595,841 research outputs found
A Subband-Selective Broadband GSC with Cosine-Modulated Blocking Matrix
Get PDFIn this paper, a novel subband-selective generalized sidelobe canceller (GSC) for partially adaptive broadband beamforming is proposed. The columns of the blocking matrix are derived from a prototype vector by cosine-modulation, and the broadside constraint is incorporated by imposing zeros on the prototype vector appropriately. These columns constitute a series of bandpass filters, which select signals with specific angles of arrival and frequencies. This results in highpass-type bandlimited spectra of the blocking matrix outputs, which is further exploited by subbands decomposition and suitably discarding the low-pass subbands prior to running independent unconstrained adaptive filters in each non-redundant subband. By these steps, the computational complexity of a GSC implementation is greatly reduced compared to fully adaptive GSC schemes, while performance is comparable or even enhanced due to subband decorrelation in both spatial and temporal domains
New Liu Estimators for the Poisson Regression Model: Method and Application
Get PDFA new shrinkage estimator for the Poisson model is introduced in this paper. This method is a generalization of the Liu (1993) estimator originally developed for the linear regression model and will be generalised here to be used instead of the classical maximum likelihood (ML) method in the presence of multicollinearity since the mean squared error (MSE) of ML becomes inflated in that situation. Furthermore, this paper derives the optimal value of the shrinkage parameter and based on this value some methods of how the shrinkage parameter should be estimated are suggested. Using Monte Carlo simulation where the MSE and mean absolute error (MAE) are calculated it is shown that when the Liu estimator is applied with these proposed estimators of the shrinkage parameter it always outperforms the ML. Finally, an empirical application has been considered to illustrate the usefulness of the new Liu estimators.Estimation; MSE; MAE; Multicollinearity; Poisson; Liu; Simulation
Time-resolved Systems Immunology Reveals a Late Juncture Linked to Fatal COVID-19. Liu et al
No full textSupplemental Tables 1-7 for the paper: Can Liu, Andrew J. Martins, William W. Lau, Nicholas Rachmaninoff, ..., John S. Tsang. (2021). "Time-resolved Systems Immunology Reveals a Late Juncture Linked to Fatal COVID-19." Cell. In Press
Test Make Sense?
No full textCorresponding author Changyu Liu should be listed as the first corresponding author.No Full Tex
Letter to 'Cozen' Richard Meyler, Ballance Street, Bristol, from Corne[liu]s Davies, Haverfordwest [Wales]
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/235345Re: Settlement of a bill.116405
Sub-Item: [1980.0075.00359] "Letter to 'Cozen' Richard Meyler, Ballance Street, Bristol, from Corne[liu]s Davies, Haverfordwest [Wales]
Toward scalable and unbiased scene graph generation : active learning and causal inference perspectives
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Scene Graph Generation (SGG) aims to construct structured representations of visual scenes by identifying objects and their pairwise relationships. Despite significant progress, SGG remains challenged by two core issues: the high cost of triplet-level annotations and the persistent biases in relationship prediction. This thesis addresses both challenges through two complementary research threads—label-efficient SGG and bias-mitigated SGG.
In the first thread, we introduce EDAL, a novel active learning framework that integrates evidential uncertainty estimation with diversity-aware sample selection. EDAL enables SGG models to achieve competitive performance using only 10\% of labeled data, dramatically reducing annotation costs while preserving generalization. In the second thread, we investigate the origin of biased predictions in SGG, which are often caused by long-tailed distributions, semantic confusion, and spurious correlations. To this end, we present a series of causal debiasing methods—TsCM, CAModule, and RcSGG—that leverage structural causal modeling, triplet-level logit adjustment, and reverse causal reasoning to systematically mitigate these biases.
Our methods are validated on multiple SGG benchmarks and backbones, achieving state-of-the-art performance on debiasing metrics while preserving overall accuracy. In addition, we highlight the potential of large-scale foundation models in future SGG research, given their success in improving generalization and alleviating annotation burdens in other vision tasks.
