Paper improves channel charting using autoencoders with spatial constraints.
problem Improving logical positioning of UEs using channel-state information.
method Representation-constrained autoencoders to enhance channel charts.
result Improved quality of learned channel charts for UE positioning.
Unified Siamese network for wireless positioning and channel charting.
problem Wireless positioning and channel charting using CSI.
method Unified Siamese neural network architecture for both supervised and unsupervised learning.
result Siamese networks achieve similar or better performance than existing methods.
Study improves scalability of cell-free massive MIMO networks by optimizing UE-AP association.
problem Optimizing UE-AP association in cell-free massive MIMO networks.
method Deep learning algorithm using Bidirectional Long Short-Term Memory cells and hybrid probabilistic weight updating.
result Enhanced scalability without retraining, robust against pilot contamination.
Federated Learning over wireless networks tackles resource allocation challenges.
problem Heterogeneity in UE data and resources in Federated Learning.
method Proposed FL algorithm for heterogeneous data, convergence rate analysis, and resource allocation optimization.
result The proposed algorithm outperforms vanilla FedAvg in convergence rate and accuracy.
Adversaries manipulate wireless power allocation to reduce user rates.
problem Adversaries exploit deep learning for power control to decrease communication rates.
method Adversaries craft perturbations to inputs of a DNN to minimize power allocation.
result Adversarial attacks are highly effective and robust to uncertainties.
We propose channel charting (CC), a novel framework in which a multi-antenna network element learns a chart of the radio geometry in its surrounding area. The channel chart captures the local spatial geometry of the area so that points that are close in space will also be close in the channel chart and vice versa. CC w…
A bandit algorithm reduces regret in noisy, communication-constrained feedback.
problem Distributed stochastic multi-armed bandit with noisy, communication-constrained feedback.
method Proposes a multi-phase bandit algorithm, UE-UCB++, that matches an information-theoretic lower bound.
result Matches an information-theoretic lower bound of Ω(√(KT/σ²)) on the minimax regret.
Popular deep learning uncertainty estimation methods often mislead on out-of-distribution data.
problem Misleading uncertainty estimates on out-of-distribution data.
method Analysis of Gaussian process, Bayesian neural networks, and Monte Carlo dropout methods.
result BNNs and MCDropout do not always provide high uncertainty estimates on out-of-distribution samples.
LNK improves uncertainty estimation for molecular dynamics, reducing errors by up to 2.5 times.
problem Uncertainty estimation for molecular force fields to improve model reliability.
method LNK: Gaussian Process-based extension to GNNs addressing six desiderata.
result LNK reduces out-of-equilibrium detection errors by up to 2.5 times compared to existing methods.
The evolution of inflation, p(t), and unemployment, UE(t), in Japan has been modeled. Both variables were represented as linear functions of the change rate of labor force, dLF/LF. These models provide an accurate description of disinflation in the 1990s and a deflationary period in the 2000s. In Japan, there exists a …
Deep learning optimizes wireless band switching without measurement gaps.
problem Wireless networks waste data during band switching due to measurement gaps.
method Online-learning based classifier models exploiting spatial and spectral correlation.
result 30% improvement in mean effective rates compared to industry standard.
A new method uses machine learning to optimize user pairing and association in multicell NOMA networks.
problem Optimizing user pairing and association in multicell non-orthogonal multiple access (NOMA) systems.
method Formulated as a combinatorial optimization problem, solved using a Pointer Network (PtrNet) trained with deep reinforcement learning.
result Achieves near-optimal performance in terms of aggregate data rate, outperforming random heuristics by up to 30%.
Joint sensing and communication network improves target localization and reduces communication load.
problem Efficiently localize multiple targets with reduced communication overhead.
method Multi-base station cooperative sensing with AI-aided clustering and tracking.
result Optimal sub-pattern assignment (OSPA) error less than 60 cm with reduced communication capacity.
A novel framework refines diffusion models iteratively for better downstream reward optimization.
problem Optimizing reward functions during inference of diffusion models.
method Iterative refinement process with noising and reward-guided denoising steps.
result Superior empirical performance in protein and DNA design.
Adversarial attacks reduce deep learning beam selection performance in mmWave 5G.
problem Adversarial attacks degrade deep learning-based beam selection in mmWave 5G.
method Generates adversarial perturbations to RSS inputs to manipulate DNN predictions.
result Significant reduction in IA performance due to adversarial perturbations.
DNNs improve localization from channel estimates, overcoming practical impairments.
problem Improving localization accuracy from channel estimates in Massive MIMO systems.
method Principled feature design for DNNs invariant to practical impairments.
result DNN achieves high localization accuracy and generalization capability.
