Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing Tenogenic Differentiation of Equine Adipose-Derived Mesenchymal Stem Cells (eq-ASC) Using TGFB3 Along with BMP Antagonists

Objective Tendon repair strategies usually are accompanied by pathological mineralization and scar tissue formation that increases the risk of re-injuries. This study aimed to establish an efficient tendon regeneration method simultaneously with a reduced risk of ectopic bone formation. Materials and Methods In this experimental study, tenogenic differentiation was induced through transforming growth factor- β3 (TGFB3) treatment in combination with the inhibiting concentrations of bone morphogenetic proteins (BMP) antagonists, gremlin-2 (GREM2), and a Wnt inhibitor, namely sclerostin (SOST). The procedure’s efficacy was evaluated using real-time polymerase chain reaction (qPCR) for expression analysis of tenogenic markers and osteo- chondrogenic marker genes. The expression level of two tenogenic markers, SCX and MKX, was also evaluated by immunocytochemistry. Sirius Red staining was performed to examine the amounts of collagen fibers. Moreover, to investigate the impact of the substrate on tenogenic differentiation, the nanofibrous scaffolds that highly resemble tendon extracellular matrix was employed. Results Aggregated features formed in spontaneous normal culture conditions followed by up-regulation of tenogenic and osteogenic marker genes, including SCX, MKX, COL1A1, RUNX2, and CTNNB1. TGFB3 treatment exaggerated morphological changes and markedly amplified tenogenic differentiation in a shorter period of time. Along with TGFB3 treatment, inhibition of BMPs by GREM2 and SOST delayed migratory events to some extent and dramatically reduced osteo-chondrogenic markers synergistically. Nanofibrous scaffolds increased tenogenic markers while declining the expression of osteo-chondrogenic genes. Conclusion These findings revealed an appropriate in vitro potential of spontaneous tenogenic differentiation of eq- ASCs that can be improved by simultaneous activation of TGFB and inhibition of osteoinductive signaling pathways.
Optimal dynamic insurance contracts

I analyze long-term contracting in insurance markets with asymmetric information. A buyer privately observes her risk type, which evolves stochastically over time. A long-term contract speciﬁes a menu of policies contingent on the history of coverage choices and contractable accident information. The optimal contract oﬀers in each period a choice between a perpetual complete coverage policy with ﬁxed premium, and a risky continuation contract in which accidents aﬀect within-period consumption (partial coverage) and, potentially, future policies. I allow for restrictions on how accident information can be used in pricing. Without restrictions, accidents and coverage choices are used as signals for the eﬃcient provision of incentives. In the presence of pricing restrictions, low coverage is rewarded, leading to menus with more attractive policies. Allocative ineﬃciency decreases along all histories. These results are used to study a model of perfect competition, where the equilibrium is unique whenever it exists, and the monopoly problem. periods I show that the ﬁrm has an incentive to reward partial coverage and reduce distortions over time. I recast the ﬁrm’s contract design problem as a cost minimization one and show that, when averaging over possible ﬁrst-period income realizations, the expected marginal cost of distortions in ﬂow contracts decreases over time, while the expected marginal cost of ﬂow utility increases over time following the choice of partial coverage. Second, on the illuminating knife-edge case of utility u ( c ) = √ c , which corresponds to a separable auxiliary cost function, and an arbitrary number of periods, consecutive choices of partial coverage — which corresponds to high-type announcements — lead to a path of insurance policies with lower distortions and increasing ﬂow utility levels over time, when averaging over possible income realizations.
Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning

We present a bi-encoder framework for named entity recognition (NER), which applies contrastive learning to map candidate text spans and entity types into the same vector representation space. Prior work predominantly approaches NER as sequence labeling or span classification. We instead frame NER as a representation learning problem that maximizes the similarity between the vector representations of an entity mention and its type. This makes it easy to handle nested and flat NER alike, and can better leverage noisy self-supervision signals. A major challenge to this bi-encoder formulation for NER lies in separating non-entity spans from entity mentions. Instead of explicitly labeling all non-entity spans as the same class $\texttt{Outside}$ ($\texttt{O}$) as in most prior methods, we introduce a novel dynamic thresholding loss. Experiments show that our method performs well in both supervised and distantly supervised settings, for nested and flat NER alike, establishing new state of the art across standard datasets in the general domain (e.g., ACE2004, ACE2005) and high-value verticals such as biomedicine (e.g., GENIA, NCBI, BC5CDR, JNLPBA). We release the code at github.com/microsoft/binder.
Spreading dynamics of a droplet impacting a sphere

