Miracle-dz/UniFAR-FTUs huggingface dataset:
Interlaboratory Study of Ice Adhesion Using Different Techniques

Low ice adhesion surfaces are a promising anti-icing strategy. However, reported ice adhesion strengths cannot be directly compared between research groups. This study compares results obtained from testing the ice adhesion strength on two types of surfaces at two different laboratories, testing two different types of ice with different ice adhesion test methods at temperatures of −10 and −18 °C. One laboratory used the centrifuge adhesion test and tested precipitation ice and bulk water ice, while the other laboratory used a vertical shear test and tested only bulk water ice. The surfaces tested were bare aluminum and a commercial icephobic coating, with all samples prepared in the same manner. The results showed comparability in the general trends, surprisingly, with the greatest differences for bare aluminum surfaces at −10 °C. For bulk water ice, the vertical shear test resulted in systematically higher ice adhesion strength than the centrifugal adhesion test. The standard deviation depends on the surface type and seems to scale with the absolute value of the ice adhesion strength. The experiments capture the overall trends in which the ice adhesion strength surprisingly decreases from −10 to −18 °C for aluminum and is almost independent of temperature for a commercial icephobic coating. In addition, the study captures similar trends in the effect of ice type on ice adhesion strength as previously reported and substantiates that ice formation is a key parameter for ice adhesion mechanisms. Repeatability should be considered a key parameter in determining the ideal ice adhesion test method.
Spin-orbit related power-law dependence of the diffusive conductivity on the carrier density in disordered Rashba two-dimensional electron systems

By using the Lanczos recursive method in momentum space which considers rigorously all multiple-scattering events, we unveil that the non-perturbative disorder effect has dramatic impact on the charge transport of a two-dimensional electron system with the Rashba spin-orbit coupling in the low-density regime. Surprisingly, our simulations find a power-law dependence of the dc longitudinal conductivity on the carrier density, with the exponent linearly dependent on the Rashba spin-orbit strength but independent of the disorder strength. This unconventional behavior is argued to be observable in surface alloys like Bi/Ag(111).
Directional Semantic Grasping of Real-World Objects: From Simulation to Reality

We present a deep reinforcement learning approach to grasp semantically meaningful objects from a particular direction. The system is trained entirely in simulation, with sim-to-real transfer accomplished by using a simulator that models physical contact and produces photorealistic imagery with domain randomized backgrounds. The system is an example of end-to-end (mapping input monocular RGB images to output Cartesian motor commands) grasping of objects from multiple pre-defined object-centric orientations, such as from the side or top. Coupled with a real-time 6-DoF object pose estimator, the eye-in-hand system is capable of grasping objects anywhere within the graspable workspace. Results are shown in both simulation and the real world, demonstrating the effectiveness of the approach.
SSSDET: Simple Short and Shallow Network for Resource Efficient Vehicle Detection in Aerial Scenes

Detection of small-sized targets is of paramount importance in many aerial vision-based applications. The commonly deployed low cost unmanned aerial vehicles (UAVs) for aerial scene analysis are highly resource constrained in nature. In this paper we propose a simple short and shallow network (SSSDet) to robustly detect and classify small-sized vehicles in aerial scenes. The proposed SSSDet is up to 4× faster, requires 4.4× less FLOPs, has 30× less parameters, requires 31× less memory space and provides better accuracy in comparison to existing state-of-the-art detectors. Thus, it is more suitable for hardware implementation in real-time applications. We also created a new airborne image dataset (ABD) by annotating 1396 new objects in 79 aerial images for our experiments. The effectiveness of the proposed method is validated on the existing VEDAI, DLR-3K, DOTA and Combined dataset. The SSSDet outperforms state-of-the-art detectors in term of accuracy, speed, compute and memory efficiency.
2-Local Hamiltonian with Low Complexity is QCMA

We prove that 2-Local Hamiltonian (2-LH) with Low Complexity problem is QCMA-complete by combining the results from the QMA-completeness[4] of 2-LH and QCMA-completeness of 3-LH with Low Complexity[6]. The idea is straightforward. It has been known that 2-LH is QMA-complete. By putting a low complexity constraint on the input state, we make the problem QCMA. Finally, we use similar arguments as in [4] to show that all QCMA problems can be reduced to our proposed problem.
Observation of Feynman scaling violations and evidence for a new resonance at RHIC

