Miracle-dz/UniFAR-FTUs huggingface dataset:
Retrieve, Reason, and Refine: Generating Accurate and Faithful Patient Instructions

The “Patient Instruction” (PI), which contains critical instructional information provided both to carers and to the patient at the time of discharge, is essential for the patient to manage their condition outside hospital. An accurate and easy-to-follow PI can improve the self-management of patients which can in turn reduce hospital readmission rates. However, writing an appropriate PI can be extremely time-consuming for physicians, and is subject to being incomplete or error-prone for (potentially overworked) physicians. Therefore, we propose a new task that can provide an objective means of avoiding incompleteness, while reducing clinical workload: the automatic generation of the PI, which is imagined as being a document that the clinician can review, modify, and approve as necessary (rather than taking the human “out of the loop”). We build a benchmark clinical dataset and propose the Re 3 Writer, which imitates the working patterns of physicians to first re trieve related working experience from historical PIs written by physicians, then re ason related medical knowledge. Finally, it re fines the retrieved working experience and reasoned medical knowledge to extract useful information, which is used to generate the PI for previously-unseen patient according to their health records during hospitalization. Our experiments show that, using our method, the performance of five different models can be substantially boosted across all metrics, with up to 20%, 11% and 19% relative improvements in BLEU-4, ROUGE-L and METEOR, respectively. Meanwhile, we show results from human evaluations to measure the effectiveness in terms of its usefulness for clinical practice. 3
Cipherfix: Mitigating Ciphertext Side-Channel Attacks in Software

Trusted execution environments (TEEs) provide an environment for running workloads in the cloud without having to trust cloud service providers, by offering additional hardware-assisted security guarantees. However, main memory encryption as a key mechanism to protect against system-level attackers trying to read the TEE's content and physical, off-chip attackers, is insufficient. The recent Cipherleaks attacks infer secret data from TEE-protected implementations by analyzing ciphertext patterns exhibited due to deterministic memory encryption. The underlying vulnerability, dubbed the ciphertext side-channel, is neither protected by state-of-the-art countermeasures like constant-time code nor by hardware fixes. Thus, in this paper, we present a software-based, drop-in solution that can harden existing binaries such that they can be safely executed under TEEs vulnerable to ciphertext side-channels, without requiring recompilation. We combine taint tracking with both static and dynamic binary instrumentation to find sensitive memory locations, and mitigate the leakage by masking secret data before it gets written to memory. This way, although the memory encryption remains deterministic, we destroy any secret-dependent patterns in encrypted memory. We show that our proof-of-concept implementation protects various constant-time implementations against ciphertext side-channels with reasonable overhead.
A non-sequential hierarchy of message-passing models

There is a wide variety of message-passing communication models, ranging from synchronous ”rendez-vous” communications to fully asynchronous/out-of-order communications. For large-scale distributed systems, the communication model is determined by the transport layer of the network, and a few classes of orders of message delivery (FIFO, causally ordered) have been identiﬁed in the early days of distributed computing. For local-scale message-passing applications, e.g., running on a single machine, the communication model may be determined by the actual implementation of message buﬀers and by how FIFO queues are used. While large-scale communication models, such as causal ordering, are deﬁned by logical axioms, local-scale models are often deﬁned by an operational semantics. In this work, we connect these two approaches, and we present a uniﬁed hierarchy of communication models encompassing both large-scale and local-scale models, based on their non-sequential behaviors. We also show that all the communication models we consider can be axiomatised in the monadic second order logic, and may therefore beneﬁt from several bounded veriﬁcation techniques based on bounded special treewidth.
Conformal Predictor for Improving Zero-Shot Text Classification Efficiency

