Miracle-dz/UniFAR-FTUs huggingface dataset:
Why is Winoground Hard? Investigating Failures in Visuolinguistic Compositionality

Recent visuolinguistic pre-trained models show promising progress on various end tasks such as image retrieval and video captioning. Yet, they fail miserably on the recently proposed Winoground dataset, which challenges models to match paired images and English captions, with items constructed to overlap lexically but differ in meaning (e.g., “there is a mug in some grass” vs. “there is some grass in a mug”). By annotating the dataset using new fine-grained tags, we show that solving the Winoground task requires not just compositional language understanding, but a host of other abilities like commonsense reasoning or locating small, out-of-focus objects in low-resolution images. In this paper, we identify the dataset’s main challenges through a suite of experiments on related tasks (probing task, image retrieval task), data augmentation, and manual inspection of the dataset. Our analysis suggests that a main challenge in visuolinguistic models may lie in fusing visual and textual representations, rather than in compositional language understanding. We release our annotation and code at https://github.com/ajd12342/why-winoground-hard.
Hypergraph Convolutional Network based Weakly Supervised Point Cloud Semantic Segmentation with Scene-Level Annotations

Point cloud segmentation with scene-level annotations is a promising but challenging task. Currently, the most popular way is to employ the class activation map (CAM) to locate discriminative regions and then generate point-level pseudo labels from scene-level annotations. However, these methods always suffer from the point imbalance among categories, as well as the sparse and incomplete supervision from CAM. In this paper, we propose a novel weighted hypergraph convolutional network-based method, called WHCN, to confront the challenges of learning point-wise labels from scene-level annotations. Firstly, in order to simultaneously overcome the point imbalance among different categories and reduce the model complexity, superpoints of a training point cloud are generated by exploiting the geometrically homogeneous partition. Then, a hypergraph is constructed based on the high-confidence superpoint-level seeds which are converted from scene-level annotations. Secondly, the WHCN takes the hypergraph as input and learns to predict high-precision point-level pseudo labels by label propagation. Besides the backbone network consisting of spectral hypergraph convolution blocks, a hyperedge attention module is learned to adjust the weights of hyperedges in the WHCN. Finally, a segmentation network is trained by these pseudo point cloud labels. We comprehensively conduct experiments on the ScanNet and S3DIS segmentation datasets. Experimental results demonstrate that the proposed WHCN is effective to predict the point labels with scene annotations, and yields state-of-the-art results in the community. The source code is available at http://zhiyongsu.github. io/Project/WHCN.html.
Deep Learning Object Detection Approaches to Signal Identification

Traditionally source identification is solved using threshold based energy detection algorithms. These algorithms frequently sum up the activity in regions, and consider regions above a specific activity threshold to be sources. While these algorithms work for the majority of cases, they often fail to detect signals that occupy small frequency bands, fail to distinguish sources with overlapping frequency bands, and cannot detect any signals under a specified signal to noise ratio. Through the conversion of raw signal data to spectrogram, source identification can be framed as an object detection problem. By leveraging modern advancements in deep learning based object detection, we propose a system that manages to alleviate the failure cases encountered when using traditional source identification algorithms. Our contributions include framing source identification as an object detection problem, the publication of a spectrogram object detection dataset, and evaluation of the RetinaNet and YOLOv5 object detection models trained on the dataset. Our final models achieve Mean Average Precisions of up to 0.906. With such a high Mean Average Precision, these models are sufficiently robust for use in real world applications.
Mean-variance portfolio selection with nonlinear wealth dynamics and random coefficients

This paper studies the continuous time mean-variance portfolio selection problem with one kind of non-linear wealth dynamics. To deal the expectation constraint, an auxiliary stochastic control problem is ﬁrstly solved by two new generalized stochastic Riccati equations from which a candidate portfolio in feedback form is constructed, and the corresponding wealth process will never cross the vertex of the parabola. In order to verify the optimality of the candidate portfolio, the convex duality (requires the monotonicity of the cost function) is established to give another more direct expression of the terminal wealth level. The variance-optimal martingale measure and the link between the non-linear ﬁnancial market and the classical linear market are also provided. Finally, we obtain the eﬃcient frontier in closed form. From our results, people are more likely to invest their money in riskless asset compared with the classical linear market.
Universal dimensional crossover of domain wall dynamics in ferromagnetic films

