Miracle-dz/UniFAR-FTUs huggingface dataset:
Electroporation of stomata: effect on drying kinetics and aroma compounds of Genovese basil leaves

The aroma components of herbs and spices are very sensitive to heat, complicating their drying process. Until now, exporters of dried herbs face the challenge of low total phenolic contents and efforts are constantly being made to improve the drying efficiency of herbs and conserve their aroma. This thesis looks at the influence of extracellular trehalose impregnated by vacuum impregnation (VI) and electroporation achieved with reversible pulsed electric field (PEF) treatment affecting stomata opening on the convective air drying kinetics, rehydration properties, colour changes and aroma preservation of basil leaves. In describing the drying kinetics of treated basil samples, attention is given to the determination of the drying rate and the effective moisture diffusivity. The results indicate that reversible electroporation of opened stomata resulted in faster drying rate (from 0.025 to 0.00039 min-1) compared to samples with opened stomata not electroporated (from 0.0202 to 0.0021 min-1) throughout the drying period. Vacuum impregnation with trehalose increased the initial drying rate of the control from 0.012 to 0.016 min-but towards the end of drying, the drying rate of the control and vacuum infused control were slightly the same (0.0025 and 0.0024 min-1) respectively. Similar trends were also observed for effective moisture diffusivity where samples with stomatal electroporation resulted in increased effective moisture diffusivity (1.857*10-9 m2 s-1) compared to samples where the opened stomata were not electroporated (1.309*10-9 m2 s-1). As a result of the increased drying efficiency, irreversible electroporation of the leaf tissue resulted in 47% reduction, reversible electroporation of opened stomata resulted in 37% reduction whilst leaves samples that were reversible electroporated but opened stomata was not electroporated resulted in 18% reduction in the drying time. The quality parameters analysed on the dried herb indicate that reversible electroporation of opened stomata resulted in less colour change, produced good rehydration properties and resulted in 1.3 to 5.7 fold increase in the major aroma compounds relative to the untreated dried samples. (Less)
Storage‐Ring Ion Trap Derived from the Linear Quadrupole Radio‐Frequency Mass Filter

The storage of protons and 3He ions in a circular storage‐ring ion trap with background gas collision‐limited loss time constants up to 8 min long under ultrahigh‐vacuum conditions is described. Storage‐ring traps were derived from the linear quadrupole rf mass filter by shaping the electrodes into circular and racetrack configurations. Operation of the traps with a trapping rf frequency Ω large compared to the secular ion oscillation frequency ω inhibited ion loss due to nonlinearities in the trapping fields. Long‐term ion storage in such traps may prove useful for radio‐frequency ion spectroscopic purposes.
Structured Prediction for CRiSP Inverse Kinematics Learning With Misspecified Robot Models

With the recent advances in machine learning, problems that traditionally would require accurate modeling to be solved analytically can now be successfully approached with data-driven strategies. Among these, computing the inverse kinematics of a redundant robot arm poses a significant challenge due to the non-linear structure of the robot, the hard joint constraints and the non-invertible kinematics map. Moreover, most learning algorithms consider a completely data-driven approach, while often useful information on the structure of the robot is available and should be positively exploited. In this work, we present a simple, yet effective, approach for learning the inverse kinematics. We introduce a structured prediction algorithm that combines a data-driven strategy with the model provided by a forward kinematics function – even when this function is misspecified – to accurately solve the problem. The proposed approach ensures that predicted joint configurations are well within the robot's constraints. We also provide statistical guarantees on the generalization properties of our estimator as well as an empirical evaluation of its performance on trajectory reconstruction tasks.
TeethTap: Recognizing Discrete Teeth Gestures Using Motion and Acoustic Sensing on an Earpiece

Teeth gestures become an alternative input modality for different situations and accessibility purposes. In this paper, we present TeethTap, a novel eyes-free and hands-free input technique, which can recognize up to 13 discrete teeth tapping gestures. TeethTap adopts a wearable 3D printed earpiece with an IMU sensor and a contact microphone behind both ears, which works in tandem to detect jaw movement and sound data, respectively. TeethTap uses a support vector machine to classify gestures from noise by fusing acoustic and motion data, and implements K-Nearest-Neighbor (KNN) with a Dynamic Time Warping (DTW) distance measurement using motion data for gesture classification. A user study with 11 participants demonstrated that TeethTap could recognize 13 gestures with a real-time classification accuracy of 90.9% in a laboratory environment. We further uncovered the accuracy differences on different teeth gestures when having sensors on single vs. both sides. Moreover, we explored the activation gesture under real-world environments, including eating, speaking, walking and jumping. Based on our findings, we further discussed potential applications and practical challenges of integrating TeethTap into future devices.
Fronthaul Compression and Passive Beamforming Design for Intelligent Reflecting Surface-aided Cloud Radio Access Networks

