Miracle-dz/UniFAR-FTUs huggingface dataset:
Sense of agency, gender, and students’ perception in open-ended physics labs

Instructional physics labs are critical junctures for many STEM majors to develop an understanding of experimentation in the sciences. Students can acquire useful experimental skills and grow their identities as scientists. However, many traditionally-instructed labs do not necessarily involve authentic physics experimentation features in their curricula. Recent research calls for a reformation in undergraduate labs to incorporate more student agency and choice in the learning processes. In our institution, we have adopted open-ended lab teaching in the introductory physics courses. By using reformed curricula that provide higher student agency, we analyzed approximately 100 students in the introductory-level lab courses to examine their views towards the open-ended physics labs. Between the start and the end of the semester, we found a statistically significant shift in students' perceptions about the agency afforded in lab activities. We also examined students' responses to "Which lab unit was your favorite and why? ". The analysis showed that majority of the students preferred Project Lab, which had the highest student agency and coding analysis showed that "freedom" was the most frequent response for students' reason for picking Project Lab. Finally, we also examined student views across gender and found no significant gender effect on students' sense of agency.
An Improved Integration Scheme for Mode-Coupling-Theory Equations

Within the mode-coupling theory (MCT) of the glass transition, we reconsider the numerical schemes to evaluate the MCT functional. Here we propose nonuniform discretizations of the wave number, in contrast to the standard equidistant grid, in order to decrease the number of grid points without losing accuracy. We discuss in detail how the integration scheme on the new grids has to be modified from standard Riemann integration. We benchmark our approach by solving the MCT equations numerically for mono-disperse hard disks and hard spheres and by computing the critical packing fraction and the nonergodicity parameters. Our results show that significant improvements in performance can be obtained by employing a nonuniform grid.
CANet: Context Aware Network for 3D Brain Tumor Segmentation

Automated segmentation of brain tumors in 3D magnetic resonance imaging plays an active role in tumor diagnosis, progression monitoring and surgery planning. Based on convolutional neural networks, especially fully convolutional networks, previous studies have shown some promising technologies for brain tumor segmentation. However, these approaches lack suitable strategies to incorporate contextual information to deal with local ambiguities, leading to unsatisfactory segmentation outcomes in challenging circumstances. In this work, we propose a novel Context-Aware Network (CANet) with a Hybrid Context Aware Feature Extractor (HCA-FE) and a Context Guided Attentive Conditional Random Field (CG-ACRF) for feature fusion. HCA-FE captures high dimensional and discriminative features with the contexts from both the convolutional space and feature interaction graphs. We adopt the powerful inference ability of probabilistic graphical models to learn hidden feature maps, and then use CG-ACRF to fuse the features of different contexts. We evaluate our proposed method on publicly accessible brain tumor segmentation datasets BRATS2017 and BRATS2018 against several state-of-the-art approaches using different segmentation metrics. The experimental results show that the proposed algorithm has better or competitive performance, compared to the standard approaches.
Evidence for the natural occurrence of treeless grasslands in the Riverina region of south-eastern Australia

Grasslands in the Riverina region of south-eastern Australia have long been thought to be derived from woodland dominated by Acacia pendula A.Cunn. ex G.Don and Atriplex nummularia Lindl. following over-grazing and clearing in the 19th Century. Despite the broad acceptance of this view, there is little evidence for such a universal change having occurred. Phytosociological and historical evidence is presented here, which suggests that, although many of the existing grassland remnants are floristically similar to remnants of A. pendula woodland and are probably derived from woodland, natural treeless grassland also probably occurs in the Riverina. Such grassland is floristically distinct from remnant woodland and generally spatially separate, being prevalent in the southern Riverina. Although the delineation of natural and derived grassland boundaries may now be difficult, grassland vegetation in general is of immense biodiversity significance, containing a large number of highly localised rare or threatened species. A decrease in rainfall during the winter and spring growing season, as predicted by climate-change models, may be detrimental to natural grassland, which is restricted to the higher rainfall portion of the Riverina.
Relaxed-Responsibility Hierarchical Discrete VAEs

Successfully training Variational Autoencoders (VAEs) with a hierarchy of discrete latent variables remains an area of active research. Leveraging insights from classical methods of inference we introduce $\textit{Relaxed-Responsibility Vector-Quantisation}$, a novel way to parameterise discrete latent variables, a refinement of relaxed Vector-Quantisation. This enables a novel approach to hierarchical discrete variational autoencoder with numerous layers of latent variables that we train end-to-end. Unlike discrete VAEs with a single layer of latent variables, we can produce realistic-looking samples by ancestral sampling: it is not essential to train a second generative model over the learnt latent representations to then sample from and then decode. Further, we observe different layers of our model become associated with different aspects of the data.
Cloning and expression of the recombinant NP24I protein from tomato fruit and study of its antimicrobial activity

