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Multimodal imaging has attracted tremendous attention in biotechnology and nanomedicine applications. Monoclinic Gd2O3:Dy3+ nanoparticles (NPs) with dual-color fluorescence imaging (FI) and magnetic resonance imaging (MRI) have been successfully synthesized by employing laser ablation in liquid (LAL) technique. The synthesized nanoparticles can be used as yellow fluorescence nanoprobe as well as blue ones due to their sharp and strong yellow and blue emissions. Magnetic resonance imaging (MRI) measurement shows that the longitudinal relaxivity (r1) of the Gd2O3:1%Dy3+NPs is 4.5 times higher than that of the commercial Gd-DTPA. The experiment of in vitro cell fluorescence imaging and magnetic resonance imaging of female Balb/c mouse indicates that the Gd2O3:Dy3+ NPs synthesized by LAL technique are promising candidates of dual-color FI nanoprobe and MRI contrast agent in further clinical applications.

The past few years in the fields of Person Re-Identification (RE-ID) have seen attention mechanism receives enormous interest as it has superior performance in obtaining discriminative feature representations. However, a wide range of state-of-the-art RE-ID attention models only focus on one-dimensional attention design method, e.g. spatial attention and channels attention, hence the produced attention maps are neither detailed enough nor discriminative enough to capture complicated interactions of visual parts. Developing multi-scale attention mechanism for RE-ID, an under-studied approach, becomes a practicable method to overcome this deficiency. Toward this goal, we propose a Multiscale Omnibearing Attention Networks (MOAN) for RE-ID which is capable of utilizing the complex fusion information acquired from the multiscale attention mechanism with features being more representative. Specifically, MOAN takes full advantage of multi-sized convolution filters to obtain discriminative holistic and local feature maps, and adaptively conducts feature information augmentation by introducing an Omnibearing Attention (OA) module. Through the OA module, spatial attention and channel attention are integrated together in a unique way where they work in a complementary way. To sum up, MOAN not only inherits the merit of two kinds of attention mechanism but also performs well in extracting comprehensive feature information. Furthermore, taking into account the robustness of model performance, we formulate a Random Drop (RD) Function to facilitate training MOAN and further increase the diversity of training model for adaptation. Furthermore, to achieve end-to-end training, we utilize trainable parameters to take place of initial fixed parameters, and the model performance is experimentally promoted. Extensive experiments have been carried out on the four mainstream RE-ID datasets. As the result shows, our method with re-ranking achieves rank-1 accuracy of 92.29% on CUHK03-NP, 97.45% on Market-1501, 93.81% on DukeMTMC-reID and 81.53% on MSMT17-V2, outperforming the state-of-the-art methods and confirming the effectiveness of our method.

RGB‐Infrared person re‐identification (RGB‐IR Re‐ID) is a task aiming to retrieve and match person images between RGB images and IR images. Since most surveillance cameras capture RGB images during the day and IR images at night, RGB‐IR Re‐ID is helpful when checking day and night surveillance for criminal investigations. Previous related work often only extracts sharable and identity‐related features in images for identification. Few researches specifically extract and make use of features that do not have the ability to distinguish identity, e.g. identity‐unrelated features derived from background and modality. In this Letter, we propose a novel and concise RGB‐IR Re‐ID network named two‐way constraint network (TWCN). Compared with traditional Re‐ID networks, TWCN not only extracts and utilises identity‐related features but also novelly makes full use of identity‐unrelated features to improve the accuracy of the experiment. TWCN uses a reverse‐triplet loss to extract identity‐unrelated features, and proposes an orthogonal constraint to remove identity‐unrelated information from identity‐related features, which improves the purity of identity‐related features. In addition, a correlation coefficient synergy and central clustering (CCSCC) loss is introduced into TWCN to extract identity‐related features effectively. Extensive experiments have been conducted to prove our method is effective.

