Miracle-dz/UniFAR-FTUs huggingface dataset:
Collective Imagination Against the Given: A Conversation with Noah De Lissovoy

JR: I’m in Berlin. Germany is again under lockdown. You’re in Austin. Texas has recently experienced mass blackouts, water and food shortages. We’re over one year into a pandemic that has shaped a large part of our lived reality. I believe it’s even more relevant under these conditions to explore what a critical pedagogy might look like today. I’d like to do this through revisiting theses regarding education in neoliberalism that you have developed in your recent work. In particular, in an essay you published in 2018, “Pedagogy of the Anxious: Rethinking Critical Pedagogy in the Context of Neoliberal Autonomy and Responsibilization,” you lay out how “the challenges posed to Paulo Freire’s conceptualization of the subject under neoliberalism have not been fully addressed,” how “we must seek to reinvent this in the context of conditions that he was not able to fully foresee,” and how this has reshaped the struggle for emancipation in terms of the “narrow autonomy that is offered by neoliberal education. ” I’m very keen to have more depth or detail around the terms of critical pedagogy in the neoliberal context, and to do this I’d like to put a series of questions to you related to each of these terms, and to your arguments in this article, even if it means that sometimes I’m asking a similar type question from a slightly different angle. Before I do this, I want to begin with a short outline of the methodology course at the heart of the work we do at Goldsmiths. This is site and context-specific in how it connects the classroom to what’s beyond the classroom and how it connects the work students do to the world this inhabits. In other words, while the work we do in Goldsmiths is specific to a particular academic context, we understand this context to be contingent and permeable and to speak to other contexts within and beyond academia. The methodology brings a reflexive gaze to this work and to how it is embedded in and engages with what you describe as “neoliberalism’s logic of scarcity” or the “relentless competition for symbolic capital and self-driven human capital development. ” In developing this
A Spatial-Temporal ECG Emotion Recognition Model Based on Dynamic Feature Fusion

Physiological signals have been widely used for emotion recognition, but current works seldom apply the feature fusion and attention technologies to ECG emotion recognition. In this paper, we propose a novel ECG emotion recognition method, which adopts a spatial and temporal ECG emotion recognition model based on dynamic feature fusion (DFF-STM) to learn spatial-temporal representations of different ECG areas. Considering the difference in roles played by the different ECG areas in ECG emotion recognition, a dynamic weight distribution layer is introduced into DFF-STM to extract ECG temporal features and learn weights to adjust (e.g., enhance or weaken) the contribution of the ECG areas at the same time. Finally, we conduct experiments using real ECG data on the AMIGOS dataset to evaluate the performance of the DFF-STM on valence and arousal labels. Experiments show that dynamic feature fusion for ECG emotion recognition is much better than those using only handcraft features and deep features.
Enhance Composed Image Retrieval via Multi-Level Collaborative Localization and Semantic Activeness Perception

Composed image retrieval (CIR) is an emerging and challenging research task that combines two modalities, a reference image, and a modification text, into one query to retrieve the target image. In online shopping scenarios, the user would use the modification text as feedback to describe the difference between the reference and the desired image. In order to handle the task, there must be two main problems needed to be addressed. One is the localization problem: how to precisely find those spatial areas of the image mentioned by the text. The other is the modification problem: how to effectively modify the image semantics based on the text. However, existing methods merely fuse information coarsely from the two-modality, while the accurate spatial and semantic correspondence between these two heterogeneous features tends to be neglected. Therefore, image details cannot be precisely located and modified. To this end, we consider integrating information from the two modalities more accurately from spatial and semantic aspects. Thus, we propose an end-to-end framework for the CIR task, which contains three key components, i.e., Multi-level Collaborative Localization module (MCL), Differential Semantics Discrimination module (DSD), and Image Difference Enhancement constraints (IDE). Specifically, to solve the localization problem, MCL precisely locates the text to the image areas by collaboratively using text positioning information on multiple image layers. For the modification problem, DSD builds a distribution to evaluate the modification possibility of each image semantic dimension, and IDE effectively learns the modification patterns of text against image embedding based on the distribution. Extensive experiments on three datasets show that the proposed method achieves outstanding performance against the SOTA methods.
Knowledge-Graph Augmented Music Representation for Genre Classification

