Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyst Non-GAAP Reporting: When Do Equity Analysts Adjust GAAP Earnings – an Application of Text Based Firm Complexity

Equity analysts adjust GAAP earnings to report non-GAAP figures that they believe better represent a firm’s current and future performance. The importance of such nonGAAP(street) figures has been determined and accepted by the literature, but the process in which analysts arrive at such figures is less understood. I apply a newly defined measure of firm complexity using text based analysis of firm annual reports and investigate its effect. When complexity increases, it becomes harder to individuate different components of a system. When that system is a firm, complexity then increases the difficulty in determining an accurate measure of performance. The effect of this type of firm characteristic on analysts has yet to be explored. I find that firms with higher complexity exhibit a greater difference between GAAP earnings and analyst adjusted non-GAAP earnings. A cross sectional test featuring earnings volatility is also found to increase the marginal effect of complexity on the difference. This suggests that firm complexity plays an important role in influencing an analyst’s decision to adjust GAAP earnings.
Toward Certifiable Motion Planning for Medical Steerable Needles

Medical steerable needles can move along 3D curvilinear trajectories to avoid anatomical obstacles and reach clinically significant targets inside the human body. Automating steerable needle procedures can enable physicians and patients to harness the full potential of steerable needles by maximally leveraging their steerability to safely and accurately reach targets for medical procedures such as biopsies and localized therapy delivery for cancer. For the automation of medical procedures to be clinically accepted, it is critical from a patient care, safety, and regulatory perspective to certify the correctness and effectiveness of the motion planning algorithms involved in procedure automation. In this paper, we take an important step toward creating a certifiable motion planner for steerable needles. We introduce the first motion planner for steerable needles that offers a guarantee, under clinically appropriate assumptions, that it will, in finite time, compute an exact, obstacle-avoiding motion plan to a specified target, or notify the user that no such plan exists. We present an efficient, resolution-complete motion planner for steerable needles based on a novel adaptation of multi-resolution planning. Compared to state-of-the-art steerable needle motion planners (none of which provide any completeness guarantees), we demonstrate that our new resolution-complete motion planner computes plans faster and with a higher success rate.
GrES: Group Evolutionary Strategies for Sample Efﬁcient Direct Policy Learning

Evolutionary Strategies (ES) have recently proven competitive with Reinforcement Learning (RL) in some tasks where their decreased computational cost offsets their lower sample efﬁciency. We demonstrate ES can be further improved in both sample efﬁciency and wall-clock time, overcoming traditional RL methods in several benchmarks. We introduce Group Evolutionary Strategies (GrES), an ES algorithm where the population is split into groups of individuals, each taking turns in the same group environment, and collecting individual episodic rewards through a credit assignment mechanism. This differs from standard ES, where each individual acts through an entire episode, and we show experimentally that the sample efﬁciency of GrES scales with the group size. In our layered implementation, each group member differs from the parent network only in one layer, so we separate the evolution along each layer. To explain the performance of GrES, we isolate the effects of the group size, the credit assignment mechanism, the length of individual turns, and the layering element on the robustness and efﬁcacy of the learned policy. In particular layering brings a big memory improvement, because instead of having to store one model for each member of the next generation, we need only store one layer. We highlight current limitations of existing DNN libraries by implementing multi-model-parallelism (MMP) on a single GPU, which provides an additional acceleration factor on the wall-clock convergence time of ES in general, and of GrES in particular. Our combined GrES and MMP implementation achieves an order of magnitude speed up factor with respect to vanilla ES, and is therefore competitive or better than RL in several benchmarks.
CZECH TECHNICAL UNIVERSITY IN PRAGUE FACULTY OF ELECTRICAL ENGINEERING DEPARTMENT OF RADIOELECTRONICS SUPPRESSION OF PHOTOBLEACHING IN SUPER-RESOLUTION OPTICAL FLUCTUATION IMAGING USING DIGITAL FILTERING DIPLOMA THESIS

