Miracle-dz/UniFAR-FTUs huggingface dataset:
Compassion satisfaction and compassion fatigue in oncology nursing staff: descriptive and correlational study

Objectives: (1) To determine the perception of Compassion Satisfaction and Fatigue of oncology nursing staff; (2) to study the correlation between Compassion Satisfaction and Fatigue and sociodemographic, professional and adaptive variables (resilience, attitudes toward death, personality); and (3) to identify predictors of the two dimensions. Method: Descriptive, correlational, cross-sectional study. 69 nurses (62.7%) and 41 auxiliary care technicians (37.3%) from the Oncology Services in Biscay (Basque Health Service) completed the ProQOL-V, CD-RISC-10, DAP-R, and NEO-FFI-3. Data were collected between September 2018 and March 2019. The statistical analysis with SPSS.22 included chi-square tests, comparison of means, Pearson ́s correlation, and multivariate logistic regression. Results: 66.4% (n=73) showed high Compassion Satisfaction, and 41.8% (n=46) were at moderate levels of Compassion Fatigue. People with prior studies related to death and/or grief were more satisfied. Compassion Satisfaction had stronger correlations with resilience and extroversion, and Compassion Fatigue did so with neuroticism and resilience. Four predictor variables emerged for Compassion Satisfaction: age, prior learning, resilience, and agreeableness; and four for Compassion Fatigue: attitudes of avoidance and escape concerning death, neuroticism, and openness. Conclusions: The high relationship found between training and resilience with Compassion Satisfaction can serve as a guide for academic and health care institutions to orient training, preventive and interventional strategies to provide oncology nursing teams with resources that allow them to optimize
Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation

Off-policy evaluation (OPE) aims to estimate the performance of hypothetical policies using data generated by a different policy. Because of its huge potential impact in practice, there has been growing research interest in this field. There is, however, no real-world public dataset that enables the evaluation of OPE, making its experimental studies unrealistic and irreproducible. With the goal of enabling realistic and reproducible OPE research, we present Open Bandit Dataset, a public logged bandit dataset collected on a large-scale fashion e-commerce platform, ZOZOTOWN. Our dataset is unique in that it contains a set of multiple logged bandit datasets collected by running different policies on the same platform. This enables experimental comparisons of different OPE estimators for the first time. We also develop Python software called Open Bandit Pipeline to streamline and standardize the implementation of batch bandit algorithms and OPE. Our open data and software will contribute to fair and transparent OPE research and help the community identify fruitful research directions. We provide extensive benchmark experiments of existing OPE estimators using our dataset and software. The results open up essential challenges and new avenues for future OPE research.
Decision-Driven Regularization A Blended Model for Predict-then-Optimize

In contextual optimization, the decision-maker seeks optimal decisions to minimize a cost, that varies based on observed features. This context is common in many business applications ranging from on-demand delivery and retail operations to portfolio optimization and inventory management. In this paper, we study the predict-then-optimize approach, which ﬁrst learns how outcomes result from the features, and then selects optimal decisions based on these outcomes. We identify in the literature an ambiguity in the deﬁnition of the cost function, arising out of the lack of access to the true outcomes. To address this, we propose a blended predict-then-optimize framework that may lead to biased predictions on the outcome, but tractably incorporates the optimization problem into the prediction stage. This is achieved by a decision-driven regularization . We critically show that there are three perspectives, from which the predict-then-optimize problem may be approached, namely the regularization, the robust optimization, and the regret minimization approaches; and prove that these perspectives are equivalent to or can be naturally approximated to arrive at our proposed model. As a consequence, our framework generalizes models such as SPO+ in Elmachtoub and Grigas (2020) and JERO in Zhu et al. (2020). Building on our framework, we propose hybrid models that we show numerically to outperform SPO+ under low mis-speciﬁcation.
Experimental confirmation of the delayed Ni demagnetization in FeNi alloy

Element-selective techniques are central for the understanding of ultrafast spin dynamics in multi-element materials, such as magnetic alloys. Recently, however, it turned out that the commonly used technique of the transverse magneto-optical Kerr effect (T-MOKE) in the extreme ultraviolet range may have issues with unwanted crosstalk between different elemental signals and energy-dependent non-linear response. This problem can be sizeable, which puts recent observations of ultrafast spin transfer from Fe to Ni sites in FeNi alloys into question. In this study, we investigate the Fe-to-Ni spin transfer in a crosstalk-free time-resolved x-ray magnetic circular dichroism (XMCD) experiment with a reliable time reference. With XMCD near the absorption maxima, we find a very similar Fe and Ni dynamics as with T-MOKE from identical samples. Considering the potential non-linearities of the T-MOKE response, such a good agreement in our findings is remarkable. Our data provide the ongoing discussion about ultrafast spin-transfer mechanisms in FeNi systems with a broader experimental basis.
Coded Privacy-Preserving Computation at Edge Networks