By unifying active learning and causal debiasing, this thesis offers a comprehensive framework for building scalable and fair SGG systems, opening new directions for structured visual understanding. Original papers Sun, S., Zhi, S., Heikkilä, J., & Liu, L. (2023). Evidential uncertainty and diversity guided active learning for scene graph generation. In ICLR 2023 : The Eleventh International Conference on Learning Representations. OpenReview.net. https://openreview.net/forum?id=xI1ZTtVOtlz Self-archived version Sun, S., Zhi, S., Liao, Q., Heikkilä, J., & Liu, L. (2023). Unbiased scene graph generation via two-stage causal modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10), 12562–12580. https://doi.org/10.1109/TPAMI.2023.3285009 https://doi.org/10.1109/TPAMI.2023.3285009 Self-archived version Liu, L., Sun, S., Zhi, S., Shi, F., Liu, Z., Heikkilä, J., & Liu, Y. (2025). A causal adjustment module for debiasing scene graph generation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(5), 4024–4043. https://doi.org/10.1109/TPAMI.2025.3537283 https://doi.org/10.1109/TPAMI.2025.3537283 Self-archived version Sun, S., Liu, L., Liu, T., Zhi, S., Cheng, M.-M., Heikkilä, J., & Liu, Y. (2025). A reverse causal framework to mitigate spurious correlations for debiasing scene graph generation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(9), 7470–7489. https://doi.org/10.1109/TPAMI.2025.3568644 https://doi.org/10.1109/TPAMI.2025.3568644 Self-archived version Tiivistelmä
Scene Graph Generation (SGG) pyrkii rakentamaan visuaalisista kohtauksista jäsenneltyjä esityksiä tunnistamalla objektit ja niiden väliset parisuhteet. Huolimatta merkittävästä edistyksestä alalla, SGG:tä vaivaavat edelleen kaksi keskeistä haastetta: kolmikkoanotointien korkeat kustannukset sekä pysyvät vinoumat suhdepäätelmissä. Tämä väitöskirja käsittelee molempia ongelmia kahden toisiaan täydentävän tutkimuslinjan kautta: tehokas annotointi (label-efficient SGG) ja vinoumien poistaminen (bias-mitigated SGG).
Ensimmäisessä tutkimuslinjassa esittelemme EDAL:n, uuden aktiiviseen oppimiseen perustuvan kehyksen, joka yhdistää todisteperusteisen epävarmuuden arvioinnin sekä monimuotoisuustietoisen näytevalinnan. EDAL mahdollistaa SGG-mallien kilpailukykyisen suorituskyvyn käyttämällä vain 10\% anotetuista tiedoista, mikä vähentää merkittävästi anotointikustannuksia ilman, että yleistettävyys kärsii. Toisessa tutkimuslinjassa tarkastelemme SGG:n vinoutuneiden ennusteiden alkuperää, jotka johtuvat usein pitkähäntäisistä jakaumista, semanttisesta sekaannuksesta ja näennäiskorrelaatioista. Esittelemme joukon kausaaliperustaisia vinoumanpoistomenetelmiä—TsCM, CAModule ja RcSGG—jotka hyödyntävät rakenteellista kausaalimallinnusta, triplatasoista logit-säätöä ja käänteistä kausaalipäättelyä näiden vinoumien systemaattiseen lievittämiseen.
Menetelmämme on validoitu useilla SGG-vertailuaineistoilla ja selkärankamalleilla, ja ne saavuttavat huipputason tuloksia vinoumametriikoissa samalla säilyttäen kokonaistarkkuuden. Lisäksi tuomme esiin suurten perusmallien (foundation models) potentiaalin tulevassa SGG-tutkimuksessa, erityisesti niiden yleistämiskyvyn ja anotointitarpeen keventämisen ansiosta muissa visuaalisissa tehtävissä.
