Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent advances on fabrication of microneedles on the flexible substrate

Microneedles are able to penetrate into the skin and offer great chances in various biomedical applications including transdermal drug delivery, stable bio-signal monitoring, sampling and bio-sensing. Owing to the micro-scale feature size, microneedles have advantages of minimal invasiveness, pain, high biosafety and simple operation. However, it is still challenging to fabricate solid microneedles on a flexible substrate. This review primarily introduces recent studies about microneedles on the flexible substrate, various fabrication methods of microneedles on the flexible substrate, including micromolding, silicon dry/wet etching, electrochemical etching, drawing-lithography, electro-drawing lithography, magnetorheological drawing lithography, three-dimensional printing, vapor–liquid–solid grown, hot embossing, and assembly, and corresponding materials have been summarized and discussed in details. In the end, regular biomedical applications of microneedle on the flexible substrate are categorized into drug delivery, vaccine delivery, electrophysiological interaction, sampling and bio-sensing. Recent advances of each application have been discussed.
Understanding occupants motivation for resilient and sustainable traditional houses

The sustainability of traditional housing in the city is threatened by the development of lifestyles and economic demands. The occupants of traditional houses consist of several generations whose needs and desires are different but must follow the times. For housing to remain comfortable for occupants and keep up with the times, this research examines how the attitudes and motivations of residents towards the existence of the house they live in. The research in this paper studies how traditional houses experience development due to changes in the lifestyle of residents and how traditional houses can survive and adapt to changing times. By collecting data through semi-structured in-depth interviews with occupants of traditional houses and observations of traditional houses in Limo Koto Kampar Riau, this study collects data on community motivations in developing traditional houses. Through the Means-End Chain analysis, this paper aims to find development in traditional houses that they do. The results showed that the portion of the house that remained did not change and the part that experienced significant changes. The paper shows how traditional houses adapt to the lifestyles of residents and new functions.
Improving Understanding on Determinant of Environmental Disclosure and Moderating Effect of Corporate Governance

Industrial activities have a positive impact on the economy; on the other hand they have a negative impact on the environment. Therefore it must be controlled and balanced. So that issues related to green industry and environmental accounting are increasingly urgent to be formulated by the government. This research aims to analyse the effect of company size, financial performance, and environmental performance on environmental disclosure using good corporate as the moderating variable. By using the purposive sampling technique, the final samples of the research are 23 companies from top 45 listed companies in Indonesia during the period of 2011-2016. Data were analysed with SEM by using Smart PLS 3.0 software. The result showed that the company size and environmental performance have a significant positive effect on the environmental disclosure. Conversely, the effect of financial performance on environmental disclosure is negative. Good Corporate Governance moderates the effect of company size on environmental disclosure. Nevertheless, good corporate governance does not moderate the effect of financial performance and environmental performance on environmental disclosure. Based on the results, top 45 companies in Indonesia are still low in reporting their environmental performance. The practical implication from this study, the authorized bodies should encourage the company by providing education and supervision on the implementation of existing regulations to increase the environmental disclosure.
LTrack: Stealthy Tracking of Mobile Phones in LTE

We introduce LTrack, a new tracking attack on LTE that allows an attacker to stealthily extract user devices' locations and permanent identifiers (IMSI). To remain stealthy, the localization of devices in LTrack is fully passive, relying on our new uplink/downlink sniffer. Our sniffer records both the times of arrival of LTE messages and the contents of the Timing Advance Commands, based on which LTrack calculates locations. LTrack is the first to show the feasibility of a passive localization in LTE through implementation on software-defined radio. Passive localization attacks reveal a user's location traces but can at best link these traces to a device's pseudonymous temporary identifier (TMSI), making tracking in dense areas or over a long time-period challenging. LTrack overcomes this challenge by introducing and implementing a new type of IMSI Catcher named IMSI Extractor. It extracts a device's IMSI and binds it to its current TMSI. Instead of relying on fake base stations like existing IMSI Catchers, which are detectable due to their continuous transmission, IMSI Extractor relies on our uplink/downlink sniffer enhanced with surgical message overshadowing. This makes our IMSI Extractor the stealthiest IMSI Catcher to date. We evaluate LTrack through a series of experiments and show that in line-of-sight conditions, the attacker can estimate the location of a phone with less than 6m error in 90% of the cases. We successfully tested our IMSI Extractor against a set of 17 modern smartphones connected to our industry-grade LTE testbed. We further validated our uplink/downlink sniffer and IMSI Extractor in a test facility of an operator.
Salient Positions based Attention Network for Image Classification