—Multi-party computation (MPC) is promising for privacy-preserving machine learning algorithms at edge networks, like federated learning . Despite their potential, existing MPC algorithms fail short of adapting to the limited resources of edge devices. A promising solution, and the focus of this work, is coded computation, which advocates the use of error-correcting codes to improve the performance of distributed computing through “smart” data redundancy. In this paper, we focus on coded privacy-preserving computation using Shamir’s secret sharing. In particular, we design novel coded privacy-preserving computation mechanisms; MatDot coded MPC (MatDot-CMPC) and PolyDot coded MPC (PolyDot-CMPC) by employing recently proposed coded computation algorithms; MatDot and PolyDot. We take advantage of the “garbage terms” that naturally arise when polynomials are constructed in the design of MatDot-CMPC and PolyDot-CMPC to reduce the number of workers needed for privacy-preserving computation. Also, we analyze MatDot-CMPC and PolyDot-CMPC in terms of their computation, storage, communication overhead as well as recovery threshold, so they can easily adapt to the limited resources of edge devices.
Text Generation with Efficient (Soft) Q-Learning

Maximum likelihood estimation (MLE) is the predominant algorithm for training text generation models. This paradigm relies on direct supervision examples, which is not applicable to many applications, such as generating adversarial attacks or generating prompts to control language models. Reinforcement learning (RL) on the other hand offers a more ﬂexible solution by allowing users to plug in arbitrary task metrics as reward. Yet previous RL algorithms for text generation, such as policy gradient ( on-policy RL) and Q -learning ( off-policy RL), are often notoriously inefﬁcient or unstable to train due to the large sequence space and the sparse reward received only at the end of sequences. In this paper, we introduce a new RL formulation for text generation from the soft Q -learning perspective. It further enables us to draw from the latest RL advances, such as path consistency learning, to combine the best of on-/off-policy updates, and learn effectively from sparse reward. We apply the approach to a wide range of tasks, including learning from noisy/negative examples, adversarial attacks, and prompt generation. Experiments show our approach consistently outperforms both task-specialized algorithms and the previous RL methods. On standard supervised tasks where MLE prevails, our approach also achieves competitive performance and stability by training text generation from scratch. 1
An evolving space framework for Oseen equations on a moving domain

This article considers non-stationary incompressible linear fluid equations in a moving domain. We demonstrate the existence and uniqueness of an appropriate weak formulation of the problem by making use of the theory of time-dependent Bochner spaces. It is not possible to directly apply established evolving Hilbert space theory due to the incompressibility constraint. After we have established the well-posedness, we derive and analyse a first order time discretisation of the system.
How Modular Should Neural Module Networks Be for Systematic Generalization?

Neural Module Networks (NMNs) aim at Visual Question Answering (VQA) via composition of modules that tackle a sub-task. NMNs are a promising strategy to achieve systematic generalization, i.e., overcoming biasing factors in the training distribution. However, the aspects of NMNs that facilitate systematic generalization are not fully understood. In this paper, we demonstrate that the degree of modularity of the NMN have large influence on systematic generalization. In a series of experiments on three VQA datasets (VQA-MNIST, SQOOP, and CLEVR-CoGenT), our results reveal that tuning the degree of modularity, especially at the image encoder stage, reaches substantially higher systematic generalization. These findings lead to new NMN architectures that outperform previous ones in terms of systematic generalization.
Cardinality-constrained optimization problems in general position and beyond

We study cardinality-constrained optimization problems (CCOP) in general position, i. e. those optimization-related properties that are fulfilled for a dense and open subset of their defining functions. We show that the well-known cardinality-constrained linear independence constraint qualification (CC-LICQ) is generic in this sense. For M-stationary points we define nondegeneracy and show that it is a generic property too. In particular, the sparsity constraint turns out to be active at all minimizers of a generic CCOP. Moreover, we describe the global structure of CCOP in the sense of Morse theory, emphasizing the strength of the generic approach. Here, we prove that multiple cells need to be attached, each of dimension coinciding with the proposed M-index of nondegenerate M-stationary points. Beyond this generic viewpoint, we study singularities of CCOP. For that, the relation between nondegeneracy and strong stability in the sense of Kojima (1980) is examined. We show that nondegeneracy implies the latter, while the reverse implication is in general not true. To fill the gap, we fully characterize the strong stability of M-stationary points under CC-LICQ by firstand second-order information of CCOP defining functions. Finally, we compare nondegeneracy and strong stability of M-stationary points with second-order sufficient conditions recently introduced in the literature.
CoDERT: Distilling Encoder Representations with Co-learning for Transducer-based Speech Recognition