Yhdistämällä aktiivisen oppimisen ja kausaalisen vinoumanpoiston tämä väitöskirja esittää kokonaisvaltaisen kehyksen skaalautuvien ja reilujen SGG-järjestelmien rakentamiseen, avaten uusia tutkimussuuntia jäsenneltyyn visuaaliseen ymmärrykseen. Osajulkaisut Sun, S., Zhi, S., Heikkilä, J., & Liu, L. (2023). Evidential uncertainty and diversity guided active learning for scene graph generation. In ICLR 2023 : The Eleventh International Conference on Learning Representations. OpenReview.net. https://openreview.net/forum?id=xI1ZTtVOtlz Rinnakkaistallennettu versio Sun, S., Zhi, S., Liao, Q., Heikkilä, J., & Liu, L. (2023). Unbiased scene graph generation via two-stage causal modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10), 12562–12580. https://doi.org/10.1109/TPAMI.2023.3285009 https://doi.org/10.1109/TPAMI.2023.3285009 Rinnakkaistallennettu versio Liu, L., Sun, S., Zhi, S., Shi, F., Liu, Z., Heikkilä, J., & Liu, Y. (2025). A causal adjustment module for debiasing scene graph generation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(5), 4024–4043. https://doi.org/10.1109/TPAMI.2025.3537283 https://doi.org/10.1109/TPAMI.2025.3537283 Rinnakkaistallennettu versio Sun, S., Liu, L., Liu, T., Zhi, S., Cheng, M.-M., Heikkilä, J., & Liu, Y. (2025). A reverse causal framework to mitigate spurious correlations for debiasing scene graph generation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(9), 7470–7489. https://doi.org/10.1109/TPAMI.2025.3568644 https://doi.org/10.1109/TPAMI.2025.3568644 Rinnakkaistallennettu versio Academic dissertation to be presented with the assent of the Doctoral Programme Committee of Information Technology and Electrical Engineering of the University of Oulu for public defence via remote access, on 27 November 2025, at 9 a.m.Abstract
Scene Graph Generation (SGG) aims to construct structured representations of visual scenes by identifying objects and their pairwise relationships. Despite significant progress, SGG remains challenged by two core issues: the high cost of triplet-level annotations and the persistent biases in relationship prediction. This thesis addresses both challenges through two complementary research threads—label-efficient SGG and bias-mitigated SGG.
In the first thread, we introduce EDAL, a novel active learning framework that integrates evidential uncertainty estimation with diversity-aware sample selection. EDAL enables SGG models to achieve competitive performance using only 10\% of labeled data, dramatically reducing annotation costs while preserving generalization. In the second thread, we investigate the origin of biased predictions in SGG, which are often caused by long-tailed distributions, semantic confusion, and spurious correlations. To this end, we present a series of causal debiasing methods—TsCM, CAModule, and RcSGG—that leverage structural causal modeling, triplet-level logit adjustment, and reverse causal reasoning to systematically mitigate these biases.
Our methods are validated on multiple SGG benchmarks and backbones, achieving state-of-the-art performance on debiasing metrics while preserving overall accuracy. In addition, we highlight the potential of large-scale foundation models in future SGG research, given their success in improving generalization and alleviating annotation burdens in other vision tasks.
By unifying active learning and causal debiasing, this thesis offers a comprehensive framework for building scalable and fair SGG systems, opening new directions for structured visual understanding.Tiivistelmä
Scene Graph Generation (SGG) pyrkii rakentamaan visuaalisista kohtauksista jäsenneltyjä esityksiä tunnistamalla objektit ja niiden väliset parisuhteet. Huolimatta merkittävästä edistyksestä alalla, SGG:tä vaivaavat edelleen kaksi keskeistä haastetta: kolmikkoanotointien korkeat kustannukset sekä pysyvät vinoumat suhdepäätelmissä. Tämä väitöskirja käsittelee molempia ongelmia kahden toisiaan täydentävän tutkimuslinjan kautta: tehokas annotointi (label-efficient SGG) ja vinoumien poistaminen (bias-mitigated SGG).
