Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Health Spending Financial Sustainability

In most OECD countries, health spending has been increasing over the last decades, often surpassing GDP growth. Current challenges faced by health systems - including the COVID-19 pandemic - pose additional concerns regarding whether societies can sustain continuous health spending growth. Such problem is particularly relevant in the context of public health spending. In the absence of significant economic growth, the room for further increases in public health spending without constraining other public spending is somewhat limited. On this paper I propose an exploratory analysis on the concept of financial sustainability of public health spending. I relate the sustainability concept with fiscal space of public finances and with the crowding-out of other public expenditures. I develop a static model where health contributes directly both to utility and output. The model suggests that increases on public health spending are not necessarily undesirable from a public finances' standpoint: the crowding-out of non-health public expenditures depends on the tax rate and coverage level of public health spending. Thus, economic growth is not a sufficient condition to achieve financial sustainability of health spending. Moreover, achieving financial sustainability by adjusting coverage levels might compromise ensuring the social sustainability of public health spending. Hence, this paper contributes to the debate on whether current increases of public health spending are desirable and sustainable.
Dash for dollars

Within-firm variation of corporate bond spreads around the Covid-19 outbreak shows that US dollar-denominated bonds experienced larger increases in spreads relative to non-dollar bonds, especially at short maturities. Differently, in the non-dollar sample it was the spreads of longer maturity bonds that widened more markedly. Price pressures arising from a liquidity-driven dash for cash alone cannot rationalize these findings. Instead, the patterns we uncover suggest a ‘dash for dollars’, in which investors sold their dollar-denominated assets first, with a consequent impact on prices. We link these dynamics to the dominant role of the US dollar in the international financial system.
How Much Ambiguity Aversion? Finding Indifferences between Ellsberg's Risky and Ambiguous Bets

Experimental results on the Ellsberg paradox typically reveal behavior that is commonly interpreted as ambiguity aversion. The experiments reported in the current paper find the objective probabilities for drawing a red ball that make subjects indifferent between various risky and uncertain Ellsberg bets. They allow us to examine the predictive power of alternative principles of choice under uncertainty, including the objective maximin and Hurwicz criteria, the sure-thing principle, and the principle of insufficient reason. Contrary to our expectations, the principle of insufficient reason performed substantially better than rival theories in our experiment, with ambiguity aversion appearing only as a secondary phenomenon.
Characterization of Silicon-Photomultipliers for a Cosmic Muon Veto detector

A Cosmic Muon Veto (CMV) detector using extruded scintillators is being designed around the mini-Iron Calorimeter detector at the transit campus of the India-based Neutrino Observatory, Madurai for measuring its efficiency at shallow depth underground experiments. The scintillation signal is transmitted through a Wavelength Shifting (WLS) fibre and readout by Hamamatsu Silicon-Photomultipliers (SiPMs). A Light Emitting Diode (LED) system is included on the front-end readout for in-situ calibration of the gain of each SiPM. A characterization system was developed for the measurement of gain and choice of the overvoltage (V ov) of SiPMs using the LED as well as a cosmic muon telescope. The V ov is obtained by studying the noise rate, the gain of the SiPM, and the muon detection efficiency. In case of any malfunction of the LED system during the operation, the SiPM can also be calibrated with the noise data as well as using radioactive sources. This paper describes the basic characteristics of the SiPM and the comparison of the calibration results using all three methods, as well as the V ov of the SiPMs and muon selection criteria for the veto detector.
A Sample Bias in Quasar Variability Studies

When a flux-limited quasar sample is observed at later times, there will be more dimmed quasars than brightened ones, due to a selection bias induced at the time of sample selection. Quasars are continuously varying and there are more fainter quasars than brighter ones. At the time of selection, even symmetrical variability will result in more quasars with their instantaneous fluxes scattered above the flux limit than those scattered below, leading to an asymmetry in flux changes over time. The same bias would lead to an asymmetry in the ensemble structure function (SF) of the sample such that the SF based on pairs with increasing fluxes will be slightly smaller than that based on pairs with decreasing fluxes. We use simulated time-symmetric quasar light curves based on the damped random walk prescription to illustrate the effects of this bias. The level of this bias depends on the sample, the threshold of magnitude changes, and the coverage of light curves, but the general behaviors are consistent. In particular, the simulations matched to recent observational studies with decade-long light curves produce an asymmetry in the SF measurements at the few percent level, similar to the observed values. These results provide a cautionary note on the reported time asymmetry in some recent quasar variability studies.
A functional mirror ascent view of policy gradient methods with function approximation

