Bottom-Flavored Mono-Tau Tails at the LHC

We study the effective field theory sensitivity of an LHC analysis for the $\tau \nu$ final state with an associated b-jet. To illustrate the improvement due to the b-tagging, we first recast the recent CMS analysis in the $\tau\nu$ channel, using an integrated luminosity of 35.9 fb$^{-1}$ at $\sqrt{s}=13$ TeV, and provide limits on all the dimension-six effective operators which contribute to the process. The expected limits from the b-tagged analysis are then derived and compared. We find an improvement of approximately $\sim 30\%$ in the bounds for operators with a b quark. We also discuss in detail possible angular observables to be used as a discriminator between dimension-six operators with different Lorentz structure. Finally, we study the impact of these limits on some simplified scenarios aimed at addressing the observed deviations from the Standard Model in lepton flavor universality ratios of semileptonic B-meson decays. In particular, we compare the collider limits on those scenarios set by our analysis either with or without the b-tagging, assuming an integrated luminosity of 300 fb$^{-1}$, with relevant low-energy flavor measurements.Comment: 41 pages, 13 figures. The complete chi-square function for our CMS recast is shared in ancillary files. v2: references added, matches the version to be published in JHE

Statistical Beamformer Exploiting Non-stationarity and Sparsity with Spatially Constrained ICA for Robust Speech Recognition

In this paper, we present a statistical beamforming algorithm as a pre-processing step for robust automatic speech recognition (ASR). By modeling the target speech as a non-stationary Laplacian distribution, a mask-based statistical beamforming algorithm is proposed to exploit both its output and masked input variance for robust estimation of the beamformer. In addition, we also present a method for steering vector estimation (SVE) based on a noise power ratio obtained from the target and noise outputs in independent component analysis (ICA). To update the beamformer in the same ICA framework, we derive ICA with distortionless and null constraints on target speech, which yields beamformed speech at the target output and noises at the other outputs, respectively. The demixing weights for the target output result in a statistical beamformer with the weighted spatial covariance matrix (wSCM) using a weighting function characterized by a source model. To enhance the SVE, the strict null constraints imposed by the Lagrange multiplier methods are relaxed by generalized penalties with weight parameters, while the strict distortionless constraints are maintained. Furthermore, we derive an online algorithm based on an optimization technique of recursive least squares (RLS) for practical applications. Experimental results on various environments using CHiME-4 and LibriCSS datasets demonstrate the effectiveness of the presented algorithm compared to conventional beamforming and blind source extraction (BSE) based on ICA on both batch and online processing.Comment: Accepted by TASL

Group Theoretic Approach to Fermion Production

We propose a universal group theoretic description of the fermion production through any type of interaction to scalar or pseudo-scalar. Our group theoretic approach relies on the group $SU(2) \times U(1)$, corresponding to the freedom in choosing representations of the gamma matrices in Clifford algebra, under which a part of the Dirac spinor function transforms like a fundamental representation. In terms of a new $SO(3)$ ($\sim SU(2)$) vector constructed out of spinor functions, we show that fermion production mechanism can be analogous to the classical dynamics of a vector precessing with the angular velocity. In our group theoretic approach, the equation of motion takes a universal form for any system, and choosing a different type of interaction or a different basis amounts to selecting the corresponding angular velocity. The expression of the particle number density is greatly simplified, compared to the traditional approach, and it provides us with a simple geometric interpretation of the fermion production dynamics. For the purpose of the demonstration, we focus on the fermion production through the derivative coupling to the pseudo-scalar.Comment: 25 pages, 4 figures, v3: version accepted to JHEP. New Section V adde

A Cosmic Window on the Dark Axion Portal

Axions and dark photons are common in many extensions of the Standard Model. The dark axion portal -- an axion coupling to the dark photon and photon -- can significantly modify their phenomenology. We study the cosmological constraints on the dark axion portal from Cosmic Microwave Background (CMB) bounds on the energy density of dark radiation, $\Delta N_\text{eff}$. By computing the axion-photon-dark photon collision terms and solving the Boltzmann equations including their effects, we find that light axions are generally more constrained by $\Delta N_\text{eff}$ than from supernova cooling or collider experiments. However, with dark photons at the MeV scale, a window of parameter space is opened up above the supernova limits and below the experimental exclusion, allowing for axion decay constants as low as $f_a \sim 10^4$ GeV. This region also modifies indirectly the neutrino energy density, thus relaxing the cosmological upper bound on the sum of neutrino masses. Future CMB measurements could detect a signal or close this open window on the dark axion portal.Comment: 27 pages, 9 figure

NeXt-TDNN: Modernizing Multi-Scale Temporal Convolution Backbone for Speaker Verification

In speaker verification, ECAPA-TDNN has shown remarkable improvement by utilizing one-dimensional(1D) Res2Net block and squeeze-and-excitation(SE) module, along with multi-layer feature aggregation (MFA). Meanwhile, in vision tasks, ConvNet structures have been modernized by referring to Transformer, resulting in improved performance. In this paper, we present an improved block design for TDNN in speaker verification. Inspired by recent ConvNet structures, we replace the SE-Res2Net block in ECAPA-TDNN with a novel 1D two-step multi-scale ConvNeXt block, which we call TS-ConvNeXt. The TS-ConvNeXt block is constructed using two separated sub-modules: a temporal multi-scale convolution (MSC) and a frame-wise feed-forward network (FFN). This two-step design allows for flexible capturing of inter-frame and intra-frame contexts. Additionally, we introduce global response normalization (GRN) for the FFN modules to enable more selective feature propagation, similar to the SE module in ECAPA-TDNN. Experimental results demonstrate that NeXt-TDNN, with a modernized backbone block, significantly improved performance in speaker verification tasks while reducing parameter size and inference time. We have released our code for future studies.Comment: Accepted by ICASSP 202

Effects of epicatechin, a crosslinking agent, on human dental pulp cells cultured in collagen scaffolds

Objective The purpose of this study was to investigate the biological effects of epicatechin (ECN), a crosslinking agent, on human dental pulp cells (hDPCs) cultured in collagen scaffolds. Material and Method To evaluate the effects of ECN on the proliferation of hDPCs, cell counting was performed using optical and fluorescent microscopy. Measurements of alkaline phosphatase (ALP) activity, alizarin red staining, and real-time polymerase chain reactions were performed to assess odontogenic differentiation. The compressive strength and setting time of collagen scaffolds containing ECN were measured. Differential scanning calorimetry was performed to analyze the thermal behavior of collagen in the presence of ECN. Results Epicatechin increased ALP activity, mineralized nodule formation, and the mRNA expression of dentin sialophosphoprotein (DSPP), a specific odontogenic-related marker. Furthermore, ECN upregulated the expression of DSPP in hDPCs cultured in collagen scaffolds. Epicatechin activated the extracellular signal-regulated kinase (ERK) and the treatment with an ERK inhibitor (U0126) blocked the expression of DSPP. The compressive strength was increased and the setting time was shortened in a dose-dependent manner. The number of cells cultured in the ECN-treated collagen scaffolds was significantly increased compared to the cells in the untreated control group. Conclusions Our results revealed that ECN promoted the proliferation and differentiation of hDPCs. Furthermore, the differentiation was regulated by the ERK signaling pathway. Changes in mechanical properties are related to cell fate, including proliferation and differentiation. Therefore, our study suggests the ECN treatment might be desirable for dentin-pulp complex regeneration