Ensimmäisessä tutkimuslinjassa esittelemme EDAL:n, uuden aktiiviseen oppimiseen perustuvan kehyksen, joka yhdistää todisteperusteisen epävarmuuden arvioinnin sekä monimuotoisuustietoisen näytevalinnan. EDAL mahdollistaa SGG-mallien kilpailukykyisen suorituskyvyn käyttämällä vain 10\% anotetuista tiedoista, mikä vähentää merkittävästi anotointikustannuksia ilman, että yleistettävyys kärsii. Toisessa tutkimuslinjassa tarkastelemme SGG:n vinoutuneiden ennusteiden alkuperää, jotka johtuvat usein pitkähäntäisistä jakaumista, semanttisesta sekaannuksesta ja näennäiskorrelaatioista. Esittelemme joukon kausaaliperustaisia vinoumanpoistomenetelmiä—TsCM, CAModule ja RcSGG—jotka hyödyntävät rakenteellista kausaalimallinnusta, triplatasoista logit-säätöä ja käänteistä kausaalipäättelyä näiden vinoumien systemaattiseen lievittämiseen.
Menetelmämme on validoitu useilla SGG-vertailuaineistoilla ja selkärankamalleilla, ja ne saavuttavat huipputason tuloksia vinoumametriikoissa samalla säilyttäen kokonaistarkkuuden. Lisäksi tuomme esiin suurten perusmallien (foundation models) potentiaalin tulevassa SGG-tutkimuksessa, erityisesti niiden yleistämiskyvyn ja anotointitarpeen keventämisen ansiosta muissa visuaalisissa tehtävissä.
Yhdistämällä aktiivisen oppimisen ja kausaalisen vinoumanpoiston tämä väitöskirja esittää kokonaisvaltaisen kehyksen skaalautuvien ja reilujen SGG-järjestelmien rakentamiseen, avaten uusia tutkimussuuntia jäsenneltyyn visuaaliseen ymmärrykseen
Difference based Ridge and Liu type Estimators in Semiparametric Regression Models
Get PDFWe consider a difference based ridge regression estimator and a Liu type estimator of the regression parameters in the partial linear semiparametric regression model, y = Xβ + f + ε. Both estimators are analysed and compared in the sense of mean-squared error. We consider the case of independent errors with equal variance and give conditions under which the proposed estimators are superior to the unbiased difference based estimation technique. We extend the results to account for heteroscedasticity and autocovariance in the error terms. Finally, we illustrate the performance of these estimators with an application to the determinants of electricity consumption in Germany.Difference based estimator; Differencing estimator, Differencing matrix, Liu estimator, Liu type estimator, Multicollinearity, Ridge regression estimator, Semiparametric model
Predator-prey model of Beddington-DeAngelis type with maturation and gestation delays
No full textA predator–prey model of Beddington–DeAngelis type with maturation and gestation delays is formulated and analyzed. This two-delay model is similar to the stage-structured model by Liu and Beretta [S. Liu, E. Beretta, Stage-structured predator–prey Model with the Beddington–DeAngelis functional response, SIAM J. Appl. Math. 66 (2006) 1101–1129] but contains an extra gestation delay term. Criteria for permanence and for predator extinction as well as the global attractiveness of the interior equilibrium are derived. The combined effects of the two delays and the degree of predator interference on the dynamical behaviors of the coexistence equilibrium are also studied both analytically and numerically. It is shown that complicated behaviors including chaotic and multi-periodic solutions may occur with the introduction of gestation delay, and that the predator interference can stabilize the system by simplifying the dynamical behaviors and enlarging the stability parameter fields
