Miracle-dz/UniFAR-FTUs huggingface dataset:
PathFinder: Discovering Decision Pathways in Deep Neural Networks

Explainability is becoming an increasingly important topic for deep neural networks. Though the operation in convolutional layers is easier to understand, processing becomes opaque in fully-connected layers. The basic idea in our work is that each instance, as it flows through the layers, causes a different activation pattern in the hidden layers and in our Paths methodology, we cluster these activation vectors for each hidden layer and then see how the clusters in successive layers connect to one another as activation flows from the input layer to the output. We find that instances of the same class follow a small number of cluster sequences over the layers, which we name ``decision paths. "Such paths explain how classification decisions are typically made, and also help us determine outliers that follow unusual paths. We also propose using the Sankey diagram to visualize such pathways. We validate our method with experiments on two feed-forward networks trained on MNIST and CELEB data sets, and one recurrent network trained on PenDigits.
Paradoxes and resolutions for semiparametric fusion of individual and summary data

Suppose we have available individual data from an internal study and various types of summary statistics from relevant external studies. External summary statistics have been used as constraints on the internal data distribution, which promised to improve the statistical inference in the internal data; however, the additional use of external summary data may lead to paradoxical results: efficiency loss may occur if the uncertainty of summary statistics is not negligible and large estimation bias can emerge even if the bias of external summary statistics is small. We investigate these paradoxical results in a semiparametric framework. We establish the semiparametric efficiency bound for estimating a general functional of the internal data distribution, which is shown to be no larger than that using only internal data. We propose a data-fused efficient estimator that achieves this bound so that the efficiency paradox is resolved. Besides, a debiased estimator is further proposed which has selection consistency property by employing adaptive lasso penalty so that the resultant estimator can achieve the same asymptotic distribution as the oracle one that uses only unbiased summary statistics, which resolves the bias paradox. Simulations and application to a Helicobacter pylori infection dataset are used to illustrate the proposed methods.
GenDexGrasp: Generalizable Dexterous Grasping

Generating dexterous grasping has been a long-standing and challenging robotic task. Despite recent progress, existing methods primarily suffer from two issues. First, most prior art focuses on a specific type of robot hand, lacking generalizable capability of handling unseen ones. Second, prior arts oftentimes fail to rapidly generate diverse grasps with a high success rate. To jointly tackle these challenges with a unified solution, we propose the GenDexGrasp, a novel hand-agnostic grasping algorithm for generalizable grasping. GenDexGrasp is trained on our proposed large-scale multi-hand grasping dataset MultiDex synthesized with force closure optimization. By leveraging the contact map as a hand-agnostic intermediate representation, GenDexGrasp efficiently generates diverse and plausible grasping poses with a high success rate and can transfer among diverse multi-fingered robotic hands. Compared with previous methods, GenDexGrasp achieves a three-way trade-off among success rate, inference speed, and diversity.
Fast and accurate predictions of the nonlinear matter power spectrum for general models of Dark Energy and Modified Gravity

We embed linear and nonlinear parametrisations of beyond standard cosmological physics in the halo model reaction framework, providing a model-independent prescription for the nonlinear matter power spectrum. As an application, we focus on Horndeski theories, using the Effective Field Theory of Dark Energy (EFTofDE) to parameterise linear and quasi-nonlinear perturbations. In the nonlinear regime we investigate both a nonlinear parameterised-post Friedmann (nPPF) approach as well as a physically motivated and approximate phenomenological model based on the error function (Erf). We compare the parameterised approaches’ predictions of the nonlinear matter power spectrum to the exact solutions, as well as state-of-the-art emulators, in an evolving dark energy scenario and two well studied modified gravity models, finding sub-percent agreement in the reaction using the Erf model at z ≤ 1 and k ≤ 5 h/Mpc. This suggests only an additional 3 free constants, above the background and linear theory parameters, are sufficient to model nonlinear, non-standard cosmology in the matter power spectrum at scales down to k ≤ 3h /Mpc within $2{{\%}}$ accuracy. We implement the parametrisations into ver.2. 0 of the ReACT code: ACTio et ReACTio.
Singular hermitian metrics and the decomposition theorem of Catanese, Fujita, and Kawamata

We prove that a torsion-free sheaf $\mathcal F$ endowed with a singular hermitian metric with semi-positive curvature and satisfying the minimal extension property admits a direct-sum decomposition $\mathcal F \simeq \mathcal U \oplus \mathcal A$ where $\mathcal U$ is a hermitian flat bundle and $\mathcal A$ is a generically ample sheaf. The result applies to the case of direct images of relative pluricanonical bundles $f_* \omega_{X/Y}^{\otimes m}$ under a surjective morphism $f\colon X \to Y$ of smooth projective varieties with $m\geq 2$. This extends previous results of Fujita, Catanese--Kawamata, and Iwai.
Construction and Evaluation of a Self-Attention Model for Semantic Understanding of Sentence-Final Particles