Super Resolution Optical Fluctuation Imaging (SOFI) is a recent microscopy method which allows one to obtain a high-resolution image beyond the diffraction limit of light. Photobleaching, which results in a reduction of the intensity of the acquired signal over time, limits the quality of the final image and therefore represents a challenging tasks for SOFI processing. The goal of this work is to develop an effective algorithm for the correction of photobleaching effect based on digital filtering. Thesis presents an overview of the current super-resolution methods with emphasis on SOFI and photobleaching. Essential basis of digital filtering is also provided in the theoretical part of the work. Five bleaching correction algorithms were developed in this work and compared with the state-of-theart methods. Proposed algorithms based on the 1 order high-pass IIR Butterworth filter and the moving average subtraction provided the best results superior to the tested state-of-the-art algorithms. An novel optimization algorithm was introduced to automatically select the best filter parameters. The proposed bleaching correction algorithms enable us to achieve higher order SOFI images which contain previously unobservable details.
Optimizing Email Marketing Campaigns in the Airline Industry using Knowledge Graph Embeddings

The use of email marketing campaigns in e-commerce is an essential aspect for driving sales and establishing or maintaining good customer service. However, ensuring their success in the airline industry is challenging, and the risk is high that the marketing campaign content is not suited to the needs of speciﬁc travelers. The traveler may rapidly feel spammed and the offer content promoted via emails may even be no longer accessed. Consequently, the use of personalised recommender systems for email marketing campaigns becomes essential to build user loyalty and to increase offer conversion. Recently, the use of knowledge graphs has proven to be an effective way to recommend products. In this work, we propose an approach that leverages knowledge graph embeddings to better target the right audience in email marketing campaigns for airline products recommendation. We conduct extensive experiments to compare our approach with the currently in-production rule-based system used by airline marketers and a supervised machine learning model based on handcrafted features as another baseline. The results suggest that the use of knowledge graph embeddings is the most effective approach.
Design of Multi-band Microstrip Patch Antennas for Mid-band 5G Wireless Communication

Recently, the best antenna structures have considered microstrip patch antenna due to their simple construction, low cost, minimum weight, and the fact that they can be effortlessly integrated with circuits. To achieve multiband operation an antenna is designed with an etching rectangle and circle slot on the surface of the patch to achieve multi-band frequency capabilities in mid-band 5G applications. Inset-fed structure type of fed of all antenna printed and fabricated on the brow of the Rogers RT5880 substrate. Then, prototype structures of the microstrip patch antenna were acquired during the design process until achieving the desired antennas. The antenna_1 achieved tri-band characteristics covering the WiMAX band including 2.51 – 2.55 GHz, WLAN, and S-band including 3.80 – 3.87 GHz and C-and X-band including 6.19 – 6.60 GHz. The antenna_2 gives dual-band characteristics covering C-band and X-band including (6.72 – 7.92 GHz) with a peak under -45 dB suitable for mid-band 5G applications. High impedance bandwidth increases between (70 MHz-1.25 GHz) for wireless applications. The proposed microstrip patch antennas were simulated using CST MWS-2015 and were experimentally tested to verify the fundamental characteristics of the proposed design, it offers multiple-band operation with high stable gain and good directional radiation characteristics results. Keywords—Band-width; microstrip; multi-band; notch slot; rectangle slot; 5 G
Viscoelasticity of Cu- and La-based bulk metallic glasses: Interpretation based on the quasi-point defects theory

aSchool of Mechanics, Civil Engineering and Architecture, Northwestern Polytechnical University, Xi’an 710072, P. R. China bState Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi’an 710072, P. R. China cUniversité de Lyon, MATEIS, UMR CNRS5510, Bat. B. Pascal, INSA-Lyon, F-69621 Villeurbanne cedex, France dInstitute for Materials Research, Tohoku University, Sendai 980-8577, Japan eDepartament de Física, Universitat Politècnica de Catalunya, 08860-Castelldfels, Barcelona, Spain fDepartment of General Physics, State Pedagogical University, Lenin Street 86, Voronezh 394043, Russia
Vector Semantic Representations as Descriptors for Visual Place Recognition

