Miracle-dz/UniFAR-FTUs huggingface dataset:
A stochastic programming approach for chemotherapy appointment scheduling

Chemotherapy appointment scheduling is a challenging problem due to the uncertainty in premedication and infusion durations. In this paper, we formulate a two‐stage stochastic mixed integer programming model for the chemotherapy appointment scheduling problem under limited availability of nurses and infusion chairs. The objective is to minimize the expected weighted sum of nurse overtime, chair idle time, and patient waiting time. The computational burden to solve real‐life instances of this problem to optimality is significantly high, even in the deterministic case. To overcome this burden, we incorporate valid bounds and symmetry breaking constraints. Progressive hedging algorithm is implemented in order to solve the improved formulation heuristically. We enhance the algorithm through a penalty update method, cycle detection and variable fixing mechanisms, and a linear approximation of the objective function. Using numerical experiments based on real data from a major oncology hospital, we compare our solution approach with several scheduling heuristics from the relevant literature, generate managerial insights related to the impact of the number of nurses and chairs on appointment schedules, and estimate the value of stochastic solution to assess the significance of considering uncertainty.
FRESH: Interactive Reward Shaping in High-Dimensional State Spaces using Human Feedback

Reinforcement learning has been successful in training autonomous agents to accomplish goals in complex environments. Although this has been adapted to multiple settings, including robotics and computer games, human players often find it easier to obtain higher rewards in some environments than reinforcement learning algorithms. This is especially true of high-dimensional state spaces where the reward obtained by the agent is sparse or extremely delayed. In this paper, we seek to effectively integrate feedback signals supplied by a human operator with deep reinforcement learning algorithms in high-dimensional state spaces. We call this FRESH (Feedback-based REward SHaping). During training, a human operator is presented with trajectories from a replay buffer and then provides feedback on states and actions in the trajectory. In order to generalize feedback signals provided by the human operator to previously unseen states and actions at test-time, we use a feedback neural network. We use an ensemble of neural networks with a shared network architecture to represent model uncertainty and the confidence of the neural network in its output. The output of the feedback neural network is converted to a shaping reward that is augmented to the reward provided by the environment. We evaluate our approach on the Bowling and Skiing Atari games in the arcade learning environment. Although human experts have been able to achieve high scores in these environments, state-of-the-art deep learning algorithms perform poorly. We observe that FRESH is able to achieve much higher scores than state-of-the-art deep learning algorithms in both environments. FRESH also achieves a 21.4% higher score than a human expert in Bowling and does as well as a human expert in Skiing.
Recovering Geometric Information with Learned Texture Perturbations

Regularization is used to avoid overfitting when training a neural network; unfortunately, this reduces the attainable level of detail hindering the ability to capture high-frequency information present in the training data. Even though various approaches may be used to re-introduce high-frequency detail, it typically does not match the training data and is often not time coherent. In the case of network inferred cloth, these sentiments manifest themselves via either a lack of detailed wrinkles or unnaturally appearing and/or time incoherent surrogate wrinkles. Thus, we propose a general strategy whereby high-frequency information is procedurally embedded into low-frequency data so that when the latter is smeared out by the network the former still retains its high-frequency detail. We illustrate this approach by learning texture coordinates which when smeared do not in turn smear out the high-frequency detail in the texture itself but merely smoothly distort it. Notably, we prescribe perturbed texture coordinates that are subsequently used to correct the over-smoothed appearance of inferred cloth, and correcting the appearance from multiple camera views naturally recovers lost geometric information.
A Toeplitz Property of Ballot Permutations and Odd Order Permutations

We give a new semi-combinatorial proof for the equality of the number of ballot permutations of length $n$ and the number of odd order permutations of length $n$, which was originally proven by Bernardi, Duplantier and Nadeau. Spiro conjectures that the descent number of ballot permutations and certain cyclic weights of odd order permutations of the same length are equi-distributed. We present a bijection to establish a Toeplitz property for ballot permutations with any fixed number of descents, and a Toeplitz property for odd order permutations with any fixed cyclic weight. This allows us to refine Spiro's conjecture by tracking the neighbors of the largest letter in permutations.
High spin temperatures at large impact parameters: Ionisation in the outskirts of galaxies