We use functional mirror ascent to propose a general framework (referred to as FMA-PG) for designing policy gradient methods. The functional perspective distinguishes between a pol-icy’s functional representation (what are its sufﬁ-cient statistics) and its parameterization (how are these statistics represented), and naturally results in computationally efﬁcient off-policy updates. For simple policy parameterizations, the FMA-PG framework ensures that the optimal policy is a ﬁxed point of the updates. It also allows us to handle complex policy parameterizations (e.g., neural networks) while guaranteeing policy improvement. Our framework uniﬁes several PG methods and opens the way for designing sample-efﬁcient variants of existing methods. Moreover, it recovers important implementation heuristics (e.g., using forward vs reverse KL divergence) in a principled way. With a softmax functional representation, FMA-PG results in a variant of TRPO with additional desirable properties. It also suggests an improved variant of PPO, whose robustness we empirically demonstrate on MuJoCo.
Fair Decision-Making for Food Inspections

We revisit the application of predictive models by the Chicago Department of Public Health to schedule restaurant inspections and prioritize the detection of critical food code violations. We perform the first analysis of the model’s fairness to the population served by the restaurants in terms of average time to find a critical violation. We find that the model treats inspections unequally based on the sanitarian who conducted the inspection and that, in turn, there are geographic disparities in the benefits of the model. We examine four alternate methods of model training and two alternative ways of scheduling using the model and find that the latter generate more desirable results. The challenges from this application point to important directions for future work around fairness with collective entities rather than individuals, the use of critical violations as a proxy, and the disconnect between fair classification and fairness in dynamic scheduling systems.
Mild Stochastic Sewing Lemma, SPDE in Random Environment, and Fractional Averaging

Our first result is a stochastic sewing lemma with quantitative estimates for mild incremental processes, with which we study SPDEs driven by fractional Brownian motions in a random environment. We obtain uniform $L^p$-bounds. Our second result is a fractional averaging principle admitting non-stationary fast environments. As an application, we prove a fractional averaging principle for SPDEs.
SIDER: Single-Image Neural Optimization for Facial Geometric Detail Recovery

We present SIDER (Single-Image neural optimization for facial geometric DEtail Recovery), a novel photometric optimization method that recovers detailed facial geometry from a single image in an unsupervised manner. Inspired by classical techniques of coarse-to-fine optimization and recent advances in implicit neural representations of 3D shape, SIDER combines a geometry prior based on statistical models and Signed Distance Functions (SDFs) to recover facial details from single images. First, it estimates a coarse geometry using a morphable model represented as an SDF. Next, it reconstructs facial geometry details by optimizing a photometric loss with respect to the ground truth image. In contrast to prior work, SIDER does not rely on any dataset priors and does not require additional supervision from multiple views, lighting changes or ground truth 3D shape. Extensive qualitative and quantitative evaluation demonstrates that our method achieves state-of-the-art on facial geometric detail recovery, using only a single in the-wild image.
Logit Attenuating Weight Normalization

Over-parameterized deep networks trained using gradient-based optimizers are a popular choice for solving classification and ranking problems. Without appropriately tuned $\ell_2$ regularization or weight decay, such networks have the tendency to make output scores (logits) and network weights large, causing training loss to become too small and the network to lose its adaptivity (ability to move around) in the parameter space. Although regularization is typically understood from an overfitting perspective, we highlight its role in making the network more adaptive and enabling it to escape more easily from weights that generalize poorly. To provide such a capability, we propose a method called Logit Attenuating Weight Normalization (LAWN), that can be stacked onto any gradient-based optimizer. LAWN controls the logits by constraining the weight norms of layers in the final homogeneous sub-network. Empirically, we show that the resulting LAWN variant of the optimizer makes a deep network more adaptive to finding minimas with superior generalization performance on large-scale image classification and recommender systems. While LAWN is particularly impressive in improving Adam, it greatly improves all optimizers when used with large batch sizes
HopfE: Knowledge Graph Representation Learning using Inverse Hopf Fibrations

Recently, several Knowledge Graph Embedding (KGE) approaches have been devised to represent entities and relations in a dense vector space and employed in downstream tasks such as link prediction. A few KGE techniques address interpretability, i.e., mapping the connectivity patterns of the relations (symmetric/asymmetric, inverse, and composition) to a geometric interpretation such as rotation. Other approaches model the representations in higher dimensional space such as four-dimensional space (4D) to enhance the ability to infer the connectivity patterns (i.e., expressiveness). However, modeling relation and entity in a 4D space often comes at the cost of interpretability. We propose HopfE, a novel KGE approach aiming to achieve the interpretability of inferred relations in the four-dimensional space. HopfE models the structural embeddings in 3D Euclidean space. Next, we map the entity embedding vector from a 3D Euclidean space to a 4D hypersphere using the inverse Hopf Fibration, in which we embed the semantic information from the KG ontology. Thus, HopfE considers the structural and semantic properties of the entities without losing expressivity and interpretability. Our empirical results on four well-known benchmarks achieve state-of-the-art performance for KG completion.
Graded dimensions and monomial bases for the cyclotomic quiver Hecke algebras