In nature, high-speed rain drops often impact and spread on curved surfaces e.g. tree leaves. Although a drop impact on a surface is a traditional topic for industrial applications, drop-impact dynamics on curved surfaces in natural situations are less known about. In the present study, we examine the time-dependent spreading dynamics of a drop onto a curved hydrophobic surface. We also observed that a drop on a curved surface is spreads farther than one on a flat surface. To understand the spreading dynamics, a new analytical model is developed based on volume conservation and temporal energy balance. This model converges to previous models at the early stage and in the final stage of droplet impact. We compared the new model with measured spreading lengths on various curved surfaces and impact speeds, which resulted in good agreement.
Coexistent Quantum Channel Characterization Using Spectrally Resolved Bayesian Quantum Process Tomography

The coexistence of quantum and classical signals over the same optical fiber with minimal degradation of the transmitted quantum information is critical for operating large-scale quantum networks over the existing communications infrastructure. Here, we systematically characterize the quantum channel that results from simultaneously distributing approximate single-photon polarization-encoded qubits and classical light of varying intensities through fiber-optic channels of up to 15~km. Using spectrally resolved quantum process tomography with a Bayesian reconstruction method we developed, we estimate the full quantum channel from experimental photon counting data, both with and without classical background. Furthermore, although we find the exact channel description to be a weak function of the pump polarization, we nevertheless show that the coexistent fiber-based quantum channel has high process fidelity with an ideal depolarizing channel when the noise is dominated by Raman scattering. These results provide a basis for the future development of quantum repeater designs and quantum error correcting codes for real-world channels and inform models used in the analysis and simulation of quantum networks.
The Effect of Anthropomorphism on Trust in an Industrial Human-Robot Interaction

Robots are increasingly deployed in spaces shared with humans, including home settings and industrial environments. In these environments, the interaction between humans and robots (HRI) is crucial for safety, legibility, and efficiency. A key factor in HRI is trust, which modulates the acceptance of the system. Anthropomorphism has been shown to modulate trust development in a robot, but robots in industrial environments are not usually anthropomorphic. We designed a simple interaction in an industrial environment in which an anthropomorphic mock driver (ARMoD) robot simulates to drive an autonomous guided vehicle (AGV). The task consisted of a human crossing paths with the AGV, with or without the ARMoD mounted on the top, in a narrow corridor. The human and the system needed to negotiate trajectories when crossing paths, meaning that the human had to attend to the trajectory of the robot to avoid a collision with it. There was a significant increment in the reported trust scores in the condition where the ARMoD was present, showing that the presence of an anthropomorphic robot is enough to modulate the trust, even in limited interactions as the one we present here.
The Glass Ceiling of Automatic Evaluation in Natural Language Generation

Automatic evaluation metrics capable of replacing human judgments are critical to allowing fast development of new methods. Thus, numerous research efforts have focused on crafting such metrics. In this work, we take a step back and analyze recent progress by comparing the body of existing automatic metrics and human metrics altogether. As metrics are used based on how they rank systems, we compare metrics in the space of system rankings. Our extensive statistical analysis reveals surprising findings: automatic metrics -- old and new -- are much more similar to each other than to humans. Automatic metrics are not complementary and rank systems similarly. Strikingly, human metrics predict each other much better than the combination of all automatic metrics used to predict a human metric. It is surprising because human metrics are often designed to be independent, to capture different aspects of quality, e.g. content fidelity or readability. We provide a discussion of these findings and recommendations for future work in the field of evaluation.
Scan-based immersed isogeometric flow analysis

This chapter reviews the work conducted by our team on scan-based immersed isogeometric analysis for flow problems. To leverage the advantageous properties of isogeometric analysis on complex scan-based domains, various innovations have been made: (i) A spline-based segmentation strategy has been developed to extract a geometry suitable for immersed analysis directly from scan data; (ii) A stabilized equal-order velocity-pressure formulation for the Stokes problem has been proposed to attain stable results on immersed domains; (iii) An adaptive integration quadrature procedure has been developed to improve computational efficiency; (iv) A mesh refinement strategy has been developed to capture small features at a priori unknown locations, without drastically increasing the computational cost of the scan-based analysis workflow. We review the key ideas behind each of these innovations, and illustrate these using a selection of simulation results from our work. A patient-specific scan-based analysis case is reproduced to illustrate how these innovations enable the simulation of flow problems on complex scan data.
Efficient Sparsely Activated Transformers

Transformer-based neural networks have achieved state-of-the-art task performance in a number of machine learning domains including natural language processing and computer vision. To further improve their accuracy, recent work has explored the integration of dynamic behavior into these networks in the form of mixture-of-expert (MoE) layers. In this paper, we explore the introduction of MoE layers to optimize a different metric: inference latency. We introduce a novel system named PLANER that takes an existing Transformer-based network and a user-deﬁned latency target and produces an optimized, sparsely-activated version of the original network that tries to meet the latency target while maintaining baseline accuracy. We evaluate PLANER on two real-world language modeling tasks using the Transformer-XL network and achieve inference latency reductions of over 2x at iso-accuracy.
TCAM: Temporal Class Activation Maps for Object Localization in Weakly-Labeled Unconstrained Videos