By including the most recent observations of H I 21-cm absorption through nearby galactic discs, we confirm our previous assertion that there is an anti-correlation between the abundance of cool neutral atomic gas and impact parameter. In comparing the measured neutral hydrogen column densities of the sample with the absorption strength, we find a peak in the mean spin temperature of ⟨Tspin/f ⟩ ≈ 2310 K at an impact parameter of ρ ≈ 14 kpc, with ⟨Tspin/f ⟩≳1000 K in the remainder of the disc. This is significantly different to the spin temperature distribution in the Milky Way, which exhibits a constant ≈250 − 400 K over ρ = 8 − 25 kpc. The measured column densities may, however, suffer from beam dilution, which we show appears to be the case for the observations of H I 21-cm emission in which the beam subtends radii of ≳10 kpc. We therefore applied the column density profile of the Milky Way, in addition to the mean of the sample, observed at sufficiently high resolution, and the mean profile for the nearby ∼1012 M⊙ galaxies in the IllustrisTNG simulations. All of the models yield a peak in the mean spin temperature at similar impact parameters (r ≈ 10 − 15 kpc) as the measured column densities. These radii are similar to those of the spiral arms where H II regions are often concentrated. We therefore suggest that the elevated spin temperatures trace the H II regions observed in the outer disc of many spiral galaxies.
Estimation of causal effects of multiple treatments in observational studies with a binary outcome

There is a dearth of robust methods to estimate the causal effects of multiple treatments when the outcome is binary. This paper uses two unique sets of simulations to propose and evaluate the use of Bayesian additive regression trees in such settings. First, we compare Bayesian additive regression trees to several approaches that have been proposed for continuous outcomes, including inverse probability of treatment weighting, targeted maximum likelihood estimator, vector matching, and regression adjustment. Results suggest that under conditions of non-linearity and non-additivity of both the treatment assignment and outcome generating mechanisms, Bayesian additive regression trees, targeted maximum likelihood estimator, and inverse probability of treatment weighting using generalized boosted models provide better bias reduction and smaller root mean squared error. Bayesian additive regression trees and targeted maximum likelihood estimator provide more consistent 95% confidence interval coverage and better large-sample convergence property. Second, we supply Bayesian additive regression trees with a strategy to identify a common support region for retaining inferential units and for avoiding extrapolating over areas of the covariate space where common support does not exist. Bayesian additive regression trees retain more inferential units than the generalized propensity score-based strategy, and shows lower bias, compared to targeted maximum likelihood estimator or generalized boosted model, in a variety of scenarios differing by the degree of covariate overlap. A case study examining the effects of three surgical approaches for non-small cell lung cancer demonstrates the methods.
Discovery of a Remarkably Powerful Broad Absorption-line Quasar Outflow in SDSS J135246.37+423923.5

Broad absorption-line (BAL) features in quasar spectra reveal an unambiguous signature of energetic outflows from central supermassive black holes, and thus, BAL quasars are prime targets for investigating the potential process of luminous quasar feedback on galaxies. We analyzed the rest-UV spectrum of an “overlapping trough” iron low-ionization broad absorption-line quasar (FeLoBAL) SDSS J135246.37+423923.5 using the novel spectral synthesis code SimBAL and discovered an extraordinarily fast and energetic BAL outflow. Our analysis revealed outflow velocities reaching with a velocity width of , which is the largest FeLoBAL outflow velocity measured to date. The column density of the outflow gas is log with the log kinetic luminosity (erg s−1), which exceeds the bolometric luminosity of the quasar and is energetic enough to effectively drive quasar feedback. The energy estimate for the outflow is far greater than the estimates from any BAL object previously reported. The object also shows “anomalous reddening” and a significant scattered component that we were able to model with SimBAL. We found the first definitive case for radiation filtering in an additional zero-velocity absorption component that required an absorbed continuum to produce the particular absorption lines observed (Mg ii, Al iii, and Al ii) without also producing the high-ionization lines such as C iv.
DynUnlock: Unlocking Scan Chains Obfuscated using Dynamic Keys

