Miracle-dz/UniFAR-FTUs huggingface dataset:
Angle-resolved photoemission spectroscopy and optical renormalizations: Phonons or spin fluctuations

Improved resolution in both energy and momentum in angular-resolved photoemission spectroscopy (ARPES) data has led to the establishment of a definite energy scale in the dressed quasiparticle dispersion relations. The observed structure around 80 meV has been taken as evidence for coupling to phonons and has refocused the debate about the mechanism of superconductivity in the cuprates. Here we address the relative merits of phonon as opposed to spin-fluctuation mechanisms. Both possibilities are consistent with ARPES. On the other hand, when the considerations are extended to infrared optical data. a spin-fluctuation mechanism provides a more natural interpretation of the combined sets of data in Bi 2 Sr 2 CaCu 2 O 8 + δ .
Vaccinating SER to Neutralize Adversarial Attacks with Self-Supervised Augmentation Strategy

Speech emotion recognition (SER) is being actively developed in multiple real-world application scenarios, and users tend to become intimately connected to these services. However, most existing SER models are vulnerable against to a growing diverse set of adversarial attacks. The degraded performances can lead to dreadful user experiences. In this work, we propose a self-supervised augmentation defense (SSAD) strategy to learn a single purify network acts as a general front-end to neutralize adversarial distortions without knowing the types of the attack beforehand. We show that our approach can robustly defend against two different gradient-based attacks at various intensities on the well-known IEMOCAP. Further, by examining metrics of protection efficacy and recovery rate, our approach shows a consistent protection behavior to prevent adverse out-comes and is capable to recover samples that are wrongly-predicted before purification.
Prosodic Information in Dialect Identification of a Tonal Language: The case of Ao

Dialect identiﬁcation has been explored profusely in major languages, such as Arabic, Chinese and Spanish. This paper presents an automatic dialect identiﬁcation system in the Ao language using prosodic features. Ao is a low-resource Tibeto-Burman tonal language spoken in Nagaland in the North-Eastern part of India. It consists of three distinct dialects: Chungli, Mongsen and Changki. Prosodic characteristics are believed to have an essential role in tonal languages. In this direction, the current work focuses to investigate the prosodic characteristics to build a discriminative system in identifying the three Ao dialects. The statistical and Low-Level Descriptors (LLD) of prosodic features are used in this work. The prosodic features such as F 0 , loudness, shimmer, jitter, voiced and unvoiced segment length, etc., are utilized in this study. The experiments are conducted using SVM and attention-based BiGRU classiﬁers in trisyllabic words and passage-level datasets, respectively. The combination of prosodic features outperforms the MFCC (baseline) feature. The Voice Quality and Temporal (VQT) feature set is the best performing prosodic feature. The statistical analysis also shows that the VQT features are statistically signiﬁcant. The performances of SVM and attention-based Bi-GRU classiﬁers indicate the signiﬁcance of prosodic information in classifying the three Ao dialects.
Non-commutative quadric surfaces

The 4-dimensional Sklyanin algebra is the homogeneous coordinate ring of a noncommutative analogue of projective 3-space. The degree-two component of the algebra contains a 2-dimensional subspace of central elements. The zero loci of those central elements, except 0, form a pencil of non-commutative quadric surfaces, We show that the behavior of this pencil is similar to that of a generic pencil of quadrics in the commutative projective 3-space. There are exactly four singular quadrics in the pencil. The singular and non-singular quadrics are characterized by whether they have one or two rulings by non-commutative lines. The Picard groups of the smooth quadrics are free abelian of rank two. The alternating sum of dimensions of Ext groups allows us to define an intersection pairing on the Picard group of the smooth noncommutative quadrics. A surprise is that a smooth noncommutative quadric can sometimes contain a "curve" having self-intersection number -2. Many of the methods used in our paper are noncommutative versions of methods developed by Buchweitz, Eisenbud and Herzog: in particular, the correspondence between the geometry of a quadric hypersurface and maximal Cohen-Macaulay modules over its homogeneous coordinate ring plays a key role. An important aspect of our work is to introduce definitions of non-commutative analogues of the familiar commutative terms used in this abstract. We expect the ideas we develop here for 2-dimensional non-commutative quadric hypersurfaces will apply to higher dimensional non-commutative quadric hypersurfaces and we develop them in sufficient generality to make such applications possible.
Automatic Bone Segmentation from MRI for Real-Time Knee Tracking in Fluoroscopic Imaging

