arXiv research

DEFINED improves wireless symbol detection with limited pilot data.

DEFINED uses decision feedback ICL to detect symbols with minimal pilot data.

CHOOSE enhances shallow Transformers for wireless symbol detection.

DeepSIC learns to detect multiple symbols in MIMO systems without assuming a specific channel model.

Data-driven symbol detection improves performance in complex channels.

This paper presents a ML-based receiver for SDR that outperforms conventional methods.

ViterbiNet uses deep learning to detect symbols without channel info.

ABBA creates a new symbolic time series representation based on Brownian bridge.

PG-IM uses neural-symbolic programs to manipulate images.

Paper presents a novel neural network for MIMO symbol detection.

A new method for spotting symbols in CAD images reduces annotation costs and improves accuracy.

Enhances safety of 3D object detection neural networks.

QABBA improves time series storage efficiency while preserving shape information.

Process Monitoring involves tracking a system's behaviors, evaluating the current state of the system, and discovering interesting events that require immediate actions. In this paper, we consider monitoring temporal system state sequences to help detect the changes of dynamic systems, check the divergence of the syste…

Explores tensor products in hyperdimensional computing.

Adaptive neural network improves MIMO detection on real-world channels.

This paper offers a general and comprehensive definition of the day-of-the-week effect. Using symbolic dynamics, we develop a unique test based on ordinal patterns in order to detect it. This test uncovers the fact that the so-called "day-of-the-week" effect is partly an artifact of the hidden correlation structure of …

Data science detects Ethereum honeypots using transaction behavior.

Model-based machine learning improves communication systems.

ECSEL learns signomial equations for explainable classification.

Data-driven factor graphs improve BCJR detection robustness.

A method to automatically and symbolically detect and resolve degenerate parameter combinations from parameter-data pairs.

A system is presented that segments, clusters and predicts musical audio in an unsupervised manner, adjusting the number of (timbre) clusters instantaneously to the audio input. A sequence learning algorithm adapts its structure to a dynamically changing clustering tree. The flow of the system is as follows: 1) segment…

We provide evidence for the conjecture that the Wodzicki-Chern classes vanish for all bundles with the group Z of invertible zeroth order pseudodifferential operators as structure group. In particular, we prove this vanishing if the structure group reduces to pseudodifferential operators with leading order symbol the i…

VaSST uses soft symbolic trees for probabilistic symbolic regression.

DILP improves fraud detection explainability without significant performance boost.

Study robustness of polynomial neural networks using algebraic geometry.

We describe dimensionally constrained symbolic regression which has been developed for mass measurement in certain classes of events in high-energy physics (HEP). With symbolic regression, we can derive equations that are well known in HEP. However, in problems with large number of variables, we find that by constraini…

Irony and sarcasm are two complex linguistic phenomena that are widely used in everyday language and especially over the social media, but they represent two serious issues for automated text understanding. Many labeled corpora have been extracted from several sources to accomplish this task, and it seems that sarcasm …

Paper closes neural-symbolic learning loop with grammar model and back-search algorithm.

Detect knots from photos using machine learning and traditional algorithms.

Reinforcement learning and symbolic planning have both been used to build intelligent autonomous agents. Reinforcement learning relies on learning from interactions with real world, which often requires an unfeasibly large amount of experience. Symbolic planning relies on manually crafted symbolic knowledge, which may …

For an arbitrary Riemannian manifold $X$ and Hermitian vector bundles $E$ and $F$ over $X$ we define the notion of the normal symbol of a pseudodifferential operator $P$ from $E$ to $F$. The normal symbol of $P$ is a certain smooth function from the cotangent bundle $T^*X$ to the homomorphism bundle $Hom (E,F)$ and dep…

New ML-based detection improves PMH signal detection in load-modulated MIMO systems.

Based on the ideas of Optimal Control, we introduce the new basic characteristic of a bracket generating distribution, the Jacobi symbol. In contrast to the classical Tanaka symbol, the set of Jacobi symbols is discrete and classifiable. We give an explicit and unified algebraic procedure for the construction of the ca…

In emerging Internet-of-Nano-Thing (IoNT), information will be embedded and conveyed in the form of molecules through complex and diffusive medias. One main challenge lies in the long-tail nature of the channel response causing inter-symbol-interference (ISI), which deteriorates the detection performance. If the channe…

Symbolic knowledge in neural models can inadvertently make them more vulnerable to adversarial attacks.

We introduce mod 3 triple Milnor invariants and triple cubic residue symbols for certain primes of the Eisenstein number field $\mathbb{Q}(\sqrt{-3})$, following the analogies between knots and primes. Our triple symbol generalizes both the cubic residue symbol and Rédei's triple symbol, and describes the decomposition…

A formula for Rademacher symbols in triangle groups is provided.

The symbolic dynamics technique is well-known for low-dimensional dynamical systems and chaotic maps, and lies at the roots of the thermodynamic formalism of dynamical systems. Here we show that this technique can also be successfully applied to time series generated by complex systems of much higher dimensionality. Ou…

Complex $b$-$6j$ symbols relate to hyperbolic tetrahedron volumes and determinants.

The paper classifies symbols of differential operators on vector bundles.

A Riemannian metric on a manifold M induces a family of Riemannian metrics on the loop space LM depending on a Sobolev space parameter s. We compute the connection forms of these metrics and the higher symbols of their curvature forms, which take values in pseudodifferential operators. These calculations are used in a …

NeSS combines neural and symbolic approaches for better compositional generalization.

Defines transverse symbols for foliated manifolds and proves their K-homology class.

Paper proposes a bijective approach for signal/symbol translation using variational auto-encoders.

The Wodzicki residue and the cut-off integral extend to classical symbol-valued forms. We show that they obey a Stokes' type property and that the extended Wodzicki residue can be interpreted as a complex residue like the ordinary one. In the case of cut-off integrals, Stokes' property (i.e. vanishing on exact forms) o…

Bayesian symbolic regression automates model discovery from data.