Miracle-dz/UniFAR-FTUs huggingface dataset:
Media Modulation Based Molecular Communication

In conventional molecular communication (MC) systems, the signaling molecules used for information transmission are stored, released, and then replenished by a transmitter (TX). However, the replenishment of signaling molecules at the TX is challenging in practice. Furthermore, in most envisioned MC applications, e.g., in the medical field, it is not desirable to insert the TX into the MC system, as this might impair natural biological processes. In this paper, we propose the concept of media modulation based MC where the TX is placed outside the channel and utilizes signaling molecules already present inside the system. The signaling molecules can assume different states which can be switched by external stimuli. Hence, in media modulation based MC, the TX modulates information into the state of the signaling molecules. In particular, we exploit the group of photochromic molecules, which undergo light-induced reversible state transitions, for media modulation. We study the usage of these molecules for information transmission in a three-dimensional duct system, which contains an eraser, a TX, and a receiver for erasing, writing, and reading of information via external light, respectively. We develop a statistical model for the received signal which accounts for the distribution of the signaling molecules in the system, the initial states of the signaling molecules, the reliability of the state control mechanism, the randomness of irrepressible, spontaneous state switching, and the randomness of molecule propagation. We adopt a maximum likelihood detector and show that it can be reduced to a threshold based detector. Furthermore, we derive analytical expressions for the optimal threshold value and the resulting bit error rate (BER), respectively. Both the statistical model and BER results are verified by computer simulations. Our results reveal that media modulation enables reliable information transmission, validating it as a promising alternative to MC based on molecule emitting TXs.
An Instance Space Analysis of Constrained Multiobjective Optimization Problems

Constrained multiobjective optimization problems (CMOPs) are generally more challenging than unconstrained problems. This in part can be attributed to the infeasible region generated by the constraint functions, the interaction between constraints and objectives, or both. In this article, we explore the relationship between the performance of constrained multiobjective evolutionary algorithms (CMOEAs) and the instance characteristics of CMOP using instance space analysis (ISA). To do this, we extend recent work on Landscape Analysis features for characterizing CMOPs. Specifically, we introduce new features to describe the multiobjective-violation landscape, formed by the interaction between constraint violation and multiobjective fitness. The detailed evaluation of the algorithm footprints, spanning eight CMOP benchmark suites and 15 CMOEAs, demonstrates that ISA effectively captures the strength and weakness of the CMOEAs. We conclude that two characteristics, the isolation of nondominate set and the correlation between constraints and objectives evolvability, have the greatest impact on algorithm performance. However, the current benchmarks problems lack of diversity to represent the real-world problems and to fully reveal the efficacy of CMOEAs evaluated.
Structural Gaussian Priors for Bayesian CT reconstruction of Subsea Pipes

A non-destructive testing (NDT) application of X-ray computed tomography (CT) is inspection of subsea pipes in operation via 2D cross-sectional scans. Data acquisition is time-consuming and costly due to the challenging subsea environment. Reducing the number of projections in a scan can yield time and cost savings, but compromises the reconstruction quality, if conventional reconstruction methods are used. In this work we take a Bayesian approach to CT reconstruction and focus on designing an effective prior to make use of available structural information about the pipe geometry. We propose a new class of structural Gaussian priors to enforce expected material properties in different regions of the reconstructed image based on independent Gaussian priors in combination with global regularity through a Gaussian Markov Random Field (GMRF) prior. Numerical experiments with synthetic and real data show that the proposed structural Gaussian prior can reduce artifacts and enhance contrast in the reconstruction compared to using only a global GMRF prior or no prior at all. We show how the resulting posterior distribution can be efficiently sampled even for large-scale images, which is essential for practical NDT applications.
Styleverse: Towards Identity Stylization across Heterogeneous Domains