In this paper we give a closed formula for the graded dimension of the cyclotomic quiver Hecke algebra $R^\Lambda(\beta)$ associated to an {\it arbitrary} symmetrizable Cartan matrix $A=(a_{ij})_{i,j}\in I$, where $\Lambda\in P^+$ and $\beta\in Q_n^+$. As applications, we obtain some {\it necessary and sufficient conditions} for the KLR idempotent $e(\nu)$ (for any $\nu\in I^\beta$) to be nonzero in the cyclotomic quiver Hecke algebra $R^\Lambda(\beta)$. We prove several level reduction results which decomposes $\dim R^\Lambda(\beta)$ into a sum of some products of $\dim R^{\Lambda^i}(\beta_i)$ with $\Lambda=\sum_i\Lambda^i$ and $\beta=\sum_{i}\beta_i$, where $\Lambda^i\in P^+, \beta^i\in Q^+$ for each $i$. We construct some explicit monomial bases for the subspaces $e(\widetilde{\nu})R^\Lambda(\beta)e(\mu)$ and $e(\widetilde{\nu})R^\Lambda(\beta)e(\mu)$ of $R^\Lambda(\beta)$, where $\mu\in I^\beta$ is {\it arbitrary} and $\widetilde{\nu}\in I^\beta$ is a certain specific $n$-tuple (see Section 4). Finally, we use our graded dimension formulae to provide some examples which show that $R^\Lambda(n)$ is in general not graded free over its natural embedded subalgebra $R^\Lambda(m)$ with $m
On the Effectiveness of Transfer Learning for Code Search

The Transformer architecture and transfer learning have marked a quantum leap in natural language processing, improving the state of the art across a range of text-based tasks. This paper examines how these advancements can be applied to and improve code search. To this end, we pre-train a BERT-based model on combinations of natural language and source code data and fine-tune it on pairs of StackOverflow question titles and code answers. Our results show that the pre-trained models consistently outperform the models that were not pre-trained. In cases where the model was pre-trained on natural language “and” source code data, it also outperforms an information retrieval baseline based on Lucene. Also, we demonstrated that the combined use of an information retrieval-based approach followed by a Transformer leads to the best results overall, especially when searching into a large search pool. Transfer learning is particularly effective when much pre-training data is available and fine-tuning data is limited. We demonstrate that natural language processing models based on the Transformer architecture can be directly applied to source code analysis tasks, such as code search. With the development of Transformer models designed more specifically for dealing with source code data, we believe the results of source code analysis tasks can be further improved.
Theory of spin–charge-coupled transport in proximitized graphene: an SO(5) algebraic approach

Establishing the conditions under which orbital, spin and lattice-pseudospin degrees of freedom are mutually coupled in realistic nonequilibrium conditions is a major goal in the emergent field of graphene spintronics. Here, we use linear-response theory to obtain a unified microscopic description of spin dynamics and coupled spin–charge transport in graphene with an interface-induced Bychkov–Rashba effect. Our method makes use of an SO(5) extension of the familiar inverse-diffuson approach to obtain a quantum kinetic equation for the single-particle density matrix that treats spin and pseudospin on equal footing and is valid for arbitrary external perturbations. As an application of the formalism, we derive a complete set of drift–diffusion equations for proximitized graphene with scalar impurities in the presence of electric and spin-injection fields which vary slowly in space and time. Our approach is amenable to a wide variety of generalizations, including the study of coupled spin–charge dynamics in layered materials with strong spin–valley coupling and spin–orbit torques in van der Waals heterostructures.
The Structure of Multiphase Galactic Winds