We report measurements of forward jets and dijets produced in Cu+Au collisions at √ sNN = 200 GeV at the Relativistic Heavy Ion Collider. We also report dijet production cross sections in p+p collisions at √ s = 510 GeV. We use the invariant dijet mass to search for indications of new particles. The p+p dijet results are compatible with string fragmentation models tuned to fit LHC data. The Cu+Au jet results far exceed Feynman scaling limits, and are compatible with models that incorporate string fusion to increase parton energy, acting as a QCD accelerator. The Cu+Au dijet results can be mostly explained by double parton scattering due to a parton flux from multiple p+p interactions with √ s >> √ sNN . Further indication of the increased parton energy is obtained from evidence of singleand double-Υ(1S) production in the forward direction in Cu+Au collisions. Finally, we report evidence for the production of a new resonance, reconstructed from its dijet decay.
Elemental abundances in the centre of the Galactic nuclear disc

We have made the first attempt to derive the chemical properties of the Galactic disc at its very central part using high-resolution infrared spectroscopic observations of four classical Cepheids. These stars are located at Galactocentric distances smaller than 1 kpc. All investigated stars show near-to-solar elemental abundances. By combining these new data with our previous studies, our results suggest that the radial distribution of iron abundance on a logarithmic scale gradually increases from the outskirts of the Galactic disc to Galactocentric distances of about 2–4 kpc. There, it reaches a maximal value of about +0.4 dex, and then declines sharply to about the solar value at the Galactic Centre.
An Optimal Assistive Control Strategy based on User's Motor Goal Estimation

In this study, we propose an optimal assistive control strategy that uses estimated user's movement intention as the terminal cost function. We estimate the movement intention by observing human user's joint angle, angluar velocity, and muscle activities for very short period of time. A task-related low-dimensional feature space is extracted from the observed user's movement data. We assume that discrete number of optimal control laws associated to different target tasks are pre-computed. Then, the optimal assistive policy is derived by blending the pre-computed optimal control laws based on the linear Bellman combination method. Coefficients that determine how to blend the control laws are derived based on the low-dimensional feature value that represents the user's movement intention. To validate our proposed method, we conducted basketball throwing tasks. In these experiments, subjects were asked to throw a basketball into a hoop placed at different throwing distances. The distances from the throwing point to the hoop were estimated as the user's movement intention and the optimal control policies were derived by using our proposed method. The results showed that the basketball throwing performances of the subjects were mostly improved.
Parity-to-charge conversion for readout of topological Majorana qubits

We theoretically study a scheme to distinguish the two ground states of a one-dimensional topological superconductor, which could serve as a basis for the readout of Majorana qubits. The scheme is based on parity-to-charge conversion, i.e., the ground-state parity of the superconductor is converted to the charge occupation on a tunnel-coupled auxiliary quantum dot. We describe how certain error mechanisms degrade the quality of the parity-to-charge conversion process. We consider (i) leakage due to a strong readout tunnel pulse, (ii) incomplete charge Rabi oscillations due to slow charge noise, and (iii) charge relaxation due to phonon emission and absorption. To describe these effects, we use simple model Hamiltonians based on the ideal Kitaev chain, and draw conclusions to generic one-dimensional topological superconductors wherever possible. In general, the effects of the error mechanisms can be minimized by choosing a smooth shape and an optimal strength for the readout tunnel pulse. In a case study based on InAs heterostructure device parameters, we estimate that the parity-to-charge conversion error is mainly due to slow charge noise for weak tunnel pulses and leakage for strong tunnel pulses.
When Provably Secure Steganography Meets Generative Models

Steganography is the art and science of hiding secret messages in public communication so that the presence of the secret messages cannot be detected. There are two provably secure steganographic frameworks, one is black-box sampling based and the other is compression based. The former requires a perfect sampler which yields data following the same distribution, and the latter needs explicit distributions of generative objects. However, these two conditions are too strict even unrealistic in the traditional data environment, because it is hard to model the explicit distribution of natural image. With the development of deep learning, generative models bring new vitality to provably secure steganography, which can serve as the black-box sampler or provide the explicit distribution of generative media. Motivated by this, this paper proposes two types of provably secure stegosystems with generative models. Specifically, we first design block-box sampling based provably secure stegosystem for broad generative models without explicit distribution, such as GAN, VAE, and flow-based generative models, where the generative network can serve as the perfect sampler. For compression based stegosystem, we leverage the generative models with explicit distribution such as autoregressive models instead, where the adaptive arithmetic coding plays the role of the perfect compressor, decompressing the encrypted message bits into generative media, and the receiver can compress the generative media into the encrypted message bits. To show the effectiveness of our method, we take DFC-VAE, Glow, WaveNet as instances of generative models and demonstrate the perfectly secure performance of these stegosystems with the state-of-the-art steganalysis methods.
Phrase-Level Class based Language Model for Mandarin Smart Speaker Query Recognition

