Miracle-dz/UniFAR-FTUs huggingface dataset:
Liquidity Supply and Demand in the Corporate Bond Market

We propose a novel framework to measure liquidity supply in the corporate bond market by jointly analyzing the price and quantity of liquidity. Motivated by theories of segmented markets, we construct the "noise" in corporate bonds as a liquidity price measure, and gross dealer positions on corporate bonds as our quantity measure. Liquidity supply and demand are identified using structural VARs based on price and quantities, together with simple sign restrictions. We find that the elasticity of liquidity supply fell very sharply during the financial crisis and remains below 50 percent of its pre-crisis level, suggesting that the corporate bond liquidity has become more fragile. Our supply-demand decomposition shows that liquidity demand shocks explain slightly less than half of time-variation in aggregate noise and dealer positions. The estimated liquidity supply shocks are negatively associated with uncertainty shocks, whereas liquidity demand shocks are associated with mutual fund flows. These results underscore the importance of separating liquidity supply effects from demand effects when assessing the liquidity condition. JEL Classification: G12, G24
The National Writing Project: Scaling Up and Scaling Down

McDonald, Buchanan, and Sterling introduce the interesting notion of “scaling up by scaling down,” by which they mean that, to succeed in a new environment, a reform that is spreading geographically must also challenge and, eventually, penetrate habitual practice in new contexts. To achieve this penetration, the National Writing Project (NWP) focuses on professional development and professional networks for writing teachers. NWP promoted both spread and depth of change through its “improvement infrastructure,” a system made up of three elements: an annual review process, the development of specialized cross-site networks, and a commitment to both internal, site-based, practitioner-directed research and external, national, and independent research as a means of learning from experience.
A role for the AP2/ERF-domain transcription factors ORA59 and ERF1 in jasmonate-ethylene mediated activation of the PDF1.2 gene in Arabidopsis

Plant defense against pathogens and herbivores depends on the action of several endogenously produced hormones, including jasmonic acid (JA) and ethylene. In certain defense responses in Arabidopsis thaliana , JA and ethylene signaling pathways synergize to activate a specific set of defense genes including plant defensin1.2 ( PDF1.2 ). The AP2/ERF domain transcription factor ORA59 acts as the integrator of the JA and ethylene signalling pathways and is the key regulator of JA- and ethylene-responsive PDF1.2 expression. In this chapter we describe studies aimed at dissecting the interaction of ORA59 and the related transcription factor ERF1 with the PDF1.2 promoter. We show that two GCC boxes in the PDF1.2 promoter are important for trans-activation by ORA59 and ERF1 in transient assays and for in vitro binding. Using the chromatin immunoprecipitation technique we were able to show that ORA59 binds to the PDF1.2 promoter in vivo. Interestingly, mutation of a single GCC box at positions -256 to -261 previously reported by others to be important for JA-responsive expression completely abolished the expression of the PDF1.2 promoter in response to JA alone or in combination with the ethylene-releasing agent ethephon. PDF1.2 by ORA59 or ERF1 in transient assays or on in vitro binding of these transcription factors. Simultaneous mutation of a
Crossover-Net: Leveraging the Vertical-Horizontal Crossover Relation for Robust Segmentation

Robust segmentation for non-elongated tissues in medical images is hard to realize due to the large variation of the shape, size, and appearance of these tissues in different patients. In this paper, we present an end-to-end trainable deep segmentation model termed Crossover-Net for robust segmentation in medical images. Our proposed model is inspired by an insightful observation: during segmentation, the representation from the horizontal and vertical directions can provide different local appearance and orthogonality context information, which helps enhance the discrimination between different tissues by simultaneously learning from these two directions. Specifically, by converting the segmentation task to a pixel/voxel-wise prediction problem, firstly, we originally propose a cross-shaped patch, namely crossover-patch, which consists of a pair of (orthogonal and overlapped) vertical and horizontal patches, to capture the orthogonal vertical and horizontal relation. Then, we develop the Crossover-Net to learn the vertical-horizontal crossover relation captured by our crossover-patches. To achieve this goal, for learning the representation on a typical crossover-patch, we design a novel loss function to (1) impose the consistency on the overlap region of the vertical and horizontal patches and (2) preserve the diversity on their non-overlap regions. We have extensively evaluated our method on CT kidney tumor, MR cardiac, and X-ray breast mass segmentation tasks. Promising results are achieved according to our extensive evaluation and comparison with the state-of-the-art segmentation models.
Towards Better Generalization: Joint Depth-Pose Learning Without PoseNet

