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Ying Guo

Ying Guo appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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cond-mat.str-elquant-phComputer VisionMachine Learningmath.APInformation Theorymath.ITNeurons and CognitionApplicationsComputationComputational Complexitycond-mat.mes-hallcond-mat.supr-concs.CYData Structures and Algorithmseess.IVMethodologyMultimediaNeural and Evolutionary Computingnucl-ex

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Published work

25 published item(s)

preprint2026arXiv

HiDream-O1-Image: A Natively Unified Image Generative Foundation Model with Pixel-level Unified Transformer

The evolution of visual generative models has long been constrained by fragmented architectures relying on disjoint text encoders and external VAEs. In this report, we present HiDream-O1-Image, a natively unified generative foundation model via pixel-space Diffusion Transformer, that pioneers a paradigm shift from modular architectures to an end-to-end in-context visual generation engine. By mapping raw image pixels, text tokens, and task-specific conditions into a single shared token space, HiDream-O1-Image achieves a structural unification of multimodal inputs within an Unified Transformer (UiT) architecture. This native encoding paradigm eliminates the need for separate VAEs or disjoint pre-trained text encoders, allowing the model to treat diverse generation and editing tasks as a consistent in-context reasoning process. Extensive experiments show that HiDream-O1-Image excels across various generation tasks, including text-to-image generation, instruction-based editing, and subject-driven personalization. Notably, with only 8B parameters, HiDream-O1-Image (8B) achieves performance parity with or even surpasses established state-of-the-art models with significantly larger parameters (e.g., 27B Qwen-Image). Crucially, to validate the immense scalability of this paradigm, we successfully scale the architecture up to over 200B parameters. Experimental results demonstrate that this massive-scale version HiDream-O1-Image-Pro (200B+) unlocks unprecedented generative capabilities and superior performance, establishing new state-of-the-art benchmarks. Ultimately, HiDream-O1-Image highlights the immense potential of natively unified architectures and charts a highly scalable path toward next-generation multimodal AI.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Quantifying the Upper Limit of Backflash Attack in Quantum Key Distribution

Quantum key distribution (QKD) provides information-theoretic security grounded in the fundamental laws of physics. Nevertheless, practical imperfections can introduce side channels that expose QKD systems to quantum hacking, especially passive attacks that are inherently difficult to detect. In this study, we experimentally and theoretically investigate the upper limit of the backflash attack-a representative passive side-channel threat. Using a fully equipped fiber-based QKD receiver, we demonstrate the feasibility of the attack and reveal its limited capability in distinguishing quantum states. We further develop a theoretical framework to quantify the maximum distinguishability achievable by an eavesdropper, taking into account the broadband spectral nature of backflash photons. The analysis shows that Eve can extract effective key information from at most 95.7% of the backflash photons. Based on these findings, we evaluate the secure key rate of a decoy-state BB84 QKD system under backflash attack. Our results provide a quantitative assessment of the vulnerability of QKD systems to backflash emissions and offer a general methodology to evaluate the practical security of QKD systems.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Continuous-variable quantum key distribution network based on entangled states of optical frequency combs

Continuous-variable quantum key distribution (CVQKD) features a high key rate and compatibility with classical optical communication. Developing expandable and efficient CVQKD networks will promote the deployment of large-scale quantum communication networks in the future. This paper proposes a CVQKD network based on the entangled states of an optical frequency comb. This scheme generates Einstein-Podolsky-Rosen entangled states with a frequency comb structure through the process of a type-II optical parametric oscillator. By combining with the scheme of entanglement in the middle, a fully connected CVQKD network capable of distributing secret keys simultaneously can be formed. We analyze the security of the system in the asymptotic case. Simulation results show that under commendable controlling of system loss and noise, the proposed scheme is feasible for deploying a short-distance fully connected CVQKD network. Loss will be the main factor limiting the system's performance. The proposed scheme provides new ideas for a multi-user fully connected CVQKD network.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning

Brain networks characterize complex connectivities among brain regions as graph structures, which provide a powerful means to study brain connectomes. In recent years, graph neural networks have emerged as a prevalent paradigm of learning with structured data. However, most brain network datasets are limited in sample sizes due to the relatively high cost of data acquisition, which hinders the deep learning models from sufficient training. Inspired by meta-learning that learns new concepts fast with limited training examples, this paper studies data-efficient training strategies for analyzing brain connectomes in a cross-dataset setting. Specifically, we propose to meta-train the model on datasets of large sample sizes and transfer the knowledge to small datasets. In addition, we also explore two brain-network-oriented designs, including atlas transformation and adaptive task reweighing. Compared to other pre-training strategies, our meta-learning-based approach achieves higher and stabler performance, which demonstrates the effectiveness of our proposed solutions. The framework is also able to derive new insights regarding the similarities among datasets and diseases in a data-driven fashion.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation

Functional magnetic resonance imaging (fMRI) is one of the most common imaging modalities to investigate brain functions. Recent studies in neuroscience stress the great potential of functional brain networks constructed from fMRI data for clinical predictions. Traditional functional brain networks, however, are noisy and unaware of downstream prediction tasks, while also incompatible with the deep graph neural network (GNN) models. In order to fully unleash the power of GNNs in network-based fMRI analysis, we develop FBNETGEN, a task-aware and interpretable fMRI analysis framework via deep brain network generation. In particular, we formulate (1) prominent region of interest (ROI) features extraction, (2) brain networks generation, and (3) clinical predictions with GNNs, in an end-to-end trainable model under the guidance of particular prediction tasks. Along with the process, the key novel component is the graph generator which learns to transform raw time-series features into task-oriented brain networks. Our learnable graphs also provide unique interpretations by highlighting prediction-related brain regions. Comprehensive experiments on two datasets, i.e., the recently released and currently largest publicly available fMRI dataset Adolescent Brain Cognitive Development (ABCD), and the widely-used fMRI dataset PNC, prove the superior effectiveness and interpretability of FBNETGEN. The implementation is available at https://github.com/Wayfear/FBNETGEN.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Identifying Critical LMS Features for Predicting At-risk Students

Learning management systems (LMSs) have become essential in higher education and play an important role in helping educational institutions to promote student success. Traditionally, LMSs have been used by postsecondary institutions in administration, reporting, and delivery of educational content. In this paper, we present an additional use of LMS by using its data logs to perform data-analytics and identify academically at-risk students. The data-driven insights would allow educational institutions and educators to develop and implement pedagogical interventions targeting academically at-risk students. We used anonymized data logs created by Brightspace LMS during fall 2019, spring 2020, and fall 2020 semesters at our college. Supervised machine learning algorithms were used to predict the final course performance of students, and several algorithms were found to perform well with accuracy above 90%. SHAP value method was used to assess the relative importance of features used in the predictive models. Unsupervised learning was also used to group students into different clusters based on the similarities in their interaction/involvement with LMS. In both of supervised and unsupervised learning, we identified two most-important features (Number_Of_Assignment_Submissions and Content_Completed). More importantly, our study lays a foundation and provides a framework for developing a real-time data analytics metric that may be incorporated into a LMS.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Improving Adversarial Transferability with Gradient Refining

Deep neural networks are vulnerable to adversarial examples, which are crafted by adding human-imperceptible perturbations to original images. Most existing adversarial attack methods achieve nearly 100% attack success rates under the white-box setting, but only achieve relatively low attack success rates under the black-box setting. To improve the transferability of adversarial examples for the black-box setting, several methods have been proposed, e.g., input diversity, translation-invariant attack, and momentum-based attack. In this paper, we propose a method named Gradient Refining, which can further improve the adversarial transferability by correcting useless gradients introduced by input diversity through multiple transformations. Our method is generally applicable to many gradient-based attack methods combined with input diversity. Extensive experiments are conducted on the ImageNet dataset and our method can achieve an average transfer success rate of 82.07% for three different models under single-model setting, which outperforms the other state-of-the-art methods by a large margin of 6.0% averagely. And we have applied the proposed method to the competition CVPR 2021 Unrestricted Adversarial Attacks on ImageNet organized by Alibaba and won the second place in attack success rates among 1558 teams.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Multi-Forgery Detection Challenge 2022: Push the Frontier of Unconstrained and Diverse Forgery Detection