The self-attention mechanism has attracted wide publicity for its most important advantage of modeling long dependency, and its variations in computer vision tasks, the non-local block tries to model the global dependency of the input feature maps. Gathering global contextual information will inevitably need a tremendous amount of memory and computing resources, which has been extensively studied in the past several years. However, there is a further problem with the self-attention scheme: is all information gathered from the global scope helpful for the contextual modelling? To our knowledge, few studies have focused on the problem. Aimed at both questions this paper proposes the salient positions-based attention scheme SPANet, which is inspired by some interesting observations on the attention maps and affinity matrices generated in self-attention scheme. We believe these observations are beneficial for better understanding of the self-attention. SPANet uses the salient positions selection algorithm to select only a limited amount of salient points to attend in the attention map computing. This approach will not only spare a lot of memory and computing resources, but also try to distill the positive information from the transformation of the input feature maps. In the implementation, considering the feature maps with channel high dimensions, which are completely different from the general visual image, we take the squared power of the feature maps along the channel dimension as the saliency metric of the positions. In general, different from the non-local block method, SPANet models the contextual information using only the selected positions instead of all, along the channel dimension instead of space dimension. Our source code is available at https://github.com/likyoo/SPANet.
Automatically Differentiable Random Coefficient Logistic Demand Estimation

We show how the random coefficient logistic demand (BLP) model can be phrased as an automatically differentiable moment function, including the incorporation of numerical safeguards proposed in the literature. This allows gradient-based frequentist and quasi-Bayesian estimation using the Continuously Updating Estimator (CUE). Drawing from the machine learning literature, we outline hitherto under-utilized best practices in both frequentist and Bayesian estimation techniques. Our Monte Carlo experiments compare the performance of CUE, 2S-GMM, and LTE estimation. Preliminary findings indicate that the CUE estimated using LTE and frequentist optimization has a lower bias but higher MAE compared to the traditional 2-Stage GMM (2S-GMM) approach. We also find that using credible intervals from MCMC sampling for the non-linear parameters together with frequentist analytical standard errors for the concentrated out linear parameters provides empirical coverage closest to the nominal level. The accompanying admest Python package provides a platform for replication and extensibility.
Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games

Measuring and promoting policy diversity is critical for solving games with strong non-transitive dynamics where strategic cycles exist, and there is no consistent winner (e.g., Rock-Paper-Scissors). With that in mind, maintaining a pool of diverse policies via open-ended learning is an attractive solution, which can generate auto-curricula to avoid being exploited. However, in conventional open-ended learning algorithms, there are no widely accepted definitions for diversity, making it hard to construct and evaluate the diverse policies. In this work, we summarize previous concepts of diversity and work towards offering a unified measure of diversity in multi-agent open-ended learning to include all elements in Markov games, based on both Behavioral Diversity (BD) and Response Diversity (RD). At the trajectory distribution level, we re-define BD in the state-action space as the discrepancies of occupancy measures. For the reward dynamics, we propose RD to characterize diversity through the responses of policies when encountering different opponents. We also show that many current diversity measures fall in one of the categories of BD or RD but not both. With this unified diversity measure, we design the corresponding diversity-promoting objective and population effectivity when seeking the best responses in open-ended learning. We validate our methods in both relatively simple games like matrix game, non-transitive mixture model, and the complex \textit{Google Research Football} environment. The population found by our methods reveals the lowest exploitability, highest population effectivity in matrix game and non-transitive mixture model, as well as the largest goal difference when interacting with opponents of various levels in \textit{Google Research Football}.
Check It Again:Progressive Visual Question Answering via Visual Entailment

While sophisticated neural-based models have achieved remarkable success in Visual Question Answering (VQA), these models tend to answer questions only according to superficial correlations between question and answer. Several recent approaches have been developed to address this language priors problem. However, most of them predict the correct answer according to one best output without checking the authenticity of answers. Besides, they only explore the interaction between image and question, ignoring the semantics of candidate answers. In this paper, we propose a select-and-rerank (SAR) progressive framework based on Visual Entailment. Specifically, we first select the candidate answers relevant to the question or the image, then we rerank the candidate answers by a visual entailment task, which verifies whether the image semantically entails the synthetic statement of the question and each candidate answer. Experimental results show the effectiveness of our proposed framework, which establishes a new state-of-the-art accuracy on VQA-CP v2 with a 7.55% improvement.
Interlacing Results for Hypergraphs