In this work, we tackle the essential problem of scale inconsistency for self supervised joint depth-pose learning. Most existing methods assume that a consistent scale of depth and pose can be learned across all input samples, which makes the learning problem harder, resulting in degraded performance and limited generalization in indoor environments and long-sequence visual odometry application. To address this issue, we propose a novel system that explicitly disentangles scale from the network estimation. Instead of relying on PoseNet architecture, our method recovers relative pose by directly solving fundamental matrix from dense optical flow correspondence and makes use of a two-view triangulation module to recover an up-to-scale 3D structure. Then, we align the scale of the depth prediction with the triangulated point cloud and use the transformed depth map for depth error computation and dense reprojection check. Our whole system can be jointly trained end-to-end. Extensive experiments show that our system not only reaches state-of-the-art performance on KITTI depth and flow estimation, but also significantly improves the generalization ability of existing self-supervised depth-pose learning methods under a variety of challenging scenarios, and achieves state-of-the-art results among self-supervised learning-based methods on KITTI Odometry and NYUv2 dataset. Furthermore, we present some interesting findings on the limitation of PoseNet-based relative pose estimation methods in terms of generalization ability. Code is available at https://github.com/B1ueber2y/TrianFlow.
Model studies on the COVID-19 pandemic in Sweden

We study the increases of infections and deaths in Sweden caused by COVID-19 with several different models: Firstly an analytical susceptible-infected (SI) model and the standard susceptible-infected-recovered (SIR) model. Then within the SIR framework we study the susceptible-infected-deceased (SID) correlations. All models reproduce well the number of infected cases and give similar predictions. What causes us deep concern is the large number of deaths projected by the SI and SID models. Our analysis shows that, irrespective of the possible uncertainty of our model prediction, the next few days can be critical for determining the future evolution of the death cases (Updated April 02).
XGLUE: A New Benchmark Datasetfor Cross-lingual Pre-training, Understanding and Generation

In this paper, we introduce XGLUE, a new benchmark dataset that can be used to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora and evaluate their performance across a diverse set of cross-lingual tasks. Comparing to GLUE(Wang et al., 2019), which is labeled in English for natural language understanding tasks only, XGLUE has two main advantages: (1) it provides 11 diversified tasks that cover both natural language understanding and generation scenarios; (2) for each task, it provides labeled data in multiple languages. We extend a recent cross-lingual pre-trained model Unicoder(Huang et al., 2019) to cover both understanding and generation tasks, which is evaluated on XGLUE as a strong baseline. We also evaluate the base versions (12-layer) of Multilingual BERT, XLM and XLM-R for comparison.
Downlink Extrapolation for FDD Multiple Antenna Systems Through Neural Network Using Extracted Uplink Path Gains

When base stations (BSs) are deployed with multiple antennas, they need to have downlink (DL) channel state information (CSI) to optimize downlink transmissions by beamforming. The DL CSI is usually measured at mobile stations (MSs) through DL training and fed back to the BS in frequency division duplexing (FDD). The DL training and uplink (UL) feedback might become infeasible due to insufficient coherence time interval when the channel rapidly changes due to high speed of MSs. Without the feedback from MSs, it may be possible for the BS to directly obtain the DL CSI using the inherent relation of UL and DL channels even in FDD, which is called DL extrapolation. Although the exact relation would be highly nonlinear, previous studies have shown that a neural network (NN) can be used to estimate the DL CSI from the UL CSI at the BS. Most of previous works on this line of research trained the NN using full dimensional UL and DL channels; however, the NN training complexity becomes severe as the number of antennas at the BS increases. To reduce the training complexity and improve DL CSI estimation quality, this paper proposes a novel DL extrapolation technique using simplified input and output of the NN. It is shown through many measurement campaigns that the UL and DL channels still share common components like path delays and angles in FDD. The proposed technique first extracts these common coefficients from the UL and DL channels and trains the NN only using the path gains, which depend on frequency bands, with reduced dimension compared to the full UL and DL channels. Extensive simulation results show that the proposed technique outperforms the conventional approach, which relies on the full UL and DL channels to train the NN, regardless of the speed of MSs.
Duality of Session Types: The Final Cut

Duality is a central concept in the theory of session types. Since a flaw was found in the original definition of duality for recursive types, several other definitions have been published. As their connection is not obvious, we compare the competing definitions, discuss tradeoffs, and prove some equivalences. Some of the results are mechanized in Agda.
BosphorusSign22k Sign Language Recognition Dataset

Sign Language Recognition is a challenging research domain. It has recently seen several advancements with the increased availability of data. In this paper, we introduce the BosphorusSign22k, a publicly available large scale sign language dataset aimed at computer vision, video recognition and deep learning research communities. The primary objective of this dataset is to serve as a new benchmark in Turkish Sign Language Recognition for its vast lexicon, the high number of repetitions by native signers, high recording quality, and the unique syntactic properties of the signs it encompasses. We also provide state-of-the-art human pose estimates to encourage other tasks such as Sign Language Production. We survey other publicly available datasets and expand on how BosphorusSign22k can contribute to future research that is being made possible through the widespread availability of similar Sign Language resources. We have conducted extensive experiments and present baseline results to underpin future research on our dataset.
Non-adiabatic Hall effect at Berry curvature hot spot