Outsourcing in semiconductor industry opened up venues for faster and cost-effective chip manufacturing. However, this also introduced untrusted entities with malicious intent, to steal intellectual property (IP), overproduce the circuits, insert hardware Trojans, or counterfeit the chips. Recently, a defense is proposed to obfuscate the scan access based on a dynamic key that is initially generated from a secret key but changes in every clock cycle. This defense can be considered as the most rigorous defense among all the scan locking techniques. In this paper, we propose an attack that remodels this defense into one that can be broken by the SAT attack, while we also note that our attack can be adjusted to break other less rigorous (key that is updated less frequently) scan locking techniques as well.
Measurement and analysis of nuclear 
γ
-ray production cross sections in proton interactions with Mg, Si, and Fe nuclei abundant in astrophysical sites over the incident energy range 
E=30–66
 MeV

Gamma-ray production cross section excitation functions have been measured for $30$, $42$, $54$ and $66$ MeV proton beams accelerated onto targets of astrophysical interest, $^{nat}$C, C + O (Mylar), $^{nat}$Mg, $^{nat}$Si and $^{56}$Fe, at the Sector Separated Cyclotron (SSC) of iThemba LABS (near Cape Town, South Africa). The AFRODITE array equipped with 8 Compton suppressed HPGe clover detectors was used to record $\gamma$-ray data. For known, intense $\gamma$-ray lines the previously reported experimental data measured up to $E_{p}\simeq$ $25$ MeV at the Washington and Orsay tandem accelerators were extended to higher proton energies. Our experimental data for the last 3 targets are reported here and discussed with respect to previous data and the Murphy \textit{et al.} compilation [ApJS 183, 142 (2009)], as well as to predictions of the nuclear reaction code TALYS. The overall agreement between theory and experiment obtained in first-approach calculations using default input parameters of TALYS has been appreciably improved by using modified optical model potential (OMP), deformation, and level density parameters. The OMP parameters have been extracted from theoretical fits to available experimental elastic/inelastic nucleon scattering angular distribution data by means of the coupled-channels reaction code OPTMAN. Experimental data for several new $\gamma$-ray lines are also reported and discussed. The astrophysical implications of our results are emphasised.
Optimal Rate of Convergence for Deep Neural Network Classifiers under the Teacher-Student Setting

Classifiers built with neural networks handle large-scale high dimensional data, such as facial images from computer vision, extremely well while traditional statistical methods often fail miserably. In this paper, we attempt to understand this empirical success in high dimensional classification by deriving the convergence rates of excess risk. In particular, a teacher-student framework is proposed that assumes the Bayes classifier to be expressed as ReLU neural networks. In this setup, we obtain a sharp rate of convergence, i.e., $\tilde{O}_d(n^{-2/3})$, for classifiers trained using either 0-1 loss or hinge loss. This rate can be further improved to $\tilde{O}_d(n^{-1})$ when the data distribution is separable. Here, $n$ denotes the sample size. An interesting observation is that the data dimension only contributes to the $\log(n)$ term in the above rates. This may provide one theoretical explanation for the empirical successes of deep neural networks in high dimensional classification, particularly for structured data.
Forming Diverse Super-Earth Systems In Situ