Recent progress in real-time tracking of knee bone structures from fluoroscopic imaging using CT templates has opened the door to studying knee kinematics to improve our understanding of patellofemoral syndrome. The problem with CT imaging is that it exposes patients to extra ionising radiation, which adds to fluoroscopic imaging. This can be solved by segmenting bone templates from MRI instead of CT by using a deep neural network architecture called 2.5D U-Net. To train the network, we used the SKI10 database from the MICCAI challenge; it contains 100 knee MRIs with their corresponding annotated femur and tibia bones as the ground truth. Since patella tracking is essential in our application, the SKI10 database was augmented with a new label named UofA Patella. Using 70 MRIs from the database, a 2.5D U-Net was trained successfully after 75 epochs with an excellent final Dice score of 98%, which compared favourably with the best state-of-the-art algorithms. A test set of 30 MRIs were segmented using the trained 2.5D U-Net and then converted into 3D mesh templates by using a marching cube algorithm. The resulting 3D mesh templates were compared to the 3D mesh model extracted from the corresponding labelled data from the augmented SKI10. Even though the final Dice score (98%) compared well with the state-of-the-art algorithms, we initially found that the Euclidean distance between the segmented MRI and SKI10 meshes was over 6 mm in many regions, which is unacceptable for our application. By optimising many of the hyper-parameters of the 2.5D U-Net, we were able to find that, by changing the threshold used in the last layer of the network, one can significantly improve the average accuracy to 0.2 mm with a variance of 0.065 mm for most of the MRI mesh templates. These results illustrate that the Dice score is not always a good predictor of the geometric accuracy of segmentation and that fine-tuning hyper-parameters is critical for improving geometric accuracy.
Laminin-5, Fibronectin, and Type IV Collagen as Potential Biomarkers of Brain Glioma Malignancy

The presented work is based on the quantification of LN-5, FN, and COL IV in blood plasma as potential biomarkers in patients diagnosed with glioma in grades G1 to G4. The obtained concentration results were compared with the protein content in the control group, which consisted of smokers of different ages. The obtained results were statistically analysed and interpreted based on the available clinical description. Quantitative determinations of LN-5, FN, and COL IV were performed with the use of SPRi biosensors specific to the tested proteins. Comparing groups K and G4, as well as G2 and G4, statistically significant relationships between changes in the concentration of individual proteins, were observed. The analysis showed significant correlations between FN and LN-5, between FN and COL IV, and between LN-5 and COL IV. There was a moderate positive correlation between individual proteins and the age of the patient. The ROC analysis distinguished patients with advanced disease from the control group. The results of the research show that LN-5, FN, and COL IV are effective biomarkers of brain glioma that may be helpful in non-invasive diagnosis and determining the grade of the disease.
Development of Bio-Nanofluids Based on the Effect of Nanoparticles’ Chemical Nature and Novel Solanum torvum Extract for Chemical Enhanced Oil Recovery (CEOR) Processes

This study aimed to develop novel bio-nanofluids using Solanum torvum extracts in synergy with nanoparticles of different chemical nature as a proposal sustainable for enhanced oil recovery (EOR) applications. For this, saponin-rich extracts (SRE) were obtained from Solanum torvum fruit using ultrasound-assisted and Soxhlet extraction. The results revealed that Soxhlet is more efficient for obtaining SRE from Solanum torvum and that degreasing does not generate additional yields. SRE was characterized by Fourier transformed infrared spectrophotometry, thermogravimetric analysis, hydrophilic–lipophilic balance, and critical micelle concentration analyses. Bio-nanofluids based on SiO2 (strong acid), ZrO2 (acid), Al2O3 (neutral), and MgO (basic) nanoparticles and SRE were designed to evaluate the effect of the chemical nature of the nanoparticles on the SRE performance. The results show that 100 mg L−1 MgO nanoparticles improved the interfacial tension up to 57% and the capillary number increased by two orders of magnitude using this bio-nanofluid. SRE solutions enhanced with MgO recovered about 21% more than the system in the absence of nanoparticles. The addition of MgO nanoparticles did not cause a loss of injectivity. This is the first study on the surface-active properties of Solanum torvum enhanced with nanomaterials as an environmentally friendly EOR process.
What is Meaning?: Fundamentals of Formal Semantics