In this work, we propose a Three-Dimensional Transmissible Attention Network (3DTANet) for Person Re-Identification, which can transmit the attention information from layer to layer and attend to the person image from a three-dimensional perspective. Main contributions of the 3DTANet are: (i) A novel Transmissible Attention (TA) mechanism is introduced, which can transfer attention information between convolution layers. Different from traditional attention mechanism, not only can it convey accumulated attention information layer by layer but also guide the network to retain holistic attention information. (ii) We propose a Three-Dimension Attention (3DA) mechanism, which is capable of extracting a three-dimensional attention map. While previous researches on image attention mechanism extracts channel or spatial attention information separately, 3DA mechanism pays attention to channel and spatial information simultaneously, thereby making them play better complementary role in attention extraction. (iii) A new loss function named L2-norm Multi-labels Loss (L2ML) is applied to acquire higher recognition accuracy calculated by multi labels of same ID and corresponding feature representation. Quite different from the common loss functions, L2-norm Multi-labels Loss is specifically good at optimizing feature distance. In brief, 3DTANet gains two-fold benefit toward higher accuracy. For one thing, the attention information is informative and can be transmitted, feature being more representative. For another, our model is computationally lightweight and can be easily applied to real scenarios. We extensively conduct experiments on four Person Re-Identification benchmark datasets. Our model achieves rank-1 accuracy of 87.50% on CUHK03, 96.23% on Market-1501, 92.50% on DukeMTMC-reID and 76.60% on MSMT17-V2 respectively. The results confirm that the 3DTANet can extract more representative features and attain a higher recognition accuracy, outperforming the state-of-the-art methods.

Person Re-identification (ReID) aims to match people across non-overlapping camera views in a public space, which is usually regarded as an image retrieval problem to match query images with pedestrian images in the gallery. It is challenging since many difficulties exist such as pose misalignments, occlusions, similar appearance when detecting people. Existing researches on ReID mainly focus on two major problems: representation learning and metric learning. In this paper, we target at learning discriminative representations and make two contributions in total. ( $i$ ) We propose a novel architecture named Deeply Associative Two-stage Representations Learning (DATRL). It contains the global re-initialization stage and fully-perceptual classification stage employing two identical CNNs associatively at the same time. On the global stage, we take on the backbone of one deep CNN e.g., dozens of layers in the front of Resnet-50 as a normal re-initialization subnetwork. Meanwhile, we apply our own proposed 3D-transpose technique into the backbone of the other CNN to form the 3D-transpose re-initialization subnetwork. The fully-perceptual stage is actually made up of the leftover layers of the original CNNs. On this stage, we take both the global representations learned at multiple hierarchies and the local representations uniformly-partitioned on the highest conv-layer into consideration, and then optimizing them separately for classification. ( $ii$ ) We introduce a new joint loss function in which our proposed Labels Interval Extension loss (LIEL) and Group loss (GL) are combined to enhance the performance of gradient decent as well as increasing the distances between image features with different identities. We apply the above DATRL, LIEL and GL to ReID thus obtaining DATRL-ReID. Experimental results on four datasets CUHK03, Market-1501, DukeMTMC-reID and MSMT17-V2 demonstrate that DATRL-ReID shows excellent performance in improving recognition accuracy and is superior to state-of-the-art methods.

Heterogeneous cluster-based multi/many-core systems (e.g., ARM big.LITTLE, supporting dynamic voltage and frequency scaling (DVFS) at cluster level and dynamic power management (DPM) at core level) have attracted much attention to optimize energy on modern embedded systems. For concurrently executing applications on such a platform, this paper aims to study how to appropriately apply the three system configurations (mapping, DVFS, and DPM) to reduce both dynamic and static energy. To this end, this paper first formulates the dependence of the three system configurations on heterogeneous cluster-based systems as a 0–1 integrated linear programming (ILP) model, taking into account run-time configuration overheads (e.g., costs of DPM mode switching and task migration). Then, with the 0–1 ILP model, different run-time strategies (e.g., considering the three configurations in fully separate, partially separate, and holistic manners) are compared based on a hierarchical management structure and design-time prepared data. Experimental case studies offer insights into the effectiveness of different management strategies on different platform sizes (e.g., #cluster × #core, 2 × 4, 2 × 8, 4 × 4, 4 × 8), in terms of application migration, energy efficiency, resource efficiency, and complexity.

The mixtures of Gd2O3:Yb3+/Er3+ and Gd2O3:Yb3+/Ho3+ nanoparticles were successfully prepared via pulsed laser ablation in liquid followed by solution mixing. Under excitation of 980 nm diode laser, tunable color from green to red emission was achieved. Based on the thermal linked energy levels, the temperature sensitive upconversion emission was observed. The fluorescence intensity ratio (FIR) of I513–530 nm/I530–580 nm increased as the elevation of temperature. The absolute sensitivity and relative sensitivity were derived from temperature dependent FIR. The results show that the mixture of Gd2O3:Yb3+/Er3+ and Gd2O3:Yb3+/Ho3+ nanoparticles are not only potential candidates for multicolor upconversion luminescence but also promising optical materials for non-contact optical thermometry.