Tuning cellular network performance against always occurring wireless impairments can dramatically improve reliability to end users. In this paper, we formulate cellular network performance tuning as a reinforcement learning (RL) problem and provide a solution to improve the performance for indoor and outdoor environme…
Many wireless networks, including 5G NR (New Radio) and future beyond 5G cellular systems, are expected to operate on multiple frequency bands. This paper considers the band assignment (BA) problem in dual-band systems, where the basestation (BS) chooses one of the two available frequency bands (centimeter-wave and mil…
Paper optimizes UAV-assisted mobile edge computing for energy efficiency.
problem Minimizing energy consumption in UAV-assisted mobile edge computing.
method Proposes CAT and RAT algorithms combining convex optimization and deep reinforcement learning.
result RAT achieves similar performance and outperforms traditional algorithms.
New method optimizes diffusion models without fine-tuning, integrating soft value functions.
problem Optimizing natural design spaces of images, molecules, DNA, RNA, and protein sequences.
method Iterative sampling method integrating soft value functions into diffusion model inference.
result Directly utilizes non-differentiable features/reward feedback, applies to discrete diffusion models.
Let G/H be a Riemannian homogeneous space. For an orthogonal representation φ of H on the Euclidean space Rk+1, there corresponds the vector bundle E=G×φRk+1→G/H with fiberwise inner product. Provided that φ is the direct sum of at most two representations which are either …
New algorithm optimizes beam and rate allocation in mmWave systems for multiple users.
problem Optimizing beam and rate allocation in mmWave systems for multiple users with limited feedback.
method Introducing SAT-CTS, a combinatorial semi-bandit policy with satisficing objective.
result SAT-CTS achieves finite-time regret bounds and reduces satisficing regret in mmWave systems.
Paper models and compresses wideband CSI feedback in FDD MIMO systems.
problem Fundamental limits of channel state information (CSI) feedback in FDD massive MIMO systems.
method Modeling CSI as a Gaussian-mixture source with latent geometry states, proposing Gaussian-mixture transform coding (GMTC).
result Near-optimal CSI compression achieved through state-adaptive transform coding without large neural encoders.
We compute the Pin(2)-equivariant monopole Floer homology for the class of plumbed 3-manifolds with at most one "bad" vertex (in the sense of Ozsvath and Szabo). We show that for these manifolds, the Pin(2)-equivariant monopole Floer homology can be calculated in terms of the Heegaard Floer/monopole Floer lattice compl…
Study finds uncertainty estimators weakly correlate with LLM hallucinations.
problem Characterizing the relationship between uncertainty estimators and LLM hallucinations.
method Systematic empirical study of diverse uncertainty estimators across hallucination types and benchmarks.
result Uncertainty estimators weakly correlate with LLM hallucinations, depending on hallucination type and LLM.
Wireless systems perform rate adaptation to transmit at highest possible instantaneous rates. Rate adaptation has been increasingly granular over generations of wireless systems. The base-station uses SINR and packet decode feedback called acknowledgement/no acknowledgement (ACK/NACK) to perform rate adaptation. SINR i…
The paper optimizes UAV path and power for QoS in cellular networks.
problem Optimizing UAV path and power for QoS in cellular networks.
method Apprenticeship learning via deep inverse reinforcement learning (IRL) combined with Q-learning and DRL.
result The proposed method achieves expert-level performance and maintains performance in unseen situations.
Graph neural networks optimize radio resource management policies for wireless networks.
problem Optimizing user selection and power control in wireless networks with fairness constraints.
method Formulated as a Lagrangian dual problem, RRM policies are parameterized by a GNN architecture trained on channel conditions.
result The method achieves superior tradeoff between average and 5th percentile rates, demonstrating fairness.
This tutorial reviews RL-based methods for optimizing diffusion models to maximize specific metrics.
problem Optimizing diffusion models to generate samples that maximize specific metrics in practical applications.
method Various RL algorithms including PPO, differentiable optimization, reward-weighted MLE, value-weighted sampling, and path consistency learning.
result Exploration of strengths and limitations of RL-based fine-tuning algorithms and their benefits compared to non-RL-based approaches.
Tutorial on optimizing diffusion model samples for specific metrics.
problem Optimizing diffusion model samples for specific downstream metrics.
method Review and exploration of inference-time guidance and alignment methods.
result Unified perspective on inference-time algorithms and novel methods.
TIE framework detects out-of-distribution samples and estimates uncertainty without external datasets.
problem Detecting and estimating uncertainty for out-of-distribution samples in neural networks.
method TIE framework extends a classifier to an (n+1)-class model, iteratively refining through training, inversion, and exclusion.
result Unified and interpretable framework for robust anomaly detection and calibrated uncertainty estimation.