The magnetic domain wall motion driven by a magnetic field is studied in (Ga,Mn)As and (Ga,Mn)(As,P) films of different thicknesses. In the thermally activated creep regime, a kink in the velocity curves and a jump of the roughness exponent evidence a dimensional crossover in the domain wall dynamics. The measured values of the roughness exponent zeta_{1d} = 0.62 +/- 0.02 and zeta_{2d} = 0.45 +/- 0.04 are compatible with theoretical predictions for the motion of elastic line (d = 1) and surface (d = 2) in two and three dimensional media, respectively.
An Active Learning Reliability Method for Systems with Partially Defined Performance Functions

In engineering design, one often wishes to calculate the probability that the performance of a system is satisfactory under uncertainty. State of the art algorithms exist to solve this problem using active learning with Gaussian process models. However, these algorithms cannot be applied to problems which often occur in the autonomous vehicle domain where the performance of a system may be undefined under certain circumstances. To solve this problem, we introduce a hierarchical model for the system performance, where undefined performance is classified before the performance is regressed. This enables active learning Gaussian process methods to be applied to problems where the performance of the system is sometimes undefined, and we demonstrate the effectiveness of our approach by testing our methodology on synthetic numerical examples for the autonomous driving domain.
Study of the 
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The decay $B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-}$ is studied in proton-proton collisions at a center-of-mass energy of $\sqrt{s}=13$ TeV using data corresponding to an integrated luminosity of 5 $\mathrm{fb}^{-1}$ collected by the LHCb experiment. In the $\Lambda_{c}^+ K^{-}$ system, the $\Xi_{c}(2930)^{0}$ state observed at the BaBar and Belle experiments is resolved into two narrower states, $\Xi_{c}(2923)^{0}$ and $\Xi_{c}(2939)^{0}$, whose masses and widths are measured to be $$ m(\Xi_{c}(2923)^{0}) = 2924.5 \pm 0.4 \pm 1.1 \,\mathrm{MeV}, \\ m(\Xi_{c}(2939)^{0}) = 2938.5 \pm 0.9 \pm 2.3 \,\mathrm{MeV}, \\ \Gamma(\Xi_{c}(2923)^{0}) = \phantom{000}4.8 \pm 0.9 \pm 1.5 \,\mathrm{MeV},\\ \Gamma(\Xi_{c}(2939)^{0}) = \phantom{00}11.0 \pm 1.9 \pm 7.5 \,\mathrm{MeV}, $$ where the first uncertainties are statistical and the second systematic. The results are consistent with a previous LHCb measurement using a prompt $\Lambda_{c}^{+} K^{-}$ sample. Evidence of a new $\Xi_{c}(2880)^{0}$ state is found with a local significance of $3.8\,\sigma$, whose mass and width are measured to be $2881.8 \pm 3.1 \pm 8.5\,\mathrm{MeV}$ and $12.4 \pm 5.3 \pm 5.8 \,\mathrm{MeV}$, respectively. In addition, evidence of a new decay mode $\Xi_{c}(2790)^{0} \to \Lambda_{c}^{+} K^{-}$ is found with a significance of $3.7\,\sigma$. The relative branching fraction of $B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-}$ with respect to the $B^{-} \to D^{+} D^{-} K^{-}$ decay is measured to be $2.36 \pm 0.11 \pm 0.22 \pm 0.25$, where the first uncertainty is statistical, the second systematic and the third originates from the branching fractions of charm hadron decays.
Pseudogap temperature T* of cuprate superconductors from the Nernst effect

