Towards Visual Syntactical Understanding

Syntax is usually studied in the realm of linguistics and refers to the arrangement of words in a sentence. Similarly, an image can be considered as a visual 'sentence', with the semantic parts of the image acting as 'words'. While visual syntactic understanding occurs naturally to humans, it is interesting to explore whether deep neural networks (DNNs) are equipped with such reasoning. To that end, we alter the syntax of natural images (e.g. swapping the eye and nose of a face), referred to as 'incorrect' images, to investigate the sensitivity of DNNs to such syntactic anomaly. Through our experiments, we discover an intriguing property of DNNs where we observe that state-of-the-art convolutional neural networks, as well as vision transformers, fail to discriminate between syntactically correct and incorrect images when trained on only correct ones. To counter this issue and enable visual syntactic understanding with DNNs, we propose a three-stage framework- (i) the 'words' (or the sub-features) in the image are detected, (ii) the detected words are sequentially masked and reconstructed using an autoencoder, (iii) the original and reconstructed parts are compared at each location to determine syntactic correctness. The reconstruction module is trained with BERT-like masked autoencoding for images, with the motivation to leverage language model inspired training to better capture the syntax. Note, our proposed approach is unsupervised in the sense that the incorrect images are only used during testing and the correct versus incorrect labels are never used for training. We perform experiments on CelebA, and AFHQ datasets and obtain classification accuracy of 92.10%, and 90.89%, respectively. Notably, the approach generalizes well to ImageNet samples which share common classes with CelebA and AFHQ without explicitly training on them

Segmented Recurrent Transformer: An Efficient Sequence-to-Sequence Model

Transformers have shown dominant performance across a range of domains including language and vision. However, their computational cost grows quadratically with the sequence length, making their usage prohibitive for resource-constrained applications. To counter this, our approach is to divide the whole sequence into segments and apply attention to the individual segments. We propose a segmented recurrent transformer (SRformer) that combines segmented (local) attention with recurrent attention. The loss caused by reducing the attention window length is compensated by aggregating information across segments with recurrent attention. SRformer leverages Recurrent Accumulate-and-Fire (RAF) neurons' inherent memory to update the cumulative product of keys and values. The segmented attention and lightweight RAF neurons ensure the efficiency of the proposed transformer. Such an approach leads to models with sequential processing capability at a lower computation/memory cost. We apply the proposed method to T5 and BART transformers. The modified models are tested on summarization datasets including CNN-dailymail, XSUM, ArXiv, and MediaSUM. Notably, using segmented inputs of varied sizes, the proposed model achieves $6-22\%$ higher ROUGE1 scores than a segmented transformer and outperforms other recurrent transformer approaches. Furthermore, compared to full attention, the proposed model reduces the computational complexity of cross attention by around $40\%$.Comment: EMNLP 2023 Finding

Learning from the Best: Active Learning for Wireless Communications

Collecting an over-the-air wireless communications training dataset for deep learning-based communication tasks is relatively simple. However, labeling the dataset requires expert involvement and domain knowledge, may involve private intellectual properties, and is often computationally and financially expensive. Active learning is an emerging area of research in machine learning that aims to reduce the labeling overhead without accuracy degradation. Active learning algorithms identify the most critical and informative samples in an unlabeled dataset and label only those samples, instead of the complete set. In this paper, we introduce active learning for deep learning applications in wireless communications, and present its different categories. We present a case study of deep learning-based mmWave beam selection, where labeling is performed by a compute-intensive algorithm based on exhaustive search. We evaluate the performance of different active learning algorithms on a publicly available multi-modal dataset with different modalities including image and LiDAR. Our results show that using an active learning algorithm for class-imbalanced datasets can reduce labeling overhead by up to 50% for this dataset while maintaining the same accuracy as classical training

Kooperativna evolucija za kvalitetno pružanje usluga u paradigmi Interneta stvari

