Deep learning has seen rapid evolution in the past decade, accomplishing tasks that were previously unimaginable. At the same time, researchers strive to better understand and interpret the underlying mechanisms of the deep models, which are often justifiably regarded as "black boxes". Overcoming this deficiency will not only serve to suggest better learning architectures and training methods, but also extend deep learning to scenarios where interpretability is key to the application. One such scenario is signal detection and estimation, with gravitational wave detection as a specific example, where classic methods are often preferred for their interpretability. Nonetheless, while classic statistical detection methods such as matched filtering excel in their simplicity and intuitiveness, they can be suboptimal in terms of both accuracy and computational efficiency. Therefore, it is appealing to have methods that achieve ``the best of both worlds'', namely enjoying simultaneously excellent performance and interpretability. In this thesis, we aim to bridge this gap between modern deep learning and classic statistical detection, by revisiting the signal detection problem from a new perspective. First, to address the perceived distinction in interpretability between classic matched filtering and deep learning, we state the intrinsic connections between the two families of methods, and identify how trainable networks can address the structural limitations of matched filtering. Based on these ideas, we propose two trainable architectures that are constructed based on matched filtering, but with learnable templates and adaptivity to unknown noise distributions, and therefore higher detection accuracy. We next turn our attention toward improving the computational efficiency of detection, where we aim to design architectures that leverage structures within the problem for efficiency gains. By leveraging the statistical structure of class imbalance, we integrate hierarchical detection into trainable networks, and use a novel loss function which explicitly encodes both detection accuracy and efficiency. Furthermore, by leveraging the geometric structure of the signal set, we consider using signal space optimization as an alternative computational primitive for detection, which is intuitively more efficient than covering with a template bank. We theoretical prove the efficiency gain by analyzing Riemannian gradient descent on the signal manifold, which reveals an exponential improvement in efficiency over matched filtering. We also propose a practical trainable architecture for template optimization, which makes use of signal embedding and kernel interpolation. We demonstrate the performance of all proposed architectures on the task of gravitational wave detection in astrophysics, where matched filtering is the current method of choice. The architectures are also widely applicable to general signal or pattern detection tasks, which we exemplify with the handwritten digit recognition task using the template optimization architecture. Together, we hope the this work useful to scientists and engineers seeking machine learning architectures with high performance and interpretability, and contribute to our understanding of deep learning as a whole.

In the legal domain, documents of various types are created in connection with a particular case, such as testimony of people, transcripts, depositions, memos, and emails. Deposition transcripts are one such type of legal document, which consists of conversations between the different parties in the legal proceedings that are recorded by a court reporter. Court reporting has been traced back to 63 B.C. It has transformed from the initial scripts of ``Cuneiform", ``Running Script", and ``Grass Script" to Certified Access Real-time Translation (CART). Since the boom of digitization, there has been a shift to storing these in the PDF/A format. Deposition transcripts are in the form of question-answer (QA) pairs and can be quite lengthy for common people to read. This gives us a need to develop some automatic text-summarization method for the same. The present-day summarization systems do not support this form of text, entailing a need to process them. This creates a need to parse such documents and extract QA pairs as well as any relevant supporting information. These QA pairs can then be converted into complete canonical sentences, i.e., in a declarative form, from which we could extract some insights and use for further downstream tasks. This work investigates the same, as well as using deep-learning techniques for such transformations. / Master of Science / In the legal domain, documents of various types are created in connection with a particular case, such as the testimony of people, transcripts, memos, and emails. Deposition transcripts are one such type of legal document, which consists of conversations between a lawyer and one of the parties in the legal proceedings, captured by a court reporter. Since the boom of digitization, there has been a shift to storing these in the PDF/A format. Deposition transcripts are in the form of question-answer (QA) pairs and can be quite lengthy. Though automatic summarization could help, present-day systems do not work well with such texts. This creates a need to parse these documents and extract QA pairs as well as any relevant supporting information. The QA pairs can then be converted into canonical sentences, i.e., in a declarative form, from which we could extract some insights and support downstream tasks. This work describes these conversions, as well as using deep-learning techniques for such transformations.