In this paper, we present the Multi-Forgery Detection Challenge held concurrently with the IEEE Computer Society Workshop on Biometrics at CVPR 2022. Our Multi-Forgery Detection Challenge aims to detect automatic image manipulations including but not limited to image editing, image synthesis, image generation, image photoshop, etc. Our challenge has attracted 674 teams from all over the world, with about 2000 valid result submission counts. We invited the Top 10 teams to present their solutions to the challenge, from which three teams are awarded prizes in the grand finale. In this paper, we present the solutions from the Top 3 teams, in order to boost the research work in the field of image forgery detection.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Enhancing discrete-modulated continuous-variable measurement-device-independent quantum key distribution via quantum catalysis

The discrete modulation can make up for the shortage of transmission distance in measurement-device-independent continuous-variable quantum key distribution (MDI-CVQKD) that has an unique advantage against all side-channel attacks but also challenging for the further performance improvement. Here we suggest a quantum catalysis (QC) approach for enhancing the performance of the discrete-modulated (DM) MDI-CVQKD in terms of the achievable secret key rate and lengthening the maximal transmission distance. The numerical simulation results show that the QC-based MDI-CVQKD with discrete modulation that involves a zero-photon catalysis (ZPC) operation can not only obtain a higher secret key rate than the original DM protocol, but also contributes to the reasonable increase of the corresponding optimal variance. As for the extreme asymmetric and symmetric cases, the secret key rate and maximal transmission distance of the ZPC-involved DM MDI-CVQKD system can be further improved under the same parameters. This approach enables the system to tolerate lower reconciliation efficiency, which will promote the practical implementations with state-of-art technology.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

HINT: A Hierarchical Independent Component Analysis Toolbox for Investigating Brain Functional Networks using Neuroimaging Data

Independent component analysis (ICA) is a popular tool for investigating brain organization in neuroscience research. In fMRI studies, an important goal is to study how brain networks are modulated by subjects' clinical and demographic variables. Existing ICA methods and toolboxes don't incorporate subjects' covariates effects in ICA estimation of brain networks, which potentially leads to loss in accuracy and statistical power in detecting brain network differences between subjects' groups. We introduce a Matlab toolbox, HINT (Hierarchical INdependent component analysis Toolbox), that provides a hierarchical covariate-adjusted ICA (hc-ICA) for modeling and testing covariate effects and generates model-based estimates of brain networks on both the population- and individual-level. HINT provides a user-friendly Matlab GUI that allows users to easily load images, specify covariate effects, monitor model estimation via an EM algorithm, specify hypothesis tests, and visualize results. HINT also has a command line interface which allows users to conveniently run and reproduce the analysis with a script. HINT implements a new multi-level probabilistic ICA model for group ICA. It provides a statistically principled ICA modeling framework for investigating covariate effects on brain networks. HINT can also generate and visualize model-based network estimates for user-specified subject groups, which greatly facilitates group comparisons.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Linear-Time Parameterized Algorithms with Limited Local Resources

We propose a new (theoretical) computational model for the study of massive data processing with limited computational resources. Our model measures the complexity of reading the very large data sets in terms of the data size N and analyzes the computational cost in terms of a parameter k that characterizes the computational power provided by limited local computing resources. We develop new algorithmic techniques that implement algorithms for solving well-known computational problems on the proposed model. In particular, we present an algorithm that finds a k-matching in a general unweighted graph in time O(N + k^{2.5}) and an algorithm that constructs a maximum weighted k-matching in a general weighted graph in time O(N + k^3 log k). Both algorithms have their space complexity bounded by O(k^2).