In this paper, we propose KGenre, a knowledge-embedded music representation learning framework for improved genre classification. We construct the knowledge graph from the metadata in the open-source FMA-medium and OpenMIC-2018 datasets, with no extra information/effort required. KGenre then mines the correlation between different genres from the knowledge graph and embeds such correlation in audio representation. To our knowledge, KGenre is the first method fusing the audio with high-level knowledge for music genre classification. Experimental results demonstrate the embedded knowledge can effectively enhance the audio feature representation, and the genre classification performance surpasses the state-of-the-art methods.
Pre-Diagnostic Saliva Microbiota of School-Aged Children Who Developed Type 1 Diabetes or Inflammatory Bowel Diseases

Altered commensal microbiota composition has been associated with pediatric type 1 diabetes mellitus (T1D) and inflammatory bowel diseases (IBD), but the causal relationship is still unclear. To search for potential pre-diagnostic biomarkers for pediatric T1D or IBD, we compared microbiota in saliva samples in a nested case-control design comprising children developing T1D (nchildren = 52) or IBD (nchildren = 21) and controls with a similar age, sex, and residential area (nchildren = 79). The pre-diagnostic saliva microbiota alpha- and beta-diversity of children who would develop T1D (nsamples = 27) or IBD (nsamples = 14) minimally varied from that of controls. The relative abundances of Abiotrophia were higher, while those of Veillonella, Actinomyces, Megasphaera, Butyrivibrio, and Candidatus ancillula were lower in children who would develop T1D. Within 2 years before diagnosis, the metabolic PWY-5677 pathway (converting succinate into butyrate) was lower in pre-T1D samples than in controls (q = 0.034). No significant pre-IBD differences were found. In conclusion, saliva microbiota diversity or composition were not successful predictors for pediatric T1D nor IBD. Intriguingly, the succinate fermentation pathway was predicted to be lowered before the onset of T1D. Thus, investigating functional pathways might provide a better approach in searching for biomarkers for autoimmune disease in the future.
Menilai pertumbuhan dan trend pada studi sustainable development goals: Analisis bibliometrik

Produksi ilmiah tentang Sustainable Development Goals telah tumbuh dalam beberapa tahun terakhir. Namun, masih sedikit penelitian yang memberikan gambaran global tentang topik ini. Tujuan penelitian adalah untuk mengidentifikasi tren penelitian serta produksi ilmiah dari waktu ke waktu. Analisis bibliometri digunakan untuk menganalisis sampel dari 3608 artikel yang diterbitkan antara tahun 1996 hingga 2022. Sustainable Development Goals merupakan kueri yang digunakan dalam judul artikel untuk mendapatkan metadata dari database bibliografi Scopus. Microsoft excel digunakan untuk melakukan analisis frekuensi, VOSviewer untuk visualisasi data dan Harzing's Publish or Perish digunakan untuk metrik kutipan. Hasil menunjukkan bahwa ada peningkatan literatur tentang Sustainable Development Goals dari tahun 1996 hingga 2022, dan peningkatan yang signifikan sejak tahun 2016. Ada berbagai penelitian yang melibatkan kolaborasi multi-penulis yang diterbitkan dalam berbagai bahasa, dengan total 32 penulis dari 141 negara yang berbeda dan 32 lembaga. Amerika Serikat adalah kontributor terbesar untuk penelitian ini. Publikasi terbanyak diterbitkan oleh Sustainability Switzerland. Temuan ini dapat membantu penelitian di masa depan terkait dengan ikhtisar penelitian Sustainable Development Goals di seluruh dunia dari waktu ke waktu.
Robust Cubature Kalman filter based on Huber M estimator

A class of robust Cubature Kalman filter(CKF) algorithm with statistical regression is proposed to solve the problem that the conventional CKF declines in accuracy and further diverges when the noise is not Gaussian noise or its prior statistic is unknown. Two kinds of robust CKFs with linear approximation regression or not are deduced and filtering steps are designed. The directly nonlinear regression overcomes the shortcoming of CKF with linear approximation of measurement align. Simulation example with a model of mixed Gaussian noise analyzes and contrasts the performances of filter with the three kinds of Cubature Kalman Filter. The results show that the two robust Cubature Kalman filters outbalance the conventional CKF in the accuracy and consistency of filtering, and the robust CKF without linear approximation owns stronger robustness and better performance compared with the other robust CKF.
Stable and Accurate Loosely-Coupled Scheme for Unsteady Fluid-Structure Interaction