Pattern formation, or the generation of coordinated, emergent behavior, is ubiquitous in nature. Researchers have long sought to understand the mechanisms behind such systems as zebra stripes, repeating flower petals, and fingers on hands, within fields such as physics and developmental biology. Notably, a diverse array of bacteria species naturally self-organize into durable macroscale patterns on solid surfaces via swarming motility—a highly coordinated, rapid movement of bacteria powered by flagella. Meanwhile, researchers in the synthetic biology field, which aims to rationally engineer living organisms for biotechnological applications, have been engineering synthetic pattern formation in microbes over the last several decades. Engineering swarming is an untapped opportunity to increase the scale and robustness of coordinated synthetic microbial systems. In this thesis, we expand the field of engineered pattern formation by applying the tools of synthetic biology and deep learning to engineer and characterize the swarming of Proteus mirabilis, which natively forms a centimeter-scale ring pattern. We engineer P. mirabilis to “write” external inputs into visible spatial records. Specifically, we engineer tunable expression of swarming-related genes that modify pattern features, and we develop quantitative approaches to decoding. Next, we develop a dual-input system that modulates two swarming-related genes simultaneously, and we apply convolutional neural networks (CNNs) to decode the resulting patterns with over 90% top-3 accuracy. We separately show growing colonies can record dynamic environmental changes which can be decoded with a U-Net model. We show the robustness of the engineered strains’ readout to fluctuations in temperature and environmental water samples. Lastly, we engineer strains which sense and respond to heavy metals. Our pCopA-flgM strain records the presence of 0 to 50 mM aqueous copper with decreased colony ring width. We conclude in this chapter that engineering native swarm patterns can thus be applied for building bacterial recorders with a visible macroscale readout. In parallel, to better characterize the swarm patterns of P. mirabilis, we develop a pipeline using deep learning approaches to segment colony images. We develop easy-to-use, semi-automated ground truth annotation and preprocessing methods. We separately segment the (1) colony background from agar and (2) the internal colony ring boundaries. The first task is achieved with a patch-classification approach; in the process, we find that the combination of the trained CNN and the “majority voting” method of label fusion achieves a test DICE score of 93% and correctly segments even faint outer swarm rings. The second task is accomplished with a U-Net which achieves over 83% test DICE. We show that our trained models easily segment a set of colonies generated at two relevant conditions, enabling automated analysis of features such as area and ring width. We apply our pipeline to analyze the more complex patterns of our engineered strains, such as the pCopA-flgM strain. The work in this chapter altogether advances the ability to analyze swarm patterns of P. mirabilis. We also aim to expand the use of our colony-characterization approaches beyond P. mirabilis to other microbes. Therefore, we present our work using deep learning to classify a set of Bacillus species isolated from soil samples. We generate datasets of the species grown under different conditions and apply transfer learning to train well-known CNN architectures such as ResNet and Inception to classify these datasets. This approach allows the models to easily learn these small datasets, and the models generalize to correctly predict a species which forms branching patterns regardless of exact growth condition. We visualize the attributions of the models with the integrated gradients method and find that model predictions are attributable to colony regions. This work sets the stage for classification, segmentation, and characterization of a wider array of microbial species with distinctive macroscale colony morphologies. Finally, we conclude by discussing ongoing efforts to expand upon the work presented in this thesis towards the sensing of dynamic inputs such as light, engineering of species other than P. mirabilis, and further optimization of the system of an engineered swarm pattern as a macroscale biosensor readout. Such work can contribute not only to the fields of synthetic pattern formation and the study of bacterial swarming, but also to the fields of engineered living materials and bio-inspired design.