Sentence-final particles serve an essential role in spoken Japanese because they express the speaker's mental attitudes toward a proposition and/or an interlocutor. They are acquired at early ages and occur very frequently in everyday conversation. However, there has been little proposal for a computational model of acquiring sentence-final particles. This paper proposes Subjective BERT, a self-attention model that takes various subjective senses in addition to language and images as input and learns the relationship between words and subjective senses. An evaluation experiment revealed that the model understands the usage of"yo", which expresses the speaker's intention to communicate new information, and that of"ne", which denotes the speaker's desire to confirm that some information is shared.
Behind the Scenes of Gradient Descent: A Trajectory Analysis via Basis Function Decomposition

This work analyzes the solution trajectory of gradient-based algorithms via a novel basis function decomposition. We show that, although solution trajectories of gradient-based algorithms may vary depending on the learning task, they behave almost monotonically when projected onto an appropriate orthonormal function basis. Such projection gives rise to a basis function decomposition of the solution trajectory. Theoretically, we use our proposed basis function decomposition to establish the convergence of gradient descent (GD) on several representative learning tasks. In particular, we improve the convergence of GD on symmetric matrix factorization and provide a completely new convergence result for the orthogonal symmetric tensor decomposition. Empirically, we illustrate the promise of our proposed framework on realistic deep neural networks (DNNs) across different architectures, gradient-based solvers, and datasets. Our key finding is that gradient-based algorithms monotonically learn the coefficients of a particular orthonormal function basis of DNNs defined as the eigenvectors of the conjugate kernel after training. Our code is available at https://github.com/jianhaoma/function-basis-decomposition.
A Smart Recycling Bin Using Waste Image Classification At The Edge

Rapid economic growth gives rise to the urgent demand for a more efficient waste recycling system. This work thereby developed an innovative recycling bin that automatically separates urban waste to increase the recycling rate. We collected 1800 recycling waste images and combined them with an existing public dataset to train classification models for two embedded systems, Jetson Nano and K210, targeting different markets. The model reached an accuracy of 95.98% on Jetson Nano and 96.64% on K210. A bin program was designed to collect feedback from users. On Jetson Nano, the overall power consumption of the application was reduced by 30% from the previous work to 4.7 W, while the second system, K210, only needed 0.89 W of power to operate. In summary, our work demonstrated a fully functional prototype of an energy-saving, high-accuracy smart recycling bin, which can be commercialized in the future to improve urban waste recycling.
(Non)-Coherent MU-MIMO Block Fading Channels With Finite Blocklength and Linear Processing

Driven by the stringent demands of future ultra reliable and low latency communication (URLLC), we provide a comprehensive study for a coherent and non-coherent multiuser multiple-input multiple-output (MU-MIMO) uplink system in the finite blocklength regime. The independent and identically distributed (i.i.d.) Gaussian codebook is assumed for each user. To be more specific, the base station (BS) first uses two popular linear processing schemes to combine the signals transmitted from all users, namely maximum-ratio combining (MRC) and zero-forcing (ZF). Following it, the matched maximum-likelihood (ML) and mismatched nearest-neighbour (NN) decoding metric for the coherent and non-coherent cases are respectively employed at the BS. Under these conditions, the refined third-order achievable coding rate, expressed as a function of the blocklength, average error probability, and the third-order term of the information density (called as the channel perturbation), is derived. With this result in hand, a detailed performance analysis is then pursued, through which, we derive the asymptotic results of the channel perturbation, achievable coding rate, channel capacity, and the channel dispersion. These theoretical results enable us to obtain a number of interesting insights related to the impact of the finite blocklength: i) in our system setting, massive MIMO helps to reduce the channel perturbation of the achievable coding rate, which can even be discarded without affecting the performance with just a small-to-moderate number of BS antennas and number of blocks; ii) under the non-coherent case, even with massive MIMO, the channel estimation errors cannot be eliminated unless the transmit powers in both the channel estimation and data transmission phases for each user are made inversely proportional to the square root of the number of BS antennas; iii) in the non-coherent case and for fixed total blocklength, the scenarios with longer coherence intervals and smaller number of blocks will offer higher achievable coding rate.
Observational strong gravity and quantum black hole structure

Quantum considerations have led many theorists to believe that classical black hole physics is modified not just deep inside black holes but at {\it horizon scales}, or even further outward. The near-horizon regime has just begun to be observationally probed for astrophysical black holes -- both by LIGO, and by the Event Horizon Telescope. This suggests exciting prospects for observational constraints on or discovery of new quantum black hole structure. This paper overviews arguments for certain such structure and these prospects.
A note on centering in subsample selection for linear regression