This paper presents a new loosely-coupled partitioned procedure for modeling fluid-structure interaction. The procedure relies on a higher-order Combined Interface Boundary Condition (CIBC) treatment for improved accuracy and stability of fluid-structure coupling. Traditionally, continuity of velocity and momentum flux along interfaces are satisfied through algebraic interface conditions applied in a sequential fashion, which is often referred to staggered computation. In existing staggered procedures, the interface conditions undermine stability and accuracy of coupled fluid-structure simulations. By utilizing the CIBC technique on the velocity and momentum flux boundary conditions, a staggered coupling procedure can be constructed with similar order of accuracy and stability of standalone computations. Introduced correction terms for velocity and momentum flux transfer can be explicitly added to the standard staggered time-stepping stencils so that the discretization is well-defined across the deformable interface. The new formulation involves a coupling parameter, which has an interval of well-performing values for both classical 1D closed- and open-elastic piston problems. The technique is also demonstrated in 2D in conjunction with the common refinement method for subsonic flow over a thin-shell structure.
Sparse Delay-Doppler Channel Estimation for OTFS Modulation Using 2D-Music

In this paper, we address the problem of estimating the delays and Doppler shifts introduced by a sparse wireless channel for orthogonal time frequency space (OTFS) modulation. We show that in the discrete time-frequency (TF) domain, the received signal resulting from an OTFS pilot signal is a superposition of two-dimensional (2D) complex exponentials, where the 2D frequencies of the complex exponentials are given by the delays and Doppler shifts of the scatterers. Thus, estimating the delays and Doppler shifts can be formulated as a 2D sinusoidal frequency estimation problem. We apply a 2D TF domain version of the well-known MUSIC algorithm to recover the delays and Doppler shifts. Since the reduced guard interval frame structure eliminates most of the interference between the pilot and data symbols, the data symbols can be filtered out from the received signal such that only the pilot signal component is used for the 2D MUSIC algorithm.
Large Covariance Matrix Estimation With Oracle Statistical Rate via Majorization-Minimization

The $\boldsymbol{\ell}_{\mathbf{1}}$ penalized covariance estimator has been widely used for estimating large sparse covariance matrices. It is recognized that $\boldsymbol{\ell}_{\mathbf{1}}$ penalty introduces a non-negligible estimation bias, while a proper utilization of non-convex penalty may lead to an estimator with a refined statistical rate of convergence. To eliminate the estimation bias, in this paper we propose to estimate large sparse covariance matrices using the non-convex penalty. It is challenging to analyze the theoretical properties of the resulting estimator because popular iterative algorithms for convex optimization no longer have global convergence guarantees for non-convex optimization. To tackle this issue, an efficient algorithm based on the majorization-minimization (MM) framework is developed by solving a sequence of convex relaxation subproblems. An approximation solution to each subproblem is obtained via the proximal gradient method with a linear convergence rate. We clearly establish the statistical properties of all the approximate solutions generated by the MM-based algorithm and prove that the proposed estimator achieves the oracle statistical rate in the Frobenius norm under weak technical assumptions. We also consider a modification of the proposed estimation method using the correlation matrix and show that the modified correlation-based covariance estimator enjoys a better rate in the spectral norm. Our theoretical findings are corroborated through extensive numerical experiments on both synthetic data and real-world datasets.
Finding and improving bounds of real functions by thermodynamic arguments

The possibility of stating the second law of thermodynamics in terms of the increasing behaviour of a physical property establishes a connection between that branch of physics and the theory of algebraic inequalities. We use this connection to show how some well-known inequalities, such as the standard bounds for the logarithmic function or generalizations of Bernoulli's inequality, can be derived by thermodynamic methods. Additionally, we show that by comparing the global entropy production in processes implemented with decreasing levels of irreversibility but subject to the same change of state of one particular system, we can find progressively better bounds for the real function that represents the entropy variation of the system. As an application, some new families of bounds for the function log (1+x) are obtained by this method.
Evaluation The Success of Artificial Insemination Using Frozen Sexed Semen Based on Different Estrus Characters