Wearable technology offers the promise of decentralized and personalized healthcare, which can both alleviate current burdens on medical resources, and also help individuals to be more informed about their health. The heterogeneity of disease phenotypes necessitates adaptations to both diagnosing and surveilling disease, but to ensure user adoption and behavioral change, there needs to be a convenient way to amass such health information continuously. This can be in part accomplished by the development of continuously monitoring, compact wearable medical sensors and analytics technology that provide updates on analyte and biosignal measurements at regular intervals in situ. This dissertation investigates methods for collecting and analyzing information from wearable devices with these principles in mind. In Aim 1, we developed new methods for analysis of cardiovascular biosignals. Current methods of estimating left ventricular mass index (LVMI, a strong risk factor for cardiac outcomes), rely on the analysis of echocardiographic signals. Though still the gold standard, echocardiography can typically only be performed in the clinic, making it inconvenient to obtain frequent measurements of LVMI. Frequent measurements can be useful for monitoring cardiac risk, particularly for high-risk individuals, so we investigated the feasibility of predicting LVMI using a deep learning-based approach through ambulatory blood pressure readings, a one-time laboratory test and demographic information. We find that adding blood pressure waveform information in conjunction with multitask learning improved prediction errors (compared to baseline linear regression and neural network models), pointing to its potential as a clinical tool. Using transfer learning, we developed a model that does not require waveform data, but achieved similar prediction accuracies as methods that do require such data – opening the door to use cases that eliminate the need for wearing a blood pressure cuff continuously during the measurement period. Overall, such a technique has the potential to provide information to individuals who are at high risk of cardiac outcomes both inside and outside the clinic. In Aims 2 and 3, we developed a minimally invasive hydrogel patch for continuous monitoring of calcium, as proof-of-concept for wearable measurement of a wide variety of analytes typically assayed in the lab – a technology that can facilitate treatment and management of many prevalent diseases. Specifically, in Aim 2, we engineered a DNA polyacrylamide hydrogel microneedle array that sensed physiologically relevant calcium levels, for potential use by individuals who have hypoparathyroidism, a condition in which blood calcium levels are low and calcium supplements are needed. A negative mold was made using a CNC mill, the positive mold was cast in silicone, and the aptamer along with acrylamide and bis-acrylamide was seeded into the silicone mold. The DNA hydrogel was then fabricated using a simple UV curing protocol. The optimized DNA hydrogel was specific to calcium, used simple fabrication methods and had a fast, reversible signal response. Finally, in Aim 3, we developed the DNA hydrogel sensor into a wearable, integrated system with real-time fluorescence monitoring for testing in vivo. The microneedle array needed to be hydrated for the DNA aptamer to function, but polyacrylamide was too weak in its hydrated state to effectively pierce through skin epidermis. We demonstrated a method for strengthening our hydrogel system with polyethylene glycol diacrylate (PEGDA), while maintaining an optically translucent gel for detection purposes. We conducted piercing studies with a skin phantom on different microneedle array sizes and shapes, and determined that a 3x3 array of beveled microneedles required the least amount of force to pierce through a skin phantom. A custom complementary metal-oxide semiconductor (CMOS) system was developed to capture real-time fluorescence signals from the microneedle array, which correlated to calcium levels in vitro. This setup was then validated in a rat study. In this dissertation, we demonstrated methods for monitoring human biosignals using signal processing techniques, material innovations and integrated sensing platforms. While a work in progress, this dissertation is a step towards realizing the goal of decentralized, connected health for earlier detection and better management of disease.

This thesis encompasses two studies on the application of computational techniques, including deep learning and physics-based methods, in the exploration of protein structure and dynamics. In Chapter 1, I will introduce the background knowledge. Chapter 2 describes the development of a deep learning method for protein loop modeling. We introduce a fast and accurate method for protein loop structure modeling and refinement using deep learning. This method, which is both fast and accurate, integrates a protein language model, a graph neural network, and attention-based modules to predict all-atom protein loop structures from sequences. Its accuracy was validated on benchmark datasets CASP14 and CAMEO, showing performance comparable to or better than the state-of-the-art method, AlphaFold2. The model’s robustness against loop structures outside of the training set was confirmed by testing on datasets after removing high-identity templates and train- ing set homologs. Moreover, it demonstrated significantly lower computational costs compared to existing methods. Application of this method in real-world scenarios included predicting anti- body complementarity-determining regions (CDR) loop structures and refining loop structures in inexact side-chain environments. The method achieved sub-angstrom or near-angstrom accuracy for most CDR loops and notably enhanced the quality of many suboptimal loop predictions in in- exact environments, marking an advancement in protein loop structure prediction and its practical applications. Chapter 3 presents a collaborative study that employs nuclear magnetic resonance (NMR) experiments, molecular dynamics (MD), and hybrid quantum mechanics/molecular mechanics (QM/MM) calculations to investigate protein conformational dynamics across varying temperatures. NMR chemical shifts provide a sensitive probe of protein structure and dynamics. Prediction of shifts, and therefore interpretation of shifts, particularly for the frequently measured amidic 15N sites, remains a tall challenge. We demonstrate that protein ¹⁵N chemical shift prediction from QM/MM predictions can be improved if conformational variation is included via