Miracle-dz/UniFAR-FTUs huggingface dataset:
DVD-Audio versus SACD: Perceptual Discrimination of Digital Audio Coding Formats

To study perceptual discrimination between two digital audio coding formats, “Direct Stream Digital” and highresolution (24-bit, 176.4 kHz) PCM, subjective listening comparison tests were conducted with specially recorded sound stimuli in stereo and surround. To guarantee their reliability, validity and objectivity, the double-blind ABX tests followed three main principles: The signal chain should be based on identical audio components as far as possible; these components should be able to convey very high audio frequencies; and the test population should consist of various groups of subjects with different listening expectations and perspectives. The results showed that hardly any of the subjects could make a reproducible distinction between the two encoding systems. Hence it may be concluded that no significant differences are audible.
Effect of large trucks on traffic safety and operations. Final report

This report presents results, findings, and recommendations concerning the operation of longer combination vehicles (LCVs) in Michigan. Various truck combinations were simulated to operate on roadways and highways that are typical of those in Michigan. Several performance modes (operating situations) were used to evaluate each truck configuration and to analyze the simulation results. Policy decisions are called for in those cases where drawing the border line between acceptable and unacceptable performance levels involves more than considerations of vehicle mechanics and dynamics. Specifically, policy decisions are needed to choose between increased no-pass striping, restricted access of various truck configurations, and acceptable reduced levels of traffic flow on free-access roads. Results are presented, and conclusions are drawn regarding combinations that might be accommodated on the current roadway, combinations that require minimal changes to the infrastructure ("optimal" combinations), and the required changes in roadways to accommodate other combinations. Costs and benefits are also addressed.
Study on lung infections of patients with cancer under chemotherapy

The aim of this study is to assess the prevalence of lung infections among a group of hospitalized cancer patients who received chemotherapy as well as to describe a population of these patients. The clinical data and demographic information were collected from the archived files of in-patients referred to hematology center / Baghdad Teaching Hospital / Medical City , ministry of health, Iraq during the period of 2018. This study was carried out on 250 patients with different types of cancer ,they were mostly of age group (40 - 49) 59 / 250 (23.6)% , (14-19) 49 /250 (19.6%) and (60-69) 41/ 250(16.4%) . The patients had two major types of hematological malignancies: leukemia 109/250(43.6%) and lymphoma 105/250(42%), only 36/250 (14.4%) of the patients had other types of cancer which were distributed as : 26/250( 10.4 %) microangiopathic and 10 /250 (4 %) Multiple myelomoa. Statistically , there was significant correlation between age parameter and types of cancer in p<0.05 (p value : 0.000). Patients with positive Lung infections , as recorded on their medical reports, were not in high frequency and found only in 48/250(19.2%) of the cases which were mostly shown in patients with leukemia 28/48( 58.3 %) . In spite of that there was no significant correlation between lung infections and type of cancer . The higher percentage of patients, who admitted to the women wards of Baghdad hospital, came from Baghdad city 132/250(52.8%) and diyala governorate 45/250(18%). The married women were 173 /250(69.2%) and mostly they were housewives 200/250(80%). The illiteracy was in 67/250(26.8% ) of patients. Low frequent lung infections as resulted during this study could be because of the good control of infections by giving suitable antibiotic drugs for the bacterial infections but most of patients with positive cases may be infected with viruses or fungi ,which are difficult to diagnose and treat . These infections occurred due to the immune defects induced by chemotherapy treatments . So the greatest hope for the future is the availability of more targeted anticancer drugs that have fewer side-effects on the immune system.
Usefulness of impulse oscillometry for the assessment of bronchodilator response in elderly patients with chronic obstructive airway disease.

Background Impulse oscillometry (IOS) is a noninvasive and convenient technique to measure both airway resistance and reactance. This study aimed to evaluate whether IOS can be used to measure bronchodilator response (BDR) in elderly patients with asthma and chronic obstructive pulmonary disease (COPD) and also describe the difference between asthma and COPD. Methods Seventy patients (30 and 40 with asthma and COPD, respectively) over 65 years of age were enrolled. IOS and spirometry measurements were obtained before and after bronchodilator administration. Correlation analysis was used to compare the percentage changes in spirometry and IOS parameters after bronchodilator administration between the asthma and COPD groups. Results The changes in IOS parameters after bronchodilator administration were strongly correlated with changes in forced expiratory volume at 1 second (FEV1) and forced expiratory flow at 25-75% (FEF25-75). However, the percentage changes in IOS parameters failed to discriminate between the asthma and COPD groups. Receiver operating characteristic curve (ROC) analysis of resistance at 5 and 20 Hz (R5-20) at the best cutoff (-15.4% change) showed both high sensitivity and specificity for BDR. Conclusions IOS serves as a reliable and useful technique for identifying BDR in elderly patients with chronic obstructive airway disease. There was a difference in IOS parameters between the asthma and COPD groups; however, it was difficult to distinguish between both diseases. Further larger studies are required to investigate the real implications of using IOS in the clinical practice.
Rembrandts in the Attic: Unlocking the Hidden Value of Patents