Pre-trained language models (PLMs) have been shown effective for zero-shot (0shot) text classification. 0shot models based on natural language inference (NLI) and next sentence prediction (NSP) employ cross-encoder architecture and infer by making a forward pass through the model for each label-text pair separately. This increases the computational cost to make inferences linearly in the number of labels. In this work, we improve the efficiency of such cross-encoder-based 0shot models by restricting the number of likely labels using another fast base classifier-based conformal predictor (CP) calibrated on samples labeled by the 0shot model. Since a CP generates prediction sets with coverage guarantees, it reduces the number of target labels without excluding the most probable label based on the 0shot model. We experiment with three intent and two topic classification datasets. With a suitable CP for each dataset, we reduce the average inference time for NLI- and NSP-based models by 25.6% and 22.2% respectively, without dropping performance below the predefined error rate of 1%.
Kadabra: Adapting Kademlia for the Decentralized Web

Blockchains have become the catalyst for a growing movement to create a more decentralized Internet. A fundamental operation of applications in a decentralized Internet is data storage and retrieval. As today's blockchains are limited in their storage functionalities, in recent years a number of peer-to-peer data storage networks have emerged based on the Kademlia distributed hash table protocol. However, existing Kademlia implementations are not efficient enough to support fast data storage and retrieval operations necessary for (decentralized) Web applications. In this paper, we present Kadabra, a decentralized protocol for computing the routing table entries in Kademlia to accelerate lookups. Kadabra is motivated by the multi-armed bandit problem, and can automatically adapt to heterogeneity and dynamism in the network. Experimental results show Kadabra achieving between 15-50% lower lookup latencies compared to state-of-the-art baselines.
Achievable Error Exponents for Almost Fixed-Length Hypothesis Testing and Classification

We revisit multiple hypothesis testing and propose a two-phase test, where each phase is a ﬁxed-length test and the second-phase proceeds only if a reject option is decided in the ﬁrst phase. We derive achievable error exponents of error probabilities under each hypothesis and show that our two-phase test bridges over ﬁxed-length and sequential tests in the similar spirit of Lalitha and Javidi (ISIT, 2016) for binary hypothesis testing. Speciﬁcally, our test could achieve the performance close to a sequential test with the asymptotic complexity of a ﬁxed-length test and such test is named the almost ﬁxed-length test. Motivated by practical applications where the generating distribution under each hypothesis is unknown , we generalize our results to the statistical classiﬁcation framework of Gutman (TIT, 1989). We ﬁrst consider binary classiﬁcation and then generalize our results to M -ary classiﬁcation. For both cases, we propose a two-phase test, derive achievable error exponents and demonstrate that our two-phase test bridges over ﬁxed-length and sequential tests. In particular, for M -ary classiﬁcation, no ﬁnal reject option is required to achieve the same exponent as the sequential test of Haghifam, Tan, and Khisti (TIT, 2021). Our results generalize the design and analysis of the almost ﬁxed-length test for binary hypothesis testing to broader and more practical families of M -ary hypothesis testing and statistical classiﬁcation.
On the Calibration of Massively Multilingual Language Models

Massively Multilingual Language Models (MMLMs) have recently gained popularity due to their surprising effectiveness in cross-lingual transfer. While there has been much work in evaluating these models for their performance on a variety of tasks and languages, little attention has been paid on how well calibrated these models are with respect to the confidence in their predictions. We first investigate the calibration of MMLMs in the zero-shot setting and observe a clear case of miscalibration in low-resource languages or those which are typologically diverse from English. Next, we empirically show that calibration methods like temperature scaling and label smoothing do reasonably well in improving calibration in the zero-shot scenario. We also find that few-shot examples in the language can further help reduce calibration errors, often substantially. Overall, our work contributes towards building more reliable multilingual models by highlighting the issue of their miscalibration, understanding what language and model-specific factors influence it, and pointing out the strategies to improve the same.
Biologically Plausible Variational Policy Gradient with Spiking Recurrent Winner-Take-All Networks