MD sampling, focusing on the antibiotic target, E. coli Dihydrofolate reductase (DHFR). Variations of up to 25 ppm in predicted ¹⁵N chemical shifts are observed over the trajectory. For solution shifts, the average of fluctuations on the low picosecond timescale results in a superior prediction to a single optimal conformation. For low-temperature solid-state measurements, the histogram of predicted shifts for locally minimized snapshots with specific solvent arrangements sampled from the trajectory explains the heterogeneous linewidths; in other words, the conformations and associated solvent are ‘frozen out’ at low temperatures and result in inhomogeneously broadened NMR peaks. We identified conformational degrees of freedom that contribute to chemical shift variation. Backbone torsion angles show high amplitude fluctuations during the trajectory on the low picosecond timescale. For a number of residues, including I60, 𝝍 varies by up to 60o within a conformational basin during the MD simulations, despite the fact that I60 (and other sites studied) are in a secondary structure element and remain well folded during the trajectory. Fluctuations in 𝝍 appear to be compensated by other degrees of freedom in the protein, including 𝝓 of the succeeding residue, resulting in “rocking” of the amide plane with changes in hydrogen bonding interactions. Good agreement for both room-temperature and low-temperature NMR spectra provides strong support for the specific approach to conformational averaging of computed chemical shifts.

This thesis investigates the problem of learning video representations for video understanding. Previous works have explored the use of data-driven deep learning approaches, which have been shown to be effective in learning useful video representations. However, obtaining large amounts of labeled data can be costly and time-consuming. We investigate self-supervised approach as for multimodal video data to overcome this challenge. Video data typically contains multiple modalities, such as visual, audio, transcribed speech, and textual captions, which can serve as pseudo-labels for representation learning without needing manual labeling. By utilizing these modalities, we can train deep representations over large-scale video data consisting of millions of video clips collected from the internet. We demonstrate the scalability benefits of multimodal self-supervision by achieving new state-of-the-art performance in various domains, including video action recognition, text-to-video retrieval, and text-to-video grounding. We also examine the limitations of these approaches, which often rely on the association assumption involving multiple modalities of data used in self-supervision. For example, the text transcript is often assumed to be about the video content, and two segments of the same video share similar semantics. To overcome this problem, we propose new methods for learning video representations with more intelligent sampling strategies to capture samples that share high-level semantics or consistent concepts. The proposed methods include a clustering component to address false negative pairs in multimodal paired contrastive learning, a novel sampling strategy for finding visually groundable video-text pairs, an investigation of object tracking supervision for temporal association, and a new multimodal task for demonstrating the effectiveness of the proposed model. We aim to develop more robust and generalizable video representations for real-world applications, such as human-to-robot interaction and event extraction from large-scale news sources.

Unfavorable environmental and (or) human displacement may engender the need for Search and Rescue (SAR). Challenges such as inaccessibility, large search areas, and heavy reliance on available responder count, limited equipment and training makes SAR a challenging problem. Additionally, SAR operations also pose significant risk to involved responders. This opens a remarkable opportunity for robotic systems to assist and augment human understanding of the harsh environments. A large body of work exists on the introduction of ground and aerial robots in visual and temporal inspection of search areas with varying levels of autonomy. Unfortunately, limited autonomy is the norm in such systems, due to the limitations presented by on-board UAV resources and networking capabilities. In this work we propose a new multi-agent approach to SAR and introduce a wearable compute cluster in the form factor of a backpack. The backpack allows offloading compute intensive tasks such as Lost Person Behavior Modelling, Path Planning and Deep Neural Network based computer vision applications away from the UAVs and offers significantly high performance computers to execute them. The backpack also provides for a strong networking backbone and task orchestrators which allow for enhanced coordination and resource sharing among all the agents in the system. On the basis of our benchmarking experiments, we observe that the backpack can significantly boost capabilities and success in modern SAR responses. / Master of Science / Unfavorable environmental and (or) human displacement may engender the need for Search and Rescue (SAR). Challenges such as inaccessibility, large search areas, and heavy reliance on available responder count, limited equipment and training makes SAR a challenging problem. Additionally, SAR operations also pose significant risk to involved responders. This opens a remarkable opportunity for robotic systems to assist and augment human understanding of the harsh environments. A large body of work exists on the introduction of ground and aerial robots in visual and temporal inspection of search areas with varying levels of autonomy. Unfortunately, limited autonomy is the norm in such systems, due to the limitations presented by on-board UAV resources and networking capabilities. In this work we propose a new multi-agent approach to SAR and introduce a wearable compute cluster in the form factor of a backpack. The backpack allows offloading compute intensive tasks such as Lost Person Behavior Modelling, Path Planning and Deep Neural Network based computer vision applications away from the UAVs and offers significantly high performance computers to execute them. The backpack also provides for a strong networking backbone and task orchestrators which allow for enhanced coordination and resource sharing among all the agents in the system. On the basis of our benchmarking experiments, we observe that the backpack can significantly boost capabilities and success in modern SAR responses.