Acknowledgements. 1. The Fundamental Question. What is a Meaning? Meanings are Out in the World. We Should Think of the Meaning of Sentences in Terms of Truth-Conditions. 2. Putting a Meaning Together from Pieces. Names Refer. Incomplete Propositions. Predication is Saturation. Compositionality. Syntax and Semantics. 3. More about Predicates. Other types of Predicates: Adjectives, Predicate Nominals. Transitive Verbs. Relative Clauses. Topicalization. Sub-atomic Semantics. Modeling Properties with Sets and Functions. 4. Modifiers. Adjective + N Combination. More Issues with Adjectives. Relative Clauses as Modifiers. Adverbs. The Form of Meanings and Their World-Describing Content. 5. Complexities of Referring Expressions. Definite NPs. Some Subtleties. A Bit about Indefinite NPs. Theories of reference. Plurals and Mass Terms. Kinds. Pronouns and Anaphora. 6. Quantifiers. Generalized Quantifiers: Predicates of Predicates, or Sets of Sets. NP Conjunction. Negative Polarity Items. Quantifiers in Object Position. 7. Extensional vs. Intensional Contexts. 8. Tense, Aspect, and Modality. Tense. Aspect. Modality. 9. Propositional Attitudes. A possible Worlds Semantics for Belief and Desire. Logical Consequences of the Modal Analysis of Propositional Attitude Verbs. Two Foundational Problems: Coreferential Terms and Logical Truths. Structure and Meaning. Or, Have We Reached the Limits of Semantics? 10. The Pragmatics of What's Given. Indexicality and Deixis. Presupposition. Speech Acts. Focus and Topic. 11. The Pragmatics of Inference. Properties of Implicature. 12. Formal Semantics Today. Diversity within Formal Semantics. Relationships with Other Varieties of Semantics. Relationships with Other Fields. Appendix: Answers to Selected Exercises. References. Index.
Speech Modification for Intelligibility in Cochlear Implant Listeners: Individual Effects of Vowel- and Consonant-Boosting

Previous research has demonstrated techniques to improve automatic speech recognition and speech-in-noise intelligibility for normal hearing (NH) and cochlear implant (CI) listeners by synthesizing Lombard Effect (LE) speech. In this study, we emulate and evaluate segment-specific modifications based on speech production characteristics observed in natural LE speech in order to improve intelligibility for CI listeners. Two speech processing approaches were designed to modify representation of vowels, consonants, and the combination using amplitude-based compression techniques in the “ electric domain ” – referring to the stimulation sequence delivered to the intracochlear electrode array that corresponds to the acoustic signal. Performance with CI listeners resulted in no significant difference using consonant-boosting and consonant- and vowel-boosting strategies with better representation of mid-frequency and high-frequency content corresponding to both formant and consonant structure, respectively. Spectral smearing and decreased amplitude variation were also observed which may have negatively impacted intelligibility. Segmental perturbations using a weighted logarithmic and sigmoid compression functions in this study demonstrated the ability to improve representation of frequency content but disrupted amplitude-based cues, regardless of comparable speech intelligibility. While there are an infinite number of acoustic domain modifications characterizing LE speech, this study demonstrates a basic framework for emulating segmental differences in the electric domain.
An Anchor-Free Network With Density Map and Attention Mechanism for Multiscale Object Detection in Aerial Images

Accurate detection of the multiple classes in aerial images has become possible with the use of anchor-based object detectors. However, anchor-based object detectors place a large number of preset anchors on images and regress the target bounding box while anchor-free object detections predict the location of objects directly and avoid the carefully predefined anchor box parameters. Object detection in aerial images is faced with two main challenges: 1) the scale diversity of the geospatial objects and 2) the cluttered background in complex scenes. In this letter, to address these challenges, we present a novel Anchor-Free Network with a Density map (DM) and attention mechanism (DA2FNet). Considering the extreme density variations of the detection instances among the different categories in aerial images, the proposed DA2FNet model conducts DM estimation with image-level supervision for the geospatial object counting, to acquire global knowledge about the scale information. A simple and effective image-level global counting loss function is also introduced. In addition, a compositional attention network (AN) is further introduced to enhance the saliency of the foreground objects. The proposed DA2FNet method was compared with the state-of-the-art object detection models, achieving excellent performance on the NWPU VHR-10, RSOD, and DOTA datasets.
Basic principles of nuclear magnetic resonance imaging

The intent of this book is to help clinicians understand the basic physical principles of magnetic resonance (MR) imaging. The book consists of the following: a discussion of elementary considerations; pulse sequencing; localization of MR signals in space; MR equipment; MR contrast agents; clinical applications; MR spectroscopy; and biological effects of MR imaging; a set of appendixes; and a bibliography. Illustrations and images are included.
Ethnic/Racial Differences in Doctoral Degree Attainment in Texas: A Multiyear Analysis

In this multiyear, statewide investigation, the degree to which changes had occurred in the numbers and percentages of doctoral degrees awarded to White, Hispanic, and Black students in Texas public postsecondary institutions from the 1999-2000 academic year through the 2018-2019 academic year was examined. The highest numbers of doctoral degrees were awarded to White students, followed by Hispanic students and Black students, respectively. Statistically significant differences were present for the percentages of doctoral degrees awarded to Hispanic and to Black students between the 1999-2000 academic year and the 2018-2019 academic year. The percentage of doctoral degrees awarded to White students decreased by nearly 21%, whereas the percentage of master’s degrees awarded to Hispanic students and to Black students increased by 11.07% and 9.39%, respectively. As such, the ethnic/racial diversity of doctoral degree recipients increased over the academic years of data analyzed herein.
Comprehensive Investigation of In-Plane and Out-of-Plane Die Shift in Flexible Fan-Out Wafer-Level Packaging Using Polydimethylsiloxane