Weakly supervised video object localization (WSVOL) allows locating object in videos using only global video tags such as object classes. State-of-art methods rely on multiple independent stages, where initial spatio-temporal proposals are generated using visual and motion cues, and then prominent objects are identified and refined. The localization involves solving an optimization problem over one or more videos, and video tags are typically used for video clustering. This process requires a model per video or per class making for costly inference. Moreover, localized regions are not necessary discriminant because these methods rely on unsupervised motion methods like optical flow, or discarded video tags from optimization. In this paper, we leverage the successful class activation mapping (CAM) methods, designed for WSOL based on still images. A new Temporal CAM (TCAM) method is introduced for training a discriminant deep learning (DL) model to exploit spatio-temporal information in videos, using an CAM-Temporal Max Pooling (CAM-TMP) aggregation mechanism over consecutive CAMs. In particular, activations of regions of interest (ROIs) are collected from CAMs produced by a pretrained CNN classifier, and generate pixel-wise pseudo-labels for training a decoder. In addition, a global unsupervised size constraint, and local constraint such as CRF are used to yield more accurate CAMs. Inference over single independent frames allows parallel processing of a clip of frames, and real-time localization. Extensive experiments1 on two challenging YouTube-Objects datasets with unconstrained videos indicate that CAM methods (trained on independent frames) can yield decent localization accuracy. Our proposed TCAM method achieves a new state-of-art in WSVOL accuracy, and visual results suggest that it can be adapted for subsequent tasks, such as object detection and tracking.
Modelling host population support for combat adversaries

Abstract We consider a model of adversarial dynamics consisting of three populations, labelled Blue, Green, and Red, which evolve under a system of first order nonlinear differential equations. Red and Blue populations are adversaries and interact via a set of Lanchester combat laws. Green is divided into three sub-populations: Red supporters, Blue supporters, and Neutral. Green support for Red and Blue leads to more combat effectiveness for either side. From Green’s perspective, if either Red or Blue exceeds a size according to the capacity of the local population to facilitate or tolerate, then support for that side diminishes; the corresponding Green population reverts to the neutral sub-population, who do not contribute to combat effectiveness of either side. The mechanism for supporters deciding if either Blue or Red is too big is given by a logistic-type interaction term. The intent of the model is to examine the role of influence in complex adversarial situations typical in counter-insurgency, where victory requires a genuine balance between maintaining combat effectiveness and support from a third party whose backing is not always assured.
A JWST/NIRCam Study of key contributors to reionization: The star-forming and ionizing properties of UV-faint z ∼ 7 − 8 galaxies

Spitzer/IRAC imaging has revealed that the brightest z ∼ 7 − 8 galaxies often exhibit young ages and strong nebular line emission, hinting at high ionizing efficiency among early galaxies. However, IRAC’s limited sensitivity has long hindered efforts to study the fainter, more numerous population often thought largely responsible for reionization. Here we use CEERS JWST/NIRCam data to characterize 116 UV-faint (median MUV = −19.5) z ∼ 6.5 − 8 galaxies. The SEDs are typically dominated by young (∼10–50 Myr), low-mass (M* ∼ 108 M⊙) stellar populations, and we find no need for extremely high stellar masses (∼1011 M⊙). Considering previous studies of UV-bright (MUV ∼ −22) z ∼ 7 − 8 galaxies, we find evidence for a strong (5–10×) increase in specific star formation rate toward lower luminosities (median sSFR=103 Gyr−1 in CEERS). The larger sSFRs imply a more dominant contribution from OB stars in the relatively numerous UV-faint population, perhaps suggesting that these galaxies are very efficient ionizing agents (median ξion = 1025.7 erg−1 Hz). In spite of the much larger sSFRs, we find little increase in [OIII]+Hβ EWs towards fainter MUV (median ≈780 $\mathrm{\mathring{A}}$). If confirmed, this may indicate that a substantial fraction of our CEERS galaxies possess extremely low metallicities (≲3% Z⊙) where [OIII] emission is suppressed. Alternatively, high ionizing photon escape fractions or bursty star formation histories can also weaken the nebular lines in a subset of our CEERS galaxies. While the majority of our objects are very blue (median β = −2.0), we identify a significant tail of very dusty galaxies (β ∼ −1) at ≈0.5 $L_\mathrm{UV}^\ast$ which may contribute significantly to the z ∼ 7 − 8 star formation rate density.
QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noise