The last decade has witnessed a tremendous increase in the amount of available biological data. Different technologies for measuring the genome, epigenome, transcriptome, proteome, metabolome, and microbiome in different organisms are producing large amounts of high-dimensional data every day. High-dimensional data provides unprecedented challenges and opportunities to gain a better understanding of biological systems. Unlike other data types, biological data imposes more constraints on researchers. Biologists are not only interested in accurate predictive models that capture complex input-output relationships, but they also seek a deep understanding of these models. In the last few years, deep models have achieved better performance in computational prediction tasks compared to other approaches. Deep models have been extensively used in processing natural data, such as images, text, and recently sound. However, application of deep models in biology is limited. Here, I propose to use deep models for output prediction, dimension reduction, and feature selection of biological data to get better interpretation and understanding of biological systems. I demonstrate the applicability of deep models in a domain that has a high and direct impact on health care. In this research, novel deep learning models have been introduced to solve pressing biological problems. The research shows that deep models can be used to automatically extract features from raw inputs without the need to manually craft features. Deep models are used to reduce the dimensionality of the input space, which resulted in faster training. Deep models are shown to have better performance and less variant output when compared to other shallow models even when an ensemble of shallow models is used. Deep models are shown to be able to process non-classical inputs such as sequences. Deep models are shown to be able to naturally process input sequences to automatically extract useful features. / Ph. D.

Building facades, serving mainly to protect occupants and structural components from natural forces, require periodic inspections for the detection and assessment of building façade anomalies. Over the past years, a growing trend of utilizing camera-equipped drones for periodical building facade inspection has emerged. Building façade anomalies, such as cracks and erosion, can be detected through analyzing drone-captured video, photographs, and infrared images. Such anomalies are known to have an impact on various building performance aspects, e.g., thermal, energy, moisture control issues. Current research efforts mainly focus on the design of drone flight schema for building inspection, 3D building model reconstruction through drone-captured images, and the detection of specific façade anomalies with these images. However, there are several research gaps impeding the improvement of automation level during the processes of building façade inspection with UAV (Unmanned Aerial Vehicle). These gaps are (1) lack effective ways to store multi-type data captured by drones with the connection to the spatial information of building facades, (2) lack high-performance tools for UAV-image analysis for the automated detection of building façade anomalies, and (3) lack a comprehensive management (i.e., storage, retrieval, analysis, and display) of large amounts and multi-media information for cyclic façade inspection. When seeking inspirations from nature, the process of drone-based facade inspection can be compared with caching birds' foraging food through spatial memory, visual sensing, and remarkable memories. This dissertation aims at investigating ways to improve the management of UAV-captured data and the automation level of drone-based façade anomaly inspection with inspirations from caching birds' foraging behavior. Firstly, a 2D spatial model of building façades was created in the geographic information system (GIS) for the registration and storage of UAV-images to assign façade spatial information to each image. Secondly, computational methods like computer vision and deep learning neural networks were applied to develop algorithms for automated extraction of visual features of façade anomalies within UAV-captured images. Thirdly, a GIS-based database was designed for the comprehensive management of heterogeneous inspection data, such as the spatial, multi-spectral, and temporal data. This research will improve the automation level of storage, retrieval, analysis, and documentation of drone-captured images to support façade inspection during a building's service lifecycle. It has promising potential for supporting the decision-making of early-intervention or maintenance strategies to prevent façade failures and improve building performance. / Doctor of Philosophy / Building facades require periodic inspections and maintenance to protect occupants and structures from natural forces like the sun, wind, rain, and snow. Over the past years, a growing trend of utilizing drones for periodical building facade inspection has emerged. Building façade anomalies, such as cracks and corrosion, can be detected from the drone-captured photographs or video. Such anomalies are known to have an impact on various building performance aspects, such as moisture issues, abnormal heat loss, and additional energy consumptions. Existing practices for detecting façade anomalies from