We propose a new challenging task namely IDentity Stylization (IDS) across heterogeneous domains. IDS focuses on stylizing the content identity, rather than completely swapping it using the reference identity. We use an effective heterogeneous-network-based framework $Styleverse$ that uses a single domain-aware generator to exploit the Metaverse of diverse heterogeneous faces, based on the proposed dataset FS13 with limited data. FS13 means 13 kinds of Face Styles considering diverse lighting conditions, art representations and life dimensions. Previous similar tasks, \eg, image style transfer can handle textural style transfer based on a reference image. This task usually ignores the high structure-aware facial area and high-fidelity preservation of the content. However, Styleverse intends to controllably create topology-aware faces in the Parallel Style Universe, where the source facial identity is adaptively styled via AdaIN guided by the domain-aware and reference-aware style embeddings from heterogeneous pretrained models. We first establish the IDS quantitative benchmark as well as the qualitative Styleverse matrix. Extensive experiments demonstrate that Styleverse achieves higher-fidelity identity stylization compared with other state-of-the-art methods.
TANDEM: Learning Joint Exploration and Decision Making With Tactile Sensors

Inspired by the human ability to perform complex manipulation in the complete absence of vision (like retrieving an object from a pocket), the robotic manipulation field is motivated to develop new methods for tactile-based object interaction. However, tactile sensing presents the challenge of being an active sensing modality: a touch sensor provides sparse, local data, and must be used in conjunction with effective exploration strategies in order to collect information. In this work, we focus on the process of guiding tactile exploration, and its interplay with task-related decision making. We propose TANDEM (TActile exploration aNd DEcision Making), an architecture to learn efficient exploration strategies in conjunction with decision making. Our approach is based on separate but co-trained modules for exploration and discrimination. We demonstrate this method on a tactile object recognition task, where a robot equipped with a touch sensor must explore and identify an object from a known set based on binary contact signals alone. TANDEM achieves higher accuracy with fewer actions than alternative methods and is also shown to be more robust to sensor noise.
Learning Robust Real-Time Cultural Transmission without Human Data

Cultural transmission is the domain-general social skill that allows agents to acquire and use information from each other in real-time with high fidelity and recall. In humans, it is the inheritance process that powers cumulative cultural evolution, expanding our skills, tools and knowledge across generations. We provide a method for generating zero-shot, high recall cultural transmission in artificially intelligent agents. Our agents succeed at real-time cultural transmission from humans in novel contexts without using any pre-collected human data. We identify a surprisingly simple set of ingredients sufficient for generating cultural transmission and develop an evaluation methodology for rigorously assessing it. This paves the way for cultural evolution as an algorithm for developing artificial general intelligence.
Learning Object Relations with Graph Neural Networks for Target-Driven Grasping in Dense Clutter

Robots in the real world frequently come across identical objects in dense clutter. When evaluating grasp poses in these scenarios, a target-driven grasping system requires knowledge of spatial relations between scene objects (e.g., proximity, adjacency, and occlusions). To efficiently complete this task, we propose a target-driven grasping system that simultaneously considers object relations and predicts 6-DoF grasp poses. A densely cluttered scene is first formulated as a grasp graph with nodes representing object geometries in the grasp coordinate frame and edges indicating spatial relations between the objects. We design a Grasp Graph Neural Network (G2N2) that evaluates the grasp graph and finds the most feasible 6-DoF grasp pose for a target object. Additionally, we develop a shape completion-assisted grasp pose sampling method that improves sample quality and consequently grasping efficiency. We compare our method against several baselines in both simulated and real settings. In real-world experiments with novel objects, our approach achieves a 77.78% grasping accuracy in densely cluttered scenarios, surpassing the best-performing baseline by more than 15%. Supplementary material is available at https://sites.google. com/umn.edu/graph-grasping.
Efficient Meta Subspace Optimization