One stream of reinforcement learning research is exploring biologically plausible models and algorithms to simulate biological intelligence and fit neuromorphic hardware. Among them, reward-modulated spike-timing-dependent plasticity (R-STDP) is a recent branch with good potential in energy efficiency. However, current R-STDP methods rely on heuristic designs of local learning rules, thus requiring task-specific expert knowledge. In this paper, we consider a spiking recurrent winner-take-all network, and propose a new R-STDP method, spiking variational policy gradient (SVPG), whose local learning rules are derived from the global policy gradient and thus eliminate the need for heuristic designs. In experiments of MNIST classification and Gym InvertedPendulum, our SVPG achieves good training performance, and also presents better robustness to various kinds of noises than conventional methods.
Stochastic Mirror Descent for Large-Scale Sparse Recovery

In this paper we discuss an application of Stochastic Approximation to statistical estimation of high-dimensional sparse parameters. The proposed solution reduces to resolving a penalized stochastic optimization problem on each stage of a multistage algorithm; each problem being solved to a prescribed accuracy by the non-Euclidean Composite Stochastic Mirror Descent (CSMD) algorithm. Assuming that the problem objective is smooth and quadratically minorated and stochastic perturbations are sub-Gaussian, our analysis prescribes the method parameters which ensure fast convergence of the estimation error (the radius of a confidence ball of a given norm around the approximate solution). This convergence is linear during the first"preliminary"phase of the routine and is sublinear during the second"asymptotic"phase. We consider an application of the proposed approach to sparse Generalized Linear Regression problem. In this setting, we show that the proposed algorithm attains the optimal convergence of the estimation error under weak assumptions on the regressor distribution. We also present a numerical study illustrating the performance of the algorithm on high-dimensional simulation data.
10 Hours Data Is All You Need

We propose a novel procedure to generate pseudo mandarin speech data named as CAMP (character audio mix up), which aims at generating audio from a character scale. We also raise a method for building a mandarin character scale audio database adaptive to CAMP named as META-AUDIO, which makes full use of audio data and can greatly increase the data diversity of the database. Experiments show that our CAMP method is simple and quite effective. For example, we train models with 10 hours of audio data in AISHELL-1 and pseudo audio data generated by CAMP, and achieve a competitive 11.07 character error rate (CER). Besides, we also perform training with only 10 hours of audio data in AIDATATANG dataset and pseudo audio data generated by CAMP, which again achieves a competitive 8.26 CER.
Programming bare-metal accelerators with heterogeneous threading models: a case study of Matrix-3000

As the hardware industry moves toward using specialized heterogeneous many-core processors to avoid the effects of the power wall, software developers are finding it hard to deal with the complexity of these systems. In this paper, we share our experience of developing a programming model and its supporting compiler and libraries for Matrix-3000, which is designed for next-generation exascale supercomputers but has a complex memory hierarchy and processor organization. To assist its software development, we have developed a software stack from scratch that includes a low-level programming interface and a high-level OpenCL compiler. Our low-level programming model offers native programming support for using the bare-metal accelerators of Matrix-3000, while the high-level model allows programmers to use the OpenCL programming standard. We detail our design choices and highlight the lessons learned from developing system software to enable the programming of bare-metal accelerators. Our programming models have been deployed in the production environment of an exascale prototype system.
Extended random ﬂights, Fourier-Bessel series, and a completeness-like relation for spherical Bessel functions

We prove a completeness-like relation for a non-orthogonal set of spherical Bessel functions which is not known to be complete -- namely, the set $\{j_m(\alpha^l_i z) \}$, with $0 \leq z \leq 1$, $l \geq m$, where $\alpha^l_i$ is the $i$-th zero of the spherical Bessel function of order $l$. This completeness-like relation arises in the study of Fourier-Bessel series expansions of the probability density functions for random flights.
Decentralized Stochastic Bilevel Optimization with Improved Per-Iteration Complexity