We propose a simple yet effective method to compress an RNN-Transducer (RNN-T) through the well-known knowledge distillation paradigm. We show that the transducer's encoder outputs naturally have a high entropy and contain rich information about acoustically similar word-piece confusions. This rich information is suppressed when combined with the lower entropy decoder outputs to produce the joint network logits. Consequently, we introduce an auxiliary loss to distill the encoder logits from a teacher transducer's encoder, and explore training strategies where this encoder distillation works effectively. We find that tandem training of teacher and student encoders with an inplace encoder distillation outperforms the use of a pre-trained and static teacher transducer. We also report an interesting phenomenon we refer to as implicit distillation, that occurs when the teacher and student encoders share the same decoder. Our experiments show 5.37-8.4% relative word error rate reductions (WERR) on in-house test sets, and 5.05-6.18% relative WERRs on LibriSpeech test sets.
The Signatures of Self-interacting Dark Matter and Subhalo Disruption on Cluster Substructure

The abundance, distribution, and inner structure of satellites of galaxy clusters can be sensitive probes of the properties of dark matter. We run 30 cosmological zoom-in simulations with self-interacting dark matter (SIDM), with a velocity-dependent cross section, to study the properties of subhalos within cluster-mass hosts. We find that the abundance of subhalos that survive in the SIDM simulations are suppressed relative to their cold dark matter (CDM) counterparts. Once the population of disrupted subhalos—which may host orphan galaxies—is taken into account, satellite galaxy populations in CDM and SIDM models can be reconciled. However, even in this case, the inner structures of subhalos are significantly different in the two dark matter models. We study the feasibility of using the weak-lensing signal from the subhalo density profiles to distinguish between the cold and self-interacting dark matter while accounting for the potential contribution of orphan galaxies. We find that the effects of self-interactions on the density profile of subhalos can appear degenerate with subhalo disruption in CDM, when orphans are accounted for. With current error bars from the Subaru Hyper Suprime-Cam Strategic Program, we find that subhalos in the outskirts of clusters (where disruption is less prevalent) can be used to constrain dark matter physics. In the future, the Vera C. Rubin Observatory Legacy Survey of Space and Time will give precise measurements of the weak-lensing profile and can be used to constrain σ T /m at the ∼1 cm2 g−1 level at v ∼ 2000 km s−1.
Revisiting Model Stitching to Compare Neural Representations

We revisit and extend model stitching (Lenc&Vedaldi 2015) as a methodology to study the internal representations of neural networks. Given two trained and frozen models $A$ and $B$, we consider a"stitched model'' formed by connecting the bottom-layers of $A$ to the top-layers of $B$, with a simple trainable layer between them. We argue that model stitching is a powerful and perhaps under-appreciated tool, which reveals aspects of representations that measures such as centered kernel alignment (CKA) cannot. Through extensive experiments, we use model stitching to obtain quantitative verifications for intuitive statements such as"good networks learn similar representations'', by demonstrating that good networks of the same architecture, but trained in very different ways (e.g.: supervised vs. self-supervised learning), can be stitched to each other without drop in performance. We also give evidence for the intuition that"more is better'' by showing that representations learnt with (1) more data, (2) bigger width, or (3) more training time can be"plugged in'' to weaker models to improve performance. Finally, our experiments reveal a new structural property of SGD which we call"stitching connectivity'', akin to mode-connectivity: typical minima reached by SGD can all be stitched to each other with minimal change in accuracy.
An Alternative Statistical Characterization of TWDP Fading Model

Two-wave with diffuse power (TWDP) is one of the most promising models for the description of small-scale fading effects in 5G networks, which employs mmWave band, and in wireless sensor networks deployed in different cavity environments. However, its current statistical characterization has several fundamental issues. Primarily, conventional TWDP parameterization is not in accordance with the model’s underlying physical mechanisms. In addition, available TWDP expressions for PDF, CDF, and MGF are given either in integral or approximate forms, or as mathematically untractable closed-form expressions. Consequently, the existing TWDP statistical characterization does not allow accurate evaluation of system performance in all fading conditions for most modulation and diversity techniques. In this regard, physically justified TWDP parameterization is proposed and used for further calculations. Additionally, exact infinite-series PDF and CDF are introduced. Based on these expressions, the exact MGF of the SNR is derived in a form suitable for mathematical manipulations. The applicability of the proposed MGF for derivation of the exact average symbol error probability (ASEP) is demonstrated with the example of M-ary PSK modulation. The derived M-ary PSK ASEP expression is further simplified for large SNR values in order to obtain a closed-form asymptotic ASEP, which is shown to be applicable for SNR > 20 dB. All proposed expressions are verified by Monte Carlo simulation in a variety of TWDP fading conditions.
A PRELIMINARY STUDY ON SOME BASIC STRUCTURAL CHARACTERISTIC OF THE MELANINS ISOLATED FROM WHITE SILKY FOWLS