We use the Nernst effect to delineate the boundary of the pseudogap phase in the temperature-doping phase diagram of hole-doped cuprate superconductors. New data for the Nernst coefficient ν(T) of YBa2Cu3Oy (YBCO), La1.8−xEu0.2SrxCuO4 (Eu-LSCO), and La1.6−xNd0.4SrxCuO4 (Nd-LSCO) are presented and compared with previously published data on YBCO, Eu-LSCO, Nd-LSCO, and La2−xSrxCuO4 (LSCO). The temperature Tν at which ν/T deviates from its high-temperature linear behavior is found to coincide with the temperature at which the resistivity ρ(T) deviates from its linear-T dependence, which we take as the definition of the pseudogap temperature T★—in agreement with the temperature at which the antinodal spectral gap detected in angle-resolved photoemission spectroscopy (ARPES) opens. We track T★ as a function of doping and find that it decreases linearly vs p in all four materials, having the same value in the three LSCO-based cuprates, irrespective of their different crystal structures. At low p,T★ is higher than the onset temperature of the various orders observed in underdoped cuprates, suggesting that these orders are secondary instabilities of the pseudogap phase. A linear extrapolation of T★(p) to p=0 yields T★(p→0)≃TN(0), the Neel temperature for the onset of antiferromagnetic order at p=0, suggesting that there is a link between pseudogap and antiferromagnetism. With increasing p,T★(p) extrapolates linearly to zero at p≃pc2, the critical doping below which superconductivity emerges at high doping, suggesting that the conditions which favor pseudogap formation also favor pairing. We also use the Nernst effect to investigate how far superconducting fluctuations extend above the critical temperature Tc, as a function of doping, and find that a narrow fluctuation regime tracks Tc, and not T★. This confirms that the pseudogap phase is not a form of precursor superconductivity, and fluctuations in the phase of the superconducting order parameter are not what causes Tc to fall on the underdoped side of the Tc dome.
Long term neutrino afterglows from gamma-ray bursts

It is widely believed that multiwavelength afterglows of gamma-ray bursts (GRBs) originate from relativistic blast waves. We here show that in such blast waves, a significant fraction of the energy of shock-accelerated protons would be lost due to pion production by interactions with afterglow photons. This could lead to long-term production of 10 1 6 -10 1 8 eV neutrinos and sub-TeV γ-rays that accompany usual afterglows, provided that the protons are accelerated to 10 1 9 eV in the blast waves.
Diversification of East Asian subtropical evergreen broadleaved forests over the last 8 million years

Abstract The evolution of subtropical evergreen broadleaved forests (EBLFs) in East Asia is interesting while complicated. Genus‐level phylogenies indicate that the origins of EBLFs could trace back to the Oligocene–Miocene boundary or even the Eocene, while population‐level phylogeographic evidence suggests that they diversified after the Miocene, particularly in the Pleistocene. Here, we review the origins of dominant plant species to better understand the evolution of EBLFs. We compiled published estimates of the timing of origin of dominant species and diversification of evergreen relict genera from East Asian EBLFs. We also traced and visualized the evolution of EBLFs in the region using dated phylogenies and geographic distributions of the reviewed taxa. Most (76.1%) of the dominant species originated after the late Miocene, ca. 8 million years ago. Of the 10 evergreen relict genera, eight diverged near the late Miocene–Pliocene boundary or during the late Pliocene, and the remaining two diverged during the Pleistocene. Over the past 8 million years, geo‐climatic changes have triggered origins of most of the dominant EBLF species and provided refugia for evergreen relict genera. Three pulsed phases of evolution are suggested by genetic studies at the genus, species, and population levels. Fossil evidence and spatiotemporal investigations should be integrated to fully understand the evolution of EBLFs in East Asia.
Using R in Regulatory Toxicology

Statistical analyses are an essential part of regulatory toxicological evaluations. While projects would be ideally monitored by both toxicologists and statisticians, this is often not possible in practice. Hence, toxicologists should be trained in some common statistical approaches but also need a tool for statistical evaluations. Due to transparency needed in regulatory processes and standard tests that can be evaluated with template approaches, the freely available open-source statistical software R may be suitable. R is a well-established software in the statistical community. The principal input method is via software code, which is both benefit and weakness of the tool. It is increasingly used by regulating authorities globally and can be easily extended by software packages, e.g., for new statistical functions and features. This manuscript outlines how R can be used in regulatory toxicology, allowing toxicologists to perform all regulatory required data evaluations in a single software solution. Practical applications are shown in case studies on simulated and experimental data. The examples cover a) Dunnett testing of treatment groups against a common control and in relation to a biological relevance threshold, assessing the test's assumptions and plotting the results; b) dose-response analysis and benchmark dose derivation for chronic kidney inflammation as a function of Pyridine; and c) graphical/exploratory data analysis of previously published developmental neurotoxicity data for Chlorpyrifos.
Quantum oscillations, colossal magnetoresistance, and the magnetoelastic interaction in bilayered Ca 3 Ru 2 O 7

