Miracle-dz/UniFAR-FTUs huggingface dataset:
On Robust Utility Maximization

This paper studies the problem of optimal investment in incomplete markets, robust with respect to stopping times. We work on a Brownian motion framework and the stopping times are adapted to the Brownian filtration. Robustness can only be achieved for logartihmic utility, otherwise a cashflow should be added to the investor s wealth. The cashflow can be decomposed into the sum of an increasing and a decreasing process. The last one can be viewed as consumption. The first one is an insurance premium the agent has to pay.
Incremental Layer-Wise Self-Supervised Learning for Efficient Unsupervised Speech Domain Adaptation On Device

Streaming end-to-end speech recognition models have been widely applied to mobile devices and show signiﬁcant improvement in efﬁciency. These models are typically trained on the server using transcribed speech data. However, the server data distribution can be very different from the data distribution on user devices, which could affect the model performance. There are two main challenges for on device training, limited reliable labels and limited training memory. While self-supervised learning algorithms can mitigate the mismatch between domains using unlabeled data, they are not applicable on mobile devices directly because of the memory constraint. In this paper, we propose an incremental layer-wise self-supervised learning algorithm for efﬁcient unsupervised speech domain adaptation on mobile devices, in which only one layer is updated at a time. Extensive experimental results demonstrate that the proposed algorithm achieves a 24 . 2% relative Word Error Rate (WER) improvement on the target domain compared to a supervised baseline and costs 95 . 7% less training memory than the end-to-end self-supervised learning algorithm.
Automatic Speaker Verification System for Dysarthria Patients

Dysarthria is one of the most common speech communication disorder associate with a neurological damage that weakens the muscles necessary for speech. In this paper, we present our efforts towards developing an automatic speaker verification (ASV) system based on x -vectors for dysarthric speakers with varying speech intelligibility (low, medium and high). For that purpose, a baseline ASV system was trained on speech data from healthy speakers since there is severe scarcity of data from dysarthric speakers. To improve the performance with respect to dysarthric speakers, data augmentation based on duration modification is proposed in this study. Duration modification with several scaling factors was applied to healthy training speech. An ASV system was then trained on healthy speech augmented with its duration modified versions. It compensates for the substantial disparities in phone duration between normal and dysarthric speakers of varying speech intelligibilty. Experiment evaluations presented in this study show that proposed duration-modification-based data augmentation resulted in a relative improvement of 22% over the baseline. Further to that, a relative improvement of 26% was obtained in the case of speakers with high severity level of dysarthria.
The Analysis of Repeated Measures: A Practical Review with Examples

Repeated measures data, in which the same response variable is recorded on each observational unit on several different occasions, occur frequently in many different disciplines. Many methods of analysis have been suggested including t-tests at each separate time point and multivariate analysis of variance. In this paper the application of a number of methods is discussed and illustrated on a variety of data sets. The approach involving the calculation of a small number of relevant summary statistics is considered to have advantages in many circumstances
Multiplanar knee kinematics-based test battery helpfully guide return-to-sports decision-making after anterior cruciate ligament reconstruction