ABSTRACT. Knowledge of farmers about estrus detection is one of the factors that affect fixed-time artificial insemination (AI). This study aims to evaluate the success rate of AI using Y sexing frozen semen based on the estrus character of cattle. Forty-five Limousin Crossed Cows involved in this research with a Body Condition Score of 3-5 (1-9 scale), 1.8-7 years old. Y sexing frozen semen is produced by the Singosari Center for Artificial Insemination using Percoll's Gradient Density Centrifugation method. Observation of estrus character was done before insemination with the deep insemination technique of AI. Artificial insemination was carried out using double doses at the 2nd and 8th hours after estrus. Rheinbio vitamins as BioATP+ are injected after AI. The results showed that the Conception Rate and Pregnancy Rate based on the estrus character was higher as indicated by the red colour of the vulva was 17.78% and 22.22%; very swollen vulva was 20% and 22.22%; abundant cervical mucus was 15.56% and 17.78%, and vaginal temperatures ranging from 38.0-38.5oC was 13.33% and 20%. In conclusion, the low percentage of pregnancy in this study was caused by various factors, mainly the genetic quality, early embryonic efficacy, maintenance management especially feed, and there were cows that experienced reproductive disorders. (Evaluasi keberhasilan inseminasi buatan menggunakan semen beku sexing berdasarkan karakter estrus yang berbeda) ABSTRAK. Pengetahuan peternak mengenai deteksi estrus merupakan salah satu faktor yang berpengaruh terhadap ketepatan waktu inseminasi. Penelitian ini bertujuan untuk mengevaluasi tingkat keberhasilan IB menggunakan semen sexing Y berdasarkan karakter estrus ternak. Akseptor yang digunakan pada penelitian ini berjumlah 45 ekor Sapi Persilangan Limousin dengan BCS 3-5 dan umur berkisar antara 1,8-7 tahun. Semen beku sexing Y merupakan hasil produksi Balai Besar Inseminasi Buatan (BBIB) Singosari menggunakan metode Sentrifugasi Densitas Gradien Percoll (SGDP). Pengamatan karakter estrus dilakukan sebelum inseminasi. Inseminasi Buatan dilakukan dengan menggunakan double dosis pada jam ke-2 dan jam ke-8 dengan teknik deep insemination. Vitamin Rheinbio sebagai BioATP+ diinjeksikan setelah IB. Hasil penelitian menunjukkan bahwa keberhasilan kebuntingan ditinjau dari nilai conception rate (CR) dan pregnancy rate (PR) berdasarkan karakter estrus ternak lebih tinggi ditunjukkan pada kondisi warna vulva merah merata yaitu 17,78% dan 22,22%; vulva yang sangat bengkak yaitu 20% dan 22,22%; banyak mengeluarkan lendir servik yaitu 15,56% dan 17,78%, serta suhu vagina yang berkisar antara 38,0-38,5oC yaitu 13,33% dan 20%. Persentase kebuntingan yang rendah pada penelitian ini disebabkan oleh berbagai faktor, utamanya kualitas genetik ternak, kematian embrio dini, manajemen pemeliharaan khususnya pakan, dan terdapat ternak yang mengalami gangguan reproduksi.
Robust GMM Parameter Estimation via the K-BM Algorithm

In this paper, we develop an expectation-maximization (EM)-like scheme, called ${\mathcal{K}}$-BM, for iterative numerical computation of the minimum ${\mathcal{K}}$-divergence estimator (M${\mathcal{K}}$DE). This estimator utilizes Parzen’s non-parameteric ${\mathcal{K}}$ernel density estimate to down weight low density areas attributed to outliers. Similarly to the standard EM algorithm, the ${\mathcal{K}}$-BM involves successive Maximizations of lower Bounds on the objective function of the M${\mathcal{K}}$DE. Differently from EM, these bounds do not rely on conditional expectations only. The proposed ${\mathcal{K}}$-BM algorithm is applied to robust parameter estimation of a finite-order multivariate Gaussian mixture model (GMM). Simulation studies illustrate the performance advantage of the ${\mathcal{K}}$-BM as compared to other state-of-the-art robust GMM estimators.
Real-Time Multichannel Speech Separation and Enhancement Using a Beamspace-Domain-Based Lightweight CNN

