Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Topology Optimization for constructing Compliant Optical Mount Mechanisms by means of Additive Manufacturing

Optical systems in high precision equipment become increasingly more complex due to the raising number of optical mounts used for mounting and positioning of optical components. Additional, the installation of electronics and inert gas purging systems to protect the optics against the abrasive optical beam extends the amount of components in the optical system, while keeping the surface area of the overall optical system as small as possible. Recent developments in optical systems lead to innovations of optical flexure mounts towards a more compact, accessible and user-specific system with integrated functionality at consistently high standards of optical stability. Additive manufacturing, commonly named 3D printing, offers new possibilities of designing optical flexure mounts, since the layer-wise manufacturing approach allows the production of highly complex parts compared to traditional processes. Topology optimization is a mathematical design tool that can help to design these increasingly complex parts, such as the optical mount, by computing optimal material distributions for a given objective with a determined set of constraints and boundary conditions. The thesis objective is bilateral: The first research objective investigates the design of compliant mechanisms for industrial optical mounts by means of additive manufacturing. The second research objective investigates the suitability of Topology Optimization for designing compliant mechanisms for optical mounts. This research is providing insights into the working principles of optical mounts and compliant mechanisms as well as topology optimization and additive manufacturing. The present case studies demonstrate and evaluate topology optimization design techniques for compliant structures and mechanisms. Further, the use of additive manufacturing for compliant optical mounts mechanisms designs is evaluated.
Lumenhancement: Exploiting Appearance for Planetary Modeling

Planetary environments are among the most hazardous, remote and uncharted in the solar system. They are also critical to the search for life, human exploration, resource extraction, infrastructure and science. These applications represent the prime unexploited opportunity for automated modeling, but robots are underutilized for this purpose. There is urgent need to explore, document, and evaluate these spaces with robots and to do so in a superior and efficient manner beyond the state-of-the-art. This thesis introduces Lumenhancement: the use of active illumination and intensity imaging with optical domain knowledge to enhance geometric modeling. While planetary environments are among the most challenging for robots, they share unique appearance constraints that can be exploited for sensing. Their dark, uniform, rocky and physically constrained nature enables a variety of physicsbased vision techniques which are not pertinent in other field environments. Synergistic integration of calibrated imagery with traditional range sensing results in models with increased accuracy, sample density and readability. By leveraging the prevalence of existing illumination – such as sunlight – and common imaging sensors along with post-processing capability, this work promises broad significance. Contributions from this thesis extend the state-of-the-art in several ways. Future discussion is anchored by experimental characterization of the planetary domain for the material and geometric properties of appearance. Material reflectance characterization using gonioreflectometry has created the first empirical BRDF database of planetary materials. Studies of surface geometry have resulted in the first expansive database of range sensor comparative performance. The correctness of common vision assumptions in this domain, implications to intensity image techniques, and relevance to other domains are addressed. Novel methods for range and image fusion are devised to enhance and optimize aspects of model quality in the context of these principles, including geometric super-resolution, image-directed optimal sampling, and material classification. New possibilities for visualizing lumenhanced models are also presented. Finally, implementations on mobile mapping robots and field experimentation at a coal mine and moon-yard are documented.
Investigation of the Antiangiogenic Properties of Zoledronic Acid by Using Chorioallantoic Membrane Model

Objective Zoledronic acid (ZA) is a bisphosphonate-derived agent used in osteoporotic clinical pathologies to prevent the development of complications such as fractures and hypercalcemia by regulating bone metabolism. Studies have been conducted on the antiangiogenic efficacy of this agent, which also has other systemic side effects. In this study, the dose-dependent antiangiogenic activity of ZA was investigated on the chorioallantoic membrane model (CAM). Methods Three doses (10−4, 10−5, and 10−6 M concentrations) of drug pellets were prepared with ZA and another pellet was prepared as the positive control group with vascular endothelial growth factor (VEGF) inhibitor agent bevacizumab (10−6 M concentration). Thereafter all pellets were placed on chorioallantoic membranes on the fourth day of egg incubation. All eggs were evaluated for capillary development four days after the drug application. Results The highest antiangiogenic effect was detected in the positive control group. Moreover, incremental antiangiogenic effects were detected with average scores of . 9, 1.1, 1.2 in 10−6, 10−5, and 10−4 M concentrations of ZA groups respectively. Conclusion Our findings supported that ZA has dose-dependent antiangiogenic effects. This result suggests that different dosing may be required in cases where angiogenesis is therapeutic.
Antifungal Activity of Sodium New Houttuyfonate Against Aspergillus fumigatus in vitro and in vivo