Thaumatin-like proteins (TLPs) constitutes a homogeneous family, members of which are produced by plants in response to different kinds of stress. NP24 protein is one of such salt-induced protein from tomato ( Solanum lycopersicum ) and it belongs to TLPs family. NP24 is a 24 kDa (207 amino acid) antimicrobial TLP found in tomato fruits. One isoform (NP24I) of NP24 was discovered in the outer pericarp of tomato fruit and is reported to play a possible role in ripening of the fruit in addition to its antimicrobial activity. In this study, the total RNA was isolated successfully from the outer pericarp of ripe (red) tomato fruit. cDNA was prepared and the gene coding for NP24I protein was amplified using conventional polymerase chain reaction (PCR). The gene was then cloned into Mach1™- T1 ® Escherichia coli cells, then subcloned into the over-expression vector pET-28a (+) using BL21 expression bacteria. Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) analysis showed that the gene was over-expressed in E. coli as inclusion bodies. Optimization of the recombinant NP24I protein solubility was achieved by cold induction through decreasing both expression temperature and Isopropyl-beta-thio galactopyranoside (IPTG) concentration. The recombinant NP24I protein was purified using Ni-NTA resin, and then the antimicrobial activity of the purified recombinant NP24I protein was tested. The aims of this work were to study the cloning and expression of NP24 protein from local tomato cultivar in a prokaryotic system and to test the activity of the recombinant NP24, as well as to prove the activity of native protein on the bacterial as well as fungal growth. Key words: Pathogenesis-related proteins (PR), low molecular weight thaumatin-like protein (TLPs), cloning, Escherichia coli, cold induction, antimicrobial activity, tomato (Solanum lycopersicum).
End-to-end training of a two-stage neural network for defect detection

Segmentation-based, two-stage neural network has shown excellent results in the surface defect detection, enabling the network to learn from a relatively small number of samples. In this work, we introduce end-to-end training of the two-stage network together with several extensions to the training process, which reduce the amount of training time and improve the results on the surface defect detection tasks. To enable end-to-end training we carefully balance the contributions of both the segmentation and the classification loss throughout the learning. We adjust the gradient flow from the classification into the segmentation network in order to prevent the unstable features from corrupting the learning. As an additional extension to the learning, we propose frequency-of-use sampling scheme of negative samples to address the issue of over- and under-sampling of images during the training, while we employ the distance transform algorithm on the region-based segmentation masks as weights for positive pixels, giving greater importance to areas with higher probability of presence of defect without requiring a detailed annotation. We demonstrate the performance of the end-to-end training scheme and the proposed extensions on three defect detection datasets-DAGM, KolektorSDD and Severstal Steel defect dataset- where we show state-of-the-art results. On the DAGM and the KolektorSDD we demonstrate 100% detection rate, therefore completely solving the datasets. Additional ablation study performed on all three datasets quantitatively demonstrates the contribution to the overall result improvements for each of the proposed extensions.
Satellite-To-Earth Quantum Key Distribution via Orbital Angular Momentum

In this work, we explore the feasibility of performing satellite-to-Earth quantum key distribution (QKD) using the orbital angular momentum (OAM) of light. Due to the fragility of OAM states the conventional wisdom is that turbulence would render OAM-QKD non-viable in a satellite-to-Earth channel. However, based on detailed phase screen simulations of the anticipated atmospheric turbulence we find that OAM-QKD is viable in some system configurations, especially if quantum channel information is utilized in the processing of post-selected states. More specifically, using classically entangled light as a probe of the quantum channel, and reasonably-sized transmitter-receiver apertures, we find that non-zero QKD rates are achievable on sea-level ground stations. Without using classical light probes, OAM-QKD is relegated to high-altitude ground stations with large receiver apertures. Our work represents the first quantitative assessment of the performance of OAM-QKD from satellites, showing under what circumstances the much-touted higher dimensionality of OAM can be utilized in the context of secure communications.
Learning Part Boundaries from 3D Point Clouds

We present a method that detects boundaries of parts in 3D shapes represented as point clouds. Our method is based on a graph convolutional network architecture that outputs a probability for a point to lie in an area that separates two or more parts in a 3D shape. Our boundary detector is quite generic: it can be trained to localize boundaries of semantic parts or geometric primitives commonly used in 3D modeling. Our experiments demonstrate that our method can extract more accurate boundaries that are closer to ground‐truth ones compared to alternatives. We also demonstrate an application of our network to fine‐grained semantic shape segmentation, where we also show improvements in terms of part labeling performance.
Enhancing Generalized Zero-Shot Learning via Adversarial Visual-Semantic Interaction

