Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-based stochastic modeling reveals sources of activity bursts in single-cell TGF-β signaling

Cells sense their surrounding by employing intracellular signaling pathways that transmit hormonal signals from the cell membrane to the nucleus. TGF-β/SMAD signaling encodes various cell fates, controls tissue homeostasis and is deregulated in diseases such as cancer. The pathway shows strong heterogeneity at the single-cell level, but quantitative insights into mechanisms underlying fluctuations at various time scales are still missing, partly due to inefficiency in the calibration of stochastic models that mechanistically describe signaling processes. In this work we analyze single-cell TGF-β/SMAD signaling and show that it exhibits temporal stochastic bursts which are dose-dependent and whose number and magnitude correlate with cell migration. We propose a stochastic modeling approach to mechanistically describe these pathway fluctuations with high computational efficiency. Employing high-order numerical integration and fitting to burst statistics we enable efficient quantitative parameter estimation and discriminate models that assume noise in different reactions at the receptor level. This modeling approach suggests that stochasticity in the internalization of TGF-β receptors into endosomes plays a key role in the observed temporal bursting. Further, the model predicts the single-cell dynamics of TGF-β/SMAD signaling in untested conditions, e.g., successfully reflects memory effects of signaling noise and cellular sensitivity towards repeated stimulation. Taken together, our computational framework based on burst analysis, noise modeling and path computation scheme is a suitable tool for the data-based modeling of complex signaling pathways, capable of identifying the source of temporal noise.
Pluto: Motion Detection for Navigation in a VR Headset

Untethered, inside-out tracking is considered a new goalpost for virtual reality, which became attainable with advent of machine learning in SLAM. Yet computer vision-based navigation is always at risk of a tracking failure due to poor illumination or saliency of the environment. An extension for a navigation system is proposed, which recognizes agents motion and stillness states with 87% accuracy from accelerometer data. 40% reduction in navigation drift is demonstrated in a repeated tracking failure scenario on a challenging dataset.
Ethylene Carbonate Adsorption and Decomposition on Pristine and Defective ZnO(101̅0) Surfaces: A First-Principles Study

Fundamental understanding of the reactivity between coating material of Li-ion battery cathode and electrolyte is important in order to obtain suitable coating candidates. Herein, we study ethylene carbonate (EC) adsorption and decomposition reactions on pristine, O vacancyand Zn vacancy-defected ZnO (1010) by means of first-principles density functional theory (DFT)
Crosslink-Net: Double-branch Encoder Segmentation Network via Fusing Vertical and Horizontal Convolutions

Accurate image segmentation plays a crucial role in medical image analysis, yet it faces great challenges of various shapes, diverse sizes, and blurry boundaries. To address these difficulties, square kernel-based encoder-decoder architecture has been proposed and widely used, but its performance remains still unsatisfactory. To further cope with these challenges, we present a novel double-branch encoder architecture. Our architecture is inspired by two observations: 1) Since the discrimination of features learned via square convolutional kernels needs to be further improved, we propose to utilize non-square vertical and horizontal convolutional kernels in the double-branch encoder, so features learned by the two branches can be expected to complement each other. 2) Considering that spatial attention can help models to better focus on the target region in a large-sized image, we develop an attention loss to further emphasize the segmentation on small-sized targets. Together, the above two schemes give rise to a novel double-branch encoder segmentation framework for medical image segmentation, namely Crosslink-Net. The experiments validate the effectiveness of our model on four datasets. The code is released at https://github.com/Qianyu1226/Crosslink-Net.
Beyond Voice Identity Conversion: Manipulating Voice Attributes by Adversarial Learning of Structured Disentangled Representations