We report magnetic and interplane transport properties of Ca 3 Ru 2 O 7 at high magnetic fields and low temperatures. Ca 3 Ru 2 O 7 with a bilayered orthorhombic structure is a Mott-like system with a narrow charge gap of 0.1eV. Of a host of unusual physical phenomena revealed in this study, a few are particularly intriguing: (1) a collapse of the c-axis lattice parameter at a metal-nonmetal transition, T M I (=48 K), and a rapid increase of T M I with low uniaxial pressure applied along the c axis; (2) quantum oscillations in the gapped, nonmetallic state for 20mK<T<6.5 K; (3) tunneling colossal magnetoresistance, which yields a precipitate drop in resistivity by as much as 3 orders of magnitude; (4) different in-plane anisotropies of the colossal magnetoresistance and magnetization. All results appear to indicate a highly anisotropic ground state and a critical role of coupling between lattice and magnetism. The implication of these phenomena is discussed.
The Scale on Chiral Symmetry Breaking

We study the relation between the scale of chiral symmetry spontaneously breaking and constituent quark mass. We argue that this relation partly reveals strong interaction origination of chiral symmetry breaking. We show that the relation can be obtained via checking unitarity region of low-energy effective field theory of QCD. This effective field theory must manifestly include consistent quark mass as parameter. Thus we derive this effective field theory from naive chiral constituent quark model. The phenomenological value obtained by this method agree with usual one determined by pion decay constant.
Kaluza-Klein dark matter and Galactic antiprotons

Extra dimensions offer new ways to address long-standing problems in beyond-the-standard-model particle physics. In some classes of extra-dimensional models, the lightest Kaluza-Klein particle is a viable dark matter candidate. In this work, we study indirect detection of Kaluza-Klein dark matter via its annihilation into antiprotons. We use a sophisticated galactic cosmic ray diffusion model whose parameters are fully constrained by an extensive set of experimental data. We find that models with universal extra dimensions remain unconstrained by cosmic ray antiprotons while low-mass weakly interacting massive particle candidates of a few tens of GeV that arise in Randall-Sundrum geometries can be probed.
Revision of the Properties of the GRS 1915+105 Jets: Clues from the Large-Scale Structure

The jets of GRS 1915+105 carry a considerable energy away from the central source into the interstellar medium (ISM). The similarity of the jets of this source and jets in radio galaxies or radio-loud quasars suggests that we should detect large-scale, synchrotron-emitting radio structures surrounding GRS 1915+105. However, these large structures have not been found. We show that by adapting a model for the radio lobes of extragalactic jet sources we predict a radio surface brightness of the equivalent structures of GRS 1915+105 below the current detection limits. The model uses an energy transport rate of the jets averaged over the jet lifetime. This transport rate is found to be considerably lower than the power of the jets during the rare major ejection events. Thus, the lobes contain less energy than would be inferred from these events and produce a lower radio luminosity. The model also predicts a lifetime of the jets of order 106 yr and a gas density of the ISM in the vicinity of GRS 1915+105 of ~150 cm-3. The impact sites of the jets are identified with two IRAS regions with a flat radio spectrum located on either side of GRS 1915+105. Observations of molecular lines and dust emission from these objects are consistent with our interpretation. Distance estimates for the IRAS regions give 6.5 kpc, and our model implies that this is also the distance to GRS 1915+105. This low distance estimate in combination with the observed motions of jet ejections on small scales yields a jet velocity of about 0.7c and an angle of 53° of the jets to our line of sight.
Designing experiments for multi-variable B-spline models