This letter studies a cloud radio access network (C-RAN) with multiple intelligent reflecting surfaces (IRS) deployed between users and remote radio heads (RRH). Specifically, we consider the uplink transmission where each RRH quantizes the received signals from the users by either point-to-point compression or Wyner-Ziv compression and then transmits the quantization bits to the BBU pool through capacity limited fronthhual links. To maximize the uplink sum rate, we jointly optimize the passive beamformers of IRSs and the quantization noise covariance matrices of fronthoul compression. An joint fronthaul compression and passive beamforming design is proposed by exploiting the Arimoto-Blahut algorithm and semidefinte relaxation (SDR). Numerical results show the performance gain achieved by the proposed algorithm.
Degree conditions forcing directed cycles

Caccetta-H\"{a}ggkvist conjecture is a longstanding open problem on degree conditions that force an oriented graph to contain a directed cycle of a bounded length. Motivated by this conjecture, Kelly, K\"uhn, and Osthus initiated a study of degree conditions forcing the containment of a directed cycle of a given length. In particular, they found the optimal minimum semidegree, that is, the smaller of the minimum indegree and the minimum outdegree, which forces a large oriented graph to contain a directed cycle of a given length not divisible by 3, and conjectured the optimal minimum semidegree for all the other cycles except the directed triangle. In this paper, we establish the best possible minimum semidegree that forces a large oriented graph to contain a directed cycle of a given length divisible by 3 yet not equal to 3, hence fully resolve the conjecture by Kelly, K\"{u}hn, and Osthus. We also find an asymptotically optimal semidegree threshold of any cycle with a given orientation of its edges with the sole exception of a directed triangle.
Refinement Type Directed Search for Meta-Interpretive-Learning of Higher-Order Logic Programs

The program synthesis problem within the Inductive Logic Programming (ILP) community has typically been seen as untyped. We consider the benefits of user provided types on background knowledge. Building on the MetaInterpretive Learning (MIL) framework, we show that type checking is able to prune large parts of the hypothesis space of programs. The introduction of polymorphic type checking to the MIL approach to logic program synthesis is validated by strong theoretical and experimental results, showing a cubic reduction in the size of the search space and synthesis time, in terms of the number of typed background predicates. Additionally we are able to infer polymorphic types of synthesized clauses and of entire programs. The other advancement is in developing an approach to leveraging refinement types in ILP. Here we show that further pruning of the search space can be achieved, though the SMT solving used for refinement type checking comes at a significant cost timewise.
Subcubic certificates for CFL reachability

Many problems in interprocedural program analysis can be modeled as the context-free language (CFL) reachability problem on graphs and can be solved in cubic time. Despite years of efforts, there are no known truly sub-cubic algorithms for this problem. We study the related certification task: given an instance of CFL reachability, are there small and efficiently checkable certificates for the existence and for the non-existence of a path? We show that, in both scenarios, there exist succinct certificates (O(n2) in the size of the problem) and these certificates can be checked in subcubic (matrix multiplication) time. The certificates are based on grammar-based compression of paths (for reachability) and on invariants represented as matrix inequalities (for non-reachability). Thus, CFL reachability lies in nondeterministic and co-nondeterministic subcubic time. A natural question is whether faster algorithms for CFL reachability will lead to faster algorithms for combinatorial problems such as Boolean satisfiability (SAT). As a consequence of our certification results, we show that there cannot be a fine-grained reduction from SAT to CFL reachability for a conditional lower bound stronger than nω, unless the nondeterministic strong exponential time hypothesis (NSETH) fails. In a nutshell, reductions from SAT are unlikely to explain the cubic bottleneck for CFL reachability. Our results extend to related subcubic equivalent problems: pushdown reachability and 2NPDA recognition; as well as to all-pairs CFL reachability. For example, we describe succinct certificates for pushdown non-reachability (inductive invariants) and observe that they can be checked in matrix multiplication time. We also extract a new hardest 2NPDA language, capturing the “hard core” of all these problems.
Imitation Learning for Robust and Safe Online Motion Planning: A Contraction Theory Approach