Hypergraphs are a generalization of graphs in which edges can connect any number of vertices. They allow the modeling of complex networks with higher-order interactions, and their spectral theory studies the qualitative properties that can be inferred from the spectrum, i.e. the multiset of the eigenvalues, of an operator associated to a hypergraph. It is expected that a small perturbation of a hypergraph, such as the removal of a few vertices or edges, does not lead to a major change of the eigenvalues. In particular, it is expected that the eigenvalues of the original hypergraph interlace the eigenvalues of the perturbed hypergraph. Here we work on hypergraphs where, in addition, each vertex--edge incidence is given a real number, and we prove interlacing results for the adjacency matrix, the Kirchoff Laplacian and the normalized Laplacian. Tightness of the inequalities is also shown.
Loss function based second-order Jensen inequality and its application to particle variational inference

Bayesian model averaging, obtained as the expectation of a likelihood function by a posterior distribution, has been widely used for prediction, evaluation of uncertainty, and model selection. Various approaches have been developed to efﬁciently capture the information in the posterior distribution; one such approach is the optimization of a set of models simultaneously with interaction to ensure the diversity of the individual models in the same way as ensemble learning. A representative approach is particle variational inference (PVI), which uses an ensemble of models as an empirical approximation for the posterior distribution. PVI iteratively updates each model with a repulsion force to ensure the diversity of the optimized models. However, despite its promising performance, a theoretical understanding of this repulsion and its association with the generalization ability remains unclear. In this paper, we tackle this problem in light of PAC-Bayesian analysis. First, we provide a new second-order Jensen inequality, which has the repulsion term based on the loss function. Thanks to the repulsion term, it is tighter than the standard Jensen inequality. Then, we derive a novel generalization error bound and show that it can be reduced by enhancing the diversity of models. Finally, we derive a new PVI that optimizes the generalization error bound directly. Numerical experiments demonstrate that the performance of the proposed PVI compares favorably with existing methods in the experiment.
Mean field system of a two-layers neural model in a diffusive regime

We study a model of interacting neurons. The structure of this neural system is composed of two layers of neurons such that the neurons of the first layer send their spikes to the neurons of the second one: if $N$ is the number of neurons of the first layer, at each spiking time of the first layer, every neuron of both layers receives an amount of potential of the form $U/\sqrt{N},$ where $U$ is a centered random variable. This kind of structure of neurons can model a part of the structure of the visual cortex: the first layer represents the primary visual cortex V1 and the second one the visual area V2. The model consists of two stochastic processes, one modelling the membrane potential of the neurons of the first layer, and the other the membrane potential of the neurons of the second one. We prove the convergence of these processes as the number of neurons~$N$ goes to infinity and obtain a convergence speed. The proofs rely on similar arguments as those used in [Erny, L\"ocherbach, Loukianova (2022)]: the convergence speed of the semigroups of the processes is obtained from the convergence speed of their infinitesimal generators using a Trotter-Kato formula, and from the regularity of the limit semigroup.
Offline Inverse Reinforcement Learning

The objective of offline RL is to learn optimal policies when a fixed exploratory demonstrations data-set is available and sampling additional observations is impossible (typically if this operation is either costly or rises ethical questions). In order to solve this problem, off the shelf approaches require a properly defined cost function (or its evaluation on the provided data-set), which are seldom available in practice. To circumvent this issue, a reasonable alternative is to query an expert for few optimal demonstrations in addition to the exploratory data-set. The objective is then to learn an optimal policy w.r. t. the expert's latent cost function. Current solutions either solve a behaviour cloning problem (which does not leverage the exploratory data) or a reinforced imitation learning problem (using a fixed cost function that discriminates available exploratory trajectories from expert ones). Inspired by the success of IRL techniques in achieving state of the art imitation performances in online settings, we exploit GAN based data augmentation procedures to construct the first offline IRL algorithm. The obtained policies outperformed the aforementioned solutions on multiple OpenAI gym environments.
Interactive modelling of volumetric musculoskeletal anatomy

We present a new approach for modelling musculoskeletal anatomy. Unlike previous methods, we do not model individual muscle shapes as geometric primitives (polygonal meshes, NURBS etc.). Instead, we adopt a volumetric segmentation approach where every point in our volume is assigned to a muscle, fat, or bone tissue. We provide an interactive modelling tool where the user controls the segmentation via muscle curves and we visualize the muscle shapes using volumetric rendering. Muscle curves enable intuitive yet powerful control over the muscle shapes. This representation allows us to automatically handle intersections between different tissues (muscle-muscle, muscle-bone, and muscle-skin) during the modelling and automates computation of muscle fiber fields. We further introduce a novel algorithm for converting the volumetric muscle representation into tetrahedral or surface geometry for use in downstream tasks. Additionally, we introduce an interactive skeleton authoring tool that allows the users to create skeletal anatomy starting from only a skin mesh using a library of bone parts.
Submodular + Concave