Subspace optimization methods have the attractive property of reducing large-scale optimization problems to a sequence of low-dimensional subspace optimization problems. However, existing subspace optimization frameworks adopt a fixed update policy of the subspace and therefore appear to be sub-optimal. In this paper, we propose a new \emph{Meta Subspace Optimization} (MSO) framework for large-scale optimization problems, which allows to determine the subspace matrix at each optimization iteration. In order to remain invariant to the optimization problem's dimension, we design an \emph{efficient} meta optimizer based on very low-dimensional subspace optimization coefficients, inducing a rule-based method that can significantly improve performance. Finally, we design and analyze a reinforcement learning (RL) procedure based on the subspace optimization dynamics whose learnt policies outperform existing subspace optimization methods.
Stable, accurate and efficient deep neural networks for inverse problems with analysis-sparse models

Solving inverse problems is a fundamental component of science, engineering and mathematics. With the advent of deep learning, deep neural networks have signiﬁcant potential to outperform existing state-of-the-art, model-based methods for solving inverse problems. However, it is known that current data-driven approaches face several key issues, notably instabilities and hallucinations, with potential impact in critical tasks such as medical imaging. This raises the key question of whether or not one can construct stable and accurate deep neural networks for inverse problems. In this work, we present a novel construction of an accurate, stable and eﬃcient neural network for inverse problems with general analysis-sparse models. To construct the network, we unroll NESTA, an accelerated ﬁrst-order method for convex optimization. Combined with a compressed sensing analysis, we prove accuracy and stability. Finally, a restart scheme is employed to enable exponential decay of the required network depth, yielding a shallower, and consequently more eﬃcient, network. We showcase this approach in the case of Fourier imaging, and verify its stability and performance via a series of numerical experiments. The key impact of this work is to provide theoretical guarantees for computing and developing stable neural networks in practice.
Mukayese: Turkish NLP Strikes Back

Having sufficient resources for language X lifts it from the under-resourced languages class, but not necessarily from the under-researched class. In this paper, we address the problem of the absence of organized benchmarks in the Turkish language. We demonstrate that languages such as Turkish are left behind the state-of-the-art in NLP applications. As a solution, we present Mukayese, a set of NLP benchmarks for the Turkish language that contains several NLP tasks. We work on one or more datasets for each benchmark and present two or more baselines. Moreover, we present four new benchmarking datasets in Turkish for language modeling, sentence segmentation, and spell checking. All datasets and baselines are available under: https://github.com/alisafaya/mukayese
Berry phase in the phase space worldline representation: The axial anomaly and classical kinetic theory

The Berry phase is analyzed for Weyl and Dirac fermions in a phase space representation of the worldline formalism. Kinetic theories are constructed for both at a classical level. Whereas the Weyl fermion case reduces in dimension, resembling a theory in quantum mechanics, the Dirac fermion case takes on a manifestly Lorentz covariant form. To achieve a classical kinetic theory for the non-Abelian Dirac fermion Berry phase a spinor construction of Barut and Zanghi is utilized. The axial anomaly is also studied at a quantum level. It is found that under an adiabatic approximation, which is necessary for facilitating a classical kinetic theory, the index of the Dirac operator for massless fermions vanishes. Even so, similarities of an axial rotation to an exact non-covariant Berry phase transform are drawn by application of the Fujikawa method to the Barut and Zanghi spinors on the worldline.
Stackelberg-Pareto Synthesis (Extended Version)