—Place recognition is the task of recognizing the current scene from a database of known places. The currently dominant algorithmic paradigm is to use (deep learning based) holistic feature vectors to describe each place and use fast vector query methods to ﬁnd matchings. We propose a novel type of image descriptor, Vector Semantic Representations (VSR), that encodes the spatial semantic layout from a semantic segmentation together with appearance properties in a, for example, 4,096 dimensional vector for place recognition. We leverage operations from the established class of Vector Symbolic Architectures to combine symbolic (e.g. class label) and numeric (e.g. feature map response) information in a common vector representation. We evaluate the proposed semantic descriptor on 13 standard mobile robotic place recognition datasets and compare to six descriptors from the literature. VSR is on par with the best compared descriptor (NetVLAD) in terms of mean average precision and superior in terms of recall and worst-case average precision. This makes the approach particularly interesting for candidate selection. For a more detailed investigation, we discuss and evaluate recall integrity as additional criterion. Further, we demonstrate that the semantic descriptor is particularly well suited for combination with existing appearance descriptors indicating that semantics provide complementary information for image matching.
Characterization of Antioxidant Properties of Common Spices and the Effect of Thermal Treatment on their Antioxidant Activity

A total of 16 spices including red pepper (Capsicum annum L.), turmeric (Curcuma longa L.), white pepper (Piper nigrum L.), black pepper (Piper nigrum L.), cardamom (Elettaria cardamomum Maton), allspice (Pimenta dioica L.), ginger (Zingiber officinale Roscoe), mustard (Brassica alba Boiss), cinnamon (Cinnamomum zeylanicum Beryn), fennel (Foeniculum vulgare Mill), fenugreek (Trigonella foenumgraecum L.), cumin (Cuminum cyminum L.), coriander (Coriandrum sativum L.), clove (Eugenia caryophyllata Thunb), nutmeg (Myristica fragrans Houtt) and mace (Myristica fragrans Houtt) were used in this experiment to characterize antioxidant properties including polyphenols and flavonoids. Among the tested spices, clove contained the highest amount of phenolics such as eugenol, acetyleugenol, gallic acid and flavonoids (quercetin, kaempferol glycosides and quercetin glycosides). Phenol contents such as gallic acid, quercetin, piperine, ferulic acid and caffeic acid were found increased after heating treatment. Significant increment was found in gallic acid for clove and allspice, ferulic acid for turmeric, caffeic acid for coriander and piperine for black pepper and white pepper. Most of the phenolic components of spices were found stable during heat treatment at 100°C.
EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following

The rapid rise of accessibility of unmanned aerial vehicles or drones pose a threat to general security and confidentiality. Most of the commercially available or custom-built drones are multi-rotors and are comprised of multiple propellers. Since these propellers rotate at a high-speed, they are generally the fastest moving parts of an image and cannot be directly"seen"by a classical camera without severe motion blur. We utilize a class of sensors that are particularly suitable for such scenarios called event cameras, which have a high temporal resolution, low-latency, and high dynamic range. In this paper, we model the geometry of a propeller and use it to generate simulated events which are used to train a deep neural network called EVPropNet to detect propellers from the data of an event camera. EVPropNet directly transfers to the real world without any fine-tuning or retraining. We present two applications of our network: (a) tracking and following an unmarked drone and (b) landing on a near-hover drone. We successfully evaluate and demonstrate the proposed approach in many real-world experiments with different propeller shapes and sizes. Our network can detect propellers at a rate of 85.1% even when 60% of the propeller is occluded and can run at upto 35Hz on a 2W power budget. To our knowledge, this is the first deep learning-based solution for detecting propellers (to detect drones). Finally, our applications also show an impressive success rate of 92% and 90% for the tracking and landing tasks respectively.
A Human-Robot Cooperative and Personalized Compliant Joint Controller for Upper-Limb Rehabilitation Robots: The Elbow Joint Validation