Bilevel optimization recently has received tremendous attention due to its great success in solving important machine learning problems like meta learning, reinforcement learning, and hyperparameter optimization. Extending single-agent training on bilevel problems to the decentralized setting is a natural generalization, and there has been a flurry of work studying decentralized bilevel optimization algorithms. However, it remains unknown how to design the distributed algorithm with sample complexity and convergence rate comparable to SGD for stochastic optimization, and at the same time without directly computing the exact Hessian or Jacobian matrices. In this paper we propose such an algorithm. More specifically, we propose a novel decentralized stochastic bilevel optimization (DSBO) algorithm that only requires first order stochastic oracle, Hessian-vector product and Jacobian-vector product oracle. The sample complexity of our algorithm matches the currently best known results for DSBO, and the advantage of our algorithm is that it does not require estimating the full Hessian and Jacobian matrices, thereby having improved per-iteration complexity.
Resolving the blazar CGRaBS J0809+5341 in the presence of telescope systematics

We analyse Very Long Baseline Interferometry (VLBI) observations of the blazar CGRaBS J0809+5341 using Bayesian inference methods. The observation was carried out at 5 GHz using 8 telescopes that form part of the European VLBI Network. Imaging and deconvolution using traditional methods imply that the blazar is unresolved. To search for source structure beyond the diffraction limit, we perform Bayesian model selection between three source models (point, elliptical Gaussian, and circular Gaussian). Our modelling jointly accounts for antenna-dependent gains and system equivalent flux densities. We obtain posterior distributions for the various source and instrumental parameters along with the corresponding uncertainties and correlations between them. We find that there is very strong evidence (>1e9 :1) in favour of elliptical Gaussian structure and using this model derive the apparent brightness temperature distribution of the blazar, accounting for uncertainties in the shape estimates. To test the integrity of our method, we also perform model selection on synthetic observations and use this to develop a Bayesian criterion for the minimum resolvable source size and consequently the maximum measurable brightness temperature for a given interferometer, dependent on the signal-to-noise ratio (SNR) of the data incorporating the aforementioned systematics. We find that calibration errors play an increasingly important role in determining the over-resolution limit for SNR>>100. We show that it is possible to exploit the resolving power of future VLBI arrays down to about 5 per cent of the size of the naturally-weighted restoring beam, if the gain calibration is precise to 1 per cent or less.
Transport Reversible Jump Proposals

Reversible jump Markov chain Monte Carlo (RJMCMC) proposals that achieve reasonable acceptance rates and mixing are notoriously difficult to design in most applications. Inspired by recent advances in deep neural network-based normalizing flows and density estimation, we demonstrate an approach to enhance the efficiency of RJMCMC sampling by performing transdimensional jumps involving reference distributions. In contrast to other RJMCMC proposals, the proposed method is the first to apply a non-linear transport-based approach to construct efficient proposals between models with complicated dependency structures. It is shown that, in the setting where exact transports are used, our RJMCMC proposals have the desirable property that the acceptance probability depends only on the model probabilities. Numerical experiments demonstrate the efficacy of the approach.
Fixed Point Iteration-An Interesting Way to Begin a Calculus Course.

How do you begin a calculus course? A precalculus review? A discussion of the slope of secant and tangent lines? An investigation of limits? In this article we offer an alternative, a discussion of Fixed Point Iteration which combines the best features of each of these three traditional approaches and which is ideal for the use of the hand calculator as well. Fixed Point Iteration (FPI, sometimes called Picard Iteration) is a root-finding algorithm discussed in most elementary numerical analysis books (e.g., [1], [3]) and in at least two newer calculus texts ([2], [5]). To illustrate our approach we examine, in detail, one example which could cover the first few days of the course. We conclude with a discussion of the advantages of this method.
COMPLETE BAYESIAN ANALYSIS OF SOME MIXTURE TIME SERIES MODELS

