Miracle-dz/UniFAR-FTUs huggingface dataset:
Dancing in the Dark: Uncertainty in Ultrafaint Dwarf Galaxy Predictions from Cosmological Simulations

The existence of ultrafaint dwarf (UFD) galaxies highlights the need to push our theoretical understanding of galaxies to extremely low mass. We examine the formation of UFDs by twice running a fully cosmological simulation of dwarf galaxies, but varying star formation. One run uses a temperature–density threshold for star formation, while the other uses an H2-based subgrid star formation model. The total number of dwarf galaxies that form is different by a factor of 2 between the two runs, but most of these are satellites, leading to a factor of 5 difference in the number of luminous UFD companions around more massive, isolated dwarfs. The first run yields a 47% chance of finding a satellite around an Mhalo ∼ 1010 M⊙ host, while the H2 run predicts only a 16% chance. Metallicity is the primary physical parameter that creates this difference. As metallicity decreases, the formation of H2 is slowed and relegated to higher-density material. Thus, our H2 run is unable to form many (and often, any) stars before reionization removes gas. These results emphasize that predictions for UFD properties made using hydrodynamic simulations, in particular regarding the frequency of satellites around dwarf galaxies, the slope of the stellar mass function at low masses, and the properties of ultrafaint galaxies occupying the smallest halos, are extremely sensitive to the subgrid physics of star formation contained within the simulation. However, upcoming discoveries of UFDs will provide invaluable constraining power on the physics of the first star formation.
The Influence of Board Size on Intellectual Capital Disclosure by Kenyan Listed Firms

Purpose – The purpose of this paper is to examine the effect of board size on firms disclosing more, rather than less, strategic and tactical intellectual capital resources using the top 26 of the 52 firms ranked by the Nairobi Stock Exchange for market capitalization in 2002 and in 2003. This study identifies intellectual capital disclosure by three separate categories: internal capital, external capital, and human capital. Hence, this study examines the influence of board size on six disclosure outcomes. Design/Methodology/Approach – The study develops hypotheses using the resource dependency theory. Using content analysis for data generation, this study classifies firms that disclose more versus those that disclose less, using the mean for all firms for each disclosure outcome. Findings – Using logistic regression, the study examines the influence of board size on each disclosure outcome and finds that firms disclosing more tactical internal capital and more strategic human capital have larger boards. Practical Implications – The findings provide insights into how a larger board size can help boards to overcome skill deficiencies in making more discretionary disclosure related to future earnings. Originality/Value – This study analyses the influence of the board size on six aspects of intellectual capital disclosure.
Axion resonances in binary pulsar systems

We investigate the extent to which resonances between an oscillating background of ultra-light axion and a binary Keplerian system can affect the motion of the latter. These resonances lead to perturbations in the instantaneous time-of-arrivals, and to secular variations in the period of the binary. While the secular changes at exact resonance have recently been explored, the instantaneous effects have been overlooked. In this paper, we examine the latter using N-body simulations including the external oscillatory forcing induced by the axion background. While the secular effects are restricted to a narrow width near the resonance, the instantaneous changes, albeit strongest close to resonances, are apparent for wide range of configurations. We compute the signal-to-noise ratio (SNR) as a function of semi-major axis for a detection of axion oscillations through the RØmer delay. The latter can be extracted from the time-of-arrivals of binary pulsars. The SNR broadly increases with increasing binary eccentricity as expected from secular expectations. However, we find that it differs significantly from the scaling a5/2 around the lowest orders of resonance. Future observations could probe these effects away from resonances and, therefore, constrain a much broader range of axion masses provided that binary pulsar systems are found near the central region of our Galaxy, and that the time-or-arrival measurement accuracy reaches ≲ 10 ns.
Interpreting Adversarial Examples by Activation Promotion and Suppression

It is widely known that convolutional neural networks (CNNs) are vulnerable to adversarial examples: images with imperceptible perturbations crafted to fool classifiers. However, interpretability of these perturbations is less explored in the literature. This work aims to better understand the roles of adversarial perturbations and provide visual explanations from pixel, image and network perspectives. We show that adversaries have a promotion-suppression effect (PSE) on neurons' activations and can be primarily categorized into three types: i) suppression-dominated perturbations that mainly reduce the classification score of the true label, ii) promotion-dominated perturbations that focus on boosting the confidence of the target label, and iii) balanced perturbations that play a dual role in suppression and promotion. We also provide image-level interpretability of adversarial examples. This links PSE of pixel-level perturbations to class-specific discriminative image regions localized by class activation mapping (Zhou et al. 2016). Further, we examine the adversarial effect through network dissection (Bau et al. 2017), which offers concept-level interpretability of hidden units. We show that there exists a tight connection between the units' sensitivity to adversarial attacks and their interpretability on semantic concepts. Lastly, we provide some new insights from our interpretation to improve the adversarial robustness of networks.
M2KD: Multi-model and Multi-level Knowledge Distillation for Incremental Learning