There is a constant need for high-performing and computationally efficient neural network models for image super-resolution: computationally efficient models can be used via low-capacity devices and reduce carbon footprints. One way to obtain such models is to compress models, e.g. quantization. Another way is a neural architecture search that automatically discovers new, more efficient solutions. We propose a novel quantization-aware procedure, the QuantNAS that combines pros of these two approaches. To make QuantNAS work, the procedure looks for quantization-friendly super-resolution models. The approach utilizes entropy regularization, quantization noise, and Adaptive Deviation for Quantization (ADQ) module to enhance the search procedure. The entropy regularization technique prioritizes a single operation within each block of the search space. Adding quantization noise to parameters and activations approximates model degradation after quantization, resulting in a more quantization-friendly architectures. ADQ helps to alleviate problems caused by Batch Norm blocks in super-resolution models. Our experimental results show that the proposed approximations are better for search procedure than direct model quantization. QuantNAS discovers architectures with better PSNR/BitOps trade-off than uniform or mixed precision quantization of fixed architectures. We showcase the effectiveness of our method through its application to two search spaces inspired by the state-of-the-art SR models and RFDN. Thus, anyone can design a proper search space based on an existing architecture and apply our method to obtain better quality and efficiency. The proposed procedure is 30\% faster than direct weight quantization and is more stable.
Quantum annealing for neural network optimization problems: A new approach via tensor network simulations

Here, we focus on the problem of minimizing complex classical cost functions associated with prototypical discrete neural networks, specifically the paradigmatic Hopfield model and binary perceptron. We show that the adiabatic time evolution of QA can be efficiently represented as a suitable Tensor Network. This representation allows for simple classical simulations, well-beyond small sizes amenable to exact diagonalization techniques. We show that the optimized state, expressed as a Matrix Product State (MPS), can be recast into a Quantum Circuit, whose depth scales only linearly with the system size and quadratically with the MPS bond dimension. This may represent a valuable starting point allowing for further circuit optimization on near-term quantum devices.
Ultrafast Photo-Ion Probing of the Relaxation Dynamics in 2-Thiouracil

In this work, we investigate the relaxation processes of 2-thiouracil after UV photoexcitation to the S2 state through the use of ultrafast, single-colour, pump-probe UV/UV spectroscopy. We place focus on investigating the appearance and subsequent decay signals of ionized fragments. We complement this with VUV-induced dissociative photoionisation studies collected at a synchrotron, allowing us to better understand and assign the ionisation channels involved in the appearance of the fragments. We find that all fragments appear when single photons with energy > 11 eV are used in the VUV experiments and hence appear through 3+ photon-order processes when 266 nm light is used. We also observe three major decays for the fragment ions: a sub-autocorrelation decay (i.e., sub-370 fs), a secondary ultrafast decay on the order of 300–400 fs, and a long decay on the order of 220 to 400 ps (all fragment dependent). These decays agree well with the previously established S2 → S1 → Triplet → Ground decay process. Results from the VUV study also suggest that some of the fragments may be created by dynamics occurring in the excited cationic state.
Sketching the Expression: Flexible Rendering of Expressive Piano Performance with Self-Supervised Learning

We propose a system for rendering a symbolic piano performance with flexible musical expression. It is necessary to actively control musical expression for creating a new music performance that conveys various emotions or nuances. However, previous approaches were limited to following the composer's guidelines of musical expression or dealing with only a part of the musical attributes. We aim to disentangle the entire musical expression and structural attribute of piano performance using a conditional VAE framework. It stochastically generates expressive parameters from latent representations and given note structures. In addition, we employ self-supervised approaches that force the latent variables to represent target attributes. Finally, we leverage a two-step encoder and decoder that learn hierarchical dependency to enhance the naturalness of the output. Experimental results show that our system can stably generate performance parameters relevant to the given musical scores, learn disentangled representations, and control musical attributes independently of each other.
Specific and non-specific effects of sodium and potassium ions on the interactions between model charged groups of proteins

Potassium and sodium ions are crucial for many physiological processes in living systems and play different roles when interacting with proteins and enzymes. Intracellular concentration of potassium is always maintained higher than that of sodium, which provides a suitable environment for biochemical machinery. These cations also possess different properties in physico-chemical Hofmeister phenomena. It is now accepted that the main physical reason for these ion-specific effects is due to formation of ion pairs. The greater ability of sodium over potassium to destabilize protein solutions was previously rationalized by sodium stronger pairing with carboxylates, which are the main anionic moiety of proteins. While ion pairing of cations with carboxylates was studied in detail previously, understanding of the molecular mechanisms of cation-specific mediation of protein-protein interactions is still lacking. In this work with the use of molecular dynamics we studied the effect of sodium and potassium on interactions between model compounds that bear typical positively and negatively charged groups of proteins, namely, methylammonium and acetate molecules. We found only a weak difference in the contacts of charged groups depending on the cation type present in the solution. Our results suggest that a strength of cation-carboxylate binding is not critical, but structural features may be more important.