Miracle-dz/UniFAR-FTUs huggingface dataset:
Assurance Methods for designing a clinical trial with a delayed treatment effect

An assurance calculation is a Bayesian alternative to a power calculation. One may be performed to aid the planning of a clinical trial, specifically setting the sample size or to support decisions about whether or not to perform a study. Immuno-oncology is a rapidly evolving area in the development of anticancer drugs. A common phenomenon that arises in trials of such drugs is one of delayed treatment effects, that is, there is a delay in the separation of the survival curves. To calculate assurance for a trial in which a delayed treatment effect is likely to be present, uncertainty about key parameters needs to be considered. If uncertainty is not considered, the number of patients recruited may not be enough to ensure we have adequate statistical power to detect a clinically relevant treatment effect and the risk of an unsuccessful trial is increased. We present a new elicitation technique for when a delayed treatment effect is likely and show how to compute assurance using these elicited prior distributions. We provide an example to illustrate how this can be used in practice and develop open-source software to implement our methods. Our methodology has the potential to improve the success rate and efficiency of Phase III trials in immuno-oncology and for other treatments where a delayed treatment effect is expected to occur.
Rigidity of Balanced Minimal Cycle Complexes

A $(d-1)$-dimensional simplicial complex $\Delta$ is balanced if its graph $G(\Delta)$ is $d$-colorable. Klee and Novik obtained the balanced lower bound theorem for balanced normal $(d-1)$-pseudomanifolds $\Delta$ with $d\geq3$ by showing that the subgraph of $G(\Delta)$ induced by the vertices colored in $T$ is rigid in $\mathbb{R}^3$ for any $3$ colors $T$. We show that the same rigidity result, and thus the balanced lower bound theorem, holds for balanced minimal $(d-1)$-cycle complexes with $d \geq 3$. Motivated by the Stanley's work on a colored system of parameters for the Stanley-Reisner ring of balanced simplicial complexes, we further investigate the infinitesimal rigidity of non-generic realization of balanced, and more broadly $\bm{a}$-balanced, simplicial complexes. Among other results, we show that for $d \geq 4$, a balanced homology $(d-1)$-manifold can be realized as an infinitesimally rigid framework in $\mathbb{R}^d$ such that each vertex of color $i$ lies on the $i$th coordinate axis.
Heritage Site Management: The Link between Visitors' Pre-Visit Perceptions, Motivations and Expectations

ABSTRACT This study attempts to explore the significance of visitors' perceptions of a site relative to their own heritage as a factor in understanding tourist behavior and for the management of heritage settings. The Anne Frank House in Amsterdam, the Netherlands, was chosen as the focus of the current research, which is composed of two field studies. The first, relates to tourists who had already decided to visit and was conducted among people waiting in line to enter the site. The second field study, conducted at different locations in Amsterdam, referred to potential visitors (had not yet decided to visit the site). The results Indicate that the individual's perception of the site relative to his/her own heritage is a key factor in understanding both motivation to visit and expectations of the interpretation provided. Implications of the findings for researchers and practitioners are presented.
mlirSynth: Automatic, Retargetable Program Raising in Multi-Level IR Using Program Synthesis

MLIR is an emerging compiler infrastructure for modern hardware, but existing programs cannot take advantage of MLIR's high-performance compilation if they are described in lower-level general purpose languages. Consequently, to avoid programs needing to be rewritten manually, this has led to efforts to automatically raise lower-level to higher-level dialects in MLIR. However, current methods rely on manually-defined raising rules, which limit their applicability and make them challenging to maintain as MLIR dialects evolve. We present mlirSynth - a novel approach which translates programs from lower-level MLIR dialects to high-level ones without manually defined rules. Instead, it uses available dialect definitions to construct a program space and searches it effectively using type constraints and equivalences. We demonstrate its effectiveness by raising C programs to two distinct high-level MLIR dialects, which enables us to use existing high-level dialect specific compilation flows. On Polybench, we show a greater coverage than previous approaches, resulting in geomean speedups of 2.5x (Intel) and 3.4x (AMD) over state-of-the-art compilation flows for the C programming language. mlirSynth also enables retargetability to domain-specific accelerators, resulting in a geomean speedup of 21.6x on a TPU.
Motion Matching for Character Animation and Virtual Reality Avatars in Unity

