107 research outputs found
Multi-domain Recommendation with Embedding Disentangling and Domain Alignment
Multi-domain recommendation (MDR) aims to provide recommendations for
different domains (e.g., types of products) with overlapping users/items and is
common for platforms such as Amazon, Facebook, and LinkedIn that host multiple
services. Existing MDR models face two challenges: First, it is difficult to
disentangle knowledge that generalizes across domains (e.g., a user likes cheap
items) and knowledge specific to a single domain (e.g., a user likes blue
clothing but not blue cars). Second, they have limited ability to transfer
knowledge across domains with small overlaps. We propose a new MDR method named
EDDA with two key components, i.e., embedding disentangling recommender and
domain alignment, to tackle the two challenges respectively. In particular, the
embedding disentangling recommender separates both the model and embedding for
the inter-domain part and the intra-domain part, while most existing MDR
methods only focus on model-level disentangling. The domain alignment leverages
random walks from graph processing to identify similar user/item pairs from
different domains and encourages similar user/item pairs to have similar
embeddings, enhancing knowledge transfer. We compare EDDA with 12
state-of-the-art baselines on 3 real datasets. The results show that EDDA
consistently outperforms the baselines on all datasets and domains. All
datasets and codes are available at https://github.com/Stevenn9981/EDDA.Comment: Accepted by CIKM'23 as a Long pape
Debiasing Recommendation with Personal Popularity
Global popularity (GP) bias is the phenomenon that popular items are
recommended much more frequently than they should be, which goes against the
goal of providing personalized recommendations and harms user experience and
recommendation accuracy. Many methods have been proposed to reduce GP bias but
they fail to notice the fundamental problem of GP, i.e., it considers
popularity from a \textit{global} perspective of \textit{all users} and uses a
single set of popular items, and thus cannot capture the interests of
individual users. As such, we propose a user-aware version of item popularity
named \textit{personal popularity} (PP), which identifies different popular
items for each user by considering the users that share similar interests. As
PP models the preferences of individual users, it naturally helps to produce
personalized recommendations and mitigate GP bias. To integrate PP into
recommendation, we design a general \textit{personal popularity aware
counterfactual} (PPAC) framework, which adapts easily to existing
recommendation models. In particular, PPAC recognizes that PP and GP have both
direct and indirect effects on recommendations and controls direct effects with
counterfactual inference techniques for unbiased recommendations. All codes and
datasets are available at \url{https://github.com/Stevenn9981/PPAC}.Comment: Accepted by WWW'24 as a research full pape
Error-Mitigated Quantum Simulation of Interacting Fermions with Trapped Ions
Quantum error mitigation has been extensively explored to increase the
accuracy of the quantum circuits in noisy-intermediate-scale-quantum (NISQ)
computation, where quantum error correction requiring additional quantum
resources is not adopted. Among various error-mitigation schemes, probabilistic
error cancellation (PEC) has been proposed as a general and systematic protocol
that can be applied to numerous hardware platforms and quantum algorithms.
However, PEC has only been tested in two-qubit systems and a superconducting
multi-qubit system by learning a sparse error model. Here, we benchmark PEC
using up to four trapped-ion qubits. For the benchmark, we simulate the
dynamics of interacting fermions with or without spins by applying multiple
Trotter steps. By tomographically reconstructing the error model and
incorporating other mitigation methods such as positive probability and
symmetry constraints, we are able to increase the fidelity of simulation and
faithfully observe the dynamics of the Fermi-Hubbard model, including the
different behavior of charge and spin of fermions. Our demonstrations can be an
essential step for further extending systematic error-mitigation schemes toward
practical quantum advantages.Comment: 15 pages, 11 figure
Unveiling causal attention in dogs' eyes with smart eyewear
Our goals are to better understand dog cognition, and to support others who share this interest. Existing investigation methods predominantly rely on human-manipulated experiments to examine dogs’ behavioral responses to visual stimuli such as human gestures. As a result, existing experimental paradigms are usually constrained to in-lab environments and may not reveal the dog’s responses to real-world visual scenes. Moreover, visual signals pertaining to dog behavioral responses are empirically derived from observational evidence, which can be prone to subjective bias and may lead to controversies. We aim to overcome or reduce the existing limitations of dog cognition studies by investigating a challenging issue: identifying the visual signal(s) from dog eye motion that can be utilized to infer causal explanations of its behaviors, namely estimating causal attention. To this end, we design a deep learning framework named Causal AtteNtIon NEtwork (CANINE) to unveil the dogs’ causal attention mechanism, inspired by the recent advance in causality analysis with deep learning. Equipped with CANINE, we developed the first eyewear device to enable inference on the vision-related behavioral causality of canine wearers. We demonstrate the technical feasibility of the proposed CANINE glasses through their application in multiple representative experimental scenarios of dog cognitive study. Various in-field trials are also performed to demonstrate the generality of the CANINE eyewear in real-world scenarios. With the proposed CANINE glasses, we collect the first large-scale dataset, named DogsView, which consists of automatically generated annotations on the canine wearer’s causal attention across a wide range of representative scenarios. The DogsView dataset is available online to facilitate research
Sciences for The 2.5-meter Wide Field Survey Telescope (WFST)
The Wide Field Survey Telescope (WFST) is a dedicated photometric survey
facility under construction jointly by the University of Science and Technology
of China and Purple Mountain Observatory. It is equipped with a primary mirror
of 2.5m in diameter, an active optical system, and a mosaic CCD camera of 0.73
Gpix on the main focus plane to achieve high-quality imaging over a field of
view of 6.5 square degrees. The installation of WFST in the Lenghu observing
site is planned to happen in the summer of 2023, and the operation is scheduled
to commence within three months afterward. WFST will scan the northern sky in
four optical bands (u, g, r, and i) at cadences from hourly/daily to
semi-weekly in the deep high-cadence survey (DHS) and the wide field survey
(WFS) programs, respectively. WFS reaches a depth of 22.27, 23.32, 22.84, and
22.31 in AB magnitudes in a nominal 30-second exposure in the four bands during
a photometric night, respectively, enabling us to search tremendous amount of
transients in the low-z universe and systematically investigate the variability
of Galactic and extragalactic objects. Intranight 90s exposures as deep as 23
and 24 mag in u and g bands via DHS provide a unique opportunity to facilitate
explorations of energetic transients in demand for high sensitivity, including
the electromagnetic counterparts of gravitational-wave events detected by the
second/third-generation GW detectors, supernovae within a few hours of their
explosions, tidal disruption events and luminous fast optical transients even
beyond a redshift of 1. Meanwhile, the final 6-year co-added images,
anticipated to reach g about 25.5 mag in WFS or even deeper by 1.5 mag in DHS,
will be of significant value to general Galactic and extragalactic sciences.
The highly uniform legacy surveys of WFST will also serve as an indispensable
complement to those of LSST which monitors the southern sky.Comment: 46 pages, submitted to SCMP
31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two
Background
The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd.
Methods
We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background.
Results
First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001).
Conclusions
In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival
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