Background: There are currently no well-established criteria to guide return to sports (RTS) after anterior cruciate ligament reconstruction (ACLR). In this study, a new test battery consisting of subjective and objective tests, especially multiplanar knee kinematics assessment, was developed to aid RTS decision making after ACLR. Methods: This study was conducted with 30 patients who were assessed a mean of 9.2 ± 0.5 months after ACLR. All patients underwent complete evaluations of both lower limbs with four objective assessments [isokinetic, hop, knee laxity, and 6–degree of freedom (6DOF, angle: flexion-extension, varus-valgus, internal-external rotation; translation: anteroposterior, proximodistal, mediolateral) knee kinematics tests] and two subjective assessments [International Knee Documentation Committee (IKDC) and Anterior Cruciate Ligament Return to Sport after Injury (ACL-RSI) questionnaires]. Limb symmetry indices (LSIs) of knee strength, hop distance, and range of motion (ROM) of knee kinematics were calculated. LSI ≥90%, IKDC scale score within the 15th percentile for healthy adults, and ACL-RSI score >56 were defined as RTS criteria. Results: Significant differences between affected and contralateral knees were observed in the quadriceps strength (p < 0.001), hamstring strength (p = 0.001), single hop distance (p < 0.001), triple hop distance (p < 0.001), and rotational ROM (p = 0.01). Only four patients fulfilled the overall RTS criteria. The percentages of patients fulfilling individual criteria were: quadriceps strength, 40%; hamstring strength, 40%; single hop distance, 30%; triple hop distance, 36.7%; knee ligament laxity, 80%; flexion-extension, 23.3%; varus-valgus rotation, 20%; internal-external rotation, 66.7%; anteroposterior translation, 20%; proximodistal translation, 33.3%; mediolateral translation, 26.7%; IKDC scale score, 53.3%; and ACL-RSI score, 33.3%. Conclusion: At an average of 9 months after ACLR, objectively and subjectively measured knee functional performance was generally unsatisfactory especially the recovery of knee kinematics, which is an important prerequisite for RTS.
THE EFFECT OF PROACTIVE PERSONALITY ON CUSTOMER-ORIENTED BEHAVIORS: THE MEDIATING ROLE OF EMPLOYEE CREATIVITY

Purpose - The study explores creativity as a mediator in the impact of proactive personality on customer orientation in light of Allport’s trait theory, 1961. Methodology/Design - The study was conducted in four- and five-star hotels in a destination resort, Cesme, Izmir. The data was obtained via a questionnaire. 194 valid questionnaires were subjected to reliability and validity test. The hypothesized relationships were tested by regression. Findings - The results show that although proactive personality increases hotel employees’ creativity, proactive personality decreases their customer-oriented behavior. In addition, the results show that employee creativity has a partial mediating effect on the relationships between proactive personality and customer orientation. Originality of the research - The results can contribute to the literature on customer orientation in the hospitality industry and assist hoteliers in hiring the right employee. They offer practical implications to encourage the proactive and creative tendencies of their current employees.
Salt stress and ambience in the culture of yellow passion-fruit

Salt stress negatively affects the emergence and initial growth of crops. However, the use of protected environments emerges as an alternative to enhance these parameters. In this sense, the objective was to evaluate the effect of different environments and the salinity of the irrigation water on the emergence and development of yellow passion-fruit seedlings. The experiment was conducted at the University for International Integration of the Afro-Brazilian Lusophony, in the city of Redenção, Ceará. The experimental design was entirely randomized in a subdivided plot scheme, with four cultivation environments under different shading screens (black screen; white screen; red screen; all with 50% shading; and full sun), and two electrical conductivities of irrigation water: 0.3 dS m-1 and 3.0 dS m-1, with five repetitions of 25 seeds. The protected environment with 50% black roofing stood out from the others providing a better percentage of emergence, seedling height, stem diameter, number of leaves and radicle length in yellow passion-fruit. The deleterious effects of salts on the rate of emergence and shoot dry matter were reduced when the 50% black shade cloth was used. Irrigation with water of higher conductivity caused reductions and retardation in the emergence of passion-fruit. Besides negatively affecting seedling height, stem diameter and shoot dry matter.
Dynamic Sliding Window Modeling for Abstractive Meeting Summarization

Summarizing spoken content using neural approaches has raised emerging research interest lately, as sequence-to-sequence approaches have improved abstractive summarization performance. However, summarizing long meeting transcripts remains challenging. Meetings are multi-party spoken discussions where information is topically diffuse, making it harder for neural models to distill and cover essential content. Such meeting summarization tasks cannot readily benefit from pre-trained language models, which typically have input length limitations. In this work, we take advantage of the intuition that the topical structure of meetings tends to correlate with the meeting agendas. Inspired by this phenomenon, we propose a dynamic sliding window strategy to elegantly decompose the long source content of meetings to smaller contextualized semantic chunks for more resourceful modeling, and propose two meth-ods without additional trainable parameters for context boundary prediction. Experimental results show that the proposed framework achieves state-of-the-art abstractive summarization performance on the AMI corpus and obtains higher factual consistency on competitive baselines.
ELO-SPHERES intelligibility prediction model for the Clarity Prediction Challenge 2022