An output-capacitorless low-dropout regulator (OCL-LDO) using a dual-active feedback frequency compensation (DAFFC) scheme with both transient and stability enhancement has been presented in this paper. The DAFFC scheme consists of two parallel active feedback paths, which creates two pole-zero pairs to effectively enhance the stability and transient response for the proposed OCL-LDO. Compared to the conventional single-path active-feedback frequency compensation method, the proposed DAFFC technique has provided one more design freedom with one more active feedback loop deployed and has been proved to be capable of obtaining better compensation effects with the same capacitor budget. Besides, the induced extra ac currents by the two active feedback loops have also enhanced the transient response of the proposed OCL-LDO. To substantiate the proposed DAFFC, a telescopic cascode output stage for error amplifier, and two on-chip compensation capacitors (5 and 1 pF, respectively) are needed. The proposed OCL-LDO has been implemented in 65-nm CMOS technology and the active chip area is 0.0105 mm2. The output voltage is 0.8 V, and the minimum input voltage is 0.95 V at 100-mA loading current. The proposed OCL-LDO can work stably in a load range of 0 to 100 mA with 14-μA quiescent current.

RISC-V is an open source instruction set architecture (ISA) based on the principles of Reduced Instruction Set Computing (RISC). High-level synthesis (HLS) can automatically synthesize high-level specifications (such as in C or C++) into low-level RTL specifications for efficient implementation in application-specific integrated circuits (ASIC) or field programmable gate arrays (FPGA). This article proposes a method to implement CPU based on HLS. Based on the RISC-V architecture, the processor is designed using C language and synthesized into RTL code through HLS. This method greatly improve the speed of design and reduce the manpower required for design. In addition, by using Universal Verification Methodology (UVM) to build a verification platform to verify the RTL code, the synthesized RTL code is reliably and fully verified.

Attention mechanisms are widely used in re-identification (reID) tasks, but few attention-based architectures have considered integrating local features with their global dependencies, that is the previous works do not model the semantic interdependencies in both spatial dimension and channel dimension. Intuitively, for person feature representations, it is important to model the interdependencies between human body semantics. In this Letter, the authors proposed a dual semantic interdependencies attention module to capture semantic interdependencies in both spatial dimension and channel dimension simultaneously. Besides, they designed a deep supervision branch to directly guide the training of the attention modules and innovatively introduce a channels random dropping mechanism in the training phase to promote the attention modules to capture more discriminative information. Extensive experimental results show that the network merging the above strategies achieves state-of-the-art results on the mainstream reID data sets.

Keyword spotting (KWS) plays a crucial role in human–machine interactions involving smart devices. In recent years, temporal convolutional networks (TCNs) have performed outstandingly with less computational complexity, in comparison with classical convolutional neural network (CNN) methods. However, it remains challenging to achieve a trade-off between a small-footprint model and high accuracy for the edge deployment of the KWS system. In this article, we propose a small-footprint model based on a modified temporal efficient neural network (TENet) and a simplified mel-frequency cepstrum coefficient (MFCC) algorithm. With the batch-norm folding and int8 quantization of the network, our model achieves the accuracy of 95.36% on Google Speech Command Dataset (GSCD) with only 18 K parameters and 461 K multiplications. Furthermore, following a hardware/model co-design approach, we propose an optimized dataflow and a configurable hardware architecture for TENet inference. The proposed accelerator implemented on Xilinx zynq 7z020 achieves an energy efficiency of 25.6 GOPS/W and reduces the runtime by 3.1× compared with state-of-the-art work.

Recently, transformer has been applied to the image caption model, in which the convolutional neural network and the transformer encoder act as the image encoder of the model, and the transformer decoder acts as the decoder of the model. However, transformer may suffer from the interference of non-critical objects of a scene and meet with difficulty to fully capture image information due to its self-attention mechanism's dense characteristics. In this Letter, in order to address this issue, the authors propose a novel transformer model with decreasing attention gates and attention fusion module. Specifically, they firstly use attention gate to force transformer to overcome the interference of non-critical objects and capture objects information more efficiently via truncating all the attention weights that smaller than gate threshold. Secondly, through inheriting attentional matrix from the previous layer of each network layer, the attention fusion module enables each network layer to consider other objects without losing the most critical ones. Their method is evaluated using the benchmark Microsoft COCO dataset and achieves better performance compared to the state-of-the-art methods.