In this thesis we consider some finite mixture time series models in which each component is following a well-known process, e.g. AR, ARMA or ARMA-GARCH process, with either normal-type errors or Student-t type errors. We develop MCMC methods and use them in the Bayesian analysis of these mixture models. We introduce some new models such as mixture of Student-t ARMA components and mixture of Student-t ARMA-GARCH components with complete Bayesian treatments. Moreover, we use component precision (instead of variance) with an additional hierarchical level which makes our model more consistent with the MCMC moves. We have implemented the proposed methods in R and give examples with real and simulated data.
E-Valuating Classifier Two-Sample Tests

We propose E-C2ST, a classifier two-sample test for high-dimensional data based on E-values. Compared to $p$-values-based tests, tests with E-values have finite sample guarantees for the type I error. E-C2ST combines ideas from existing work on split likelihood ratio tests and predictive independence testing. The resulting E-values incorporate information about the alternative hypothesis. We demonstrate the utility of E-C2ST on simulated and real-life data. In all experiments, we observe that when going from small to large sample sizes, as expected, E-C2ST starts with lower power compared to other methods but eventually converges towards one. Simultaneously, E-C2ST's type I error stays substantially below the chosen significance level, which is not always the case for the baseline methods. Finally, we use an MRI dataset to demonstrate that multiplying E-values from multiple independently conducted studies leads to a combined E-value that retains the finite sample type I error guarantees while increasing the power.
A Signature of Planetary Migration: The Origin of Asymmetric Capture in the 2:1 Resonance

The spatial distribution of Kuiper Belt objects (KBOs) in 2 : 1 exterior resonance with Neptune constrains that planet's migration history. Numerical simulations demonstrate that fast planetary migration generates a larger population of KBOs trailing rather than leading Neptune in orbital longitude. This asymmetry corresponds to a greater proportion of objects caught into asymmetric resonance such that their resonance angles ϕ librate about values greater than π (trailing) as opposed to less than π (leading). We provide, for the first time, an explanation of this phenomenon, using physical, analytic, and semianalytic arguments. Central to our understanding is how planetary migration shifts the equilibrium points of the superposed direct and indirect potentials. Symmetric libration, in which ϕ librates about ~π, precedes capture into asymmetric resonance. As a particle transitions from symmetric to asymmetric libration, if ϕ exceeds the value ψ at the unstable point of asymmetric resonance, then the particle is caught into trailing resonance, while if ϕ < ψ, the particle is caught into leading resonance. The probability that the KBO is caught into trailing resonance is determined by the fraction of time it spends with ϕ > ψ while in symmetric libration. This fractional time increases with faster migration because migration not only shifts ψ to values less than π but also shifts the stable point of symmetric libration to values greater than π. Smaller eccentricities prior to capture strengthen the effect of these shifts. Large capture asymmetries appear for exponential timescales of migration τ shorter than ~107 yr. The observed distribution of 2 : 1 KBOs (two trailing and seven leading) excludes τ ≤ 106 yr with 99.65% confidence.
Time-Varying ALIP Model and Robust Foot-Placement Control for Underactuated Bipedal Robotic Walking on a Swaying Rigid Surface

Controller design for bipedal walking on dynamic rigid surfaces (DRSes), which are rigid surfaces moving in the inertial frame (e.g., ships and airplanes), remains largely underexplored. This paper introduces a hierarchical control approach that achieves stable underactuated bipedal walking on a horizontally oscillating DRS. The highest layer of our approach is a real-time motion planner that generates desired global behaviors (i.e., center of mass trajectories and footstep locations) by stabilizing a reduced-order robot model. One key novelty of this layer is the derivation of the reduced-order model by analytically extending the angular momentum based linear inverted pendulum (ALIP) model from stationary to horizontally moving surfaces. The other novelty is the development of a discrete-time foot-placement controller that exponentially stabilizes the hybrid, linear, time-varying ALIP. The middle layer translates the desired global behaviors into the robot’s full-body reference trajectories for all directly actuated degrees of freedom, while the lowest layer exponentially tracks those reference trajectories based on the full-order, hybrid, nonlinear robot model. Simulations confirm that the proposed framework ensures stable walking of a planar underactuated biped under different swaying DRS motions and gait types.