Neochauliodes punctatolosus Liu & Yang
No full textNeochauliodes punctatolosus Liu & Yang (Fig. 9) Neochauliodes punctatolosus Liu & Yang, 2006b: 193. Type locality: Yunnan (Jingdong). Diagnosis. Head and pronotum entirely orange. Forewing with dense small brownish spots, posterior two RP branches distinctly curved posteriad. Male fused gonocoxites 10 slender suboblong with distal portion broadened in lateral view. See an additional description and other information in Liu & Yang (2006b) and Liu et al. (2010a). Materials examined. 2♀, MYANMAR: N, 21 km E Putao, 550 m, Nan Sa Bon vill., 1-5.V.1998, S. Murzin & V. Siniaev (LDPC); 1♀, MYANMAR: N, 25 km E Putao, 800 m, env. Nan Sa Bon vill., 6-9.V.1998, S. Murzin & V. Siniaev (LDPC). Distribution. China (Yunnan); Laos (Houaphan, Louang Namtha, Vientiane, Xieng Khoang); Myanmar (Kachin); Thailand (Chanthaburi, Chiang Mai); Vietnam (Son La).Published as part of Liu, Xingyue & Dvorak, Libor, 2017, New species and records of Corydalidae (Insecta: Megaloptera) from Myanmar, pp. 428-436 in Zootaxa 4306 (3) on page 433, DOI: 10.11646/zootaxa.4306.3.9, http://zenodo.org/record/84454
1, 2-H shift in benzylchlorocarbene: isotope effect and influence of the solvent
No full textLaser flash photolysis of 3-chloro-3-benzyldiazirine and 3-chloro-3-(phenyldideuteriomethyl)diazirine in isooctane over the 60 to -80-degrees-C temperature range gives rise to curved Arrhenius plots for both 1,2-H and 1,2-D migration in benzylchlorcarbene. The k(H)/k(D) values increase smoothly from 0.87 to 2.62 when the temperature increases from -60 to +30-degrees-C. The k(H)/k(D) value is approximately 4 for most of the temperatures studied if a solvent correction is applied. Quantum mechanical tunnelling or the influence of the solvent may be a possible explanation for these observations.PT: J; CR: BONNEAU R, 1989, J AM CHEM SOC, V111, P5973 BONNEAU R, 1992, J PHOTOCH PHOTOBIO A, V68, P97 DIX EJ, 1993, J AM CHEM SOC, V115, P10424 EVANSECK JD, 1990, J PHYS CHEM-US, V94, P5518 GRAHAM WH, 1965, J AM CHEM SOC, V87, P4396 JACKSON JE, 1994, ADV CARBENE CHEM JONES M, 1980, REACTIVE INTERMEDIAT, V2 KIRMSE W, 1971, CARBENE CHEM LIU MTH, 1984, TETRAHEDRON, V40, P887 LIU MTH, 1990, J AM CHEM SOC, V112, P3915 LIU MTH, 1992, J PHOTOCH PHOTOBIO A, V63, P115 LIU MTH, 1992, J PHYS ORG CHEM, V15, P285 LIU MTH, 1994, RES CHEM INTERMEDIAT, V20, P195 MODARELLI DA, 1992, J AM CHEM SOC, V114, P7034 MOSS RA, 1992, TETRAHEDRON LETT, V33, P4287 MOSS RA, 1994, ADV CARBENE CHEM MUROV SL, 1973, HDB PHOTOCHEMISTRY NICKON A, 1993, ACCOUNTS CHEM RES, V26, P84 SALIS GA, 1968, J PHYS CHEM-US, V72, P752 SANDER W, 1994, UNPUB SCHAEFER HF, 1979, ACCOUNTS CHEM RES, V12, P288 SCHOLLER WW, 1989, HOUBEN WEYL METHODEN, P41 SHIMANOUCHI T, 1972, TABLES MOL VIBRATION, V1 SUGIYAMA MH, 1992, J AM CHEM SOC, V114, P966 WIERLACHER S, 1993, J AM CHEM SOC, V115, P8943; NR: 25; TC: 20; J9: J PHOTOCHEM PHOTOBIOL A-CHEM; PG: 5; GA: PV021Source type: Electronic(1
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