Real-time animation of virtual characters has traditionally been accomplished by playing short sequences of animations structured in the form of a graph. These methods are time-consuming to set up and scale poorly with the number of motions required in modern virtual environments. The ever-increasing need for highly-realistic virtual characters in fields such as entertainment, virtual reality, or the metaverse has led to significant advances in the field of data-driven character animation. Techniques like Motion Matching have provided enough versatility to conveniently animate virtual characters using a selection of features from an animation database. Data-driven methods retain the quality of the captured animations, thus delivering smoother and more natural-looking animations. In this work, we researched and developed a Motion Matching technique for the Unity game engine. In this thesis, we present our findings on how to implement an animation system based on Motion Matching. We also introduce a novel method combining body orientation prediction with Motion Matching to animate avatars for consumer-grade virtual reality systems.
Diffusing on Two Levels and Optimizing for Multiple Properties: A Novel Approach to Generating Molecules with Desirable Properties

In the past decade, Artificial Intelligence driven drug design and discovery has been a hot research topic, where an important branch is molecule generation by generative models, from GAN-based models and VAE-based models to the latest diffusion-based models. However, most existing models pursue only the basic properties like validity and uniqueness of the generated molecules, a few go further to explicitly optimize one single important molecular property (e.g. QED or PlogP), which makes most generated molecules little usefulness in practice. In this paper, we present a novel approach to generating molecules with desirable properties, which expands the diffusion model framework with multiple innovative designs. The novelty is two-fold. On the one hand, considering that the structures of molecules are complex and diverse, and molecular properties are usually determined by some substructures (e.g. pharmacophores), we propose to perform diffusion on two structural levels: molecules and molecular fragments respectively, with which a mixed Gaussian distribution is obtained for the reverse diffusion process. To get desirable molecular fragments, we develop a novel electronic effect based fragmentation method. On the other hand, we introduce two ways to explicitly optimize multiple molecular properties under the diffusion model framework. First, as potential drug molecules must be chemically valid, we optimize molecular validity by an energy-guidance function. Second, since potential drug molecules should be desirable in various properties, we employ a multi-objective mechanism to optimize multiple molecular properties simultaneously. Extensive experiments with two benchmark datasets QM9 and ZINC250k show that the molecules generated by our proposed method have better validity, uniqueness, novelty, Fr\'echet ChemNet Distance (FCD), QED, and PlogP than those generated by current SOTA models.
Augmented Flexible Krylov Subspace methods with applications to Bayesian inverse problems

This paper presents two new augmented flexible (AF)-Krylov subspace methods, AF-GMRES and AF-LSQR, to compute solutions of large-scale linear discrete ill-posed problems that can be modeled as the sum of two independent random variables, exhibiting smooth and sparse stochastic characteristics respectively. Following a Bayesian modelling approach, this corresponds to adding a covariance-weighted quadratic term and a sparsity enforcing $\ell_1$ term in the original least-squares minimization scheme. To handle the $\ell_1$ regularization term, the proposed approach constructs a sequence approximating quadratic problems that are partially solved using augmented flexible Krylov-Tikhonov methods. Compared to other traditional methods used to solve this minimization problem, such as those based on iteratively reweighted norm schemes, the new algorithms build a single (augmented, flexible) approximation (Krylov) subspace that encodes information about the different regularization terms through adaptable"preconditioning". The solution space is then expanded as soon as a new problem within the sequence is defined. This also allows for the regularization parameters to be chosen on-the-fly at each iteration. Compared to most recent work on generalized flexible Krylov methods, our methods offer theoretical assurance of convergence and a more stable numerical performance. The efficiency of the new methods is shown through a variety of experiments, including a synthetic image deblurring problem, a synthetic atmospheric transport problem, and fluorescence molecular tomography reconstructions using both synthetic and real-world experimental data.
Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting

This work considers a rather general and broad class of Markov chains, Ito chains that look like Euler-Maryama discretization of some Stochastic Differential Equation. The chain we study is a unified framework for theoretical analysis. It comes with almost arbitrary isotropic and state-dependent noise instead of normal and state-independent one, as in most related papers. Moreover, our chain's drift and diffusion coefficient can be inexact to cover a wide range of applications such as Stochastic Gradient Langevin Dynamics, sampling, Stochastic Gradient Descent, or Stochastic Gradient Boosting. We prove an upper bound for $W_{2}$-distance between laws of the Ito chain and the corresponding Stochastic Differential Equation. These results improve or cover most of the known estimates. Moreover, for some particular cases, our analysis is the first.
The foundations of statistical physics: entropy, irreversibility, and inference

Statistical physics aims to describe properties of macroscale systems in terms of distributions of their microscale agents. Its central tool is the maximization of entropy, a variational principle. We review the history of this principle, first considered as a law of nature, more recently as a procedure for inference in model-making. And while equilibria (EQ) have long been grounded in the principle of Maximum Entropy (MaxEnt), until recently no equally foundational generative principle has been known for non-equilibria (NEQ). We review evidence that the variational principle for NEQ is Maximum Caliber. It entails maximizing \textit{path entropies}, not \textit{state entropies}. We also describe the role of entropy in characterizing irreversibility, and describe the relationship between MaxCal and other prominent approaches to NEQ physics, including Stochastic Thermodynamics (ST), Large Deviations Theory (LDT), Macroscopic Fluctuation Theory (MFT), and non-extensive entropies.
Optimization methods for the capacitated refueling station location problem with routing

The energy transition in transportation benefits from demand-based models to determine the optimal placement of refueling stations for alternative fuel vehicles such as battery electric trucks. A formulation known as the refueling station location problem with routing (RSLP-R) is concerned with minimizing the number of stations necessary to cover a set of origin-destination trips such that the transit time does not exceed a given threshold. In this paper we extend the RSLP-R by station capacities to limit the number of vehicles that can be refueled at individual stations. The solution to the capacitated RSLP-R (CRSLP-R) avoids congestion of refueling stations by satisfying capacity constraints. We devise two optimization methods to deal with the increased difficulty to solve the CRSLP-R. The first method extends a prior branch-and-cut approach and the second method is a branch-cut-and-price algorithm based on variables associated with feasible routes. We evaluate both our methods on instances from the literature as well as a newly constructed network and find that the relative performance of the algorithms depends on the strictness of the capacity constraints. Furthermore, we show some runtime improvements over prior work on uncapacitated instances.
The Reinforce Policy Gradient Algorithm Revisited

We revisit the Reinforce policy gradient algorithm that works with full cost returns obtained over random length episodes. We propose a new Reinforce type algorithm that estimates the policy gradient using a function measurement over a perturbed parameter using a smoothed functional based gradient estimator. We observe that even though we estimate the gradient of the performance objective using sample performance (and not the sample gradient), the algorithm converges to a neighborhood of a local minimum. We further describe the main convergence result.
A variable speed of sound formulation for weakly compressible smoothed particle hydrodynamics

We present a Weakly Compressible SPH (WCSPH) formulation with a temporally variable speed of sound. The benefits of a time-varying sound speed formulation and the weaknesses of a constant sound speed formulation are worked out. It is shown how a variable sound speed can improve the performance, accuracy, and applicability of the WCSPH method. In our novel Uniform Compressible SPH (UCSPH) method, the required artificial speed of sound is calculated at each time step based on the current flow field. The method's robustness, performance, and accuracy are demonstrated with three test cases: a Taylor-Green vortex flow, a falling droplet impact, and a dam break. For all showcases, we observe at least similar accuracy as computed with WCSPH at strongly improved computational performance.
Human-Robot Gym: Benchmarking Reinforcement Learning in Human-Robot Collaboration