The problems of speech separation and enhancement concern the extraction of the speech emitted by a target speaker when placed in a scenario where multiple interfering speakers or noise are present, respectively. A plethora of practical applications such as home assistants and teleconferencing require some sort of speech separation and enhancement pre-processing before applying Automatic Speech Recognition (ASR) systems. In the recent years, most techniques have focused on the application of deep learning to either time-frequency or time-domain representations of the input audio signals. In this paper we propose a real-time multichannel speech separation and enhancement technique, which is based on the combination of a directional representation of the sound field, denoted as beamspace, with a lightweight Convolutional Neural Network (CNN). We consider the case where the Direction-Of-Arrival (DOA) of the target speaker is approximately known, a scenario where the power of the beamspace-based representation can be fully exploited, while we make no assumption regarding the identity of the talker. We present experiments where the model is trained on simulated data and tested on real recordings and we compare the proposed method with a similar state-of-the-art technique.
Differentiable Adaptive Short-Time Fourier Transform with Respect to the Window Length

This paper presents a gradient-based method for on-the-fly optimization for both per-frame and per-frequency window length of the short-time Fourier transform (STFT), related to previous work in which we developed a differentiable version of STFT by making the window length a continuous parameter. The resulting differentiable adaptive STFT possesses commendable properties, such as the ability to adapt in the same time-frequency representation to both transient and stationary components, while being easily optimized by gradient descent. We validate the performance of our method in vibration analysis.
Research Progresses on the Function and Detection Methods of Insect Gut Microbes

The insect gut is home to an extensive array of microbes that play a crucial role in the digestion and absorption of nutrients, as well as in the protection against pathogenic microorganisms. The variety of these gut microbes is impacted by factors such as age, diet, pesticides, antibiotics, sex, and caste. Increasing evidence indicates that disturbances in the gut microbiota can lead to compromised insect health, and that its diversity has a far-reaching impact on the host’s health. In recent years, the use of molecular biology techniques to conduct rapid, qualitative, and quantitative research on the host intestinal microbial diversity has become a major focus, thanks to the advancement of metagenomics and bioinformatics technologies. This paper reviews the main functions, influencing factors, and detection methods of insect gut microbes, in order to provide a reference and theoretical basis for better research utilization of gut microbes and management of harmful insects.
Hidden $U(1)_Y$ Ward-Takahashi identities, absence of Brout-Englert-Higgs fine-tuning and decoupling of certain heavy particles due to spontaneous symmetry breaking I: The extended Abelian Higgs model

This work is dedicated to the memory of R. Stora who co-concieved its seminal ideas, and expressed his intention to join as a co-author. The weak-scale $U(1)_Y$ Abelian Higgs model (AHM) is the SSB gauge theory of a complex scalar $\phi=(H+i\pi)/\sqrt{2}={\tilde H}/\sqrt{2}exp[i{\tilde \pi}/ ]$ and a vector $A^\mu$. The extended AHM (E-AHM) adds heavy ($M>>m_{Weak}$) $U(1)_Y$ scalar $\Phi$ and fermion $\psi$ particles. In Lorenz gauge, the SSB E-AHM has a massless pseudo-scalar $\pi$, a conserved $U(1)_Y$ global current and a Goldstone Theorem. $\tilde\pi$ becomes a Nambu-Goldstone boson (NGB). Since Slavnov-Taylor IDs guarantee that on-shell T-matrix elements of physical states are independent of anomaly-free $U(1)_Y$ gauge transformations, we observe that they are therefore also independent of anomaly-free $U(1)_Y$ rigid/global transformations. Two towers of resulting $U(1)_Y$ Ward-Takahashi Identities (WTI) give relations among Green's functions and among T-matrix elements, and constraints on the $\phi$-sector dynamics. All UV-divergent and finite relevant operators originating in virtual loops are absorbed into a pseudo-Nambu-Goldstone Boson (NGB) mass-squared $m_\pi^2$ that appears in intermediate calculational steps. The Goldstone Theorem enforces $m_\pi^2=0$, so all relevant operators vanish! After quartic-coupling renormalization, $\Phi$ and $\psi$ contribute only irrelevant operators and decouple. The renormalized gauge-independent H pole-mass and VEV are therefore not fine-tuned. The NGB decouples from the observable particle spectrum as usual, when the physical vector particle absorbs it, as if it were a gauge transformation. Our WTI are then "hidden" from observable particle physics, but those Hidden WTI and the embedded shift symmetry for ${\tilde\pi}$, have protected the physics of the low-energy SSB AHM weak-scale theory from loop contributions of heavy particles!