Super-Earths and mini-Neptunes exhibit great diversity in their compositional and orbital properties. Their bulk densities span a large range, from those dense enough to be purely rocky to those needing a substantial contribution from volatiles to their volumes. Their orbital configurations range from compact, circular multitransiting systems like Kepler-11 to systems like our solar system’s terrestrial planets with wider spacings and modest but significant eccentricities and mutual inclinations. Here we investigate whether a continuum of formation conditions resulting from variation in the amount of solids available in the inner disk can account for the diversity of orbital and compositional properties observed for super-Earths, including the apparent dichotomy between single and multitransiting systems. We simulate in situ formation of super-Earths via giant impacts and compare to the observed Kepler sample. We find that intrinsic variations among disks in the amount of solids available for in situ formation can account for the orbital and compositional diversity observed among Kepler’s transiting planets. Our simulations can account for the planets’ distributions of orbital period ratios, transit duration ratios, and transit multiplicity; higher eccentricities for planets in single transiting systems than for those in multitransiting systems; smaller eccentricities for larger planets; scatter in the mass–radius relation, including lower densities for planets with masses measured with transit timing variations instead of with radial velocity; and similarities in planets’ sizes and spacings within each system. Our findings support the theory that variation among super-Earth and mini-Neptune properties is primarily locked in by different in situ formation conditions, rather than arising stochastically through subsequent evolution.
Multicharged-ion–water-molecule collisions in a classical-trajectory time-dependent mean-field theory

A recently proposed classical-trajectory dynamical screening model for the description of multiple ionization and capture during ion-water molecule collisions is extended to incorporate dynamical screening on both the multi-center target potential and the projectile ion. Comparison with available experimental data for He$^{2+}$ + H$_2$O collisions at intermediate energies (10-150 keV/u) and Li$^{3+}$ + H$_2$O at higher energies (100-850 keV/u) demonstrates the importance of both screening mechanisms. The question of how to deal with the repartitioning of the capture flux into allowed capture channels is addressed. The model also provides insights for data on highly charged projectile ions (C$^{6+}$, O$^{8+}$, Si$^{13+}$) in the MeV/u range where the question of saturation effects in net ionization was raised in the literature.
Lyceum: An efficient and scalable ecosystem for robot learning

We introduce Lyceum, a high-performance computational ecosystem for robot learning. Lyceum is built on top of the Julia programming language and the MuJoCo physics simulator, combining the ease-of-use of a high-level programming language with the performance of native C. In addition, Lyceum has a straightforward API to support parallel computation across multiple cores and machines. Overall, depending on the complexity of the environment, Lyceum is 5-30x faster compared to other popular abstractions like OpenAI's Gym and DeepMind's dm-control. This substantially reduces training time for various reinforcement learning algorithms; and is also fast enough to support real-time model predictive control through MuJoCo. The code, tutorials, and demonstration videos can be found at: www.lyceum. ml.
The H IX galaxy survey

Context. This paper presents the analysis of optical integral field spectra for the H I eXtreme (H IX) galaxy sample. H IX galaxies host at least 2.5 times more atomic gas (H I) than expected from their optical R-band luminosity. Previous examination of their star formation activity and H I kinematics suggested that these galaxies stabilise their large H I discs (radii up to 94 kpc) against star formation due to their higher than average baryonic specific angular momentum. A comparison to semi-analytic models further showed that the elevated baryonic specific angular momentum is inherited from the high spin of the dark matter host. Aims. In this paper we now turn to the gas-phase metallicity as well as stellar and ionised gas kinematics in H IX galaxies to gain insights into recent accretion of metal-poor gas or recent mergers. Methods. We compare the stellar, ionised, and atomic gas kinematics, and examine the variation in the gas-phase metallicity throughout the stellar disc of H IX galaxies. Results. We find no indication for counter-rotation in any of the components, the central metallicities tend to be lower than average, but as low as expected for galaxies of similar H I mass. Metallicity gradients are comparable to other less H I-rich, local star forming galaxies. Conclusions. We conclude that H IX galaxies show no conclusive evidence for recent major accretion or merger events. Their overall lower metallicities are likely due to being hosted by high spin halos, which slows down their evolution and thus the enrichment of their interstellar medium.
Synthesis of Fe$_3$O$_4$ nanoparticles and their magnetic properties

Fe$_3$O$_4$ magnetic nanoparticles (MNPs) were synthesized by a co-precipitation method using sodium citrate and oleic acid as modifiers. Phase composition and microstructure analysis indicate that the sodium citrate and oleic acid have been successfully grafted onto the surface of Fe$_3$O$_4$ MNPs. The magnetic behaviors reveal that the modification can decrease the saturation magnetization of Fe$_3$O$_4$ MNPs due to the surface effect. Fe$_3$O$_4$ MNPs modified by sodiumcitrate and oleic acid show excellent dispersion capability, which should be ascribed to the great reduction of high surface energy and dipolar attraction of the nanoparticles.
Multiwavelength behaviour of the blazar 3C 279: decade-long study from γ-ray to radio