Voice conversion (VC) consists of digitally altering the voice of an individual to manipulate part of its content, primarily its identity, while maintaining the rest unchanged. Research in neural VC has accomplished considerable breakthroughs with the capacity to falsify a voice identity using a small amount of data with a highly realistic rendering. This paper goes beyond voice identity and presents a neural architecture that allows the manipulation of voice attributes (e.g., gender and age). Leveraging the latest advances on adversarial learning of structured speech representation, a novel structured neural network is proposed in which multiple auto-encoders are used to encode speech as a set of idealistically independent linguistic and extra-linguistic representations, which are learned adversariarly and can be manipulated during VC. Moreover, the proposed architecture is time-synchronized so that the original voice timing is preserved during conversion which allows lip-sync applications. Applied to voice gender conversion on the real-world VCTK dataset, our proposed architecture can learn successfully gender-independent representation and convert the voice gender with a very high efficiency and naturalness.
Learn to Focus: Hierarchical Dynamic Copy Network for Dialogue State Tracking

Recently, researchers have explored using the encoder-decoder framework to tackle dialogue state tracking (DST), which is a key component of task-oriented dialogue systems. However, they regard a multi-turn dialogue as a flat sequence, failing to focus on useful information when the sequence is long. In this paper, we propose a Hierarchical Dynamic Copy Network (HDCN) to facilitate focusing on the most informative turn, making it easier to extract slot values from the dialogue context. Based on the encoder-decoder framework, we adopt a hierarchical copy approach that calculates two levels of attention at the word- and turn-level, which are then renormalized to obtain the final copy distribution. A focus loss term is employed to encourage the model to assign the highest turn-level attention weight to the most informative turn. Experimental results show that our model achieves 46.76% joint accuracy on the MultiWOZ 2.1 dataset.
Integer-Programming-Based Narrow-Passage Multi-Robot Path Planning with Effective Heuristics

We study optimal Multi-robot Path Planning (MPP) on graphs, in order to improve the efficiency of multi-robot system (MRS) in the warehouse-like environment. We propose a novel algorithm, OMRPP (One-way Multi-robot Path Planning) based on Integer programming (IP) method. We focus on reducing the cost caused by a set of robots moving from their initial configuration to goal configuration in the warehouse-like environment. The novelty of this work includes: (1) proposing a topological map extraction based on the property of warehouse-like environment to reduce the scale of constructed IP model; (2) proposing one-way passage constraint to prevent the robots from having unsolvable collisions in the passage. (3) developing a heuristic architecture that IP model can always have feasible initial solution to ensure its solvability. Numerous simulations demonstrate the efficiency and performance of the proposed algorithm.
Achilles Heels for AGI/ASI via Decision Theoretic Adversaries

As progress in AI continues to advance, it is important to know how advanced systems will make choices and in what ways they may fail. Machines can already outsmart humans in some domains, and understanding how to safely build ones which may have capabilities at or above the human level is of particular concern. One might suspect that artificially generally intelligent (AGI) and artificially superintelligent (ASI) will be systems that humans cannot reliably outsmart. As a challenge to this assumption, this paper presents the Achilles Heel hypothesis which states that even a potentially superintelligent system may nonetheless have stable decision-theoretic delusions which cause them to make irrational decisions in adversarial settings. In a survey of key dilemmas and paradoxes from the decision theory literature, a number of these potential Achilles Heels are discussed in context of this hypothesis. Several novel contributions are made toward understanding the ways in which these weaknesses might be implanted into a system.
QGo: Scalable Quantum Circuit Optimization Using Automated Synthesis