This paper describes and evaluates the ELO-SPHERES project sentence intelligibility model for the Clarity Prediction Challenge 2022. The aim of the model is to make predictions of the intelligibility of enhanced speech to hearing impaired listeners. Input to the model are binaural processed audio of short sentences generated in a simulated noisy and reverberant environment together with the original source audio. Output of the model is a prediction of the intelligibility of each sentence in terms of percentage words correct for a known hearing-impaired listener characterized by a pure-tone audiogram. Models are evaluated in terms of the root mean squared error of prediction. We approached this problem in three stages: (i) evaluation of the influences of the scene metadata on scores, (ii) construction of classifiers for estimation of scene metadata from audio, and (iii) training a non-linear regression model on the challenge data and evaluation using 5-fold cross validation. On the test data, a baseline system using only the standard short-time objective intelligibility metric on the better ear achieved a RMS prediction error of 27%, while our model that also took into account given and estimated scene data achieved an RMS error of 22%.
Multi-Pair Computation for C-RAN With Intra-Cloud and Inter-Cloud Communications

Most existing works on cloud radio access networks (C-RAN) design uplink or downlink transmission schemes separately. However, joint uplink and downlink transmission scheme design may offer additional benefits, especially in C-RANs with intra-cloud communications. In this letter, a multi-pair computation based transmission scheme is proposed for C-RANs with combined intra-cloud and inter-cloud communications, where the uplink and downlink are designed jointly while taking into account message source-destination information. The achievable rates are derived, optimized, and evaluated numerically under limited fronthaul capacity and quality of service (QoS) constraints. Simulation results reveal significant sum rate improvements compared to the state-of-the-art.
Algorithms and Lower Bounds for Submodular Cuts and Approximating Submodular Functions (Combinatorial Optimization and Discrete Algorithms)

We study the submodular sparsest cut problem, which is a generalization of the classical sparsest cut problem obtained by replacing the graph cut function with a general submodular function, and establish matching upper and lower bounds for its approximability. Then we apply the approximation algorithm for submodular sparsest cut to obtain bicriteria approximation results for a related problem, submodular balanced cut, which generalizes the balanced cut problem on graphs. We also give an improved lower bound for the problem of approximating a monotone submodular function everywhere. Then we present an algorithm for approximating monotone submodular functions with special structure, called two-partition functions. This algorithm’s guarantee is close to the lower bound, which uses two-partition functions, and therefore applies even in this special case.
VCSE: Time-Domain Visual-Contextual Speaker Extraction Network

Speaker extraction seeks to extract the target speech in a multi-talker scenario given an auxiliary reference. Such reference can be auditory, i.e., a pre-recorded speech, visual, i.e., lip movements, or contextual, i.e., phonetic sequence. References in different modalities provide distinct and complementary information that could be fused to form top-down attention on the target speaker. Previous studies have introduced visual and contextual modalities in a single model. In this paper, we propose a two-stage time-domain visual-contextual speaker extraction network named VCSE, which incorporates visual and self-enrolled contextual cues stage by stage to take full advantage of every modality. In the first stage, we pre-extract a target speech with visual cues and estimate the underlying phonetic sequence. In the second stage, we refine the pre-extracted target speech with the self-enrolled contextual cues. Experimental results on the real-world Lip Reading Sentences 3 (LRS3) database demonstrate that our proposed VCSE network consistently outperforms other state-of-the-art baselines.
Evaluasi Tingkat Kesiapan Keamanan Informasi Menggunakan Indeks Kami 4.2 dan ISO/IEC 27001:2013 (Studi Kasus : Diskominfo Kabupaten Gianyar)