We present a novel analytic framework to model the steady-state structure of multiphase galactic winds comprised of a hot, volume-filling component and a cold, clumpy component. We first derive general expressions for the structure of the hot phase for arbitrary mass, momentum, and energy source terms. Next, informed by recent simulations, we parameterize the cloud–wind mass transfer rates, which are set by the competition between turbulent mixing and radiative cooling. This enables us to cast the cloud–wind interaction as a source term for the hot phase and thereby simultaneously solve for the evolution of both phases, fully accounting for their bidirectional influence. With this model, we explore the nature of galactic winds over a broad range of conditions. We find that (i) with realistic parameter choices, we naturally produce a hot, low-density wind that transports energy while entraining a significant flux of cold clouds, (ii) mixing dominates the cold cloud acceleration and decelerates the hot wind, (iii) during mixing thermalization of relative kinetic energy provides significant heating, (iv) systems with low hot phase mass loading factors and/or star formation rates can sustain higher initial cold phase mass loading factors, but the clouds are quickly shredded, and (v) systems with large hot phase mass loading factors and/or high star formation rates cannot sustain large initial cold phase mass loading factors, but the clouds tend to grow with distance from the galaxy. Our results highlight the necessity of accounting for the multiphase structure of galactic winds, both physically and observationally, and have important implications for feedback in galactic systems.
Measurement Integrity in Peer Prediction: A Peer Assessment Case Study

We propose measurement integrity, a property related to ex post reward fairness, as a novel desideratum for peer prediction mechanisms in many natural applications. Like robustness against strategic reporting, the property that has been the primary focus of the peer prediction literature, measurement integrity is an important consideration for understanding the practical performance of peer prediction mechanisms. We perform computational experiments, both with an agent-based model and with real data, to empirically evaluate peer prediction mechanisms according to both of these important properties. Our evaluations simulate the application of peer prediction mechanisms to peer assessment---a setting in which ex post fairness concerns are particularly salient. We find that peer prediction mechanisms, as proposed in the literature, largely fail to demonstrate significant measurement integrity in our experiments. We also find that theoretical properties concerning robustness against strategic reporting are somewhat noisy predictors of empirical performance. Further, there is an apparent trade-off between our two dimensions of analysis. The best-performing mechanisms in terms of measurement integrity are highly susceptible to strategic reporting. Ultimately, however, we show that supplementing mechanisms with realistic parametric statistical models can, in some cases, improve performance along both dimensions of our analysis and result in mechanisms that strike the best balance between them.
Scalable pragmatic communication via self-supervision

Models of context-sensitive communication often use the Rational Speech Act framework (RSA; Frank & Goodman, 2012), which formulates listeners and speakers in a cooperative reasoning process. However, the standard RSA formulation can only be applied to small domains, and large-scale applications have relied on imitating human behavior. Here, we propose a new approach to scalable pragmatics, building upon recent theoretical results (Zaslavsky et al., 2020) that characterize pragmatic reasoning in terms of general information-theoretic principles. Specifically, we propose an architecture and learning process in which agents acquire pragmatic policies via self-supervision instead of imitating human data. This work suggests a new principled approach for equipping artificial agents with pragmatic skills via self-supervision, which is grounded both in pragmatic theory and in information theory.
m-RevNet: Deep Reversible Neural Networks with Momentum

In recent years, the connections between deep residual networks and first-order Ordinary Differential Equations (ODEs) have been disclosed. In this work, we further bridge the deep neural architecture design with the second-order ODEs and propose a novel reversible neural network, termed as m-RevNet, that is characterized by inserting momentum update to residual blocks. The reversible property allows us to perform backward pass without access to activation values of the forward pass, greatly relieving the storage burden during training. Furthermore, the theoretical foundation based on second-order ODEs grants m-RevNet with stronger representational power than vanilla residual networks, which potentially explains its performance gains. For certain learning scenarios, we analytically and empirically reveal that our m-RevNet succeeds while standard ResNet fails. Comprehensive experiments on various image classification and semantic segmentation benchmarks demonstrate the superiority of our m-RevNet over ResNet, concerning both memory efficiency and recognition performance.
Exploring Head-based Mode-Switching in Virtual Reality

Mode-switching supports multilevel operations using a limited number of input methods. In Virtual Reality (VR) head-mounted displays (HMD), common approaches for mode-switching use buttons, controllers, and users’ hands. However, they are inefficient and challenging to do with tasks that require both hands (e.g., when users need to use two hands during drawing operations). Using head gestures for mode-switching can be an efficient and cost-effective way, allowing for a more continuous and smooth transition between modes. In this paper, we explore the use of head gestures for mode-switching especially in scenarios when both users’ hands are performing tasks. We present a first user study that evaluated eight head gestures that could be suitable for VR HMD with a dual-hand line-drawing task. Results show that move forward, move backward, roll left, and roll right led to better performance and are preferred by participants. A second study integrating these four gestures in Tilt Brush, an open-source painting VR application, is conducted to further explore the applicability of these gestures and derive insights. Results show that Tilt Brush with head gestures allowed users to change modes with ease and led to improved interaction and user experience. The paper ends with a discussion on some design recommendations for using head-based mode-switching in VR HMD.