—The current phase of quantum computing is in the Noisy Intermediate-Scale Quantum (NISQ) era. On NISQ devices, two-qubit gates such as CNOTs are much noisier than single-qubit gates, so it is essential to minimize their count. Quantum circuit synthesis is a process of decomposing an arbitrary unitary into a sequence of quantum gates, and can be used as an optimization tool to produce shorter circuits to improve overall circuit ﬁdelity. However, the time-to-solution of synthesis grows exponentially with the number of qubits. As a result, synthesis is intractable for circuits on a large qubit scale. In this paper, we propose a hierarchical, block-by-block opti- mization framework, QGo, for quantum circuit optimization. Our approach allows an exponential cost optimization to scale to large circuits. QGo uses a combination of partitioning and synthesis: 1) partition the circuit into a sequence of independent circuit blocks; 2) re-generate and optimize each block using quantum synthesis; and 3) re-compose the ﬁnal circuit by stitching all the blocks together. We perform our analysis and show the ﬁdelity improvements in three different regimes: small-size circuits on real devices, medium-size circuits on noise simulations, and large-size circuits on analytical models. Using a set of NISQ benchmarks, we show that QGo can reduce the number of CNOT gates by 29.9% on average and up to 50% when compared with industrial compilers such as t | ket (cid:105) . When executed on the IBM Athens system, shorter depth leads to higher circuit ﬁdelity. We also demonstrate the scalability of our QGo technique to optimize circuits of 60+ qubits. Our technique is the ﬁrst demonstration of successfully employing and scaling synthesis in the compilation toolchain for large circuits. Overall, our approach is robust for direct incorporation in production compiler toolchains.
Adaptive Identification of Legged Robotic Kinematic Structure

Model-based control usually relies on an accurate model, which is often obtained from CAD and actuator models. The more accurate the model the better the control performance. However, in bipedal robots that demonstrate high agility actions, such as running and hopping, the robot hardware will suffer from impacts with the environment and deform in vulnerable parts, which invalidates the predefined model. Thus, it is desired to have an adaptable kinematic structure that takes deformation into consideration. To account for this we propose an approach that models all of the robotic joints as 6-DOF joints and develop an algorithm that can identify the kinematic structure from motion capture data. We evaluate the algorithm's performance both in simulation - a three link pendulum, and on a bipedal robot - ATRIAS. In the simulated case the algorithm produces a result that has a 3.6% error compared to the ground truth, and on the real life bipedal robot the algorithm's result confirms our prior assumption where the joint deforms on out-of-plane degrees of freedom. In addition our algorithm is able to predict torques and forces using the reconstructed joint mode.
Two Headed Dragons: Multimodal Fusion And Cross Modal Transactions

As the field of remote sensing is evolving, we witness the accumulation of information from several modalities, such as multispectral (MS), hyperspectral (HSI), LiDAR etc. Each of these modalities possess its own distinct characteristics and when combined synergistically, perform very well in the recognition and classification tasks. However, fusing multiple modalities in remote sensing is cumbersome due to highly disparate domains. Furthermore, the existing methods do not facilitate cross-modal interactions. To this end, we propose a novel transformer based fusion method for HSI and LiDAR modalities. The model is composed of stacked auto encoders that harness the cross key-value pairs for HSI and LiDAR, thus establishing a communication between the two modalities, while simultaneously using the CNNs to extract the spectral and spatial information from HSI and LiDAR. We test our model on Houston (Data Fusion Contest – 2013) and MUUFL Gulfport datasets and achieve competitive results.
Near-Optimal Average-Case Approximate Trace Reconstruction from Few Traces

In the standard trace reconstruction problem, the goal is to \emph{exactly} reconstruct an unknown source string $\mathsf{x} \in \{0,1\}^n$ from independent"traces", which are copies of $\mathsf{x}$ that have been corrupted by a $\delta$-deletion channel which independently deletes each bit of $\mathsf{x}$ with probability $\delta$ and concatenates the surviving bits. We study the \emph{approximate} trace reconstruction problem, in which the goal is only to obtain a high-accuracy approximation of $\mathsf{x}$ rather than an exact reconstruction. We give an efficient algorithm, and a near-matching lower bound, for approximate reconstruction of a random source string $\mathsf{x} \in \{0,1\}^n$ from few traces. Our main algorithmic result is a polynomial-time algorithm with the following property: for any deletion rate $0<\delta<1$ (which may depend on $n$), for almost every source string $\mathsf{x} \in \{0,1\}^n$, given any number $M \leq \Theta(1/\delta)$ of traces from $\mathrm{Del}_\delta(\mathsf{x})$, the algorithm constructs a hypothesis string $\widehat{\mathsf{x}}$ that has edit distance at most $n \cdot (\delta M)^{\Omega(M)}$ from $\mathsf{x}$. We also prove a near-matching information-theoretic lower bound showing that given $M \leq \Theta(1/\delta)$ traces from $\mathrm{Del}_\delta(\mathsf{x})$ for a random $n$-bit string $\mathsf{x}$, the smallest possible expected edit distance that any algorithm can achieve, regardless of its running time, is $n \cdot (\delta M)^{O(M)}$.
Opinions and dark energy: A mechanism of long-range repulsion in a toy opinion particle model