Centring is a commonly used technique in linear regression analysis. With centred data on both the responses and covariates, the ordinary least squares estimator of the slope parameter can be calculated from a model without the intercept. If a subsample is selected from a centred full data, the subsample is typically uncentred. In this case, is it still appropriate to fit a model without the intercept? The answer is yes, and we show that the least squares estimator on the slope parameter obtained from a model without the intercept is unbiased and it has a smaller variance covariance matrix in the Loewner order than that obtained from a model with the intercept. We further show that for noninformative weighted subsampling when a weighted least squares estimator is used, using the full data weighted means to relocate the subsample improves the estimation efficiency.
Distributed Graph Neural Network Training with Periodic Stale Representation Synchronization

Despite the recent success of Graph Neural Networks, it remains challenging to train a GNN on large graphs with millions of nodes and billions of edges, which are prevalent in many graph-based applications. Traditional sampling-based methods accelerate GNN training by dropping edges and nodes, which impairs the graph integrity and model performance. Differently, distributed GNN algorithms accelerate GNN training by utilizing multiple computing devices and can be classified into two types:"partition-based"methods enjoy low communication costs but suffer from information loss due to dropped edges, while"propagation-based"methods avoid information loss but suffer from prohibitive communication overhead caused by the neighbor explosion. To jointly address these problems, this paper proposes DIGEST (DIstributed Graph reprEsentation SynchronizaTion), a novel distributed GNN training framework that synergizes the complementary strength of both categories of existing methods. We propose to allow each device to utilize the stale representations of its neighbors in other subgraphs during subgraph parallel training. This way, our method preserves global graph information from neighbors to avoid information loss and reduce communication costs. Our convergence analysis demonstrates that DIGEST enjoys a state-of-the-art convergence rate. Extensive experimental evaluation on large, real-world graph datasets shows that DIGEST achieves up to 21.82 speedups without compromising performance compared to state-of-the-art distributed GNN training frameworks.
Uncertainty Detection and Reduction in Neural Decoding of EEG Signals

EEG decoding systems based on deep neural networks have been widely used in decision making of brain computer interfaces (BCI). Their predictions, however, can be unreliable given the signiﬁcant variance and noise in EEG signals. Previous works on EEG analysis mainly focus on the exploration of noise pattern in the source signal, while the uncertainty during the decoding process is largely un-explored. Automatically detecting and reducing such decoding uncertainty is important for BCI motor imagery applications such as robotic arm control etc. In this work, we proposed an uncertainty estimation and reduction model (UNCER) to quantify and mitigate the uncertainty during the EEG decoding process. It utilized a combination of dropout oriented method and Bayesian neural network for uncertainty estimation to incorporate both the uncertainty in the input signal and the uncertainty in the model parameters. We further proposed a data augmentation based approach for uncertainty reduction. The model can be integrated into current widely used EEG neural decoders without change of architecture. We performed extensive experiments for uncertainty estimation and its reduction in both intra-subject EEG decoding and cross-subject EEG decoding on two public motor imagery datasets, where the proposed model achieves signiﬁcant improvement both on the quality of estimated uncertainty and the effectiveness of uncertainty reduction.
Interactions between upstream-propagating guided jet waves and shear-layer instability waves near the nozzle of subsonic and nearly ideally expanded supersonic free jets with laminar boundary layers

Abstract The interactions between upstream-propagating guided jet waves and shear-layer instability waves near the nozzle of subsonic and nearly ideally expanded supersonic, isothermal free jets are investigated for jets at Mach numbers between 0.50 and 2 with fully laminar exit boundary layers of different thicknesses. The velocity spectra in the shear layers downstream of the nozzle exhibit strong narrow peaks for the first azimuthal modes, associated with growing Kelvin–Helmholtz instability waves. The frequencies of the predominant peaks are close, but not necessarily equal, to those of the most amplified instability waves predicted from the mean flow fields using linear stability analysis. They also fall in most cases within or very near the allowable frequency bands of the free-stream upstream-propagating guided jet waves obtained using a vortex-sheet model and jump from one band to another as the Mach number increases. At these frequencies, moreover, high levels organized into elongated stripes are found in the jet potential core and standing-wave patterns are visible at the edges of the shear layers in the power spectral densities of pressure and velocity fluctuations. Therefore, the free-stream upstream-propagating guided jet waves appear to interact with and excite the instability waves near the nozzle of the present jets, as in screeching and impinging jets. This explains the disparities of the frequencies and azimuthal modes of the instability waves dominating early on in the shear layers of initially laminar jets and their discontinuous changes and staging behaviours as the jet velocity varies.
A Family of Relaxed Concurrent Queues for Low-Latency Operations and Item Transfers