Collaborative representation based classification (CRC) has been widely used and shown good performance in face recognition (FR). Afterwards, hierarchical representation based classification has recently been proposed and aims to enhance the classification performance of the CRC method. However, these methods highly depend on the over-complete dictionary comprised of sufficient training samples, and cannot be directly applied for single-sample FR. In this study, the authors propose a novel CRC-based FR framework to address this issue, which is named hierarchical extended collaborative representation based classification (HECRC). Firstly, they integrate hierarchical representation based model with low-rank constrained variation dictionaries. Secondly, they select training samples that are the nearest neighbours of test images to obtain a more discriminative training dictionary, where an adaptive scheme is introduced to select proper samples automatically instead of setting a predefined number in traditional methods. Finally, the refined training dictionary and the learned variation dictionaries are jointly utilised to represent the test sample. Moreover, they combined the proposed HECRC with deep features to further improve the recognition rate. Experiments have been conducted on the AR and FERET datasets, and the results show that the proposed method has a substantial improvement over existing algorithms for single-sample FR.

Yb3+:Ho3+ co-doped Gd2O3 nanoparticles were successfully synthesized by pulsed laser ablation in water under different laser energy. The phase structure, morphology, crystallization and upconversion photoluminescence properties of obtained samples were investigated using X-Ray diffraction (XRD), transmission electron microscopy (TEM), selected area electron diffraction (SAED) and photoluminescence spectra. The mechanism of the upconversion process was discussed based on the energy level diagram and power dependent upconversion emission. Upconversion mechanisms and thermal effects caused by absorption of excitation laser were discussed. Temperature dependent green and red emissions of Yb3+:Ho3+ co-doped Gd2O3 nanoparticles under the excitation of 980 nm were investigated in the low temperature range of 130 K–280 K. Non-radiative decay rate theory was used to explain the difference of quenching rates of green and red emissions. A further study on temperature sensing properties based on fluorescence intensity ratio (FIR) of green and red emissions was carried out. The FIR as a function of temperature can be well fitted by the model based on the thermal quenching theory. The relative sensitivity reaches its maximum value of 0.804% K−1 at 216 K.

The large number of energy levels available to lanthanide ions makes the emission mechanism of lanthanide-doped luminescent materials extremely complicated. Herein, we focus on the discussion and analysis of luminescence manipulation in Gd2O3:Yb3+/Er3+ nanocrystals and systematically investigate ion-ion interaction–induced energy transfer in up- and downconversion processes to shed light on the manipulation mechanism, showing that the luminescence of the above nanocrystals can be controlled by varying the content of Yb3+. Based on Dexter's energy transfer theory and the fact that excitation of Er3+ ions results in the appearance of emission attributed to the 2F5/2→2F7/2 transition of Yb3+ in the near-infrared region (950–1100 nm), this behavior is ascribed to the occurrence of energy transfer from Er3+ to Yb3+.

Early compressor trees based on carry-save adders and single-column parallel counters show good performance in ASIC design, but do not adapt well to modern field-programmable gate arrays (FPGAs). Recently, compressor trees built from generalized parallel counters (GPCs) were synthesized on FPGAs to address this issue. Despite the improved timing performance of GPC-based compressor trees, area reduction is not as significant as delay, and can be further optimized. In this paper, we propose improved GPC mappings as well as new approaches for GPC cascading and binding for Xilinx FPGAs. With these improvements, we develop an integer linear programming (ILP) method for FPGA synthesis of GPC-based compressor trees that supports cascading and binding between GPCs. Experimental results show that the single-cycle compressor trees produced by the proposed ILP can reduce the average area by 42.40% compared with those generated by existing heuristic method, but are 13.16% slower; the pipelined compressor trees produced by the proposed ILP can reduce the average area by 33.43% at the cost of an average 14.35% decrease in maximum clock frequency compared with those obtained by existing heuristic method.