The concept of opinion particles can be introduced by studying timecontinuous versions of Bayesian-inspired opinion dynamics methods. Here, we use opinion particles to further explore how information and Bayesian methods can contribute new ideas to physical problems. Continuous-time limits are shown to correspond to the case with little trust between the original agents, and a synchronous version of the model is studied. The interaction between two particles is defined as attractive. However, space and distances are not well represented in the model. We define a local measure of distances based on the parameters. That allows distant particles to be repelled in the natural coordinates of any central particle, similar to the observed cosmological effects identified as dark energy.
Numerical Investigation of Accuracy, Convergence, and Conservation of Weakly-Compressible SPH

Despite the many advances in the use of weakly-compressible smoothed particle hydrodynamics (SPH) to simulate incompressible fluid flow, it is still challenging to obtain second-order numerical convergence. In this paper we perform a systematic numerical study of convergence and accuracy of kernelbased approximation, discretization operators, and weakly-compressible SPH (WCSPH) schemes. We explore the origins of the errors and issues preventing second-order convergence. Based on the study, we propose several new variations of the basic WCSPH scheme that are all second-order accurate. Additionally, we investigate the linear and angular momentum conservation property of the WCSPH schemes. Our results show that one may construct accurate WCSPH schemes that demonstrate second-order convergence through a judicious choice of kernel, smoothing length, and discretization operators in the discretization of the governing equations.
Adding air attenuation to simulated room impulse responses: A modal approach

Air absorption is an important effect to consider when simulating room acoustics as it leads to significant attenuation in high frequencies. In this study, an offline method for adding air absorption to simulated room impulse responses is devised. The proposed method is based on a modal scheme for a system of one-dimensional dissipative wave equations, which can be used to post-process a room impulse response simulated without air absorption, thereby incorporating missing frequency-dependent distance-based air attenuation. Numerical examples are presented to evaluate the proposed method, along with comparisons to existing filter-based methods.
Playtesting: What is Beyond Personas

Playtesting is an essential step in the game design process. Game designers use the feedback from playtests to refine their designs. Game designers may employ procedural personas to automate the playtesting process. In this article, we present two approaches to improve automated playtesting. First, we propose developing persona, which allows a persona to progress to different goals. In contrast, the procedural persona is fixed to a single goal. Second, a human playtester knows which paths she has tested before, and during the consequent tests, she may test different paths. However, reinforcement learning (RL) agents disregard these previous paths. We propose a novel methodology that we refer to as alternative path finder (APF). We train APF with previous paths and employ APF during the training of an RL agent. APF modulates the reward structure of the environment, while preserving the agent’s goal. When evaluated, the agent generates a different trajectory that achieves the same goal. We use the general video game artificial intelligence and VizDoom frameworks to test our proposed methodologies. We use proximal policy optimization RL agent during experiments. First, we compare the playtest data generated by developing and procedural persona. Our experiments show that developing persona provides better insight into the game and how different players would play. Second, we present the alternative paths found using APF and argue why traditional RL agents cannot learn those paths.
Invariant cones for semigroups and controllability of bilinear control systems