The performance of generative zero-shot methods mainly depends on the quality of generated features and how well the model facilitates knowledge transfer between visual and semantic domains. The quality of generated features is a direct consequence of the ability of the model to capture the several modes of the underlying data distribution. To address these issues, we propose a new two-level joint maximization idea to augment the generative network with an inference network during training which helps our model capture the several modes of the data and generate features that better represent the underlying data distribution. This provides strong cross-modal interaction for effective transfer of knowledge between visual and semantic domains. Furthermore, existing methods train the zero-shot classifier either on generate synthetic image features or latent embeddings produced by leveraging representation learning. In this work, we unify these paradigms into a single model which in addition to synthesizing image features, also utilizes the representation learning capabilities of the inference network to provide discriminative features for the final zero-shot recognition task. We evaluate our approach on four benchmark datasets i.e. CUB, FLO, AWA1 and AWA2 against several state-of-the-art methods, and show its performance. We also perform ablation studies to analyze and understand our method more carefully for the Generalized Zero-shot Learning task.
Deep Representation Learning and Clustering of Traffic Scenarios

Determining the traffic scenario space is a major challenge for the homologation and coverage assessment of automated driving functions. In contrast to current approaches that are mainly scenario-based and rely on expert knowledge, we introduce two data driven autoencoding models that learn a latent representation of traffic scenes. First is a CNN based spatio-temporal model that autoencodes a grid of traffic participants' positions. Secondly, we develop a pure temporal RNN based model that auto-encodes a sequence of sets. To handle the unordered set data, we had to incorporate the permutation invariance property. Finally, we show how the latent scenario embeddings can be used for clustering traffic scenarios and similarity retrieval.
Whither Fair Clustering?

Within the relatively busy area of fair machine learning that has been dominated by classification fairness research, fairness in clustering has started to see some recent attention. In this position paper, we assess the existing work in fair clustering and observe that there are several directions that are yet to be explored, and postulate that the state-of-the-art in fair clustering has been quite parochial in outlook. We posit that widening the normative principles to target for, characterizing shortfalls where the target cannot be achieved fully, and making use of knowledge of downstream processes can significantly widen the scope of research in fair clustering research. At a time when clustering and unsupervised learning are being increasingly used to make and influence decisions that matter significantly to human lives, we believe that widening the ambit of fair clustering is of immense significance.
Cloud-Based Privacy-Preserving Collaborative Consumption for Sharing Economy

Cloud computing has been a dominant paradigm for a variety of information processing platforms, particularly for enabling various popular applications of sharing economy. However, there is a major concern regarding data privacy on these cloud-based platforms. This work presents novel cloud-based privacy-preserving solutions to support collaborative consumption applications for sharing economy. In typical collaborative consumption, information processing platforms need to enable fair cost-sharing among multiple users for utilizing certain shared facilities and communal services. Our cloud-based privacy-preserving protocols, based on homomorphic Paillier cryptosystems, can ensure that the cloud-based operator can only obtain an aggregate schedule of all users in facility sharing, or a service schedule conforming to service provision rule in communal service sharing, but is unable to track the personal schedules or demands of individual users. More importantly, the participating users are still able to settle cost-sharing among themselves in a fair manner for the incurred costs, without knowing each other’s private schedules or demands. Our privacy-preserving protocols involve no other third party who may compromise privacy. We also provide an extensive evaluation study and a proof-of-concept system prototype of our protocols.
Efficient Online Estimation of Empowerment for Reinforcement Learning

Training artificial agents to acquire desired skills through model-free reinforcement learning (RL) depends heavily on domain-specific knowledge, and the ability to reset the system to desirable configurations for better reward signals. The former hinders generalization to new domains; the latter precludes training in real-life conditions because physical resets are not scalable. Recently, intrinsic motivation was proposed as an alternative objective to alleviate the first issue, but there has been no reasonable remedy for the second. In this work, we present an efficient online algorithm for a type of intrinsic motivation, known as empowerment, and address both limitations. Our method is distinguished by its significantly lower sample and computation complexity, along with improved training stability compared to the relevant state of the art. We achieve this superior efficiency by transforming the challenging empowerment computation into a convex optimization problem through neural networks. In simulations, our method manages to train policies with neither domain-specific knowledge nor manual intervention. To address the issue of resetting in RL, we further show that our approach boosts learning when there's no early termination. Our proposed method opens doors for studying intrinsic motivation for policy training and scaling up model-free RL training in real-life conditions.
Potential effects of prescribed savannah burning on the diet selection of forest buffalo (Syncerus caffer nanus) in Lopé National Park, Gabon