It has been well established that first order optimization methods can converge to the maximal objective value of concave functions and provide constant factor approximation guarantees for (non-convex/non-concave) continuous submodular functions. In this work, we initiate the study of the maximization of functions of the form $F(x) = G(x) +C(x)$ over a solvable convex body $P$, where $G$ is a smooth DR-submodular function and $C$ is a smooth concave function. This class of functions is a strict extension of both concave and continuous DR-submodular functions for which no theoretical guarantee is known. We provide a suite of Frank-Wolfe style algorithms, which, depending on the nature of the objective function (i.e., if $G$ and $C$ are monotone or not, and non-negative or not) and on the nature of the set $P$ (i.e., whether it is downward closed or not), provide $1-1/e$, $1/e$, or $1/2$ approximation guarantees. We then use our algorithms to get a framework to smoothly interpolate between choosing a diverse set of elements from a given ground set (corresponding to the mode of a determinantal point process) and choosing a clustered set of elements (corresponding to the maxima of a suitable concave function). Additionally, we apply our algorithms to various functions in the above class (DR-submodular + concave) in both constrained and unconstrained settings, and show that our algorithms consistently outperform natural baselines.
POSSIBLE ROUTES FOR THE FORMATION OF PREBIOTIC MOLECULES IN THE HORSEHEAD NEBULA

This article presents the results of a theoretical study concerning interstellar molecules which are useful for the bookkeeping of the organic content of the universe and for providing a glimpse into prebiotic conditions on Earth and in other environments in the universe. Performing the PDR Meudon code, we explored production channels for astrobiological relevant nitrogen-bearing cyclic molecules (N-heterocycles), e. g. pyrrole and pyridine. The present simulations demonstrate how the exploration of a few possible routes of production of Nheterocycles resulted in significant abundances for these species. One particularly efficient class of channels for the production of N-heterocycles incorporates polycyclic aromatic hydrocarbons (PAHs) as catalysts. Thereby, an exploration of a variety of production paths should reveal more species to be target of astrophysical observations.
Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation

This paper presents a simple yet effective approach to modeling space-time correspondences in the context of video object segmentation. Unlike most existing approaches, we establish correspondences directly between frames without re-encoding the mask features for every object, leading to a highly efficient and robust framework. With the correspondences, every node in the current query frame is inferred by aggregating features from the past in an associative fashion. We cast the aggregation process as a voting problem and find that the existing inner-product affinity leads to poor use of memory with a small (fixed) subset of memory nodes dominating the votes, regardless of the query. In light of this phenomenon, we propose using the negative squared Euclidean distance instead to compute the affinities. We validated that every memory node now has a chance to contribute, and experimentally showed that such diversified voting is beneficial to both memory efficiency and inference accuracy. The synergy of correspondence networks and diversified voting works exceedingly well, achieves new state-of-the-art results on both DAVIS and YouTubeVOS datasets while running significantly faster at 20+ FPS for multiple objects without bells and whistles.
Verification of a Merkle Patricia Tree Library Using F

A Merkle tree is a data structure for representing a key-value store as a tree. Each node of a Merkle tree is equipped with a hash value computed from those of their descendants. A Merkle tree is often used for representing a state of a blockchain system since it can be used for efficiently auditing the state in a trustless manner. Due to the safety-critical nature of blockchains, ensuring the correctness of their implementation is paramount. We show our formally verified implementation of the core part of Plebeia using F*. Plebeia is a library to manipulate an extension of Merkle trees (called Plebeia trees). It is being implemented as a part of the storage system of the Tezos blockchain system. To this end, we gradually ported Plebeia to F*; the OCaml code extracted from the modules ported to F* is linked with the unverified part of Plebeia. By this gradual porting process, we can obtain a working code from our partially verified implementation of Plebeia; we confirmed that the binary passes all the unit tests of Plebeia. More specifically, we verified the following properties on the implementation of Plebeia: (1) Each tree-manipulating function preserves the invariants on the data structure of a Plebeia tree and satisfies the functional requirements as a nested key-value store; (2) Each function for serializing/deserializing a Plebeia tree to/from the low-level storage is implemented correctly; and (3) The hash function for a Plebeia tree is relatively collision-resistant with respect to the cryptographic safety of the blake2b hash function. During porting Plebeia to F*, we found a bug in an old version of Plebeia, which was overlooked by the tests bundled with the original implementation. To the best of our knowledge, this is the first work that verifies a production-level implementation of a Merkle-tree library by F*.