Aspergillus fumigatus is an important pathogen causing invasive aspergillosis, which is associated with high morbidity and mortality in immunocompromised people. However, the treatment of A. fumigatus infection is a growing challenge, owing to the limited availability antifungal agents and the continual emergence of drug-resistant strains. Drug repurposing is a potential strategy to solve this current problem. Sodium new houttuyfonate (SNH), derived from houttuynin, extracted from Houttuynia cordata, has anti-bacterial and anti-Candida albicans effects. However, whether it has anti-A. fumigatus activity had not been reported. In this study, the antifungal properties of SNH against A. fumigatus, including the standard strain AF293, itraconazole resistant clinical strains, and voriconazole resistant clinical strains, were evaluated in vitro and in vivo. Moreover, the potential mechanism of SNH was characterized. SNH exhibited significant fungicidal activity toward various A. fumigatus strains. SNH also inhibited fungal growth, sporulation, conidial germination and pigment formation, and biofilm formation. Further investigations revealed that SNH interfered with the A. fumigatus cell steroid synthesis pathway, as indicated by transcriptomic and quantitative real-time polymerase chain reaction analyses, and inhibited ergosterol synthesis, as indicated by cell membrane stress assays and ergosterol quantification. Moreover, daily gastric gavage of SNH significantly decreased the fungal burden in mice with disseminated infection (kidney, liver, and lung) and local tissue damage. In addition, the application of SNH downregulated the production of IL-6 and IL-17A. Together, these findings provided the first confirmation that SNH may be a promising antifungal agent for the treatment of A. fumigatus infection.
Contextual Search in the Presence of Adversarial Corruptions

Contextual search is a generalization of binary search, which captures settings such as feature-based dynamic pricing. In this paradigm, a decision maker repeatedly interacts with a set of agents; in the pricing example, the decision maker first observes the context/ features of an item, then sets a feature-dependent price, and the agent responds by either buying or not. Standard formulations of this problem assume that agents act in accordance with a specific homogeneous response model. In practice, however, some responses may be adversarially corrupted (e.g., when some agents exhibit outlier behavior). Existing algorithms heavily depend on the assumed response model being (approximately) accurate for all agents and have poor performance in the presence of even a few such outliers. We initiate the study of contextual search in the presence of such adversarial corruptions. Our algorithms’ performance is near-optimal in the absence of outliers and degrades gracefully with their number.
New Constraint on the Tensor-to-scalar Ratio from the Planck and BICEP/Keck Array Data Using the Profile Likelihood

Motivated by the discrepancy between Bayesian and frequentist upper limits on the tensor-to-scalar ratio parameter r found by the SPIDER collaboration, we investigate whether a similar trend is also present in the latest Planck and BICEP/Keck Array data. We derive a new upper bound on r using the frequentist profile likelihood method. We vary all the relevant cosmological parameters of the ΛCDM model, as well as the nuisance parameters. Unlike the Bayesian analysis using Markov Chain Monte Carlo (MCMC), our analysis is independent of the choice of priors. Using Planck Public Release 4, BICEP/Keck Array 2018, Planck cosmic microwave background lensing, and baryon acoustic oscillation data, we find an upper limit of r < 0.037 at 95% Confidence Level (C.L.), similar to the Bayesian MCMC result of r < 0.038 for a flat prior on r and a conditioned Planck lowlEB covariance matrix.
How Platform-User Power Relations Shape Algorithmic Accountability: A Case Study of Instant Loan Platforms and Financially Stressed Users in India

Accountability, a requisite for responsible AI, can be facilitated through transparency mechanisms such as audits and explainability. However, prior work suggests that the success of these mechanisms may be limited to Global North contexts; understanding the limitations of current interventions in varied socio-political conditions is crucial to help policymakers facilitate wider accountability. To do so, we examined the mediation of accountability in the existing interactions between vulnerable users and a ‘high-risk’ AI system in a Global South setting. We report on a qualitative study with 29 financially-stressed users of instant loan platforms in India. We found that users experienced intense feelings of indebtedness for the ‘boon’ of instant loans, and perceived huge obligations towards loan platforms. Users fulfilled obligations by accepting harsh terms and conditions, over-sharing sensitive data, and paying high fees to unknown and unverified lenders. Users demonstrated a dependence on loan platforms by persisting with such behaviors despite risks of harms such as abuse, recurring debts, discrimination, privacy harms, and self-harm to them. Instead of being enraged with loan platforms, users assumed responsibility for their negative experiences, thus releasing the high-powered loan platforms from accountability obligations. We argue that accountability is shaped by platform-user power relations, and urge caution to policymakers in adopting a purely technical approach to fostering algorithmic accountability. Instead, we call for situated interventions that enhance agency of users, enable meaningful transparency, reconfigure designer-user relations, and prompt a critical reflection in practitioners towards wider accountability. We conclude with implications for responsibly deploying AI in FinTech applications in India and beyond.
Towards Self-Adaptive Game Logic