Background: Appropriate training modalities for post-stroke upper-limb rehabil6 itation are key features for effective recovery after the acute event. This work presents a novel 7 human-robot cooperative control framework that promotes compliant motion and renders differ8 ent high-level human-robot interaction rehabilitation modalities under a unified low-level control 9 scheme. 10 Methods: The presented control law is based on a loadcell-based impedance controller provided 11 with positive-feedback compensation terms for disturbances rejection and dynamics compensation. 12 We developed an elbow flexion-extension experimental setup, and we conducted experiments to 13 evaluate the controller performances. Seven high-level modalities, characterized by different levels of 14 (i) impedance-based corrective assistance, (ii) weight counterbalance assistance, and (iii) resistance, 15 have been defined and tested with 14 healthy volunteers. 16 Results: The unified controller demonstrated suitability to promote good transparency and 17 render compliant and high-impedance behavior at the joint. Superficial electromyography results 18 showed different muscular activation patterns according to the rehabilitation modalities. Results 19 suggested to avoid weight counterbalance assistance, since it could induce different motor relearning 20 with respect to purely impedance-based corrective strategies. 21 Conclusion: We proved that the proposed control framework could implement different phys22 ical human-robot interaction modalities and promote the assist-as-needed paradigm, helping the 23 user to accomplish the task, while maintaining physiological muscular activation patterns. Future 24 insights involve the extension to multiple degrees of freedom robots and the investigation of an 25 adaptation control law that makes the controller learn and adapt in a therapist-like manner. 26
Semantic Abstraction-Guided Motion Planning for scLTL Missions in Unknown Environments

Complex mission specifications can be often specified through temporal logics, such as Linear Temporal Logic and its syntactically co-safe fragment, scLTL. Finding trajectories that satisfy such specifications becomes hard if the robot is to fulfil the mission in an initially unknown environment, where neither locations of regions or objects of interest in the environment nor the obstacle space are known a priori. We propose an algorithm that, while exploring the environment, learns important semantic dependencies in the form of a semantic abstraction, and uses it to bias the growth of an Rapidly-exploring random graph towards faster mission completion. Our approach leads to finding trajectories that are much shorter than those found by the sequential approach, which first explores and then plans. Simulations comparing our solution to the sequential approach, carried out in 100 randomized office-like environments, show more than 50% reduction in the trajectory length.
Malware Detection Based on Opcode Dynamic Analysis

Malware detection is an important problem in the field of information security. Opcodes are the most direct information reflecting the execution behavior of malware, The malware based on the dynamic analysis of opcodes also faces some challenges: the acquisition of the operating code information in the execution process of the malware; the high false alarm rate in the detection process and the large system overhead caused by the malware detection in the application layer. In order to deal with the above problems, this paper proposes a new scheme for dynamic opcode acquisition, the opcode information obtained from the software runtime is used for offline analysis. The detection accuracy of off-line malware can reach 99.85%, which is superior to the traditional technology. Moreover, this paper proposes an online detection scheme: CPU built-in malware monitoring model (CBMM), which can solve the problem that it is difficult to accurately identify the execution trajectory of malware in the current malware detection process, at the same time, this model can monitor malware in real time. Finally, we implement our model by VerilogHDL, functional simulation was carried out in modelsim simulation software and its implementation cost was analyzed.
Gene tree and species tree reconciliation with endosymbiotic gene transfer