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Multi-label learning for improving discretely-modulated continuous-variable quantum key distribution

Discretely-modulated continuous-variable quantum key distribution (CVQKD) is more suitable for long-distance transmission compared with its Gaussian-modulated CVQKD counterpart. However, its security can only be guaranteed when modulation variance is very small, which limits its further development. To solve this problem, in this work, we propose a novel scheme for discretely-modulated CVQKD using multi-label learning technology, called multi-label learning-based CVQKD (ML-CVQKD). In particular, the proposed scheme divides the whole quantum system into state learning and state prediction. The former is used for training and estimating quantum classifier, and the latter is used for generating final secret key. A quantum multi-label classification (QMLC) algorithm is also designed as an embedded classifier for distinguishing coherent state. Feature extraction for coherent state and related machine learning-based metrics for the quantum classifier are successively suggested. Security analysis shows that QMLC-embedded ML-CVQKD is able to immune intercept-resend attack so that small modulation variance is no longer compulsively required, thereby improving the performance of discretely-modulated CVQKD system.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Continuous-variable measurement-device-independent multipartite quantum communication

A continuous variable measurement device independent multi-party quantum communication protocol is investigated in this paper. Utilizing distributed continuous variable Greenberger-Horne-Zeilinger state, this protocol can implement both quantum cryptographic conference and quantum secret sharing. We analyze the security of the protocol against both entangling cloner attack and coherent attack. Entangling cloner attack is a practical individual attack, and coherent attack is the optimal attack Eve can implement. Simulation results show that coherent attack can greatly reduce the secret key rate. Different kinds of entangled attacks are compared and we finally discuss the optimal coherent attacks.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Modeling Covariate Effects in Group Independent Component Analysis with Applications to Functional Magnetic Resonance Imaging

Independent component analysis (ICA) is a powerful computational tool for separating independent source signals from their linear mixtures. ICA has been widely applied in neuroimaging studies to identify and characterize underlying brain functional networks. An important goal in such studies is to assess the effects of subjects' clinical and demographic covariates on the spatial distributions of the functional networks. Currently, covariate effects are not incorporated in existing group ICA decomposition methods. Hence, they can only be evaluated through ad-hoc approaches which may not be accurate in many cases. In this paper, we propose a hierarchical covariate ICA model that provides a formal statistical framework for estimating and testing covariate effects in ICA decomposition. A maximum likelihood method is proposed for estimating the covariate ICA model. We develop two expectation-maximization (EM) algorithms to obtain maximum likelihood estimates. The first is an exact EM algorithm, which has analytically tractable E-step and M-step. Additionally, we propose a subspace-based approximate EM, which can significantly reduce computational time while still retain high model-fitting accuracy. Furthermore, to test covariate effects on the functional networks, we develop a voxel-wise approximate inference procedure which eliminates the needs of computationally expensive covariance estimation. The performance of the proposed methods is evaluated via simulation studies. The application is illustrated through an fMRI study of Zen meditation.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Quality Atomic Resolution Scanning Tunneling Microscope Imaging up to 27 T in Water-cooled Magnet

We report the achievement of the first atomically resolved scanning tunneling microscope (STM) imaging in a water-cooled magnet (WM), where the extremely harsh vibrations and noises have been the major challenge. This homebuilt WM-STM features an ultra-rigid and compact scan head in which the coarse approach is driven by our new design of the TunaDrive piezoelectric motor. A three-level spring hanging system is exploited for vibration isolation. Room-temperature raw-data images of graphite with quality atomic resolution were obtained in very high magnetic fields up to 27 T in a 32 mm bore WM whose absolute maximum field is 27.5 T at the power rating of 10 MW. This record of 27 T has exceeded the maximum field strength of the conventional superconducting magnets. Besides, our WM-STM has also paved the way to the STM imaging in the 45 T, 32 mm bore hybrid magnet, which is the world's flagship magnet and can produces the highest steady magnetic field at present.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Concentration on Surfaces for a Singularly Perturbed Neumann Problem in Three-Dimensional Domains