The success of speech assistants requires precise recognition of a number of entities on particular contexts. A common solution is to train a class-based n-gram language model and then expand the classes into specific words or phrases. However, when the class has a huge list, e.g., more than 20 million songs, a fully expansion will cause memory explosion. Worse still, the list items in the class need to be updated frequently, which requires a dynamic model updating technique. In this work, we propose to train pruned language models for the word classes to replace the slots in the root n-gram. We further propose to use a novel technique, named Difference Language Model (DLM), to correct the bias from the pruned language models. Once the decoding graph is built, we only need to recalculate the DLM when the entities in word classes are updated. Results show that the proposed method consistently and significantly outperforms the conventional approaches on all datasets, esp. for large lists, which the conventional approaches cannot handle.
Learned Semantic Multi-Sensor Depth Map Fusion

Volumetric depth map fusion based on truncated signed distance functions has become a standard method and is used in many 3D reconstruction pipelines. In this paper, we are generalizing this classic method in multiple ways: 1) Semantics: Semantic information enriches the scene representation and is incorporated into the fusion process. 2) Multi-Sensor: Depth information can originate from different sensors or algorithms with very different noise and outlier statistics which are considered during data fusion. 3) Scene denoising and completion: Sensors can fail to recover depth for certain materials and light conditions, or data is missing due to occlusions. Our method denoises the geometry, closes holes and computes a watertight surface for every semantic class. 4) Learning: We propose a neural network reconstruction method that unifies all these properties within a single powerful framework. Our method learns sensor or algorithm properties jointly with semantic depth fusion and scene completion and can also be used as an expert system, e.g. to unify the strengths of various photometric stereo algorithms. Our approach is the first to unify all these properties. Experimental evaluations on both synthetic and real data sets demonstrate clear improvements.
The Role of Housing in Community Health Promotion Among the Aged

Abstract A total of 89 aged residents living independently in single and double occupancy housing in an historic, traditionally planned neighborhood in New Orleans were surveyed with respect to their engagement in the outdoor realm in their immediate neighborhood. An index was developed, comprised of variables spanning the scale of the private dwelling to neighborhood scale. Certain dwelling attributes, health status factors, lifestyle factors, and neighborhood factors were found to be associated with a disinclination to walk outdoors in the community. Among the findings, improperly designed porches and insufficient semi-private exterior space adjacent to the dwelling function as strong deterrents to health-promoting walking activity outdoors, closely followed by fear associated with being victimized by crime in one's immediate neighborhood. Such conditions were found to pose a barrier to full engagement with the community in what on the surface would otherwise appear to be an imminently pedestrian-scaled residential setting. Study limitations, and directions for future research within the environmental design disciplines are discussed for both historic neighborhood settings and in new development.
Möbius Randomness Law for Frobenius Traces

Recently E. Bombieri and N. M. Katz (2010) have demonstrated that several well-known results about the distribution of values of linear recurrence sequences lead to interesting statements for Frobenius traces of algebraic curves. Here we continue this line of study and establish the M\"obius randomness law quantitatively for the normalised form of Frobenius traces.
Reinforcement Learning-based Automatic Diagnosis of Acute Appendicitis in Abdominal CT

Acute appendicitis characterized by a painful inflammation of the vermiform appendix is one of the most common surgical emergencies. Localizing the appendix is challenging due to its unclear anatomy amidst the complex colon-structure as observed in the conventional CT views, resulting in a time-consuming diagnosis. End-to-end learning of a convolutional neural network (CNN) is also not likely to be useful because of the negligible size of the appendix compared with the abdominal CT volume. With no prior computational approaches to the best of our knowledge, we propose the first computerized automation for acute appendicitis diagnosis. In our approach, we utilize a reinforcement learning agent deployed in the lower abdominal region to obtain the appendix location first to reduce the search space for diagnosis. Then, we obtain the classification scores (i.e., the likelihood of acute appendicitis) for the local neighborhood around the localized position, using a CNN trained only on a small appendix patch per volume. From the spatial representation of the resultant scores, we finally define a region of low-entropy (RLE) to choose the optimal diagnosis score, which helps improve the classification accuracy showing robustness even under high appendix localization error cases. In our experiment with 319 abdominal CT volumes, the proposed RLE-based decision with prior localization showed significant improvement over the standard CNN-based diagnosis approaches.
It’s All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution

This paper treats gender bias latent in word embeddings. Previous mitigation attempts rely on the operationalisation of gender bias as a projection over a linear subspace. An alternative approach is Counterfactual Data Augmentation (CDA), in which a corpus is duplicated and augmented to remove bias, e.g. by swapping all inherently-gendered words in the copy. We perform an empirical comparison of these approaches on the English Gigaword and Wikipedia, and find that whilst both successfully reduce direct bias and perform well in tasks which quantify embedding quality, CDA variants outperform projection-based methods at the task of drawing non-biased gender analogies by an average of 19% across both corpora. We propose two improvements to CDA: Counterfactual Data Substitution (CDS), a variant of CDA in which potentially biased text is randomly substituted to avoid duplication, and the Names Intervention, a novel name-pairing technique that vastly increases the number of words being treated. CDA/S with the Names Intervention is the only approach which is able to mitigate indirect gender bias: following debiasing, previously biased words are significantly less clustered according to gender (cluster purity is reduced by 49%), thus improving on the state-of-the-art for bias mitigation.
Encode, Tag, Realize: High-Precision Text Editing