Abstract Motivation It is largely established that all extant mitochondria originated from a unique endosymbiotic event integrating an α−proteobacterial genome into an eukaryotic cell. Subsequently, eukaryote evolution has been marked by episodes of gene transfer, mainly from the mitochondria to the nucleus, resulting in a significant reduction of the mitochondrial genome, eventually completely disappearing in some lineages. However, in other lineages such as in land plants, a high variability in gene repertoire distribution, including genes encoded in both the nuclear and mitochondrial genome, is an indication of an ongoing process of Endosymbiotic Gene Transfer (EGT). Understanding how both nuclear and mitochondrial genomes have been shaped by gene loss, duplication and transfer is expected to shed light on a number of open questions regarding the evolution of eukaryotes, including rooting of the eukaryotic tree. Results We address the problem of inferring the evolution of a gene family through duplication, loss and EGT events, the latter considered as a special case of horizontal gene transfer occurring between the mitochondrial and nuclear genomes of the same species (in one direction or the other). We consider both EGT events resulting in maintaining (EGTcopy) or removing (EGTcut) the gene copy in the source genome. We present a linear-time algorithm for computing the DLE (Duplication, Loss and EGT) distance, as well as an optimal reconciled tree, for the unitary cost, and a dynamic programming algorithm allowing to output all optimal reconciliations for an arbitrary cost of operations. We illustrate the application of our EndoRex software and analyze different costs settings parameters on a plant dataset and discuss the resulting reconciled trees. Availability and implementation EndoRex implementation and supporting data are available on the GitHub repository via https://github.com/AEVO-lab/EndoRex.
Language Identiﬁcation using Ensembled Deep Neural Networks

Language identiﬁcation (LI) is a multi-class classiﬁcation problem where the task is to determine the natural language of the given text. Various other natural language processing tasks assume that the language of the text is known. Language identiﬁcation enables automatically detecting the source language in tools like Google Translate before any further translation. We examine the use of an ensemble of deep neural networks for this task of language identiﬁcation in a monolingual benchmark dataset. Results show that deep neural networks with the help of an integrated ensemble of bidirectional LSTMs with varying feature extraction techniques achieve better accuracy of over 98 percent for language identiﬁcation.
mHealth Applications for Childhood Cancer Support and Self-management: Persuasive Systems Design Features

The wide accessibility of mobile devices and the potential for self-management and informational support through mHealth applications provides an opportunity to address unmet informational needs, achieve patient self-management, and provide long-term care for young cancer patients and their parents/caregivers. The aim of this paper is to examine the functionalities and features offered by mHealth applications for the support and self-management of childhood and adolescent cancer patients. In order to evaluate the influence of these apps on the user’s motivation for behaviour change, an extensive review was conducted and the features of the selected applications are further analysed using the Persuasive Systems Design. The review provided in this paper found a number of mobile health applications fulfilling a variety of functions and needs for childhood and young adult cancer patients and their families. Most of the analysed applications provide one or few features from the Persuasive Systems Design primary task support, dialogue, or social support category.
PHYTOCHEMICAL ANALYSIS OF RUELLIA PATULA USING GAS CHROMATOGRAPHY‑MASS SPECTROMETRY

ABSTRACT Objective: Ruellia patula (Jacq.) a medicinally important plant belongs to the family Acanthaceae. Traditionally, the leaves are used in the treatment of many diseases such as insect bites, itches, eye diseases, and skin problems. In the present study, the qualitative and quantitative analysis of bioactive compounds by gas chromatography (GC) and mass spectroscopy (MS) techniques. Methods: The chemical compositions of the ethanol extract of R. patula leaves were investigated using Perkin–Elmer GC‑MS. Result: In the GC‑MS analysis of ethanol extract of R. patula L. reported the presence of 15 compounds. The major compound constituents are 3, 7, 11, 15‑tetramethyl‑2‑hexadecen‑1‑ol (28.75%), and α‑sitosterol (14.35%). Conclusion: Identification of these compounds can help in the development of new drugs. Keywords: Ruellia patula, Ethanol extract, Phytochemical compounds, gas chromatography‑mass spectroscopy analysis, Biological activity of phyto‑components.