To facilitate the automation process in the Internet of Things, the research issue of distinguishing prospective services out of many “similar” services, and identifying needed services w.r.t the criteria of Quality of Service (QoS), becomes very important. To address this aim, we propose heuristic optimization, as a robust and efficient approach for solving complex real world problems. Accordingly, this paper devises a cooperative evolution approach for service composition under the restrictions of QoS. A series of effective strategies are presented for this problem, which include an enhanced local best first strategy and a global best strategy that introduces perturbations. Simulation traces collected from real measurements are used for evaluating the proposed algorithms under different service composition scales that indicate that the proposed cooperative evolution approach conducts highly efficient search with stability and rapid convergence. The proposed algorithm also makes a well-designed trade-off between the population diversity and the selection pressure when the service compositions occur on a large scale.Kako bi se automatizirali procesi u internetu stvati, nužno je rezlikovati bitne usluge u moru sličnih kao i identificirati potrebne usluge u pogledu kvalitete usluge (QoS). Kako bi doskočili ovome problemu prdlaže se heuristička optimizacija kao robustan i efikasan način rješavajne kompleksnih problema. Nadalje, u članku je predložen postupak kooperativne evolucije za slaganje usluga uz ograničenja u pogledu kvalutete usluge. Predstavljen je niz efektivnih strategija za spomenuti problem uključujući strategije najboljeg prvog i najboljeg globalnog koje unose perturbacije u polazni problem. Simulacijski rezultati kao i stvarni podatci su korišteni u svrhu evaluacije prodloženog algoritma kako bi se osigurala efikasna pretraga uz stabilnost i brzu konvergenciju. Predloženi algoritam tako.er vodi računa o odnosu izme.u različitosti populacije i selekcijskog pritiska kada je potrebno osigurati slaganje usluga na velikoj skali

To Sense or to Transmit: A Learning-Based Spectrum Management Scheme for Cognitive Radiomesh Networks

Abstract—Wireless mesh networks, composed of interconnected clusters of mesh router (MR) and multiple associated mesh clients (MCs), may use cognitive radio equipped transceivers, allowing them to choose licensed frequencies for high bandwidth communication. However, the protection of the licensed users in these bands is a key constraint. In this paper, we propose a reinforcement learning based approach that allows each mesh cluster to independently decide the operative channel, the durations for spectrum sensing, the time of switching, and the duration for which the data transmission happens. The contributions made in this paper are threefold. First, based on accumulated rewards for a channel mapped to the link transmission delays, and the estimated licensed user activity, the MRs assign a weight to each of the channels, thereby selecting the channel with highest performance for MCs operations. Second, our algorithm allows dynamic selection of the sensing time interval that optimizes the link throughput. Third, by cooperative sharing, we allow the MRs to share their channel table information, thus allowing a more accurate learning model. Simulations results reveal significant improvement over classical schemes which have pre-set sensing and transmission durations in the absence of learning. I

Origin of Ferroelectricity in Orthorhombic LuFeO3_3

We demonstrate that small but finite ferroelectric polarization ($\sim$0.01 $\mu$C/cm$^2$) emerges in orthorhombic LuFeO$_3$ ($Pnma$) at $T_N$ ($\sim$600 K) because of commensurate (k = 0) and collinear magnetic structure. The synchrotron x-ray and neutron diffraction data suggest that the polarization could originate from enhanced bond covalency together with subtle contribution from lattice. The theoretical calculations indicate enhancement of bond covalency as well as the possibility of structural transition to the polar $Pna2_1$ phase below $T_N$. The $Pna2_1$ phase, in fact, is found to be energetically favorable below $T_N$ in orthorhombic LuFeO$_3$ ($albeit$ with very small energy difference) than in isostructural and nonferroelectric LaFeO$_3$ or NdFeO$_3$. Application of electric field induces finite piezostriction in LuFeO$_3$ via electrostriction resulting in clear domain contrast images in piezoresponse force microscopy.Comment: 12 pages, 8 figure