Extends positive and almost positive links to successively almost positive ones.
problem Extending properties of positive and almost positive diagrams and links.
method Introducing successively almost positive diagrams and links, and analyzing their properties.
result Improves known results of positive and almost positive links.
The paper extends positivity results from vector bundles to Kobayashi positive ones.
problem Extending positivity results from vector bundles to Kobayashi positive ones.
method Using convexity of Kobayashi positive Finsler metrics and duality for convex Finsler metrics.
result The quotient and tensor product of Kobayashi positive vector bundles are also Kobayashi positive.
The study establishes conditions for positive and quasi-positive links.
problem Characterizing and testing positive and quasi-positive links.
method Proves necessary conditions for link concordance and positivity.
result Characterizes positive links with unlinking number 1 and 2, and tests positive links as closures of positive braids.
The paper defines new types of positivity and proves properties of Schur forms for vector bundles.
problem Defining and characterizing new types of positivity for vector bundles.
method Introducing and characterizing two types of strongly decomposable positivity, proving properties of Schur forms.
result Schur forms of strongly decomposable positive vector bundles are positive or weakly positive, answering a question of Griffiths.
New characterizations of partial positivity using Hörmander's L2-estimate.
problem Characterizing partial positivity in complex geometry.
method Using a twisted version of Hörmander's L2-estimate. result New characterizations of partial positivity, including uniform q-positivity and RC-positivity. Introduces Θ-positivity in Lie groups, generalizing Lusztig's positivity.
problem Generalizing Lusztig's total positivity to a broader class of Lie groups.
method Introduces and studies Θ-positivity in real simple Lie groups. result Four families of Lie groups admit Θ-positive structures. New bounds on Jones polynomial positivity for specific links.
problem Determining when Jones polynomial of positive links is non-negative.
method Developed new bounds and used them to obstruct positivity for infinitely many almost-positive diagrams.
result Infinitely many knots are classified as almost-positive.
Uniform RC-positivity results for direct image bundles.
problem Understanding the relation between rational connectedness and RC-positivity.
method Analyzing vector bundles and their direct images, using weak RC-positivity as a starting point.
result Uniform RC-positivity of direct image bundles under weak RC-positivity conditions.
We introduce the notion of a positive opetope and positive opetopic cardinals as certain finite combinatorial structures. The positive opetopic cardinals to positive-to-one polygraphs are like simple graphs to free omega-categories over omega-graphs, c.f. [MZ]. In particular, they allow us to give an explicit combinato…
Characterizes a subset of links using quasipositive and homogeneous properties.
problem Understanding the properties of T-positive links.
method Characterization through strongly quasipositive and T-homogeneous braids.
result T-positive links are precisely the strongly quasipositive links that are closures of T-homogeneous braids.
Positive braid knots have simple knot Floer homology.
problem Computing knot Floer homology for positive braids.
method Computed the next-to-top term of knot Floer homology.
result Rank is 1 for any prime positive braid knot.
Establishes geometric properties of elements in the positive semigroup of a general real semisimple Lie group.
problem Generalizing Lusztig's total positivity to the setting of general real semisimple Lie groups.
method Classifying Lie groups admitting a positive structure and establishing key properties of unipotent positive semigroups.
result Establishes key geometric properties of elements in the positive semigroup.
Extends Perelman's theorem to positive intermediate curvature conditions.
problem Positive intermediate curvature conditions and their implications.
method Generalization of Perelman's gluing theorem to positive intermediate curvature conditions.
result Observer moduli space can have non-trivial higher homotopy groups.
An oriented link is positive if it has a link diagram whose crossings are all positive. An oriented link is almost positive if it is not positive and has a link diagram with exactly one negative crossing. It is known that the Rasmussen invariant, 4-genus and 3-genus of a positive knot are equal. In this paper, we p…
Satellite links of fully positive braids are characterized.
problem Characterizing satellite links of fully positive braids.
method Analyzing fully positive braids and their satellites.
result Satellite links of fully positive braids are characterized by specific conditions.
New Θ-positive representations of surface groups discovered.
problem Generalizing Lusztig's total positivity to surface groups.
method Introducing Θ-positivity and proving properties of Θ-positive representations. result Discrete and faithful Θ-positive representations exist and form open sets in representation varieties. Workshop notes on positivity in Lie groups and its applications.
problem Understanding total and Θ-positivity in semisimple Lie groups. method Discussion and analysis of existing theories and recent developments.
result Progress in classifying higher Teichmüller spaces through Θ-positivity.