We report the results of decade-long (2008–2018) γ-ray to 1 GHz radio monitoring of the blazar 3C 279, including GASP/WEBT, Fermi and Swift data, as well as polarimetric and spectroscopic data. The X-ray and γ-ray light curves correlate well, with no delay $\gtrsim 3$ h, implying general cospatiality of the emission regions. The γ-ray–optical flux–flux relation changes with activity state, ranging from a linear to a more complex dependence. The behaviour of the Stokes parameters at optical and radio wavelengths, including 43 GHz Very Long Baseline Array images, supports either a predominantly helical magnetic field or motion of the radiating plasma along a spiral path. Apparent speeds of emission knots range from 10 to 37c, with the highest values requiring bulk Lorentz factors close to those needed to explain γ-ray variability on very short time-scales. The Mg ii emission line flux in the ‘blue’ and ‘red’ wings correlates with the optical synchrotron continuum flux density, possibly providing a variable source of seed photons for inverse Compton scattering. In the radio bands, we find progressive delays of the most prominent light-curve maxima with decreasing frequency, as expected from the frequency dependence of the τ = 1 surface of synchrotron self-absorption. The global maximum in the 86 GHz light curve becomes less prominent at lower frequencies, while a local maximum, appearing in 2014, strengthens toward decreasing frequencies, becoming pronounced at ∼5 GHz. These tendencies suggest different Doppler boosting of stratified radio-emitting zones in the jet.
Electromagnetic transition rates of 
C12
 and 
O16
 in rotational-vibrational models

We develop a formalism to calculate electromagnetic (EM) transition rates for rotational-vibrational models of nuclei. The formalism is applied to recently proposed models of Carbon-12 and Oxygen-16 which are inspired by nuclear dynamics in the Skyrme model. We compare the results to experimental data, as well as other nuclear models. The results for Carbon-12 are in good agreement with the data across all models, making it difficult to differentiate the models. More experimental data is needed to do this, and we suggest which transitions would be most interesting to measure. The models of Oxygen-16 are less successful in describing the data, and we suggest some possible improvements to our approximations which may help.
Single headed attention based sequence-to-sequence model for state-of-the-art results on Switchboard-300

It is generally believed that direct sequence-to-sequence (seq2seq) speech recognition models are competitive with hybrid models only when a large amount of data, at least a thousand hours, is available for training. In this paper, we show that state-of-the-art recognition performance can be achieved on the Switchboard-300 database using a single headed attention, LSTM based model. Using a cross-utterance language model, our single-pass speaker independent system reaches 6.4% and 12.5% word error rate (WER) on the Switchboard and CallHome subsets of Hub5'00, without a pronunciation lexicon. While careful regularization and data augmentation are crucial in achieving this level of performance, experiments on Switchboard-2000 show that nothing is more useful than more data.
PARAFAC-Based Channel Estimation for Intelligent Reflective Surface Assisted MIMO System

Intelligent reflective surface (IRS) is an emergent technology for future wireless communications. It consists of a large 2D array of passive scattering elements that control the electromagnetic properties of radio-frequency waves so that the reflected signals add coherently at the intended receiver or destructively to reduce co-channel interference. The promised gains of IRS-assisted communications depend on the accuracy of the channel state information. In this paper, we propose two novel channel estimation methods for an IRS-assisted multiple-input multiple-output (MIMO) communication system. Assuming a structured time-domain pattern of pilots and IRS phase shifts, we show that the received signal follows a parallel factor (PARAFAC) tensor model that can be exploited to estimate the involved communication channels in closed-form or iteratively. Numerical results corroborate the effectiveness of the proposed channel estimation methods and highlight the involved tradeoffs.