Incremental learning targets at achieving good performance on new categories without forgetting old ones. Knowledge distillation has been shown critical in preserving the performance on old classes. Conventional methods, however, sequentially distill knowledge only from the last model, leading to performance degradation on the old classes in later incremental learning steps. In this paper, we propose a multi-model and multi-level knowledge distillation strategy. Instead of sequentially distilling knowledge only from the last model, we directly leverage all previous model snapshots. In addition, we incorporate an auxiliary distillation to further preserve knowledge encoded at the intermediate feature levels. To make the model more memory efficient, we adapt mask based pruning to reconstruct all previous models with a small memory footprint. Experiments on standard incremental learning benchmarks show that our method preserves the knowledge on old classes better and improves the overall performance over standard distillation techniques.
Efficient Verification of Dicke States

Among various multipartite entangled states, Dicke states stand out because their entanglement is maximally persistent and robust under particle losses. Although much attention has been attracted for their potential applications in quantum information processing and foundational studies, the characterization of Dicke states remains as a challenging task in experiments. Here, we propose efficient and practical protocols for verifying arbitrary $n$-qubit Dicke states in both adaptive and nonadaptive ways. Our protocols require only two distinct settings based on Pauli measurements besides permutations of the qubits. To achieve infidelity $\epsilon$ and confidence level $1-\delta$, the total number of tests required is only $O(n\epsilon^{-1}\ln\delta^{-1})$. This performance is much more efficient than all known protocols based on local measurements, including quantum state tomography and direct fidelity estimation, and is comparable to the best global strategy. Our protocols are readily applicable with current experimental techniques and are able to verify Dicke states of hundreds of qubits.
Polarised radio emission associated with HESS J1912+101

The shell-type TeV source HESS J1912+101 was tentatively identified as an old supernova remnant, but is missing counterparts at radio and other frequencies. We analysed the Sino-German Urumqi λ6 cm survey and the Effelsberg λ11 cm and λ21 cm surveys to identify radio emission from HESS J1912+101 to clarify the question of a supernova origin. We find a partial shell of excessive polarisation at λ6 cm at the periphery of HESS J1912+101. At λ11 cm, its polarised emission is faint and suffers from depolarisation, while at λ21 cm, no related polarisation is seen. We could not separate the shell’s total intensity signal from the confusing intense diffuse emission from the inner Galactic plane. However, a high percentage of polarisation in the shell’s synchrotron emission is indicated. Our results support earlier suggestions that HESS J1912+101 is an old supernova remnant. The synchrotron emission is highly polarised, which is typical for evolved supernova remnants with low surface brightness.
Expanding Polynomials and Pairs of Polynomials in Characteristic 0

We begin a generalized study of sum-product type phenomenon in different fields by considering pairs $P(x,y)$ and $Q(x,y)$ of two variable polynomials that simultaneously exhibit small symmetric expansion. Our first result is that such $P(x,y)$ and $Q(x,y)$ over $\mathbb{R}$ and $\mathbb{C}$ have very similar structure, obtained by employing semi-algebraic geometry/o-minimality. Then using model-theoretic transfer and basic Galois theory we deduce results for fields of characteristic $0$ and characteristic $p$ when $p$ is large. We obtain as corollaries a generalization of Elekes-Ronyai type structural results to arbitrary characteristic 0 fields, and a strengthening of these classic results in a symmetric case of natural interest. We note a related bound of $5/4$ in the exponent for the sum-product problem in finite fields of large characteristic, although a lower bound for this characteristic cannot be computed from our methods.
Value-Added Trade, Exchange Rate Pass-Through and Trade Elasticity: Revisiting the Trade Competitiveness

How is exchange rate pass-through (ERPT) measures affected by increasing participation in global value chains? This paper measures ERPT for value-added trade, where intermediate inputs are shared among sectors and countries in a back-and-forth manner for producing a single final product. Estimation of pass-through was done using World Input-Output Database (WIOD), World Economic Outlook (WEO), and OECD statistics. Empirically estimated findings suggest that ignoring the value-added trade will cause a systematic upward bias in the estimation of ERPT. From empirical investigation, it is also evident that there exists substantial heterogeneity in pass-through rates across sectors: sectors with high-integration into global market functions with a lower rate of exchange in comparison to sectors with less integration.
Target-Aware Deep Tracking