The Melanins were isolated from three strains of white silky fowls with a modified method, and their basic structural characteristics were investigated by means of element analysis, X-ray photoelectron spectroscopy(XPS), and IR spectroscopy. The electron spectra showed that in the melanin molecules, there are two valence states of sulfur, i. e. S"and S' ,and S' occupies a dominant position, there is only one valence state for nitrogen. In the IR spectra, five groups of distinct absorption peaks were observed, and the maximum ab-sorbance lies between 1619-1632cm-1, a region corresponding to the structure of an indole ring.The results of element analysis showed the average gram atomic ratio of C:H:N :O is 10:12:1:3, which matches with the elemental composition of an indole polymer. The results mentioned above revealed that the melanins studied here are sulfur-containing irregular high-polymers consisting of indole structure.
On testing quantum gravity with interactive information sensing

We show that the atom interferometric coherence revival test suggested in [arXiv:2101.11629 [quant-ph] (2021)] does not test the quantum nature of the gravitational field when the atoms are coupled to a mechanical oscillator prepared in a thermal state. Specifically we clarify that the same coherence revivals take place in a model where the atoms are coupled to a classical oscillator through a classical gravitational field. We further elucidate the quantum mechanical calculation, showing that entanglement is not the source of the revivals. The suggested test is thus only relevant for pure initial quantum states of the oscillator. In this regime, numerical estimates show that it is unfeasible to do a test of the proposed type.
Influence of Levels of Supplemental Urea on Characteristics of Digestion and Growth Performance of Feedlot Steers Fed a Fat-Supplemented High-Energy Finishing Diet

Four Holstein steers (382 kg) with cannulas in the rumen and proximal duodenum were used in a 4 × 4 Latin square experiment. Treatments consisted of 90% concentrate (steam-flaked cornbased) diet supplemented with either 5.5% soybean meal (SBM) or . 8, 1.2, or 1.6% urea (dry matter basis). All diets contained 4% tallow. There were no treatment effects (P>.10) on ruminal pH and ammonia concentrations. Ruminal digestion of organic matter, acid detergent fiber, and starch tended to increase with supplemental urea level. There was a quadratic component (P .10) across treatments. Nonammonia nitrogen entering the small intestine/nitrogen intake decreased linearly (P .10) on daily weight gain, dry matter intake, and diet net energy. Observed:expected net energy increased (P<.10) with increasing urea level. Urea can be efficiently utilized as the sole source of supplemental nitrogen in fat-supplemented highenergy finishing diets for feedlot cattle. Furthermore, feedlot cattle growth performance may be enhanced by supplementing diets with urea at levels beyond that necessary for maximal microbial growth.
Accurate modelling of 3D-trench silicon sensor with enhanced timing performance and comparison with test beam measurements

This paper presents the detailed simulation of a double-pixel structure for charged particle detection based on the 3D-trench silicon sensor developed for the TIMESPOT project and a comparison of the simulation results with measurements performed at the π-M1 beam at PSI laboratory. The simulation is based on the combined use of several software tools (TCAD, GEANT4, TCoDe and TFBoost) which allow to fully design and simulate the device physics response in very short computational time, O(1–100 s) per simulated signal, by exploiting parallel computation using single or multi-thread processors. This allowed to produce large samples of simulated signals, perform detailed studies of the sensor characteristics and make precise comparisons with experimental results.
Magnetic-buoyancy-induced mixing in AGB stars: Fluorine nucleosynthesis at different metallicities

Asymptotic giant branch (AGB) stars are considered to be among the most significant contributors to the fluorine budget in our Galaxy. While observations and theory agree at close-to-solar metallicity, stellar models at lower metallicities overestimate the fluorine production with respect to that of heavy elements. We present 19F nucleosynthesis results for a set of AGB models with different masses and metallicities in which magnetic buoyancy acts as the driving process for the formation of the 13C neutron source (the so-called 13C pocket). We find that 19F is mainly produced as a result of nucleosynthesis involving secondary 14N during convective thermal pulses, with a negligible contribution from the 14N present in the 13C pocket region. A large 19F production is thus prevented, resulting in lower fluorine surface abundances. As a consequence, AGB stellar models with mixing induced by magnetic buoyancy at the base of the convective envelope agree well with available fluorine spectroscopic measurements at low and close-to-solar metallicity.