We study the framework of two-player Stackelberg games played on graphs in which Player 0 announces a strategy and Player 1 responds rationally with a strategy that is an optimal response. While it is usually assumed that Player 1 has a single objective, we consider here the new setting where he has several. In this context, after responding with his strategy, Player 1 gets a payoff in the form of a vector of Booleans corresponding to his satisfied objectives. Rationality of Player 1 is encoded by the fact that his response must produce a Pareto-optimal payoff given the strategy of Player 0. We study for several kinds of $\omega$-regular objectives the Stackelberg-Pareto Synthesis problem which asks whether Player 0 can announce a strategy which satisfies his objective, whatever the rational response of Player 1. We show that this problem is fixed-parameter tractable for games in which objectives are all reachability, safety, B\"uchi, co-B\"uchi, Boolean B\"uchi, parity, Muller, Streett or Rabin objectives. We also show that this problem is NEXPTIME-complete except for the cases of B\"uchi objectives for which it is NP-complete and co-B\"uchi objectives for which it is in NEXPTIME and NP-hard. The problem is already NP-complete in the simple case of reachability objectives and graphs that are trees.
A Conformer Based Acoustic Model for Robust Automatic Speech Recognition

This study addresses robust automatic speech recognition (ASR) by introducing a Conformer-based acoustic model. The proposed model builds on the wide residual bi-directional long short-term memory network (WRBN) with utterance-wise dropout and iterative speaker adaptation, but employs a Conformer encoder instead of the recurrent network. The Conformer encoder uses a convolution-augmented attention mechanism for acoustic modeling. The proposed system is evaluated on the monaural ASR task of the CHiME-4 corpus. Coupled with utterance-wise normalization and speaker adaptation, our model achieves $6.25\%$ word error rate, which outperforms WRBN by $8.4\%$ relatively. In addition, the proposed Conformer-based model is $18.3\%$ smaller in model size and reduces total training time by $79.6\%$.
A Multi-Scale Time-Frequency Spectrogram Discriminator for GAN-based Non-Autoregressive TTS

The generative adversarial network (GAN) has shown its outstanding capability in improving Non-Autoregressive TTS (NAR-TTS) by adversarially training it with an extra model that discriminates between the real and the generated speech. To maximize the benefits of GAN, it is crucial to find a powerful discriminator that can capture rich distinguishable information. In this paper, we propose a multi-scale time-frequency spectrogram discriminator to help NAR-TTS generate high-fidelity Mel-spectrograms. It treats the spectrogram as a 2D image to exploit the correlation among different components in the time-frequency domain. And a U-Net-based model structure is employed to discriminate at different scales to capture both coarse-grained and fine-grained information. We conduct subjective tests to evaluate the proposed approach. Both multi-scale and time-frequency discriminating bring significant improvement in the naturalness and fidelity. When combining the neural vocoder, it is shown more effective and concise than fine-tuning the vocoder. Finally, we visualize the discriminating maps to compare their difference to verify the effectiveness of multi-scale discriminating.
Improving the Diversity of Bootstrapped DQN via Noisy Priors

—Q-learning is one of the most well-known Reinforcement Learning algorithms. There have been tremendous efforts to develop this algorithm using neural networks. Bootstrapped Deep Q-Learning Network is amongst one of them. It utilizes multiple neural network heads to introduce diversity into Q-learning. Diversity can sometimes be viewed as the amount of reasonable moves an agent can take at a given state, analogous to the deﬁnition of the exploration ratio in RL. Thus, the performance of Bootstrapped Deep Q-Learning Network is deeply connected with the level of diversity within the algorithm. In the original research, it was pointed out that a random prior could improve the performance of the model. In this article, we further explore the possibility of treating priors as a special type of noise and sample priors from a Gaussian distribution to introduce more diversity into this algorithm. We conduct our experiment on the Atari benchmark and compare our algorithm to both the original and other related algorithms. The results show that our modiﬁcation of the Bootstrapped Deep Q-Learning algorithm achieves signiﬁcantly higher evaluation scores across different types of Atari games. Thus, we conclude that noisy priors can improve Bootstrapped Deep Q-Learning’s performance by ensuring the integrity of diversities.
Optimal deletion-robust coreset for submodular maximization