Seasonality and management are factors that may affect the diet selection of the forest buffalo (Syncerus caffer nanus). Fire is considered a major driving force in savannah systems and prescribed burning is a commonly applied conservation tool in protected areas such as Lope National Park, Gabon. Prescribed annual fires contribute to the maintenance of open areas and provide high-quality forage for forest buffalo, a major herbivore in the park. We used microhistological faecal analysis to determine the diet selection of forest buffalo and measured the extent of variation between a dry season, preburn and a wet season, postburn sampling period. The buffalo diet comprised mainly of monocotyledons, primarily grasses (Poaceae) and sedges (Cyperaceae). Intake of open-area-associated plant species was higher in the wet season, postburn treatment sampling period (97%) than the dry season, preburn sampling period (87%), which corresponded conversely to a reduction in forest-associated Marantaceae plants (10% versus 1%). High proportions of grasses and sedges in the diet signify the importance of open areas for forest buffalo. Controlled burning as tool for maintenance of open areas may play a key role in the meta-population management of the forest buffalo.
High-Throughput VLSI Architecture for GRAND

Guessing Random Additive Noise Decoding (GRAND) is a recently proposed universal decoding algorithm for linear error correcting codes. Since GRAND does not depend on the structure of the code, it can be used for any code encountered in contemporary communication standards or may even be used for random linear network coding. This property makes this new algorithm particularly appealing. Instead of trying to decode the received vector, GRAND attempts to identify the noise that corrupted the codeword. To that end, GRAND relies on the generation of test error patterns that are successively applied to the received vector. In this paper, we propose the first hardware architecture for the GRAND algorithm. Considering GRAND with ABandonment (GRANDAB) that limits the number of test patterns, the proposed architecture only needs $2+{\sum}_{i=2}^n \ \left\lfloor\frac{i}{2} \right\rfloor$ time steps to perform the ${\sum}_{i=1}^3 \ \binom{n}{i}$ queries required when AB = 3. For a code length of 128, our proposed hardware architecture demonstrates only a fraction (1.2%) of the total number of performed queries as time steps. Synthesis result using TSMC 65nm CMOS technology shows that average throughputs of 32 Gbps to 64 Gbps can be achieved at an SNR of 10 dB for a code length of 128 and code rates rate higher than 0.75, transmitted over an AWGN channel. Comparisons with a decoder tailored for a (79, 64) BCH code show that the proposed architecture can achieve a slightly higher average throughput at high SNRs, while obtaining the same decoding performance.
On the hop-constrained Steiner tree problems

The hop-constrained Steiner tree problem is a generalization of the classical Steiner tree problem, and asks for minimum cost subtree that spans some specified nodes of a given graph, such that the number of edges between each node of the tree and its root respects a given hop limit. This NP-hard problem has many variants, which are often modeled as integer linear programs. Two of the models are so called assignment and partial-ordering based models, which yield (up to our knowledge) the best two state-of-the-art formulations for the problem variant Steiner tree problem with revenues, budgets and hop constraints. We show that the linear programming relaxation of the partial-ordering based model is stronger than that of the assignment model for the hop-constrained Steiner tree problem; this remains true also for a class of hop-constrained problems, including the hop-constrained minimum spanning tree problem, the Steiner tree problem with revenues, budgets and hop constraints.
Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors

In this paper, we present a novel developmental reinforcement learning-based controller for a quadcopter with thrust vectoring capabilities. This multirotor UAV design has tilt-enabled rotors. It utilizes the rotor force magnitude and direction to achieve the desired state during flight. The control policy of this robot is learned using the policy transfer from the learned controller of the quadcopter (comparatively simple UAV design without thrust vectoring). This approach allows learning a control policy for systems with multiple inputs and multiple outputs. The performance of the learned policy is evaluated by physics-based simulations for the tasks of hovering and way-point navigation. The flight simulations utilize a flight controller based on reinforcement learning without any additional PID components. The results show faster learning with the presented approach as opposed to learning the control policy from scratch for this new UAV design created by modifications in a conventional quadcopter, i.e., the addition of more degrees of freedom (4-actuators in conventional quadcopter to 8-actuators in tilt-rotor quadcopter). We demonstrate the robustness of our learned policy by showing the recovery of the tilt-rotor platform in the simulation from various non-static initial conditions in order to reach a desired state. The developmental policy for the tilt-rotor UAV also showed superior fault tolerance when compared with the policy learned from the scratch. The results show the ability of the presented approach to bootstrap the learned behavior from a simpler system (lower-dimensional action-space) to a more complex robot (comparatively higher-dimensional action-space) and reach better performance faster.