Preface Acknowledgments 1. The New Competitive Battlefield 2. Restoring the Lost Art of Patent Strategy 3. The New CEO Challenge 4. Supercharging R&D through Patent Planning 5. Financial Leverage through Patent Mining 6. Patent Mapping Your Business Development Strategy 7. Surviving the Internet Patent Wars Notes Index
Optimization-based Approach to Cross-layer Resource Management in Wireless Networked Control Systems

Abstract : Wireless Networked Control Systems (NCS) are increasingly deployed to monitor and control Cyber-Physical Systems (CPS), as wireless network provides great convenience in terms of fully mobile operation, rapid deployment and flexible installation. To support mission critical operations of CPS, NCS need to achieve and maintain a desirable level of performance. In the wireless network however, resources are constrained by limited bandwidth and power; dynamic user behaviors and resource interference also aggravate network uncertainties and introduce random packet loss and time-varying delay. These resource constraints and network dynamics pose significant challenges and require a fresh treatment to the design of wireless NCS.
Facial Emotion Recognition using Deep Residual Networks in Real-World Environments

Automatic affect recognition using visual cues is an important task towards a complete interaction between humans and machines. Applications can be found in tutoring systems and human computer interaction. A critical step towards that direction is facial feature extraction. In this paper, we propose a facial feature extractor model trained on an in-the-wild and massively collected video dataset provided by the RealEyes company. The dataset consists of a million labelled frames and 2,616 thousand subjects. As temporal information is important to the emotion recognition domain, we utilise LSTM cells to capture the temporal dynamics in the data. To show the favourable properties of our pre-trained model on modelling facial affect, we use the RECOLA database, and compare with the current state-of-the-art approach. Our model provides the best results in terms of concordance correlation coefficient.
A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding

Speech self-supervised models such as wav2vec 2.0 and HuBERT are making revolutionary progress in Automatic Speech Recognition (ASR). However, they have not been totally proven to produce better performance on tasks other than ASR. In this work, we explored partial fine-tuning and entire fine-tuning on wav2vec 2.0 and HuBERT pre-trained models for three non-ASR speech tasks: Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding. With simple proposed downstream frameworks, the best scores reached 79.58% weighted accuracy on speaker-dependent setting and 73.01% weighted accuracy on speaker-independent setting for Speech Emotion Recognition on IEMOCAP, 2.36% equal error rate for Speaker Verification on VoxCeleb1, 89.38% accuracy for Intent Classification and 78.92% F1 for Slot Filling on SLURP, showing the strength of fine-tuned wav2vec 2.0 and HuBERT on learning prosodic, voice-print and semantic representations.
LVIS Challenge Track Technical Report 1st Place Solution: Distribution Balanced and Boundary Refinement for Large Vocabulary Instance Segmentation

This report introduces the technical details of the team FuXi-Fresher for LVIS Challenge 2021. Our method focuses on the problem in following two aspects: the long-tail distribution and the segmentation quality of mask and boundary. Based on the advanced HTC instance segmentation algorithm, we connect transformer backbone(Swin-L) through composite connections inspired by CBNetv2 to enhance the baseline results. To alleviate the problem of long-tail distribution, we design a Distribution Balanced method which includes dataset balanced and loss function balaced modules. Further, we use a Mask and Boundary Refinement method composed with mask scoring and refine-mask algorithms to improve the segmentation quality. In addition, we are pleasantly surprised to find that early stopping combined with EMA method can achieve a great improvement. Finally, by using multi-scale testing and increasing the upper limit of the number of objects detected per image, we achieved more than 45.4% boundary AP on the val set of LVIS Challenge 2021. On the test data of LVIS Challenge 2021, we rank 1st and achieve 48.1% AP. Notably, our APr 47.5% is very closed to the APf 48.0%.
Towards dynamic multi-modal phenotyping using chest radiographs and physiological data

The healthcare domain is characterized by heterogeneous data modalities, such as imaging and physiological data. In practice, the variety of medical data assists clinicians in decision-making. However, most of the current state-of-the-art deep learning models solely rely upon carefully curated data of a single modality. In this paper, we propose a dynamic training approach to learn modality-specific data representations and to integrate auxiliary features, instead of solely relying on a single modality. Our preliminary experiments results for a patient phenotyping task using physiological data in MIMIC-IV&chest radiographs in the MIMIC- CXR dataset show that our proposed approach achieves the highest area under the receiver operating characteristic curve (AUROC) (0.764 AUROC) compared to the performance of the benchmark method in previous work, which only used physiological data (0.740 AUROC). For a set of five recurring or chronic diseases with periodic acute episodes, including cardiac dysrhythmia, conduction disorders, and congestive heart failure, the AUROC improves from 0.747 to 0.798. This illustrates the benefit of leveraging the chest imaging modality in the phenotyping task and highlights the potential of multi-modal learning in medical applications.
A study of student perceptions and awareness of sustainability issues