In recent years we have witnessed an increase on the development of methods for submodular optimization, which have been motivated by the wide applicability of submodular functions in real-world data-science problems. In this paper, we contribute to this line of work by considering the problem of robust submodular maximization against unexpected deletions , which may occur due to privacy issues or user preferences. Speciﬁcally, we study and derive bounds for the minimum number of items a streaming algorithm has to remember, in order to achieve a non-trivial approximation guarantee against adversarial deletions up to d items. We call the set of items that are kept by the algorithm before adversarial deletions a deletion-robust coreset . We propose a single-pass streaming algorithm that yields a (1 − 2 ǫ ) / (4 p ) -approximation for maximizing a non-decreasing submodular function under a general p - matroid constraint and requires a coreset size k + d/ǫ , where k is the maximum size of a feasible solution. To the best of our knowledge, this is the ﬁrst work to achieve an (asymptotically) optimal coreset size, as no constant approximation is possible with a coreset of size sublinear in d .
Image analysis for automatic measurement of crustose lichens

Lichens, organisms resulting from a symbiosis between a fungus and an algae, are frequently used as age estimators, especially in recent geological deposits and archaeological structures, using the correlation between lichen size and age. Current non-automated manual lichen and measurement (with ruler, calipers or using digital image processing tools) is a time-consuming and laborious process, especially when the number of samples is high. This work presents a workflow and set of image acquisition and processing tools developed to efficiently identify lichen thalli in flat rocky surfaces, and to produce relevant lichen size statistics (percentage cover, number of thalli, their area and perimeter). The developed workflow uses a regular digital camera for image capture along with specially designed targets to allow for automatic image correction and scale assignment. After this step, lichen identification is done in a flow comprising assisted image segmentation and classification based on interactive foreground extraction tool (GrabCut) and automatic classification of images using Simple Linear Iterative Clustering (SLIC) for image segmentation and Support Vector Machines (SV) and Random Forest classifiers. Initial evaluation shows promising results. The manual classification of images (for training) using GrabCut show an average speedup of 4 if compared with currently used techniques and presents an average precision of 95\%. The automatic classification using SLIC and SVM with default parameters produces results with average precision higher than 70\%. The developed system is flexible and allows a considerable reduction of processing time, the workflow allows it applicability to data sets of new lichen populations.
Controlling the Focus of Pretrained Language Generation Models

The finetuning of pretrained transformer-based language generation models are typically conducted in an end-to-end manner, where the model learns to attend to relevant parts of the input by itself. However, there does not exist a mechanism to directly control the model’s focus. This work aims to develop a control mechanism by which a user can select spans of context as “highlights” for the model to focus on, and generate relevant output. To achieve this goal, we augment a pretrained model with trainable “focus vectors” that are directly applied to the model’s embeddings, while the model itself is kept fixed. These vectors, trained on automatic annotations derived from attribution methods, act as indicators for context importance. We test our approach on two core generation tasks: dialogue response generation and abstractive summarization. We also collect evaluation data where the highlight-generation pairs are annotated by humans. Our experiments show that the trained focus vectors are effective in steering the model to generate outputs that are relevant to user-selected highlights.
MASALAH ETNISITAS DAN TATA RUANG DI INDONESIA

Wacana tentang keberadaan emis dalam tata ruang merupakan suatu hal yang masih langka. Namun, dari beberapa sumber di kota-kota di negara-negara Eropa menunjukkan adanya perhatian pada masalah ini. Sebagaimana diketahui. kota-kota yang berkembang di Eropa Baral dilatarbelakangi dengan industrialisasi yang membawa konsekuensi pada kebutuhan tenaga kerja asing, yang berasal dari negara lain.Berbeda dengan Indonesia, kota-kota besar di Indonesia lebih banyak menarik perhatian penduduk berbagai wilayah. Sebaliknya, beberapa wilayah pedesaan di luar Jawa juga dijadikan tempat sebagai wilayah transmigrasi dari Jawa dan Bali. Akibatnya. kontak budaya antara penduduk asli dengan pendatang tidak dapat dihindarkan. Demikian pula konsekuensi secara spasial.