We consider the following singularly perturbed elliptic problem $$ \varepsilon^2\triangle\tilde{u}-\tilde{u}+\tilde{u}^p=0, \ \tilde{u}>0\quad \mbox{in} \ Ω,\ \ \ \frac{\partial\tilde{u}}{\partial \mathbf{n}}=0 \quad \mbox{on}\ \partialΩ, $$ where $Ω$ is a bounded domain in $\mathbb{R}^3$ with smooth boundary, $\varepsilon$ is a small parameter, $\mathbf{n}$ denotes the inward normal of $ \partialΩ$ and the exponent $p>1$. Let $Γ$ be a hypersurface intersecting $\partialΩ$ in the right angle along its boundary $\partialΓ$ and satisfying a {\em non-degenerate condition}. We establish the existence of a solution $u_\varepsilon$ concentrating along a surface $\tildeΓ$ close to $Γ$, exponentially small in $\varepsilon$ at any positive distance from the surface $\tildeΓ$, provided $\varepsilon$ is small and away from certain {\em critical numbers}. The concentrating surface $\tildeΓ$ will collapse to $Γ$ as $\varepsilon\rightarrow 0$.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Interlayer magnetic frustration driven quantum spin disorder in honeycomb compound In$_{3}$Cu$_{2}$VO$_{9}$

We present electronic and magnetic properties of a honeycomb compound In$_{3}$Cu$_{2}$VO$_{9}$ in this paper. We find that the parent phase is a charge transfer insulator with an energy gap of about 1.5 eV. Singly occupied d$_{3z^{2}-r^{2}}$ electrons of copper ions contribute an $S$ = 1/2 spin, while vanadium ions show nonmagnetism. Oxygen 2$p$ orbitals hybridizing with a small fraction of Cu 3$d$ orbitals dominate the density of states near $E_{F}$. The planar nearest-neighbor, next-nearest-neighbor and interplane superexchange couplings of Cu spins are $J_{1}$ $\approx$ 16.2 meV, $J_{2}$ $\approx$ 0.3 meV and $J_{z}$ $\approx$ 1.2 meV, suggesting a low-dimensional antiferromagnet \cite{Sondhi10}. We propose that the magnetic frustration along the c-axis leads to a quantum spin disorder in In$_{3}$Cu$_{2}$VO$_{9}$, in accordance with the recent experiments. {abstract}

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Ab initio study of the giant ferroelectric distortion and pressure induced spin-state transition in BiCoO3

Using configuration-state-constrained electronic structure calculations based on the generalized gradient approximation plus Hubbard U method, we sought the origin of the giant tetragonal ferroelectric distortion in the ambient phase of the potentially multiferroic material BiCoO3 and identified the nature of the pressure induced spin-state transition. Our results show that a strong Bi-O covalency drives the giant ferroelectric distortion, which is further stabilized by an xy-type orbital ordering of the high-spin (HS) Co3+ ions. For the orthorhombic phase under 5.8 GPa, we find that a mixed HS and low-spin (LS) state is more stable than both LS and intermediate-spin (IS) states, and that the former well accounts for the available experimental results. Thus, we identify that the pressure induced spin-state transition is via a mixed HS+LS state, and we predict that the HS-to-LS transition would be complete upon a large volume decrease of about 20%.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Apologizing Comment on `Quantum Quasi-Cyclic Low-Density Parity-Check codes$\,$"

In our recent paper entitled "Quantum Quasi-Cyclic Low-Density Parity-Check codes" [ICIC 2009. LNCS 5754], it was claimed that some new quantum codes can be constructed via the CSS encoding/decoding approach with various lengths and rates. However, the further investigation shows that the proposed construction may steal some ideas from the paper entitled "Quantum Quasi-Cyclic LDPC codes" [quant-ph/0701020v2]. We feel that the apologizing point of the original protocol is that some results are almost similar to that of construction methods with algebraic combinatorics although we suggest the different approach for improving them. Also, there is a weak point of the original coding approach while considering the application of codes in imperfect channels.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

High-rate Space-Time-Frequency Codes Achieving Full-Diversity with Partial Interference Cancellation Group Decoding