drone-captured photographs mainly rely on manual checking by going through numerous façade images and repetitively zooming in and out these high-resolution images, which is time-consuming and labor-intensive with potential risks of human errors. Besides, this manual checking process impedes the management of drone-captured data and the documentation of façade inspection activities. At the same time, the emerging technologies of computer vision (CV) and artificial intelligence (AI) have provided many opportunities to improve the automation level of façade anomaly detection and documentation. Previous research efforts have explored the image-based generation of 3D building models using computer vision techniques, as well as image-based detection of specific anomalies using deep learning techniques. However, few studies have looked into the comprehensive management, including the storage, retrieval, analysis, and display, of drone-captured images with the spatial coordinate information of building facades; there is also a lack of high-performance image analytics tools for the automated detection of building façade anomalies. This dissertation aims at investigating ways to improve the automation level of analyzing and managing drone-captured images as well as documenting building façade inspection information. To achieve this goal, a building façade model was created in the geographic information system (GIS) for the semi-automated registration and storage of drone-captured images with spatial coordinates by using computer vision techniques. Secondly, deep learning was applied for automated detection of façade anomalies in drone-captured images. Thirdly, a GIS-based database was designed as the platform for the automated analysis and management of heterogeneous data for drone-captured images, façade model information, and detected façade anomalies. This research will improve the automation level of drone-based façade inspection throughout a building's service lifecycle. It has promising potential for supporting the decision-making of maintenance strategies to prevent façade failures and improve building performance.

Many animal species are known for their unparalleled abilities to encode sensory information that supports fast, reliable action in complex environments, but the mechanisms remain often unclear. Through fast ear motions, bats can encode information on target direction into time-frequency Doppler signatures. These species were thought to be evolutionarily tuned to Doppler shifts generated by a prey's wing beat. Self-generated Doppler shifts from the bat's own flight motion were for the most part considered a nuisance that the bats compensate for. My findings indicate that these Doppler-based biosonar systems may be more complicated than previously thought because the animals can actively inject Doppler shifts into their input signals. The work in this dissertation presents a novel nonlinear principle for sensory information encoding in bats. Up to now, sound-direction finding has required either multiple signal frequencies or multiple pressure receivers. Inspired by bat species that add Doppler shifts to their biosonar echoes through fast ear motions, I present a source-direction finding paradigm based on a single frequency and a single pressure receiver. Non-rigid ear motions produce complex Doppler signatures that depend on source direction but are difficult to interpret. To demonstrate that deep learning can solve this problem, I have combined a soft-robotic microphone baffle that mimics a deforming bat ear with a CNN for regression. With this integrated cyber-physical setup, I have able to achieve a direction-finding accuracy of 1 degree based on a single baffle motion. / Doctor of Philosophy / Bats are well-known for their intricate biosonar system that allow the animals to navigate even the most complex natural environments. While the mechanism behind most of these abilities remains unknown, an interesting observation is that some bat species produce fast movements of their ears when actively exploring their surroundings. By moving their pinna, the bats create a time-variant reception characteristic and very little research has been directed at exploring the potential benefits of such behavior so far. One hypothesis is that the speed of the pinna motions modulates the received biosonar echoes with Doppler-shift patterns that could convey sensory information that is useful for navigation. This dissertation intends to explore this hypothetical dynamic sensing mechanism by building a soft-robotic biomimetic receiver to replicate the dynamics of the bat pinna. The experiments with this biomimetic pinna robot demonstrate that the non-rigid ear motions produce Doppler signatures that contain information about the direction of a sound source. However, these patterns are difficult to interpret because of their complexity. By combining the soft-robotic pinna with a convolutional neural network for processing the Doppler signatures in the time-frequency domain, I have been able to accurately estimate the source direction with an error margin of less than one degree. This working system, composed of a soft-robotic biomimetic ear integrated with a deep neural net, demonstrates that the use of Doppler signatures as a source of sensory information is a viable hypothesis for explaining the sensory skills of bats.