In a range of practical applications where a response cannot be adequately described by a low order polynomial, B-spline regression models for a single variable have proved useful for prediction. In this paper identifiable models for several explanatory variables are considered which are formulated from B-spline and monomial basis functions of known degree and with specified knots. The use of search methods to find efficient designs under the V-, G- and D-optimality criteria is investigated. Two methods of constructing lists of feasible candidate points are described and compared across a variety of examples.
Terahertz magnetic field enhancement in an asymmetric spiral metamaterial

We use finite element simulations in both the frequency and the time-domain to study the terahertz resonance characteristics of a metamaterial (MM) comprising a spiral connected to a straight arm. The MM acts as a RLC circuit whose resonance frequency can be precisely tuned by varying the characteristic geometrical parameters of the spiral: inner and outer radius, width and number of turns. We provide a simple analytical model that uses these geometrical parameters as input to give accurate estimates of the resonance frequency. Finite element simulations show that linearly polarized terahertz radiation efficiently couples to the MM thanks to the straight arm, inducing a current in the spiral, which in turn induces a resonant magnetic field enhancement at the center of the spiral. We observe a large (approximately 40 times) and uniform (over an area of ∼10 μm2) enhancement of the magnetic field for narrowband terahertz radiation with frequency matching the resonance frequency of the MM. When a broadband, single-cycle terahertz pulse propagates towards the MM, the peak magnetic field of the resulting band-passed waveform still maintains a six-fold enhancement compared to the peak impinging field. Using existing laser-based terahertz sources, our MM design allows to generate magnetic fields of the order of 2 T over a time scale of several picoseconds, enabling the investigation of nonlinear ultrafast spin dynamics in table-top experiments. Furthermore, our MM can be implemented to generate intense near-field narrowband, multi-cycle electromagnetic fields to study generic ultrafast resonant terahertz dynamics in condensed matter.
Cardiac Development in the Presence of Cadmium: An in Vitro Study Using Human Embryonic Stem Cells and Cardiac Organoids

Background: Exposure to cadmium (Cd) is associated with cardiovascular diseases. Maternal Cd exposure is a significant risk factor for congenital heart disease. However, mechanisms of Cd on developmental cardiotoxicity are not well defined. Objectives: We evaluated the effects of Cd on the different stages (mesoderm, cardiac induction, cardiac function) of cardiac development using an early embryo development in vitro model and two- or three-dimensional (2- or 3D) cardiomyocyte and cardiac organoid formation models mimicking early cardiac development. Methods: Embryonic stem cells (ESCs) form 3D aggregates, called embryoid bodies, that recapitulate events involved with early embryogenesis (e.g., germ layer formation). This model was used for early germ layer formation and signaling pathway identification. The 2D cardiomyocyte differentiation from the NKX2-5eGFP/w human ESCs model was used to explore the effects of Cd exposure on cardiomyocyte formation and to model mesoderm differentiation and cardiac induction, allowing us to explore different developmental windows of Cd toxicity. The 3D cardiac organoid model was used in evaluating the effects of Cd exposure on contractility and cardiac development. Results: Cd (0.6μM; 110 ppb) lowered the differentiation of embryoid bodies to mesoderm via suppression of Wnt/β-catenin–signaling pathways. During early mesoderm induction, the mesoderm-associated transcription factors MESP1 and EOMES showed a transient up-regulation, which decreased later in the cardiac induction stage. Cd (0.15μM) lowered mesoderm formation and cardiac induction through suppression of the transcription factors and mesoderm marker genes HAND1, SNAI2, HOPX, and the cardiac-specific genes NKX2-5, GATA4, troponin T, and alpha-actinin. In addition, Cd-induced histone modifications for both gene activation (H3K4me3) and repression (H3K27me3), which play vital roles in regulating mesoderm commitment markers. The effects of Cd inhibition on cardiomyocyte differentiation were confirmed in 3D cardiac organoids. Discussion: In conclusion, using a human ESC-derived 2D/3D in vitro differentiation model system and cardiac organoids, we demonstrated that low-dose Cd suppressed mesoderm formation through mesoderm gene histone modification, thus inhibiting cardiomyocyte differentiation and cardiac induction. The studies provide valuable insights into cellular events and molecular mechanisms associated with Cd-induced congenital heart disease. https://doi.org/10.1289/EHP11208