Hot spot of Berry curvature is usually found at Bloch band anti-crossings, where the Hall effect due to the Berry phase can be most pronounced. With small gaps there, the adiabatic limit for the existing formulations of Hall current can be exceeded in a moderate electric field. Here we present a theory of non-adiabatic Hall effect, capturing non-perturbatively the across gap electron-hole excitations by the electric field. We find a general connection between the field induced electron-hole coherence and intrinsic Hall velocity. In coherent evolution, the electron-hole coherence can manifest as a sizeable ac Hall velocity. When environmental noise is taken into account, its joint action with the electric field favors a form of electron-hole coherence that is function of wavevector and field only, leading to a dc non-linear Hall effect. The Hall current has all odd order terms in field, and still retains the intrinsic role of the Berry curvature. The quantitative demonstration uses the example of gapped Dirac cones, and our theory can be used to describe the bulk pseudospin Hall current in insulators with gapped edge such as graphene and 2D MnBi2Te4.
Towards a Parallel-in-Time Calculation of Time-Periodic Solutions with Unknown Period

This paper presents a novel parallel-in-time algorithm able to compute time-periodic solutions of problems where the period is not given. Exploiting the idea of the multiple shooting method, the proposed approach calculates the initial values at each subinterval as well as the corresponding period iteratively. As in the Parareal method, parallelization in the time domain is performed using discretization on a two-level grid. A special linearization of the time-periodic system on the coarse grid is introduced to speed up the computations. The iterative algorithm is verified via its application to the Colpitt oscillator model.
Vacuum arbitrariness and the Hubble tension

We show that the energy density of the superhorizon modes released in a non-Bunch-Davies vacuum can be arbitrarily large during inflation and it decreases like ln(a)/a^4 in the subsequent radiation dominated era. This may constitute a dark radiation component which can sufficiently alter the early cosmological evolution to alleviate the Hubble tension.
A Tidal Disruption Event Candidate Discovered in the Active Galactic Nucleus SDSS J022700.77-042020.6

We report the discovery of a tidal disruption event (TDE) candidate occurring in the active galactic nucleus (AGN) SDSS J022700.77-042020.6. A sudden increase in flux from J0227-0420 during the second half of 2009 appears in the long-term optical, UV, and near-IR (NIR) light curves. A plateau phase, following an initial decline, is seen in the near-UV (NUV) and optical light curves. We find possible evidence that the plateau in the NUV band may lag behind the optical ones by ∼70–80 days with also a much shorter duration, that is, ∼7–15 days against ∼40–50 days. The long-term NUV/optical (after the plateau phase), NIR, and mid-infrared (MIR) light curves can be well characterized with a form of L(t) ∝ t−β, consistent with the expectation of a TDE. The plateaus can be explained if stellar streams collide with the preexisting disk at different radii. Though the overall fallback rate decreases, the material in the outer disk drifts inward and increases the local accretion rate in the inner region, producing the NUV/optical plateaus. The possible lag is then attributed to viscosity delay. The index β of the NIR J, H, and Ks bands (∼1.4–3.3) is steeper than that of the UV/optical (∼0.7–1.3) and MIR bands (∼0.9–1.8), which may suggest that a certain fraction of the dust in the inner region of the torus may be sublimated during the TDE phase. Our results indicate that, due to collisions between stellar debris and the preexisting disk, the light curves of TDEs occurring in AGNs may show distinctive features, which may shed new light on the accretion process.
Mapping Three Decades of Intellectual Change in Academia

Research on the development of science has focused on the creation of multidisciplinary teams. However, while this coming together of people is symmetrical, the ideas, methods, and vocabulary of science have a directional flow. We present a statistical model of the text of dissertation abstracts from 1980 to 2010, revealing for the first time the large-scale flow of language across fields. Results of the analysis include identifying methodological fields that export broadly, emerging topical fields that borrow heavily and expand, and old topical fields that grow insular and retract. Particular findings show a growing split between molecular and ecological forms of biology and a sea change in the humanities and social sciences driven by the rise of gender and ethnic studies.
An alternating lock-down strategy for sustainable mitigation of COVID-19