Existing deep trackers mainly use convolutional neural networks pre-trained for the generic object recognition task for representations. Despite demonstrated successes for numerous vision tasks, the contributions of using pre-trained deep features for visual tracking are not as significant as that for object recognition. The key issue is that in visual tracking the targets of interest can be arbitrary object class with arbitrary forms. As such, pre-trained deep features are less effective in modeling these targets of arbitrary forms for distinguishing them from the background. In this paper, we propose a novel scheme to learn target-aware features, which can better recognize the targets undergoing significant appearance variations than pre-trained deep features. To this end, we develop a regression loss and a ranking loss to guide the generation of target-active and scale-sensitive features. We identify the importance of each convolutional filter according to the back-propagated gradients and select the target-aware features based on activations for representing the targets. The target-aware features are integrated with a Siamese matching network for visual tracking. Extensive experimental results show that the proposed algorithm performs favorably against the state-of-the-art methods in terms of accuracy and speed.
The Medical Deconfounder: Assessing Treatment Effect with Electronic Health Records (EHRs)

Causal estimation of treatment effect has an important role in guiding physicians' decision process for drug prescription. While treatment effect is classically assessed with randomized controlled trials (RCTs), the availability of electronic health records (EHRs) bring an unprecedented opportunity for more efficient estimation. However, the presence of unobserved confounders makes treatment effect assessment from EHRs a challenging task. Confounders are the variables that affect both drug prescription and the patient's outcome; examples include a patient's gender, race, social economic status and comorbidities. When these confounders are unobserved, they bias the estimation. To adjust for unobserved confounders, we develop the medical deconfounder, a machine learning algorithm that unbiasedly estimates treatment effect from EHRs. The medical deconfounder first constructs a substitute confounder by modeling which drugs were prescribed to each patient; this substitute confounder is guaranteed to capture all multi-drug confounders, observed or unobserved (Wang and Blei, 2018). It then uses this substitute confounder to adjust for the confounding bias in the analysis. We validate the medical deconfounder on simulations and two medical data sets. The medical deconfounder produces closer-to-truth estimates in simulations and identifies effective medications that are more consistent with the findings reported in the medical literature compared to classical approaches.
Household Finances and Social Interaction: Bayesian Analysis of Household Panel Data

We investigate the relationship between social interaction and household finances using data from the British Household Panel Survey. We contribute to the existing literature by exploring the relationship between a wide range of aspects of household finances and social interaction, rather than focusing one particular facet of household finances, such as the holding of stocks and shares. To be specific, we develop a Bayesian statistical framework to simultaneously explore both sides of the household balance sheet, i.e. liabilities and assets. Additionally, we allow the influence of social interaction on household finances to be time dependent, which enables us to model the effects of social interaction from a dynamic perspective. We also develop a two-part model to jointly investigate the influence of social interaction on the amount of different types of debt and financial assets held conditional on holding the different types of debt and assets. The analysis suggests that the effect of social interaction is not just restricted to share ownership, with positive effects found for both assets and liabilities. Our analysis also suggests that social interaction is associated with households holding larger amounts of both debt (unsecured and secured) and assets (property and non-housing), even after conditioning on the probability of possessing liabilities and/or assets.
The EUV spectrum of the Sun: Quiet- and active-Sun irradiances and chemical composition

We benchmark new atomic data against a selection of irradiances obtained from medium-resolution quiet-Sun spectra in the extreme ultraviolet (EUV), from 60 to 1040 Å. We used as a baseline the irradiances measured during solar minimum on 2008 April 14 by the prototype (PEVE) of the Solar Dynamics Observatory Extreme ultraviolet Variability Experiment (EVE). We took into account some inconsistencies in the PEVE data, using flight EVE data and irradiances we obtained from Solar and Heliospheric Observatory (SoHO) Coronal Diagnostics Spectrometer (CDS) data. We performed a differential emission measure and find overall excellent agreement (to within the accuracy of the observations, about 20%) between predicted and measured irradiances in most cases, although we point out several problems with the currently available ion charge-state distributions. We used the photospheric chemical abundances of Asplund et al. (2009, ARA&A, 47, 481). The new atomic data are nearly complete in this spectral range for medium-resolution irradiance spectra. Finally, we used observations of the active Sun in 1969 to show that the composition of the solar corona up to 1 MK is nearly photospheric in this case as well. Variations of a factor of 2 are present for higher-temperature plasma, which is emitted within active regions. These results are in excellent agreement with our previous findings.
Retired A Stars and Their Companions. VIII. 15 New Planetary Signals around Subgiants and Transit Parameters for California Planet Search Planets with Subgiant Hosts