Deep reinforcement learning (RL) has shown promising results in robot motion planning with first attempts in human-robot collaboration (HRC). However, a fair comparison of RL approaches in HRC under the constraint of guaranteed safety is yet to be made. We, therefore, present human-robot gym, a benchmark for safe RL in HRC. Our benchmark provides eight challenging, realistic HRC tasks in a modular simulation framework. Most importantly, human-robot gym includes a safety shield that provably guarantees human safety. We are, thereby, the first to provide a benchmark to train RL agents that adhere to the safety specifications of real-world HRC. This bridges a critical gap between theoretic RL research and its real-world deployment. Our evaluation of six environments led to three key results: (a) the diverse nature of the tasks offered by human-robot gym creates a challenging benchmark for state-of-the-art RL methods, (b) incorporating expert knowledge in the RL training in the form of an action-based reward can outperform the expert, and (c) our agents negligibly overfit to training data.
Model Complexity of Program Phases

In resource limited computing systems, sequence prediction models must operate under tight constraints. Various models are available that cater to prediction under these conditions that in some way focus on reducing the cost of implementation. These resource constrained sequence prediction models, in practice, exhibit a fundamental tradeoff between the cost of implementation and the quality of its predictions. This fundamental tradeoff seems to be largely unexplored for models for different tasks. Here we formulate the necessary theory and an associated empirical procedure to explore this tradeoff space for a particular family of machine learning models such as deep neural networks. We anticipate that the knowledge of the behavior of this tradeoff may be beneficial in understanding the theoretical and practical limits of creation and deployment of models for resource constrained tasks.
Systematic Search for Water Fountain Candidates Using the Databases of Circumstellar Maser Sources

Water fountains (WFs) are thought to be objects in the morphological evolution of the circumstellar envelopes of low- and intermediate-mass evolved stars, transitioning from spherically symmetric to asymmetric shapes. We used databases of circumstellar 1612 MHz OH and 22.235 GHz H2O maser sources to search for new WF candidates using the criterion of a larger velocity range of the H2O maser emission compared to that of the OH maser emission. Thus, it is in principle possible to identify WFs with H2O velocity ranges smaller than those for the previously known WFs. For the OH maser line, we analyzed database entries of 8474 observations from 2195 sources, and 6085 observations from 3642 sources for the H2O maser line. After a close examination of the velocity ranges and line profiles, we identified 11 sources that meet the criterion mentioned above. We examined the IRAS colors of the selected sources and found that two of them (IRAS 19069+0916 and IRAS 19319+2214) are in the color region for post-AGB stars. We find that the maser velocity criterion can discover other astrophysically interesting objects than just WFs. Such objects may include peculiar planetary nebulae with maser emissions and stellar merger remnants.
Deterministic Clustering in High Dimensional Spaces: Sketches and Approximation