Dinas Komunikasi dan Informatika atau Diskominfo Kabupaten Gianyar bertugas membantu Bupati menjalankan kebijakan bidang komunikasi dan informatika, termasuk mengelola dan mengamankan informasi dalam menjalankan pemerintahan daerah sesuai Peraturan Daerah Kabupaten Gianyar Nomor 5 Tahun 2016. Mengacu pada Peraturan Kemenkominfo Nomor 4 Tahun 2016, tentang Sistem Manajemen Pengamanan Informasi, bahwa “Penyelenggara Sistem Elektronik (PSE) harus melakukan penerapan keamanan terhadap informasi kepentingan umum, pelayanan publik, kelancaran penyelenggaraan negara atau pertahanan dan keamanan negara”. Diskominfo Kabupaten Gianyar harus menerapkan dan memperhatikan keamanan informasi yang dikelola selaku Penyelenggara Sistem Elektronik. Penelitian ini melakukan evaluasi pengamanan informasi yang mengacu pada peraturan BSSN Nomor 8 Tahun 2021 mengenai Penggunaan Indeks Keamanan Informasi (KAMI) sebagai alat evaluasi standar untuk mengevaluasi Tingkat Kesiapan pengamanan informasi guna terjaganya kerahasiaan, keaslian, keutuhan, dan kenirsangkalan informasi. Penelitian dilakukan menggunakan versi terbaru Indeks KAMI yaitu Versi 4.2. Hasil evaluasi akan dibandingkan dengan Kontrol ISO/IEC 27001:2013 untuk mendapatkan rekomendasi perbaikan. Berdasarkan hasil evaluasi diketahui Diskominfo Kabupaten Gianyar mendapatkan Skor Akhir sebesar 190 dengan Status Tingkat Kesiapan “Tidak Layak” untuk memenuhi Standar ISO/IEC 27001. Rekomendasi perbaikan yang diberikan diharapkan dapat meningkatkan efektivitas kinerja Pengamanan Informasi Diskominfo Kabupaten Gianyar
STABILITY OF NONLINEAR HAWKES PROCESSES

We address the problem of the convergence to equilibrium of a general class of point processes, containing, in particular, the nonlinear mutually exciting point processes, an extension of the linear Hawkes processes, and give general conditions guaranteeing the existence of a stationary version and the convergence to equilibrium of a nonstationary version, both in distribution and in variation. We also give a new proof of a result of Kerstan concerning point processes with bounded intensity and general nonlinear dynamics satisfying a Lipschitz condition.
Markov Mesh Models for Filtering and Forecasting with Leading Indicators

In this paper, a new approach for forecasting a time series based on a Markov mesh model that incorporates some key features of a leading indicator series is proposed. The features of the leading indicator series are incorporated via a weighting scheme in which the weights are assessed by a Bayesian approach. The Bayesian approach is implemented by a Gibbs sampling technique. The overall scheme proposed here can be viewed as Kaiman Filtering in two dimensions using the raster scan method.
OsTBP2.1, a TATA-Binding Protein, Alters the Ratio of OsNRT2.3b to OsNRT2.3a and Improves Rice Grain Yield

The OsNRT2.3a and OsNRT2.3b isoforms play important roles in the uptake and transport of nitrate during rice growth. However, it is unclear which cis-acting element controls the transcription of OsNRT2.3 into these specific isoforms. In this study, we used a yeast one-hybrid assay to obtain the TATA-box binding protein OsTBP2.1, which binds to the TATA-box of OsNRT2.3, and verified its important role through transient expression and RNA-seq. We found that the TATA-box of OsNRT2.3 mutants and binding protein OsTBP2.1 together increased the transcription ratio of OsNRT2.3b to OsNRT2.3a. The overexpression of OsTBP2.1 promoted nitrogen uptake and increased rice yield compared with the wild-type; however, the OsTBP2.1 T-DNA mutant lines exhibited the opposite trend. Detailed analyses demonstrated that the TATA-box was the key cis-regulatory element for OsNRT2.3 to be transcribed into OsNRT2.3a and OsNRT2.3b. Additionally, this key cis-regulatory element, together with the binding protein OsTBP2.1, promoted the development of rice and increased grain yield.
Autoencoder-Based Tongue Shape Estimation During Continuous Speech