In this paper we present necessary and sufficient conditions to guarantee the existence of invariant cones, for semigroup actions, in the space of the k-fold exterior product. As consequence we establish a necessary and sufficient condition for controllability of a class of bilinear control systems. AMS 2020 subject classification: 22E46, 93B05, 20M20
Restless Bandits with Many Arms: Beating the Central Limit Theorem

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Reversible Electrochemical Intercalation of Aluminum in Mo6S8

The Chevrel phase Mo6S8 is prepared by reaction of CuCl2 and (NH4)2MoS4 in DMF (90 °C, 6 h) followed by heating of the precipitate in 5% H2/Ar atmosphere (1000 °C, 7 h).
Testing Pushdown Systems

Testing on reactive systems is a well-known laborious activity on software development due to their asynchronous interaction with the environment. In this setting model based testing has been employed when checking conformance and generating test suites of such systems using labeled transition system as a formalism as well as the classical ioco conformance relation. In this work we turn to a more complex scenario where the target systems have an auxiliary memory, a stack. We then studied a more powerful model, the Visibly Pushdown Labeled Transition System (VPTS), its variant Input/Output VPTS (IOVPTS), its associated model Visibly Pushdown Automaton (VPA), and aspects of conformance testing and test suite generation. This scenario is much more challenge since the base model has a pushdown stack to capture more complex behaviors which commonly found on reactive systems. We then defined a more general conformance relation for pushdown reactive systems such that it prevents any observable implementation behavior that was not already present in the given specification. Further we gave an efficient algorithm to check conformance in this testing scenario and also showed that it runs in worst case asymptotic polynomial time in the size of both the given specification and the implementation that are put under test.
Sub‐10 nm Wide Cellulose Nanofibers for Ultrathin Nanoporous Membranes with High Organic Permeation

There are increasing requirements for highly efficient and solvent‐resistant nanoporous membranes in various separation processes. Traditional membranes usually have a poor solvent resistance and a thick skin layer leading to a low permeation flux. Currently, the major challenge lies in fabrication of ultrathin few‐nanometers‐pore membranes for fast organic filtration. Herein, a facile approach is presented to prepare ultrafine cellulose nanofibers for fabrication of ultrathin nanoporous membranes. The obtained nanofibers have a uniform diameter of 7.5 ± 2.5 nm and are homogeneously dispersed in aqueous solutions that are favorable to the fabrication of ultrathin nanoporous membranes. The resulting cellulose nanoporous membranes have an adjustable thickness down to 23 nm and pore sizes ranging from 2.5 to 12 nm. They allow fast permeation of water and organics during pressure‐driven filtration. Typically, the 30 nm thick membrane has high fluxes of 1.14 and 3.96 × 104 L h−1 m−2 bar−1 for pure water and acetone respectively. Furthermore, the as‐prepared cellulose nanofibers are easily employed to produce a novel syringe filter with sub‐10 nm pores that have a wide application in fast separation and purification of nanoparticles on few‐nanometers scale.
Use of speaker recognition approaches for learning timbre representations of musical instrument sounds from raw waveforms

—Timbre representations of musical instruments, essential for diverse applications such as musical audio synthesis and separation, might be learned as bottleneck features from an instrumental recognition model. Given the similarities between speaker recognition and musical instrument recognition, in this paper, we investigate how to adapt successful speaker recognition algorithms to musical instrument recognition to learn meaningful instrumental timbre representations. To address the mismatch between musical audio and models devised for speech, we introduce a group of trainable ﬁlters to generate proper acoustic features from input raw waveforms, making it easier for a model to be optimized in an input-agnostic and end-to-end manner. Through experiments on both the NSynth and RWC databases in both musical instrument closed-set identiﬁcation and open-set veriﬁcation scenarios, the modiﬁed speaker recognition model was capable of generating discriminative embeddings for instrument and instrument-family identities. We further conducted extensive experiments to characterize the encoded information in learned timbre embeddings.