The partial interference cancellation (PIC) group decoding has recently been proposed to deal with the decoding complexity and code rate trade-off on the basis of space-time block code (STBC) design criterion when full diversity is achieved. It provides a framework to arrange the rate-complexity-performance tradeoff by choosing a suitable size of information symbol groups. In this paper, a simple design of a linear dispersive space-time-frequency (STF) code is proposed with a design criterion to achieve high rate for frequency-selective channels in terms of multipath when the PIC group decoding is applied at receiver. With an appropriate grouping scheme as well as the PIC group decoding, the proposed STF code is shown to obtain the similar diversity gain as the maximum likelihood (ML) decoding, namely full-dimensional sphere decoding, but have a low decoding complexity. It seems as an intermediate decoding between the ML receiver and zero-forcing (ZF) receiver. The proposed grouping design criterion for the PIC group decoding to achieve full diversity deploying the orthogonal-frequency-division multiplexing (OFDM) technique is also an intermediate condition between the loosest ML full rank criterion of codewords and the strongest ZF linear independence condition of the column vectors for the equivalent frequency-selective channel matrix. It can achieves full diversity with the PIC group decoding for any number of sub-carriers and the data rate can be made high. Several code design examples are illustrated for the feasibility of this coding scheme. Simulation results show that the proposed STF code can well address the rate-performance-complexity tradeoff of the multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) communication system.

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preprint2011arXiv

KAgF3: quasi-one-dimensional magnetism in three-dimensional magnetic ions sublattice

The electronic structure and magnetic properties of the Jahn-Teller-distorted perovskite KAgF3 have been investigated using the full-potential linerized aug- mented plane-wave method. It is found that KAgF3 exhibits significant quasi-one- dimensional antiferromagnetism with the ratio of exchange constant jJ?j (perpen- dicular to the z axis) and J (along the z axis) about 0.04, although the sublattice of magnetic ion is three-dimensional. The strong quasi-one-dimensional antiferromag- netism originates from the C-antiferro-distortive orbital ordering of the Ag2+ 4d9 ions. The orbital ordered antiferromagnetic insulating state in KAgF3 is determined by on-site Coulomb repulsion to a large extent.

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preprint2010arXiv

The spin states of Co ions in La1.5Ca0.5CoO4 from first-principles

The spin states and electronic structure of layered perovskite La1.5Ca0.5CoO4 are investigated using fullpotential linearized augmented plane-wave method. All the computational results indicate that the Co2+ ion is in a high-spin state and the Co3+ in a low-spin state. The Co2+ t2g orbitals with a small crystal-field splitting are mixed by spin-orbit coupling, which accounts for the observed easy in-plane magnetism. The nonmagnetic LS-Co3+ state, which is stabilized by a strong crystal field, provides a natural explanation for the observed low magnetic ordering temperature and a spin-blockade phenomenon of the electron hopping. Furthermore, we find that the intermediate-spin state of Co3+ has a large multiplet splitting. But the lowest-lying IS state of Co3+ is still higher in energy than the LS ground state by a few hundred millielectron volts and the HS state of Co3+ is even less stable, both in sharp contrast to a recent experimental study which suggested the HS+IS mixed Co3+ ground state. We note that either the IS-Co3+ or HS-Co3+ states or their mixture would produce a wrong out-of-plane magnetic anisotropy and a much higher magneticordering temperature than observed. Thus, the present work sheds light on this material concerning its electronic and magnetic structure, and it would stimulate different experiments to settle this intriguing spin-state issue.

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preprint2005arXiv

Identified particle correlation studies in central Au + Au collisions at sqrt(S)= 200 GeV

We present correlations between strange baryons and mesons with charged hadrons in AuAu and pp collisions at sqrt(s)=200 GeV for different pT ranges and centralities. We observe that for central AuAu collisions, the large width of the back side correlations indicates a collapse of the jet structure. This is consistent with previous measurement of suppression of the back side correlation for unidentified charged hadrons. However, in the medium pT range the same side correlations show large AA/pp ratios and wek centrality dependencies which indicate different medium modification of the soft jet components. For different leading particle species we have found no significant difference for both same side and back side correlations. These results are comparable to PHENIX's measurements in similar pT range

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