Supervised Contrastive Learning on Blended Images for Long-tailed Recognition

Real-world data often have a long-tailed distribution, where the number of samples per class is not equal over training classes. The imbalanced data form a biased feature space, which deteriorates the performance of the recognition model. In this paper, we propose a novel long-tailed recognition method to balance the latent feature space. First, we introduce a MixUp-based data augmentation technique to reduce the bias of the long-tailed data. Furthermore, we propose a new supervised contrastive learning method, named Supervised contrastive learning on Mixed Classes (SMC), for blended images. SMC creates a set of positives based on the class labels of the original images. The combination ratio of positives weights the positives in the training loss. SMC with the class-mixture-based loss explores more diverse data space, enhancing the generalization capability of the model. Extensive experiments on various benchmarks show the effectiveness of our one-stage training method

Refractive Spatialisation: The Digital Picturesque, the Online-Reality Gap and Gentrification in Seoul

Building on the work of Henri Lefebvre, this research introduces the concept of refractive spatialisation in describing and articulating the deep interconnections between urbanisation, online space, social practices, and the relational (re)making of place-images as a result of technological innovations in information, communications and vehicles. The concept is coined in reference to the process through which symbolic space (online blog spaces) and physical space (the built environment) become co-dependent and co-generative in terms of rapid urban transformations driven by the touristification of previously mundane spaces. In relation to Seoul, South Korea these processes are shown to alter the built environment and drive processes of gentrification in tandem with state-led infrastructure projects: Airport Railroad Express (AREX) and Gyeongui Line Forest Park (GLFP). Focusing on Yeonnam- dong (total population of 15,769), a neighbourhood of Seoul, the research shows how urban regeneration, the consumption tastes of competitive young urbanites, and the representation and rearrangement of place-images online (led by online influencers) interact in the re- making of place. This involves a transformation in the place-image of Yeonnam-dong from an everyday “hidden”, working-class neighbourhood to an Instagrammable space produced and re-valued in relation to other places. The thesis analyses urban regeneration efforts in Yeonnam-dong from 2010-2018, online blog data (2,425 posts)) over the same period, and qualitative interviews with 42 interviewees from six categories: 1) 20-30s millennials; 2) Business owners and artists; 3) Property experts and local agencies; 4) Blog influencers; 5) Local residents; and 6) Seoul Metropolitan Government officials. The research articulates a new process of the uneven production of contemporary urban space influenced by the reconfiguration of spatial characteristics, property values, economic systems, social practices, and online imagery and preferences, based on the symbolic and information economy in post-industrial cities. In the new process of gentrifying Yeonnam-dong, refractive spatialisation functions as a key driver for interconnecting spatial change with new practices (aesthetic tourism and the digital picturesque), flows (selective online data of translocal representations), and logic (capitalist dynamics of aesthetic and hyper-realistic space triggered by the gap between online images and physical spaces – the online-reality gap)

Knowledge-Augmented Language Model Verification

Recent Language Models (LMs) have shown impressive capabilities in generating texts with the knowledge internalized in parameters. Yet, LMs often generate the factually incorrect responses to the given queries, since their knowledge may be inaccurate, incomplete, and outdated. To address this problem, previous works propose to augment LMs with the knowledge retrieved from an external knowledge source. However, such approaches often show suboptimal text generation performance due to two reasons: 1) the model may fail to retrieve the knowledge relevant to the given query, or 2) the model may not faithfully reflect the retrieved knowledge in the generated text. To overcome these, we propose to verify the output and the knowledge of the knowledge-augmented LMs with a separate verifier, which is a small LM that is trained to detect those two types of errors through instruction-finetuning. Then, when the verifier recognizes an error, we can rectify it by either retrieving new knowledge or generating new text. Further, we use an ensemble of the outputs from different instructions with a single verifier to enhance the reliability of the verification processes. We validate the effectiveness of the proposed verification steps on multiple question answering benchmarks, whose results show that the proposed verifier effectively identifies retrieval and generation errors, allowing LMs to provide more factually correct outputs. Our code is available at https://github.com/JinheonBaek/KALMV.Comment: EMNLP 202

Structural, Magnetic, Magnetocaloric, and Magnetostrictive Properties of Pb_1-xSr_xMnBO₄(x = 0, 0.5, and 1.0)

The solid solution Pb1-xSrxMnBO4 is reported with an orthorhombic, Pnma, structure throughout; here studies on compounds with x = 0, 0.5 and 1 are described. The structure contains chains of MnO6 octahedra that exhibit intra-chain ferromagnetic (FM) order at low temperatures. Neutron powder diffraction (NPD) reveals dominant FM order in PbMnBO4 (Tc = 30 K), whereas SrMnBO4 is primarily antiferromagnetic (AFM) with TN = 16 K; the difference is related to the link between the chains that involves the BO3 groups. PbMnBO4 has its moment along a but also has a previously unreported AFM contribution along c (magnetic space group Pnm'a'), whereas SrMnBO4 has its moment along a but also a FM canting along c (magnetic space group Pn'm'a). The end members show distinct magnetostriction at Tc/TN which correlates with the different magnetic exchange in these compounds. NPD in variable applied magnetic field shows that SrMnBO4 is converted to fully FM at 8 T. The behavior above the magnetic ordering temperature is consistent with short-range FM correlations within the chains, which is particularly apparent in PbMnBO4. The magnetocaloric effect (MCE) has been measured and compared with those previously reported for the mineral gaudefroyite. PbMnBO4 has excellent MCE behavior, especially near Tc, 30 K. The strong FM exchange within the chains and FM correlations above Tc are vital for the MC properties