Self-adaptive systems (SAS) can reconfigure at run time in response to changing situations to express acceptable behaviors in the face of uncertainty. With respect to game design, such situations may include user input, emergent behaviors, performance concerns, and combinations thereof. Typically an SAS is modeled as a feedback loop that functions within an existing system, with operations including monitoring, analyzing, planning, and executing (i.e., MAPE-K) to enable online reconfiguration. This paper presents a conceptual approach for extending software engineering artifacts to be self-adaptive within the context of game design. We have modified a game developed for creative coding education to include a MAPE-K self-adaptive feedback loop, comprising run-time adaptation capabilities and the software artifacts required to support adaptation.
Private Hypothesis Testing for Social Sciences

While running any experiment, we often have to consider the statistical power to ensure an effective study. Statistical power or power ensures that we can observe an effect with high probability if such a true effect exists. However, several studies lack the appropriate planning for determining the optimal sample size to ensure adequate power. Thus, careful planning ensures that the power remains high even under high measurement errors while keeping the type 1 error constrained. We study the impact of differential privacy on experiments and theoretically analyze the change in sample size required due to the Gaussian mechanisms. Further, we provide an empirical method to improve the accuracy of private statistics with simple bootstrapping.
KETOD: Knowledge-Enriched Task-Oriented Dialogue

Existing studies in dialogue system research mostly treat task-oriented dialogue and chit-chat as separate domains. Towards building a human-like assistant that can converse naturally and seamlessly with users, it is important to build a dialogue system that conducts both types of conversations effectively. In this work, we investigate how task-oriented dialogue and knowledge-grounded chit-chat can be effectively integrated into a single model. To this end, we create a new dataset, KETOD (Knowledge-Enriched Task-Oriented Dialogue), where we naturally enrich task-oriented dialogues with chit-chat based on relevant entity knowledge. We also propose two new models, SimpleToDPlus and Combiner, for the proposed task. Experimental results on both automatic and human evaluations show that the proposed methods can significantly improve the performance in knowledge-enriched response generation while maintaining a competitive task-oriented dialog performance. We believe our new dataset will be a valuable resource for future studies. Our dataset and code are publicly available at \url{https://github.com/facebookresearch/ketod}.
Numerical solution of the exact background collisional Boltzmann equation for dark matter-baryon scattering

Linear cosmological observables can be used to probe elastic scattering of dark matter (DM) with baryons. Availability of high-precision data requires a critical reassessment of any assumptions that may impact the accuracy of constraints. The standard formalism for constraining DM-baryon scattering pre-recombination is based on assuming a Maxwell-Boltzmann (MB) velocity distribution for DM. This assumption is not always physically justified, and does not allow for probing DM self-interactions simultaneously with its interactions with baryons. Lifting the MB assumption requires solving the full collisional Boltzmann equation (CBE), which is highly non-trivial. Earlier work proposed a more tractable Fokker-Planck (FP) approximation to the CBE, but its accuracy remained unknown. In this paper, we numerically solve the exact CBE for the first time, in a homogeneous expanding background. We consider DM-baryon scattering cross-sections that are positive power-laws of relative velocity. We derive analytical expressions for the collision operator in the case of isotropic differential scattering cross-sections. We then solve the background CBE numerically, and use our solution for the DM velocity distribution to compute the DM-baryon heat-exchange rate, which we compare against those obtained with the MB assumption and FP approximation a . Over a broad range of DM-to-baryon mass ratios, we find that the FP approximation leads to a maximum error of 17% , significantly better than the up to 160% error introduced by the MB assumption. While our results strictly apply only to the background evolution, the accuracy of the FP approximation is likely to carry over to perturbations. This motivates its implementation into cosmological Boltzmann codes, where it can supersede the much less accurate MB assumption, and allow for a more general exploration of DM interactions with baryons and with itself.
Chiral standing spin waves in skyrmion lattice

This work studies the resonance excitations of the three-dimensional skyrmions lattice in the finite thickness plate of an isotropic chiral magnet using spin dynamics simulations. We found that the absorption spectra and resonance modes differ from those predicted by the two-dimensional model and the model of the unconfined bulk crystal. The features observed on the spectra can be explained by the formation of chiral standing spin waves, which, contrary to conventional standing spin waves, are characterized by the helical profile of dynamic magnetization of fixed chirality that is defined by the Dzyaloshinskii–Moriya interaction. In this case, the dynamic susceptibility becomes a function of the plate thickness, which gives rise to an interesting effect that manifests itself in periodical fading of the intensity of corresponding modes and makes excitation of these modes impossible at specific thicknesses.
Unsupervised machine learning for physical concepts