The producer-consumer communication over shared memory is a critical function of current scalable systems. Queues that provide low latency and high throughput on highly utilized systems can improve the overall performance perceived by the end users. In order to address this demand, we set as priority to achieve both high operation performance and item transfer speed. The Relaxed Concurrent Queues (RCQs) are a family of queues that we have designed and implemented for that purpose. Our key idea is a relaxed ordering model that splits the enqueue and dequeue operations into a stage of sequential assignment to a queue slot and a stage of concurrent execution across the slots. At each slot, we apply no order restrictions among the operations of the same type. We define several variants of the RCQ algorithms with respect to offered concurrency, required hardware instructions, supported operations, occupied memory space, and precondition handling. For specific RCQ algorithms, we provide pseudo-code definitions and reason about their correctness and progress properties. Additionally, we theoretically estimate and experimentally validate the worst-case distance between an RCQ algorithm and a strict first-in-first-out (FIFO) queue. We developed prototype implementations of the RCQ algorithms and experimentally compare them with several representative strict FIFO and relaxed data structures over a range of workload and system settings. The RCQS algorithm is a provably linearizable lock-free member of the RCQ family. We experimentally show that RCQS achieves factors to orders of magnitude advantage over the state-of-the-art strict or relaxed queue algorithms across several latency and throughput statistics of the queue operations and item transfers.
Competing collinear magnetic structures in superconducting FeSe by first-principles quantum Monte Carlo calculations

Resolving the interplay between magnetic interactions and structural properties in strongly correlated materials through a quantitatively accurate approach has been a major challenge in condensed-matter physics. Here we apply highly accurate first-principles quantum Monte Carlo (QMC) techniques to obtain structural and magnetic properties of the iron selenide (FeSe) superconductor under pressure. Where comparable, the computed properties are very close to the experimental values. Of potential ordered magnetic configurations, collinear spin configurations are the most energetically favorable over the explored pressure range. They become nearly degenerate in energy with bicollinear spin orderings at around 7 GPa, when the experimental critical temperature ${T}_{c}$ is the highest. On the other hand, ferromagnetic, checkerboard, and staggered dimer configurations become relatively higher in energy as the pressure increases. The behavior under pressure is explained by an analysis of the local charge compressibility and the orbital occupation as described by the QMC many-body wave function, which reveals how spin, charge, and orbital degrees of freedom are strongly coupled in this compound. This remarkable pressure evolution suggests that stripelike magnetic fluctuations may be responsible for the enhanced ${T}_{c}$ in FeSe and that higher ${T}_{c}$ is associated with nearness to a crossover between collinear and bicollinear ordering.
Extracellular vesicle propagation in acidic tumor microenvironment

A complex and dynamic tumor microenvironment (TME) plays a crucial role in angiogenesis, proliferation, and invasion of tumors. The acidity of the TME has a key impact on the release of extracellular vesicles (EVs) which, in turn, provide novel opportunities to combat tumors. This observation opens up the development of efficient cancer treatment strategies where knowing the EV distribution becomes essential. However, an analytical model of the EV biodistribution which accounts for the effects of extracellular pH (pHe) has been missing. Here we propose a simple model for an EV-mediated molecular communications (MC) system which comprises tumor cells as transceivers and the extracellular matrix of the TME as the EV propagating medium. We account for the effect of the acidity considering the pHe-modulated EV release from tumor cells, and analytically describe the EV propagation dynamics using a partial differential equation that we solve analytically and verify its solution using the numerical method based on finite element analysis. The obtained results show that the TME acidity characterized with pH values largely affects the EV biodistribution in the TME.
Hybrid deep learning-based feature-augmented detection for molecular communication systems

This manuscript presents a hybrid Deep learning algorithm to detect transmitted symbols from experimental measured data in a biological molecular communication (MC) testbed. The algorithm consists of two deep neural networks capturing different aspects of the time signal, which are combined based on their relative confidence. The MC testbed uses a transfected Escherichia coli (E. coli) bacteria that express the light-driven proton pump gloeorhodopsin from Gloeobacter violaceus. Given an external controllable light stimulus, driven from a light-emitting diode (Transmitter), the bacteria secrete protons that change the pH level of the environment. A pH detector (Receiver) measures the pH of the environment. Modelling such a biological real system accurately is not feasible. Thus, in order to detect the transmitted bits we use both a convolutional and a recurrent neural network in tandem. This paper discusses the data augmentation, processing, and neural networks pertinent to a practical MC problem. The trained algorithm detects the transmitted bits with an accuracy above 99.9%.