In this paper we present the findings of an investigation into third year construction and property management students’ perceptions and awareness of sustainability issues. The students were all enrolled in BUIL 1217 Research and Sustainability; a core third-year construction management course at RMIT University. In this course students learn about sustainability principles, with a focus on sustainable construction and green building design, using different research methods. They also visit innovative green building projects, undertake building audits, question experts in the field, and study their own impact on the environment using interactive web-based tools. This learning and teaching approach was adopted in order to facilitate greater understanding of the often complicated decision making that surrounds sustainability issues. Students were asked to complete a survey at the beginning and end of the semester during which they studied “Research and Sustainability”. The survey asked students to respond to a series of questions pertaining to sustainability. The intention was to gain an understanding of their perceptions and awareness of sustainability issues before and after the course and to explore if undertaking the course was associated with more informed and/or transformed perceptions and awareness of sustainability. Results show that some students found it easier to articulate their knowledge about and views on sustainability; by responding with increased complexity to the questions posed at the end of the course, and some shifted the focus of their responses to reflect the course content. Findings will be used to inform approaches to learning and teaching and further develop our knowledge of student perceptions of sustainability.
An Interpretable Graph Generative Model with Heterophily

Many models for graphs fall under the framework of edge-independent dot product models. These models output the probabilities of edges existing between all pairs of nodes, and the probability of a link between two nodes increases with the dot product of vectors associated with the nodes. Recent work has shown that these models are unable to capture key structures in real-world graphs, particularly heterophilous structures, wherein links occur between dissimilar nodes. We propose the ﬁrst edge-independent graph generative model that is a) expressive enough to capture heterophily, b) produces nonnegative embeddings, which allow link predictions to be interpreted in terms of communities, and c) optimizes effectively on real-world graphs with gradient descent on a cross-entropy loss. Our theoretical results demonstrate the expressiveness of our model in its ability to exactly reconstruct a graph using a number of clusters that is linear in the maximum degree, along with its ability to capture both heterophily and homophily in the data. Further, our experiments demonstrate the effectiveness of our model for a variety of important application tasks such as multi-label clustering and link prediction.
What really makes an accretion disc MAD

Magnetically arrested accretion discs (MADs) around black holes (BH) have the potential to stimulate the production of powerful jets and account for recent ultra-high-resolution observations of BH environments. Their main properties are usually attributed to the accumulation of dynamically significant net magnetic (vertical) flux throughout the arrested region, which is then regulated by interchange instabilities. Here we propose instead that it is mainly a dynamically important {\it toroidal} field -- the result of dynamo action triggered by the significant but still relatively weak vertical field -- that defines and regulates the properties of MADs. We suggest that rapid convection-like instabilities, involving interchange of toroidal flux tubes and operating concurrently with the magnetorotatonal instability (MRI), can regulate the structure of the disc and the escape of net flux. We generalize the convective stability criteria and disc structure equations to include the effects of a strong toroidal field and show that convective flows could be driven towards two distinct marginally stable states, one of which we associate with MADs. We confirm the plausibility of our theoretical model by comparing its quantitative predictions to simulations of both MAD and SANE (strongly magnetized but not"arrested") discs, and suggest a set of criteria that could help to distinguish MADs from other accretion states. Contrary to previous claims in the literature, we argue that MRI is not suppressed in MADs and is probably responsible for the existence of the strong toroidal field.
Fast Solution Methods for Fractional Differential Equations in the Modeling of Viscoelastic Materials

Fractiona1 order models have proven to be a very useful tool for the modeling of the mechanical behaviour of viscoelastic materials. Traditional numerical solution methods exhibit various undesired properties due to the non-locality of the fractional differential operators, in particular regarding the high computational complexity and the high memory requirements. The infinite state representation is an approach on which one can base numerical methods that overcome these obstacles. Such algorithms contain a number of parameters that influence the final result in nontrivial ways. Based on numerical experiments, we initiate a study leading to good choices of these parameters.
Citizen ASAS-SN Data Release. I. Variable Star Classification Using Citizen Science