We propose LaserTagger - a sequence tagging approach that casts text generation as a text editing task. Target texts are reconstructed from the inputs using three main edit operations: keeping a token, deleting it, and adding a phrase before the token. To predict the edit operations, we propose a novel model, which combines a BERT encoder with an autoregressive Transformer decoder. This approach is evaluated on English text on four tasks: sentence fusion, sentence splitting, abstractive summarization, and grammar correction. LaserTagger achieves new state-of-the-art results on three of these tasks, performs comparably to a set of strong seq2seq baselines with a large number of training examples, and outperforms them when the number of examples is limited. Furthermore, we show that at inference time tagging can be more than two orders of magnitude faster than comparable seq2seq models, making it more attractive for running in a live environment.
Enhancing AMR-to-Text Generation with Dual Graph Representations

Generating text from graph-based data, such as Abstract Meaning Representation (AMR), is a challenging task due to the inherent difficulty in how to properly encode the structure of a graph with labeled edges. To address this difficulty, we propose a novel graph-to-sequence model that encodes different but complementary perspectives of the structural information contained in the AMR graph. The model learns parallel top-down and bottom-up representations of nodes capturing contrasting views of the graph. We also investigate the use of different node message passing strategies, employing different state-of-the-art graph encoders to compute node representations based on incoming and outgoing perspectives. In our experiments, we demonstrate that the dual graph representation leads to improvements in AMR-to-text generation, achieving state-of-the-art results on two AMR datasets
Health Related Quality of Life Instruments

The current availability of plethora of measurement tools in the area of outcome research often daunts a new comer. The instruments are classified as disease specific and generic. There is always a need to select at least one from each category as these scales are often complimentary in disclosure. The generic scales aim at assessing the fundamental status of quality of life, whereas disease specific instruments aim at assessing the degree of illness along with improvement due to treatments. Health related quality of life instruments gather and provide information from the patients about their experiences with the disease and its treatment. Because of the unique perspective offered by health related quality of life instruments, direct measurement of health from the patient's perspective is popular and has replaced more objective measures as the primary outcome of interest for a broad spectrum of clinical conditions. For the purpose of evaluating and conducting studies that include patient-reported outcomes, it is important to understand the fundamentals of reliability, validity, and responsiveness of the outcome measure being used. There is no single measure of health related quality of life that is found to be appropriate for use in all circumstances but it is possible that any measure of health related quality of life can be designed to obtain self-reported information. There is no doubt that this information is important for pharmacists in pharmacoeconomics and outcomes research.
Into the Battlefield: Quantifying and Modeling Intra-community Conflicts in Online Discussion

Over the last decade, online forums have become primary news sources for readers around the globe, and social media platforms are the space where these news forums find most of their audience and engagement. Our particular focus in this paper is to study conflict dynamics over online news articles in Reddit, one of the most popular online discussion platforms. We choose to study how conflicts develop around news inside a discussion community, the \em r/news subreddit. Mining the characteristics of these engagements often provide useful insights into the behavioral dynamics of large-scale human interactions. Such insights are useful for many reasons -- for news houses to improvise their publishing strategies and potential audience, for data analytics to get a better introspection over media engagement as well as for social media platforms to avoid unnecessary and perilous conflicts. In this work, we present a novel quantification of conflict in online discussion. Unlike previous studies on conflict dynamics, which model conflict as a binary phenomenon, our measure is continuous-valued, which we validate with manually annotated ratings. We address a two-way prediction task. Firstly, we predict the probable degree of conflict a news article will face from its audience. We employ multiple machine learning frameworks for this task using various features extracted from news articles.Secondly, given a pair of users and their interaction history, we predict if their future engagement will result in a conflict. We fuse textual and network-based features together using a support vector machine which achieves an AUC of 0.89. Moreover, we implement a graph convolutional model which exploits engagement histories of users to predict whether a pair of users who never met each other before will have a conflicting interaction, with an AUC of 0.69. We perform our studies on a massive discussion dataset crawled from the Reddit news community, containing over $41k$ news articles and $5.5$ million comments. Apart from the prediction tasks, our studies offer interesting insights on the conflict dynamics -- how users form clusters based on conflicting engagements, how different is the temporal nature of conflict over different online news forums, how is contribution of different language based features to induce conflict, etc. In short, our study paves the way towards new methods of exploration and modeling of conflict dynamics inside online discussion communities.