Vocal tract shape estimation is a necessary step for articulatory speech synthesis. However, the literature on the topic is scarce, and most current methods lack adequacy to many physical constraints related to speech production. This study proposes an alternative approach to the task to solve speciﬁc issues faced in the previous work, especially those related to critical ar-ticulators. We present an autoencoder-based method for tongue shape estimation during continuous speech. An autoencoder is trained to learn the data’s encoding and serves as an auxiliary network for the principal one, which maps phonemes to the shapes. Instead of predicting the exact points in the target curve, the neural network learns how to predict the curve’s main components, i.e., the autoencoder’s representation. We show how this approach allows imposing critical articulators’ constraints, controlling the tongue shape through the latent space, and generating a smooth output without relying on any postprocessing method.
Post-processing of additive manufactured parts: a numerical strategy applied in maxillary implantology

To obtain a functional part from additive manufacturing (AM) technologies, some surfaces require post-processing by machining. An approach is developed using additive manufacturing supports as a clamping device for the milling operation. A model combining an analytical approach to determine the cutting forces with a finite element model (FEM) to predict the dynamical response of the workpiece-supports system is proposed. The complex structure of the supports is homogenized with a simplified geometry with equivalent stiffness and mechanical properties. A case study from the biomedical field is proposed: the finishing operation of a custom-made maxillary reconstruction plate is simulated. A parametric study is proposed with: (1) two different lattice geometries used as support structures; (2) up and down milling; (3) different depths of cut.
Real-time three-dimensional acoustic imaging system with a reflector and its applications

Acknowledgments The author wishes to express his appreciation to Professor Toru Sato for his constant guidance and constructive discussions throughout the present work. It would have been impossible to accomplish this thesis without his suggestions and criticisms. The author deeply thanks to Profs. Tetsuya Matsuda and Toshitaka Tsuda for their advice and suggestions for the present work. The author thanks Associate Professor Seiji Norimatsu and Research Associate Takuya Sakamoto for their helpful advice and continuous encouragement. The author thanks the colleagues of Prof. Sato's Laboratory for their collaborative discussions and for participating the experiment of tactile stimulation. Especially the author deeply appreciates the members of radar research group for their useful suggestions and comments. Preface The presentation of detailed environmental information allows the visually impaired to live their lives independently. To realize a robust vision substitute system with high time and spatial resolution, we propose a through-air acoustic imaging system with a reflector mirror and a 2-D element array. In the proposed system, a transmitter radiates a wide beam to the entire measurement field. The echo from a target is gathered by a concave mirror. The distance between the reflector mirror and the element array is fixed, and thus the defocused image is distributed along the array. The image of the target is reconstructed from the received signal on the array by numerical back projection. Since the proposed imager measures the entire target field in a transmit and receive event, the imager has significantly high time resolution. Furthermore, in this system most of the echo arriving at the mirror is gathered and received on the array. Thus the proposed imager has higher spatial resolution than that of a conventional imager with the same size element array. In this paper we evaluate the spatial resolution of the proposed imager using numerical calculation based on physical optics. In order to transfer detailed environmental information to the visually impaired, we propose two stimulation methods to enhance transfer information using a 2-D tactile array. In the first method, stimulators are divided into several groups. Since each stimulator group is activated alternately, the interval of stimulations can be shortened to less than the two-point discrimination threshold. In cases in which stimulators are divided into two and four groups, for example, the number of stimulators increases to two and four times, respectively, of that in the case of the two-point discrimination threshold. In the second method, …