steel which possess high strength, more hardenability and good wear resistance properties. It has a wide variety of applications in automobile and tool and die making industries such as blanking and forming tools, high stressed cutting, deep drawing and press tools. The novel ways of machining these steels and finding out the optimum process parameters to yield good output is of practical importance in the field of research. This research work explores an attempt to identify the optimized process parameter combinations in end milling of AISI D3 steel to yield low surface roughness and maximum dish angle using trochoidal milling tool path, which is considered as a novelty in this study. 20 experimental trials based on face centered central composite design (CCD) of response surface methodology (RSM) were executed by varying the input process factors such as cutting speed, feed rate and trochoidal step. Analysis of variance (ANOVA) was adopted to study the significance of selected process parameters and its relative interactions on the performance measures. Desirability-based multiple objective optimization was performed and the mathematical models were developed for prediction purposes. The developed mathematical model was statistically significant with optimum conditions of cutting speed of 41m/min, feed rate of 120 mm/min and trochoidal step of 0.9 mm. It was also found that the deviation between the experimental and predicted values is 6.10% for surface roughness and 1.33% for dish angle, respectively.
2 2 0 A pr 2 00 6 New Limits on Fault-Tolerant Quantum Computation

We show that quantum circuits cannot be made fault-tolerant against a depolarizing noise level of θ̂ = (6 − 2 √ 2)/7 ≈ 45%, thereby improving on a previous bound of 50% (due to Razborov [18]). Our precise quantum circuit model enables perfect gates from the Clifford group (CNOT, Hadamard, S, X , Y , Z) and arbitrary additional one-qubit gates that are subject to depolarizing noise θ̂. We prove that this set of gates cannot be universal for arbitrary (even classical) computation, from which the upper bound on the noise threshold for fault-tolerant quantum computation follows.
Partner-Specific Dependency and Guilt as Predictors of Forgiveness in Spanish University Women

Abstract Interpersonal transgressions often threaten the stability of the relationship. Within the scope of romantic relationships, physical violence and sexual infidelity have been considered the most difficult transgressions to forgive. Similarly, two variables considered relevant for forgiveness within the context of the couple are partner-specific dependency and the guilt experienced by the offended person. In that way, this research aims to approach the understanding of the forgiveness process of such transgressions. To this end, an experimental study was designed (N = 173 university women; Mage = 21.36, SD = 2.83), by which three indicators of forgiveness corresponding to the Transgression-Related Interpersonal Motivations Scale–18–Item Form (TRIM–18; “Revenge”, “Avoidance” and “Benevolence”), partner-specific dependency and sense of guilt of the offended person were examined in the face of the transgressions of physical violence and sexual infidelity. Results revealed that violence (vs. infidelity) is less forgiven (higher “Revenge”, p = .017, ηp2 = .034). In addition, the results showed that high partner-specific dependency leads to further guilt which, in turn, leads to greater forgiveness towards the partner (less “Avoidance”, CIE = –.094, SE = .042, 95% CI [–.201, –.029]; and higher “Benevolence”, CIE = .080, SE = .037, 95% CI [.024, .173]) in light of violence (vs. infidelity). Last but not least, the previous findings and their possible implications for romantic relationships are discussed.
Comparison of DFT and Space Vector Method for Synchrophasor Measurement

Power system networks are rapidly and continuously changing, hence management and control system requires more accurate and reliable information on operating status of the grid for management. This information can be acquired in many ways and can consist of different types of data. Phasor Measurement Units (PMUs) are becoming prominent devices for electrical power system network. They are capable of measuring current and voltage amplitude, Frequency and Rate of Change of Frequency (ROCOF) in synchronized way. Various compliance classes and performance matrices for PMUs are prescribed as per the IEEE standard C37.118. 1 for power system synchrophasor. This paper reviews the role of PMUs and compares DFT and space vector methods for synchrophasor estimation.
The Early Diagnostic Value of Procalcitonin in Pneumonia After Off-Pump Coronary Artery Bypass Grafting Surgery

Background The incidence of early postoperative pneumonia (EPOP) after off-pump coronary artery bypass grafting surgery (CABG) is relatively high, but its diagnosis by traditional methods remains difficult, which could be deleterious to the prognosis. Moreover, few data exist regarding procalcitonin (PCT) in early diagnosis of pneumonia after off-pump CABG. Thus, this study was performed to evaluate the value of PCT in diagnosing EPOP after off-pump CABG. Material/Methods A total of 402 consecutive patients undergoing off-pump CABG were retrospectively enrolled. Forty-four patients were diagnosed with EPOP and 112 patients were diagnosed with systemic inflammatory response syndrome (SIRS). Chest roentgenogram, serum PCT, white blood cells, neutral granulocyte ratio, and daily maximum body temperature were recorded. The ability of PCT to diagnose EPOP was evaluated by receiver operating characteristic (ROC) analyses in comparison with traditional methods. Clinical net benefits were estimated via decision curve analysis (DCA). Results PCT presented satisfying accuracy in diagnosing EPOP with a cutoff value of 1.585 ng/mL (area under the curve [AUC] 0.808, 95% confidence interval [CI] 0.724–0.891, sensitivity 73%, specificity 86%). PCT performed better in diagnosing EPOP among SIRS patients (AUC 0.868, 95% CI 0.748–0.988, sensitivity 85%, specificity 89%). DCA showed valuable clinical net benefits of PCT in diagnosing EPOP after off-pump CABG regardless of threshold selected. Conclusions PCT could be a diagnostic marker for EPOP after off-pump CABG. The optimal cutoff value for diagnosing EPOP was 1.585 ng/mL. The application of PCT in diagnosing EPOP in SIRS patients was also satisfying with a cutoff value of 1.775 ng/mL.