Recent advances in deep learning, especially deep convolutional neural networks, have led to great performance improvement over semantic segmentation systems. Unfortunately, training deep neural networks (DNNs) requires a humongous amount of labeled data, which is laborious and costly to collect and annotate. Thus, plenty of works have proposed an alternative solution to ease the training set creation by using synthetic data. However, models trained on these kinds of data usually under-perform on real images due to the well-known issue of domain shift. To address it, we propose a generative adversarial network (GAN)-based framework called category-level adversarial adaptation networks (CAA-Nets) for domain adaptation in the context of semantic segmentation. Considering semantic predictions that contain spatial and structure information of images, our idea is to make use of this character by imposing discriminators on the semantic predictions. Different from existing works, the proposed framework utilizes a category-level discriminator in the output space to shrink the gap between real and synthetic images. Similar to reinforcement learning, we take final results as a guide to update parameters in the right direction by use of the output-based discriminator. Moreover, to further enhance the performance, we construct an image-based generator and discriminator pair to distill the feature representations obtained by a DNN. Taking advantage of these modules, our model can achieve competitive performance compared with some existing methods. To showcase the generality and scalability of our approach, we evaluate the proposed method on the Cityscapes dataset by adapting from GTAV and SYNTHIA datasets, where the results demonstrate the effectiveness of our method.

In this paper, we propose a novel image inpainting framework that takes advantage of holistic and structure information of the broken input image. Different from the existing models that complete the broken pictures using the holistic features of the input, our method adopts Patch-generative adversarial networks (GANs) equipped with multi-scale discriminators and edge process function to extract holistic, structured features, and restore the damaged images. After pre-training our Patch-GANs, the proposed network encourages our generator to find the best encoding of the broken input images in the latent space using a combination of a reconstruction loss, an edge loss, and global and local guidance losses. Besides, the reconstruction and the global guidance losses ensure the pixel reliability of the generated images, and the remaining losses guarantee the contents consistency between the local and global parts. The qualitative and quantitative experiments on multiple public datasets show that our approach has the ability to produce more realistic images compared with some existing methods, demonstrating the effectiveness and superiority of our method.

In this paper, we propose a Resetting-Label Network based on Fast Group Loss for Person Re-Identification (RLFGL-ReID). The major challenge of Re-ID lies in how to preserve the similarity of the same person against large variations caused by complex background, different illuminations, and various view angles while discriminating different individuals. To address the above-mentioned problems, we propose the RLFGL-ReID that includes resetting-label (RL) and fast group loss (FGL). Two main contributions of our network are as follows. First, a new method, the resetting-label method, which resets the ID labels, is proposed for the Re-ID network. Resetting the ID labels of each pedestrian is beneficial in maximizing the inter-group distances between each people, achieving better performance on classifying different individuals. Second, a fast group loss, i.e., an advanced version of variance group loss (VGL), is proposed to simplify the training process and accelerate the loss computation. By doing so, the network can eliminate the restriction of inputting the whole group of data when training the network. To confirm the effectiveness of our method, we extensively conduct our method on several widely used person Re-ID benchmark datasets. As the result shows, our method achieves rank@1 accuracy of 98.38% on CUHK03, 95.46% on Market1501, and 91.2% on DukeMTMC-reID, outperforming the state-of-the-art methods and confirming the advantage of our method.

A high image-rejection in-phase/quadrature (IQ) modulator and a wideband demodulator with large dynamic range operating in W-band is implemented in 65-nm CMOS technology. The modulator demonstrates a flat conversion gain of 12.5±0.7dB, a minimum image-rejection ratio of 40 dBc and a maximum LO-to-RF leakage of 38 dB from 90 to 98 GHz. The conversion gain of the demodulator is digitally controlled from 15 to 46 dB with a noise figure from 13.5 to 11.5 dB and an input 1dB compression point (IP1dB) from −13 to −39 dBm. The modulator and the demodulator are synthesized by two on-chip single-pole-double-throw (SPDT) switches and one IF switch. The entire system occupies 1.4 mm2 chip area with a total power consumption of 165 mW.

Dualmodal contrast agents of rare earth doped gadolinium oxide (Gd

Limited by applicable force-detection techniques, imaging of electronical or magnetic properties on sub-10 nanometer scale with scanning force microscopy in ambient is challenging in experiment and...

In this Letter, the authors propose a novel attention mechanism combined with a classical generative adversarial network (GAN) model to improve the visual quality of generated samples. This novel attention model is named regional attention GAN. The proposed mechanism can build dependencies between the high‐level representations extracted from attention regions of real images and corresponding feature maps of the generative network. By modelling these dependencies, the generative network can be facilitated to learn feature mapping and fit the distribution of real data. They conduct extensive experiments on widely used datasets CIFAR‐10, STL‐10, and CelebA. The quantitative and qualitative performance improvement over state‐of‐the‐art methods demonstrates the validity of the proposed attention mechanism in improving the quality of generated images.