Lacking a drug or vaccine, our current strategy to contain the COVID-19 pandemic is by means of social distancing, specifically mobility restrictions and lock-downs. Such measures impose a hurtful toll on the economy, and are difficult to sustain for extended periods. The challenge is that selective isolation of the sick, an often viable and effective strategy, is insufficient against COVID-19, due to its relatively long incubation period, in which exposed individuals experience no symptoms, but still contribute to the spread. Here we propose an alternating lock-down strategy, in which at every instance, half of the population remains under lock-down while the other half continues to be active, maintaining a routine of weekly succession between activity and lock-down. All symptomatic individuals continue to remain in isolation. Under this regime, if an individual was exposed during their active week, by the time they complete their lock-down they will already begin to exhibit symptoms. Hence this strategy isolates the majority of exposed individuals during their asymptomatic phase. We find that this strategy not only overcomes the pandemic, but also allows for some level of flexibility, withstanding a fraction of defectors or essential workers that remain continuously active. We examine our strategy based on current epidemiological models with parameters relevant for COVID-19. We wish, however, following this communication, to further test and fine-tune our scheme based more refined data, and assess its actual effectiveness.
Relating spin squeezing to multipartite entanglement criteria for particles and modes

Entanglement witnesses based on first and second moments exist in the form of spin-squeezing criteria for the detection of particle entanglement from collective measurements, and in form of modified uncertainty relations for the detection of mode entanglement or steering. By revealing a correspondence between them, we show that metrologically useful spin squeezing reveals multimode entanglement in symmetric spin states that are distributed into addressable modes. We further derive tight state-independent multipartite entanglement bounds on the spin-squeezing coefficient and point out their connection to widely-used entanglement criteria that depend on the state's polarization. Our results are relevant for state-of-the-art experiments where symmetric entangled states are distributed into a number of addressable modes, such as split spin-squeezed Bose-Einstein condensates.
Deep White-Balance Editing

We introduce a deep learning approach to realistically edit an sRGB image's white balance. Cameras capture sensor images that are rendered by their integrated signal processor (ISP) to a standard RGB (sRGB) color space encoding. The ISP rendering begins with a white-balance procedure that is used to remove the color cast of the scene's illumination. The ISP then applies a series of nonlinear color manipulations to enhance the visual quality of the final sRGB image. Recent work by [3] showed that sRGB images that were rendered with the incorrect white balance cannot be easily corrected due to the ISP's nonlinear rendering. The work in [3] proposed a k-nearest neighbor (KNN) solution based on tens of thousands of image pairs. We propose to solve this problem with a deep neural network (DNN) architecture trained in an end-to-end manner to learn the correct white balance. Our DNN maps an input image to two additional white-balance settings corresponding to indoor and outdoor illuminations. Our solution not only is more accurate than the KNN approach in terms of correcting a wrong white-balance setting but also provides the user the freedom to edit the white balance in the sRGB image to other illumination settings.
Enhancement of Reconfiguration Problem Using Harmony Search Algorithm for Power Distribution System

This paper introduces a new, fast, Harmony Search Algorithm (HSA) for the purpose of solving the DSR problem in normal operation by satisfying all objectives simultaneously with a relatively small number of generations and relatively short computation time. The objectives of the problem are to minimize real power losses and improve the voltage profile and load balancing index with minimum switching operations. Instead of generating several ranks from the non dominated set of solutions, this algorithm deals with only one rank; then the most suitable solution is chosen according to the operator's wishes. If there is no preference and all objectives have the same degree of importance, the best solution is determined by simply considering the sum of the normalized objective values. Also, a HSA operation is applied instead of a random one to speed up convergence. To test the algorithm, it was applied to widely studied test systems and a real one. The results show the efficiency of this algorithm as compared to other methods in terms of achieving all the goals simultaneously with reasonable population and generation sizes and without using a mutation rate, which is usually problem dependent.
Growing Random Graphs with Quantum Rules

Random graphs are a central element of the study of complex dynamical networks such as the internet, the brain, or socioeconomic phenomena. New methods to generate random graphs can spawn new applications and give insights into more established techniques. We propose two variations of a model to grow random graphs and trees, based on continuous-time quantum walks on the graphs. After a random characteristic time, the position of the walker(s) is measured and new nodes are attached to the nodes where the walkers collapsed. Such dynamical systems are reminiscent of the class of spontaneous collapse theories in quantum mechanics. We investigate several rates of this spontaneous collapse for an individual quantum walker and for two non-interacting walkers. We conjecture (and report some numerical evidence) that the models are scale-free.
Towards democratizing music production with AI-Design of Variational Autoencoder-based Rhythm Generator as a DAW plugin

There has been significant progress in the music generation technique utilizing deep learning. However, it is still hard for musicians and artists to use these techniques in their daily music-making practice. This paper proposes a Variational Autoencoder\cite{Kingma2014}(VAE)-based rhythm generation system, in which musicians can train a deep learning model only by selecting target MIDI files, then generate various rhythms with the model. The author has implemented the system as a plugin software for a DAW (Digital Audio Workstation), namely a Max for Live device for Ableton Live. Selected professional/semi-professional musicians and music producers have used the plugin, and they proved that the plugin is a useful tool for making music creatively. The plugin, source code, and demo videos are available online.