We present the first results from Citizen ASAS-SN, a citizen science project for the All-Sky Automated Survey for Supernovae (ASAS-SN) hosted on the Zooniverse platform. Citizen ASAS-SN utilizes the newer, deeper, higher cadence ASAS-SN g-band data and tasks volunteers to classify periodic variable star candidates based on their phased light curves. We started from 40,640 new variable candidates from an input list of ∼7.4 million stars with δ < −60° and the volunteers identified 10,420 new discoveries which they classified as 4234 pulsating variables, 3132 rotational variables, 2923 eclipsing binaries, and 131 variables flagged as Unknown. They classified known variable stars with an accuracy of 89% for pulsating variables, 81% for eclipsing binaries, and 49% for rotational variables. We examine user performance, agreement between users, and compare the citizen science classifications with our machine learning classifier updated for the g-band light curves. In general, user activity correlates with higher classification accuracy and higher user agreement. We used the user’s “Junk” classifications to develop an effective machine learning classifier to separate real from false variables, and there is a clear path for using this “Junk” training set to significantly improve our primary machine learning classifier. We also illustrate the value of Citizen ASAS-SN for identifying unusual variables with several examples.
A Constant-Factor Approximation for Quasi-bipartite Directed Steiner Tree on Minor-Free Graphs

We give the first constant-factor approximation algorithm for quasi-bipartite instances of Directed Steiner Tree on graphs that exclude fixed minors. In particular, for $K_r$-minor-free graphs our approximation guarantee is $O(r\cdot\sqrt{\log r})$ and, further, for planar graphs our approximation guarantee is 20. Our algorithm uses the primal-dual scheme. We employ a more involved method of determining when to buy an edge while raising dual variables since, as we show, the natural primal-dual scheme fails to raise enough dual value to pay for the purchased solution. As a consequence, we also demonstrate integrality gap upper bounds on the standard cut-based linear programming relaxation for the Directed Steiner Tree instances we consider.
Global Optimality and Finite Sample Analysis of Softmax Off-Policy Actor Critic under State Distribution Mismatch

In this paper, we establish the global optimality and convergence rate of an off-policy actor critic algorithm in the tabular setting without using density ratio to correct the discrepancy between the state distribution of the behavior policy and that of the target policy. Our work goes beyond existing works on the optimality of policy gradient methods in that existing works use the exact policy gradient for updating the policy parameters while we use an approximate and stochastic update step. Our update step is not a gradient update because we do not use a density ratio to correct the state distribution, which aligns well with what practitioners do. Our update is approximate because we use a learned critic instead of the true value function. Our update is stochastic because at each step the update is done for only the current state action pair. Moreover, we remove several restrictive assumptions from existing works in our analysis. Central to our work is the finite sample analysis of a generic stochastic approximation algorithm with time-inhomogeneous update operators on time-inhomogeneous Markov chains, based on its uniform contraction properties.
Computational thresholds for the fixed-magnetization Ising model

The ferromagnetic Ising model is a model of a magnetic material and a central topic in statistical physics. It also plays a starring role in the algorithmic study of approximate counting: approximating the partition function of the ferromagnetic Ising model with uniform external field is tractable at all temperatures and on all graphs, due to the randomized algorithm of Jerrum and Sinclair. Here we show that hidden inside the model are hard computational problems. For the class of bounded-degree graphs we find computational thresholds for the approximate counting and sampling problems for the ferromagnetic Ising model at fixed magnetization (that is, fixing the number of +1 and −1 spins). In particular, letting βc(Δ) denote the critical inverse temperature of the zero-field Ising model on the infinite Δ-regular tree, and ηΔ,β,1+ denote the mean magnetization of the zero-field + measure on the infinite Δ-regular tree at inverse temperature β, we prove, for the class of graphs of maximum degree Δ: (i) for β < βc(Δ) there is an FPRAS and efficient sampling scheme for the fixed-magnetization Ising model for all magnetizations η. (ii) For β > βc(Δ), there is an FPRAS and efficient sampling scheme for the fixed-magnetization Ising model for magnetizations η such that |η| >ηΔ,β,1+. (iii) For β > βc(Δ), there is no FPRAS for the fixed-magnetization Ising model for magnetizations η such that |η|
Voice Conversion Can Improve ASR in Very Low-Resource Settings

Voice conversion (VC) could be used to improve speech recognition systems in low-resource languages by using it to augment limited training data. However, VC has not been widely used for this purpose because of practical issues such as compute speed and limitations when converting to and from unseen speakers. Moreover, it is still unclear whether a VC model trained on one well-resourced language can be applied to speech from another low-resource language for the aim of data augmentation. In this work we assess whether a VC system can be used cross-lingually to improve low-resource speech recognition. We combine several recent techniques to design and train a practical VC system in English, and then use this system to augment data for training speech recognition models in several low-resource languages. When using a sensible amount of VC augmented data, speech recognition performance is improved in all four low-resource languages considered. We also show that VC-based augmentation is superior to SpecAugment (a widely used signal processing augmentation method) in the low-resource languages considered.