An unusual behaviour of a ring oscillator (RO) is described, namely the RF interference in the supply can lock the operational frequency of the RO. The lock is not an oscillator-level lock, but a gate-level lock. Transistor-level simulations are performed on various technologies. The locking phenomenon appears in all simulated cases and follows certain rules. Moreover, the model is also verified with an RO test board. In contrast to traditional injection locking, where the injection is applied to the signal pin of the oscillator, the locking behaviour introduced is caused by the injection in the supply. To the best of knowledge, this type of locking phenomenon has been reported and demonstrated for the first time.

Multimodal image-to-image translation is a class of vision and graphics problems where the goal is to learn a one-to-many mapping between the source domain and target domain. Given an image in the source domain, the model aims to produce as many diverse results as possible. It is an important and challenging problem in the task of image translation. To this end, recent works utilise Gaussian vectors to produce diverse results but with a small difference. It is because of the special probabilistic nature of Gaussian distribution. In this work, the authors propose linearly distributed latent codes instead of conventional Gaussian vectors, which control the style of generated images. Taking advantage of linear distribution, their model can produce much more diverse results and outperform the state-of-the-art baselines in terms of diversity. Qualitative and quantitative comparisons against baselines demonstrate the effectiveness and superiority of their method.

Periodic noise is one of the most common interference sources in the power distribution network of integrated circuits. It is well known that this noise can deleteriously affect timing margins and limit the circuit performance. This paper presents a theory on the response of ring oscillators in presence of periodic interference on the power supply. The theory links the cycle time of ring oscillators to the probability density function of the interference. Various waveforms of interference are studied, such as sinusoidal, triangular, and square. In addition, symmetrical and asymmetrical interference waveforms are also considered. Both transistor level HSPICE simulations and real measurements are performed to validate the theory. The obtained results are suitable with the model over a wide range of interference frequency. The valid range of the model, multi-tone interference, and methods to reduce the sensitivity of ring oscillators are also discussed.

The lateral resolutions of electrostatic force microscopy (EFM) are systematically simulated using the boundary element method, considering a bimetallic sample with surface potential inhomogeneitie...

One-chip measurements without modifying the physical structure of packaged integrated circuits such as field-programmable gate arrays (FPGA) is challenging. This paper proposes a sensor for detecting the radio frequency interference (RFI) on the supply inside the FPGA chip. The core of the sensor is a ring oscillator built with FPGA look-up tables. The paper proposes a model to predict the response of the ring oscillator to power supply RFI, and shows that the normalized frequency shift of the ring oscillator resulting from the interference is determined by the amplitude of the interference. This relationship is independent of the interference frequency and the size of the ring oscillator. To verify the model, simulations on transistor-level look-up tables of 130-nm and 40-nm technologies were performed. The simulation results matched well with the model. In addition to simulation, an FPGA test board was fabricated. Measurements of FPGA RFI response were performed and the results were consistent with the theoretical model. The effect of the interference on the ring oscillator provided a mechanism to detect the amplitude of the supply interference on the FPGA chip. The frequency of the ring oscillator was monitored through the supply pin by measuring the spectrum of the supply noise. The properties of the sensor, such as constant response in a wide frequency range, insensitiveness to the oscillator size, ease of implementation, and minimal modification requirement of the physical structure, made it suitable for performing on-chip FPGA measurements.

The development of upconversion nanoparticles (UCNs) for theranostics application is a new strategy toward the accurate diagnosis and efficient treatment of cancer. Here, magnetic and fluorescent lanthanide-doped gadolinium oxide (Gd2O3) UCNs with bright upconversion luminescence (UCL) and high longitudinal relaxivity (r1) are used for simultaneous magnetic resonance imaging (MRI)/UCL dual-modal imaging and photodynamic therapy (PDT). In vitro and in vivo MRI studies show that these products can serve as good MRI contrast agents. The bright upconversion luminescence of the products allows their use as fluorescence nanoprobes for live cells imaging. We also utilized the luminescence-emission capability of the UCNs for the activation of a photosensitizer to achieve significant PDT results. To the best of our knowledge, this study is the first use of lanthanide-doped Gd2O3 UCNs in a theranostics application. This investigation provides a useful platform for the development of Gd2O3-based UCNs for clinical diagnosis, treatment, and imaging-guided therapy of cancer.