We present the discovery of seven new planets and eight planet candidates around subgiant stars, as additions to the known sample of planets around “retired A stars. ” Among these are the possible first three-planet systems around subgiant stars, HD 163607 and HD 4917. Additionally, we present calculations of possible transit times, durations, depths, and probabilities for all known planets around subgiant ( ) stars, focused on possible transits during the TESS mission. While most have transit probabilities of 1%–2%, we find that there are three planets with transit probabilities >9%.
Inertial migration of an electrophoretic rigid sphere in a two-dimensional Poiseuille flow

There has been a recent interest in integrating external fields with inertial microfluidic devices to tune particle focusing. In this work, we analyse the inertial migration of an electrophoretic particle in a two-dimensional Poiseuille flow with an electric field applied parallel to the walls. For a thin electrical double layer, the particle exhibits a slip-driven electrokinetic motion along the direction of the applied electric field, which causes the particle to lead or lag the flow (depending on its surface charge). The fluid disturbance caused by this slip-driven motion is characterized by a rapidly decaying source-dipole field which alters the inertial lift on the particle. We determine this inertial lift using the reciprocal theorem. Assuming no wall effects, we derive an analytical expression for a ‘phoretic lift’ which captures the modification to the inertial lift due to electrophoresis. We also take wall effects into account, at the leading order, using the method of reflections. We find that for a leading particle, the phoretic lift acts towards the regions of high shear (i.e. walls), while the reverse is true for a lagging particle. Using an order-of-magnitude analysis, we obtain different components of the inertial force and classify them on the basis of the interactions from which they emerge. We show that the dominant contribution to the phoretic lift originates from the interaction of the source-dipole field (generated by the electrokinetic slip at the particle surface) with the stresslet field (generated due to particle’s resistance to strain in the background flow). Furthermore, to contrast the slip-driven phenomenon (electrophoresis) from the force-driven phenomenon (buoyancy) in terms of their influence on the inertial migration, we also study a non-neutrally buoyant particle. We show that the gravitational effects alter the inertial lift primarily through the interaction of the background shear with the buoyancy-induced Stokeslet field.
Subword-Level Language Identification for Intra-Word Code-Switching

Language identification for code-switching (CS), the phenomenon of alternating between two or more languages in conversations, has traditionally been approached under the assumption of a single language per token. However, if at least one language is morphologically rich, a large number of words can be composed of morphemes from more than one language (intra-word CS). In this paper, we extend the language identification task to the subword-level, such that it includes splitting mixed words while tagging each part with a language ID. We further propose a model for this task, which is based on a segmental recurrent neural network. In experiments on a new Spanish–Wixarika dataset and on an adapted German–Turkish dataset, our proposed model performs slightly better than or roughly on par with our best baseline, respectively. Considering only mixed words, however, it strongly outperforms all baselines.
Unsupervised Deep Tracking

We propose an unsupervised visual tracking method in this paper. Different from existing approaches using extensive annotated data for supervised learning, our CNN model is trained on large-scale unlabeled videos in an unsupervised manner. Our motivation is that a robust tracker should be effective in both the forward and backward predictions (i.e., the tracker can forward localize the target object in successive frames and backtrace to its initial position in the first frame). We build our framework on a Siamese correlation filter network, which is trained using unlabeled raw videos. Meanwhile, we propose a multiple-frame validation method and a cost-sensitive loss to facilitate unsupervised learning. Without bells and whistles, the proposed unsupervised tracker achieves the baseline accuracy of fully supervised trackers, which require complete and accurate labels during training. Furthermore, unsupervised framework exhibits a potential in leveraging unlabeled or weakly labeled data to further improve the tracking accuracy.
Path mutual information for a class of biochemical reaction networks

Living cells encode and transmit information in the temporal dynamics of signaling molecules. Gaining a quantitative understanding of how intracellular networks process dynamic signals requires measures that capture the interdependence between complete time trajectories of network components. Mutual information provides such a measure but its calculation in the context of stochastic reaction networks is associated with computational challenges. Here we propose a method to calculate the mutual information between complete time-continuous paths of two molecular species that interact with each other through chemical reactions. We demonstrate our approach using three simple case studies.