In all state-of-the-art sketching and coreset techniques for clustering, as well as in the best known fixed-parameter tractable approximation algorithms, randomness plays a key role. For the classic k-median and k-means problems, there are no known deterministic dimensionality reduction procedure or coreset construction that avoid an exponential dependency on the input dimension d, the precision parameter $\varepsilon^{-1}$ or k. Furthermore, there is no coreset construction that succeeds with probability $1-1/n$ and whose size does not depend on the number of input points, n. This has led researchers in the area to ask what is the power of randomness for clustering sketches [Feldman WIREs Data Mining Knowl. Discov’20]. Similarly, the best approximation ratio achievable deterministically without a complexity exponential in the dimension are $1+\sqrt{2}$ for k-median [Cohen-Addad, Esfandiari, Mirrokni, Narayanan, STOC’22] and 6.12903 for k-means [Grandoni, Ostrovsky, Rabani, Schulman, Venkat, Inf. Process. Lett. ’22]. Those are the best results, even when allowing a complexity FPT in the number of clusters k: this stands in sharp contrast with the $(1+\varepsilon)$-approximation achievable in that case, when allowing randomization.In this paper, we provide deterministic sketches constructions for clustering, whose size bounds are close to the best-known randomized ones. We show how to compute a dimension reduction onto $\varepsilon^{-O(1)} \log k$ dimensions in time $k^{O\left(\varepsilon^{-O(1)}+\log \log k\right)}$ poly $(n d)$, and how to build a coreset of size $O\left(k^{2} \log ^{3} k \varepsilon^{-O(1)}\right)$ in time $2^{\varepsilon^{O(1)} k \log ^{3} k}+k^{O\left(\varepsilon^{-O(1)}+\log \log k\right)}$ poly $(n d)$. In the case where k is small, this answers an open question of [Feldman WIDM’20] and [Munteanu and Schwiegelshohn, Künstliche Intell. ’18] on whether it is possible to efficiently compute coresets deterministically.We also construct a deterministic algorithm for computing $(1+$ $\varepsilon)$-approximation to k-median and k-means in high dimensional Euclidean spaces in time $2^{k^{2} \log ^{3} k / \varepsilon^{O(1)}}$ poly $(n d)$, close to the best randomized complexity of $2^{(k / \varepsilon)^{O(1)}}$ nd (see [Kumar, Sabharwal, Sen, JACM 10] and [Bhattacharya, Jaiswal, Kumar, TCS’18]). Furthermore, our new insights on sketches also yield a randomized coreset construction that uses uniform sampling, that immediately improves over the recent results of [Braverman et al. FOCS ’22] by a factor k.
Just-in-Time Flaky Test Detection via Abstracted Failure Symptom Matching

We report our experience of using failure symptoms, such as error messages or stack traces, to identify flaky test failures in a Continuous Integration (CI) pipeline for a large industrial software system, SAP HANA. Although failure symptoms are commonly used to identify similar failures, they have not previously been employed to detect flaky test failures. Our hypothesis is that flaky failures will exhibit symptoms distinct from those of non-flaky failures. Consequently, we can identify recurring flaky failures, without rerunning the tests, by matching the failure symptoms to those of historical flaky runs. This can significantly reduce the need for test reruns, ultimately resulting in faster delivery of test results to developers. To facilitate the process of matching flaky failures across different execution instances, we abstract newer test failure symptoms before matching them to the known patterns of flaky failures, inspired by previous research in the fields of failure deduplication and log analysis. We evaluate our symptom-based flakiness detection method using actual failure symptoms gathered from CI data of SAP HANA during a six-month period. Our method shows the potential of using failure symptoms to identify recurring flaky failures, achieving a precision of at least 96%, while saving approximately 58% of the machine time compared to the traditional rerun strategy. Analysis of the false positives and the feedback from developers underscore the importance of having descriptive and informative failure symptoms for both the effective deployment of this symptom-based approach and the debugging of flaky tests.
End-to-End Lip Reading in Romanian with Cross-Lingual Domain Adaptation and Lateral Inhibition

Lip reading or visual speech recognition has gained significant attention in recent years, particularly because of hardware development and innovations in computer vision. While considerable progress has been obtained, most models have only been tested on a few large-scale datasets. This work addresses this shortcoming by analyzing several architectures and optimizations on the underrepresented, short-scale Romanian language dataset called Wild LRRo. Most notably, we compare different backend modules, demonstrating the effectiveness of adding ample regularization methods. We obtain state-of-the-art results using our proposed method, namely cross-lingual domain adaptation and unlabeled videos from English and German datasets to help the model learn language-invariant features. Lastly, we assess the performance of adding a layer inspired by the neural inhibition mechanism.