—This paper presents Learning-based Autonomous Guidance with Robustness, Optimality, and Safety guarantees (LAG-ROS), a real-time robust motion planning algorithm for safety-critical nonlinear systems perturbed by bounded disturbances. The LAG-ROS method consists of three phases: 1) Control Lyapunov Function (CLF) construction via contraction theory; 2) imitation learning of the CLF-based robust feed-back motion planner; and 3) its real-time and decentralized implementation with a learning-based model predictive safety ﬁlter. For the CLF, we exploit a neural-network-based method of Neural Contraction Metrics (NCMs), which provides a differential Lyapunov function to minimize an upper bound of the steady-state Euclidean distance between perturbed and unperturbed system trajectories. The NCM ensures the perturbed state to stay in bounded error tubes around given desired trajectories, where we sample training data for imitation learning of the NCM-CLF-based robust centralized motion planner. Using local observations in training also enables its decentralized implementation. Simulation results for perturbed nonlinear systems show that the LAG-ROS achieves higher control performance and task success rate with faster execution speed for real-time computation, when compared with the existing real-time robust MPC and learning-based feedforward motion planners.
Non-invasive Cognitive-level Human Interfacing for the Robotic Restoration of Reaching & Grasping

Assistive and Wearable Robotics have the potential to support humans with different types of motor impairments to become independent and fulfil their activities of daily living successfully. The success of these robot systems, however, relies on the ability to meaningfully decode human action intentions and carry them out appropriately. Neural interfaces have been explored for use in such system with several successes, however, they tend to be invasive and require training periods in the order of months. We present a robotic system for human augmentation, capable of actuating the user's arm and fingers for them, effectively restoring the capability of reaching, grasping and manipulating objects; controlled solely through the user's eye movements. We combine wearable eye tracking, the visual context of the environment and the structural grammar of human actions to create a cognitive-level assistive robotic setup that enables the users in fulfilling activities of daily living, while conserving interpretability, and the agency of the user. The interface is worn, calibrated and ready to use within 5 minutes. Users learn to control and make successful use of the system with an additional 5 minutes of interaction. The system is tested with 5 healthy participants, showing an average success rate of 96.6% on first attempt across 6 tasks.
On continual single index learning

In this paper, we generalize the problem of single index model to the context of continual learning in which a learner is challenged with a sequence of tasks one by one and the dataset of each task is revealed in an online fashion. We propose a strategy that is able to learn a common single index for all tasks and a speciﬁc link function for each task. The common single index allows to transfer the informaton gained from the previous tasks to a new one. We provide a theoretical analysis of our proposed strategy by proving some regret bounds. Moreover, as a by-product from our work to provide an example of a within-task algorithm, we develop a novel online algorithm for learning single index model in an online setting and provide its regret bound.
New insights on binary black hole formation channels after GWTC-2: young star clusters versus isolated binaries

With the recent release of the Second Gravitational Wave Transient Catalog (GWTC-2), which introduced dozens of new detections, we are at a turning point of gravitational wave astronomy, as we are now able to directly infer constraints on the astrophysical population of compact objects. Here, we tackle the burning issue of understanding the origin of binary black hole (BBH) mergers. To this effect, we make use of state-of-the-art population synthesis and N-body simulations, to represent two distinct formation channels: BBHs formed in the field (isolated channel) and in young star clusters (dynamical channel). We then use a Bayesian hierarchical approach to infer the distribution of the mixing fraction f, with f = 0 (f = 1) in the pure dynamical (isolated) channel. We explore the effects of additional hyperparameters of the model, such as the spread in metallicity σZ and the parameter σsp, describing the distribution of spin magnitudes. We find that the dynamical model is slightly favoured with a median value of f = 0.26, when σsp = 0.1 and σZ = 0.4. Models with higher spin magnitudes tend to strongly favour dynamically formed BBHs (f ≤ 0.1 if σsp = 0.3). Furthermore, we show that hyperparameters controlling the rates of the model, such as σZ, have a large impact on the inference of the mixing fraction, which rises from 0.18 to 0.43 when we increase σZ from 0.2 to 0.6, for a fixed value of σsp = 0.1. Finally, our current set of observations is better described by a combination of both formation channels, as a pure dynamical scenario is excluded at the $99{{\ \rm per\ cent}}$ credible interval, except when the spin magnitude is high.
Maskcyclegan-VC: Learning Non-Parallel Voice Conversion with Filling in Frames