Die shift in fan-out wafer-level packaging (FOWLP) is a major roadblock, limiting package scaling and performance. It not only limits the wire pitch but also results in reliability problems and yield loss. In this work, we systematically investigate and predict the in-plane and out-of-plane die shift through numerical and experimental analysis in FlexTrate, which is a flexible hybrid electronics (FHE) platform based on FOWLP for flexible heterogeneous integration. First, the in-plane die shift is studied through simulation of the molding process and the substrate cure shrinkage. The molding process is simulated through a fluid analysis which explores the shear force. In addition, a thermo-mechanical analysis simulates the polydimethylsiloxane (PDMS) curing process followed by handler release. Based on this model, the effect of die size, die spacing, die thickness, and PDMS thickness on die shift are analyzed. The results indicate that smaller die size has a larger impact on die shift compared to the other factors. The predicted die shift is compared with the experimental results to verify the validity. Further, the experimental characterization of out-of-plane die shift is analyzed through the study of substrate warpage and die placement force which reveal the deleterious effect of high temperature processes. Finally, using the developed numerical model, the die placement is optimized for a surface electromyography (sEMG) system. This detailed analysis sheds light on the various factors which influence die shift, and hence is an important step to push the boundaries of current FHE to new limits.
Speech Separation for an Unknown Number of Speakers Using Transformers With Encoder-Decoder Attractors

Speaker-independent speech separation for single-channel mixtures with an unknown number of multiple speakers in the waveform domain is considered in this paper. To deal with the unknown number of sources, we incorporate an encoder-decoder attractor (EDA) module into a speech separation network. The neural network architecture consists of a trainable encoder-decoder pair and a masking network. The mask network in the proposed approach is inspired by the transformer-based SepFormer separation system. It contains a dual-path block and a triple path block, each block modeling both short-time and long-time dependencies in the signal. The EDA module first summarises the dual-path block output using an LSTM encoder and generates one attractor vector per speaker in the mixture using an LSTM decoder. The attractors are combined with the dual-path block output to generate speaker channels, which are processed jointly by the triple-path block to predict the mask. Further, a linear-sigmoid layer, with attractors as the input, predicts a binary output to indicate a stopping criterion for attractor generation. The proposed approach is evaluated on the WSJ0-mix dataset with mixtures of up to five speakers. State-of-the-art results are obtained in the speech separation quality and speaker counting for all the mixtures.
A Simplified Algorithm for Setting the Observer Parameters for Second-Order Systems with Persistent Disturbances Using a Robust Observer

The properties of the convergence region of the estimation error of a robust observer for second-order systems are determined, and a new algorithm is proposed for setting the observer parameters, considering persistent but bounded disturbances in the two observation error dynamics. The main contributions over closely related studies of the stability of state observers are: (i) the width of the convergence region of the observer error for the unknown state is expressed in terms of the interaction between the observer parameters and the disturbance terms of the observer error dynamics; (ii) it was found that this width has a minimum point and a vertical asymptote with respect to one of the observer parameters, and their coordinates were determined. In addition, the main advantages of the proposed algorithm over closely related algorithms are: (i) the definition of observer parameters is significantly simpler, as the fulfillment of Riccati equation conditions, solution of LMI constraints, and fulfillment of eigenvalue conditions are not required; (ii) unknown bounded terms are considered in the dynamics of the observer error for the known state. Finally, the algorithm is applied to a model of microalgae culture in a photobioreactor for the estimation of biomass growth rate and substrate uptake rate based on known concentrations of biomass and substrate.
Robust incremental optical flow

This thesis addresses the problem of recovering 2D image velocity, or optical flow, robustly over long image sequences. We develop a robust estimation framework for improving the reliability of motion estimates and an incremental minimization framework for recovering flow estimates over time. Attempts to improve the robustness of optical flow have focused on detecting and accounting for motion discontinuities in the optical flow field. We show that motion discontinuities are one example of a more general class of model violations and that by formulating the optical flow problem as one of robust estimation the problems posed by motion discontinuities can be reduced, and the violations can be detected. Additionally, robust estimation provides a powerful framework for early vision problems that generalizes the popular "line process" approaches. We formulate a temporal continuity constraint, which reflects the fact that the motion of a surface changes gradually over time. We exploit this constraint to develop a new incremental minimization framework and show how it is related to standard recursive estimation techniques. Within this framework we implement two incremental algorithms for minimizing non-convex objective functions over time; Incremental Stochastic Minimization (ISM) and Incremental Graduated Non-Convexity (IGNC). With this approach, motion estimates are always available; they are refined over time, the algorithm adapts to scene changes, and the amount of computation between frames is kept fixed. The psychophysical implications of temporal continuity are discussed and the power of the incremental minimization framework is demonstrated by extending image feature extraction over time.