In recent years, machine learning methods have been used to assist scientists in scientific research. Human scientific theories are based on a series of concepts. How machine learns the concepts from experimental data will be an important first step. We propose a hybrid method to extract interpretable physical concepts through unsupervised machine learning. This method consists of two stages. At first, we need to find the Betti numbers of experimental data. Secondly, given the Betti numbers, we use a variational autoencoder network to extract meaningful physical variables. We test our protocol on toy models and show how it works.
Valveless Pumping at Low Reynolds Numbers

Pumping at low Reynolds number is a ubiquitously encountered feature, both in biological organisms and engineered devices. Generating net flow requires the presence of an asymmetry in the system, which traditionally comes from geometric flow rectifiers. Here, we study a valveless system of $N$ oscillating pumps in series, where the asymmetry comes not from the geometry but from time, that is the phase shifts between the pumps. Experimental and theoretical results are in very good agreement. We provide the optimal phase shifts leading to the maximal net flow in the continuous $N\rightarrow \infty$ limit, larger by 25\% than that of a traditional peristaltic sinusoidal wave. Our results pave the way for the design of more efficient microfluidic pumps.
An Evaluation of Strategic Alliances in Liner Shipping-An Empirical Study of CKYH

The activities of alliances between liner carriers have increased intensively since the last decade, satisfying a whole new requirement of shippers stemmed from the industrial globalization. The development of strategic alliances is very important to the liner industry, especially based on a perspective of the booming market-Asia. For finding co-operative niches, possible disadvantages, successful factors, and the future development of the alliance co-operation, the investigation in this paper focused on members of the CKYH, a potential strategic alliance group with four major carriers in Asia. We employed the Delphi method to conduct two rounds of survey for procuring the consensus opinions of participants among these four companies. Their responses reveal that strategic alliances have become an essential tool for large carriers to extend their service ranges in the global markets. Mutual trust between partners is a cornerstone to ensure the success of alliances, and this tendency will continue to develop for supporting the global requirement of the maritime logistic system.
Best of Both Worlds: Multi-Task Audio-Visual Automatic Speech Recognition and Active Speaker Detection

Under noisy conditions, automatic speech recognition (ASR) can greatly benefit from the addition of visual signals coming from a video of the speaker’s face. However, when multiple candidate speakers are visible this traditionally requires solving a separate problem, namely active speaker detection (ASD), which entails selecting at each moment in time which of the visible faces corresponds to the audio. Recent work has shown that we can solve both problems simultaneously by employing an attention mechanism over the competing video tracks of the speakers’ faces, at the cost of sacrificing some accuracy on active speaker detection. This work closes this gap in active speaker detection accuracy by presenting a single model that can be jointly trained with a multi-task loss. By combining the two tasks during training we reduce the ASD classification accuracy by approximately 25%, while simultaneously improving the ASR performance when compared to the multi-person baseline trained exclusively for ASR.
Exploring local explanations of nonlinear models using animated linear projections

The increased predictive power of machine learning models comes at the cost of increased complexity and loss of interpretability, particularly in comparison to parametric statistical models. This trade-off has led to the emergence of eXplainable AI (XAI) which provides methods, such as local explanations (LEs) and local variable attributions (LVAs), to shed light on how a model use predictors to arrive at a prediction. These provide a point estimate of the linear variable importance in the vicinity of a single observation. However, LVAs tend not to effectively handle association between predictors. To understand how the interaction between predictors affects the variable importance estimate, we can convert LVAs into linear projections and use the radial tour. This is also useful for learning how a model has made a mistake, or the effect of outliers, or the clustering of observations. The approach is illustrated with examples from categorical (penguin species, chocolate types) and quantitative (soccer/football salaries, house prices) response models. The methods are implemented in the R package cheem, available on CRAN.
A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning

While reinforcement learning (RL) provides a framework for learning through trial and error, translating RL algorithms into the real world has remained challenging. A major hurdle to real-world application arises from the development of algorithms in an episodic setting where the environment is reset after every trial, in contrast with the continual and non-episodic nature of the real-world encountered by embodied agents such as humans and robots. Prior works have considered an alternating approach where a forward policy learns to solve the task and the backward policy learns to reset the environment, but what initial state distribution should the backward policy reset the agent to? Assuming access to a few demonstrations, we propose a new method, MEDAL, that trains the backward policy to match the state distribution in the provided demonstrations. This keeps the agent close to the task-relevant states, allowing for a mix of easy and difficult starting states for the forward policy. Our experiments show that MEDAL matches or outperforms prior methods on three sparse-reward continuous control tasks from the EARL benchmark, with 40% gains on the hardest task, while making fewer assumptions than prior works.