Non-parallel voice conversion (VC) is a technique for training voice converters without a parallel corpus. Cycle-consistent adversarial network-based VCs (CycleGAN-VC and CycleGAN-VC2) are widely accepted as benchmark methods. However, owing to their insufficient ability to grasp time-frequency structures, their application is limited to mel-cepstrum conversion and not mel-spectrogram conversion despite recent advances in mel-spectrogram vocoders. To overcome this, CycleGAN-VC3, an improved variant of CycleGAN-VC2 that incorporates an additional module called time-frequency adaptive normalization (TFAN), has been proposed. However, an increase in the number of learned parameters is imposed. As an alternative, we propose MaskCycleGAN-VC, which is another extension of CycleGAN-VC2 and is trained using a novel auxiliary task called filling in frames (FIF). With FIF, we apply a temporal mask to the input mel-spectrogram and encourage the converter to fill in missing frames based on surrounding frames. This task allows the converter to learn time-frequency structures in a self-supervised manner and eliminates the need for an additional module such as TFAN. A subjective evaluation of the naturalness and speaker similarity showed that MaskCycleGAN-VC outperformed both CycleGAN-VC2 and CycleGAN-VC3 with a model size similar to that of CycleGAN-VC2.1
The Logical Options Framework

Learning composable policies for environments with complex rules and tasks is a challenging problem. We introduce a hierarchical reinforcement learning framework called the Logical Options Framework (LOF) that learns policies that are satisfying, optimal, and composable. LOF efficiently learns policies that satisfy tasks by representing the task as an automaton and integrating it into learning and planning. We provide and prove conditions under which LOF will learn satisfying, optimal policies. And lastly, we show how LOF's learned policies can be composed to satisfy unseen tasks with only 10-50 retraining steps. We evaluate LOF on four tasks in discrete and continuous domains, including a 3D pick-and-place environment.
Quantitative in vivo imaging to enable tumor forecasting and treatment optimization

Current clinical decision-making in oncology relies on averages of large patient populations to both assess tumor status and treatment outcomes. However, cancers exhibit an inherent evolving heterogeneity that requires an individual approach based on rigorous and precise predictions of cancer growth and treatment response. To this end, we advocate the use of quantitative in vivo imaging data to calibrate mathematical models for the personalized forecasting of tumor development. In this chapter, we summarize the main data types available from both common and emerging in vivo medical imaging technologies, and how these data can be used to obtain patient-specific parameters for common mathematical models of cancer. We then outline computational methods designed to solve these models, thereby enabling their use for producing personalized tumor forecasts in silico, which, ultimately, can be used to not only predict response, but also optimize treatment. Finally, we discuss the main barriers to making the above paradigm a clinical reality.
Survival of the Fittest: Numerical Modeling of SN 2014C

Initially classified as a Type Ib supernova (SN), ∼100 days after the explosion SN 2014C made a transition to a Type II SN, presenting a gradual increase in the Hα emission. This has been interpreted as evidence of interaction between the SN shock wave and a massive shell previously ejected from the progenitor star. In this paper we present numerical simulations of the propagation of the SN shock through the progenitor star and its wind, as well as the interaction of the SN ejecta with the massive shell. To determine with high precision the structure and location of the shell, we couple a genetic algorithm to a hydrodynamic and a bremsstrahlung radiation transfer code. We iteratively modify the density stratification and location of the shell by minimizing the variance between X-ray observations and synthetic predictions computed from the numerical model, allowing the shell structure to be completely arbitrary. By assuming spherical symmetry, we found that our best-fit model has a shell mass of 2.6 M ⊙; extends from 1.6 × 1016 cm to 1.87 × 1017 cm, implying that it was ejected ∼ 60/(v w /100 km s−1) yr before the SN explosion; and has a density stratification with an average behavior ∼r −3 but presenting density fluctuations larger than one order of magnitude. Finally, we predict that if the density stratification follows the same power-law behavior, the SN will break out from the shell by mid-2022, i.e., 8.5 yr after explosion.
Blocks World Revisited: The Effect of Self-Occlusion on Classification by Convolutional Neural Networks

Despite the recent successes in computer vision, there remain new avenues to explore. In this work, we propose a new dataset to investigate the effect of self-occlusion on deep neural networks. With TEOS (The Effect of Self-Occlusion), we propose a 3D blocks world dataset that focuses on the geometric shape of 3D objects and their omnipresent self-occlusion. We designed TEOS to investigate the role of self-occlusion in the context of object classification. In the real-world, self-occlusion of 3D objects still presents significant challenges for deep learning approaches. However, humans deal with this by deploying complex strategies, for instance, by changing the viewpoint or manipulating the scene to gather necessary information. With TEOS, we present a dataset with two subsets (L1 and L2), containing 36 and 12 objects, respectively. We provide 768 uniformly sampled views of each object, their mask, object and camera position, orientation, amount of self-occlusion, as well as the CAD model of each object. We present baseline evaluations with five well-known classification deep neural networks and show that TEOS poses a significant challenge for all of them. The dataset, as well as the pre-trained models, are made publicly available for the scientific community under https: //data.nvision. eecs.yorku. ca/TEOS.