Developing agents capable of navigating fragmented, multi-source information remains challenging, primarily due to the scarcity of benchmarks reflecting hybrid workflows combining database querying with external APIs.To bridge this gap, we introduce ReCoQA, a largescale benchmark of 29,270 real-estate instances featuring machine-verifiable supervision for intermediate steps, including structured intent labels, SQL queries, and API calls.Complementarily, we propose HIRE-Agent, a hierarchical framework instantiating an understand-plan-execute architecture as a strong baseline.By orchestrating a Front-end parser, a planning Supervisor, and execution Specialists, HIRE-Agent effectively integrates heterogeneous evidence.Extensive experiments demonstrate that HIRE-Agent constitutes a strong baseline and substantiates the necessity of hierarchical collaboration for complex, real-world reasoning tasks.The benchmark and source code are available at: https://github.com/ Husky-989/ReCoQA

Retinopathy of Prematurity (ROP) is a retina disorder that affects premature infants with lower weights. If the patient cannot get the treatment in time when the illness reaches the last stage, irreversible vision loss will be caused. Nevertheless, there has been relatively little consideration given to the segmentation of the ridge, the key clinical characteristic of the illness. Additionally, existing research has not adequately addressed several segmentation issues, such as fragmentary topology, class imbalance, and false positives. This paper proposes a Densely Dilated U-Net (DD-UNet) improved from U-Net to tackle these challenges. Furthermore, the post-processing techniques based on the spatial relationship between vessels and ridges, along with the relative pixel counts of ridges and false positive results is integrated to mitigate false positive results in the predicted ridge. To enhance the precision of thin vessel, a sliding window sampling method is introduced for refined training. Compared with the state-of-the-art models in medical image segmentation, DD-UNet performs well in curvilinear structure segmentation of fundus image. For instance, our DD-UNet outperforms the Attention U-Net by 6.26% in terms of sensitivity and exhibits a 1.85% higher dice score in ridge segmentation.

Discovery of transcription factor binding sites (TFBSs) is of primary importance for understanding the underlying binding mechanic and gene regulation process. Growing evidence indicates that apart from the primary DNA sequences, DNA shape landscape has a significant influence on transcription factor binding preference. To effectively model the co-influence of sequence and shape features, we emphasize the importance of position information of sequence motif and shape pattern. In this paper, we propose a novel deep learning-based architecture, named hybridShape eDeepCNN, for TFBS prediction which integrates DNA sequence and shape information in a spatially aligned manner. Our model utilizes the power of the multi-layer convolutional neural network and constructs an independent subnetwork to adapt for the distinct data distribution of heterogeneous features. Besides, we explore the usage of continuous embedding vectors as the representation of DNA sequences. Based on the experiments on 20 in-vitro datasets derived from universal protein binding microarrays (uPBMs), we demonstrate the superiority of our proposed method and validate the underlying design logic.

Cooperative communication is becoming an attractive technology as it can greatly improve the spatial diversity without additional antennas. This novel communication paradigm can effectively reduce power consumption via multi-node cooperation and resource allocation. This paper studies the energy-efficient node-disjoint multi-path routing for a given source-destination pair by joint route construction, relay assignment and power allocation methods. We first define a new bandwidth-power aware cooperative multi-path routing (BP-CMPR) problem, and formally prove its NP-hardness. The paper then presents a polynomial-time heuristic algorithm CMPR to solve the above problem. The algorithm adopts the Suurballe's method to find k minimal-weight node-disjoint paths from source to destination on a weighted graph. Then, dynamic programming is used to implement relay assignment and power allocation. The theoretical analysis shows that CMPR can reach approximation factors of 2 and \frac{4}{3} for BP-CMPR under the amplify-and-forward and decode-and-forward schemes respectively. The distributed version of the algorithm DCMPR is also presented for this problem. We also prove that both CMPR and DCMPR construct the same cooperative multi-path routing, and show via simulations that the performance of the proposed scheme is more than 15% better than that of a traditional multi-path routing scheme, and close to the optimal result for BP-CMPR in variety of situations.

In CRAHNs (Cognitive Radio Ad Hoc Networks), cognitive users coexist with primary users who have priority in accessing a set of licensed bands. Cognitive users can use those bands only when the primary users don't occupied them. And also, these occupied bands should be vacated immediately as soon as corresponding primary users wake up. Therefore, the more frequently primary user changes its activities, the less stable spectrum availability the cognitive user has. This makes route stability become one of the most concerned problems in CRAHNs. Some works have considered such kind of stability, but the overall performance of the network hasn't been taken into account when multiple cognitive user pairs (also called multi-sessions in the following) have routing requirements. Thus, in this paper, we investigate a joint routing and spectrum scheduling problem, in which multi-sessions exist, and the route stability is required. To solve this problem, a greedy strategy named Distributed Stability Algorithm (DSA) is proposed, which aims to maximize the route stability of the session, whose route stability is the minimal. The message complexity of our algorithm DSA is O((L+2)·Tmax·κ). Here, κ represents the number of transmission pairs, L and Tmaxare both two constants. Numerical results have shown that in a 100-node networks, compared with SAMER, our scheme can increase the route stability by 48.5% on average.

Recently, cooperative communication is shown to be a promising technology to achieve spatial diversity by keeping each node with only one antenna and having a node exploit a relay node's antenna. Most previous works assume that there are multiple source-destination pairs and multiple relay nodes in a network where each node only serves as one role: either transmission node or relay node. However, this assumption will not always hold in practice where each node has its own information to transmit. This paper studies the maximal lifetime scheduling problem for cooperative communication with guaranteed throughput service in a network environment where there are multiple source-destination pairs and each node can serve as both two roles: transmission node and "relay" node. We first analyze the power allocation of optimal lifetime for a single source-destination pair with a bounded throughput in different communication modes, and then design an optimal polynomial-time algorithm to maximize the network lifetime. This algorithm is based on iteration and tries to find a better solution than that before in each iteration. After that, the optimality of this algorithm has been proved formally. The simulation results show that the algorithm can prolong about 199% lifetime than that of direct transmission.

Because of rapid channel attenuation in lossy environment, there is a severe packet drop loss problem in wireless ad hoc networks. Though MIMO (Multi-Input Multi-Output) technology can alleviate this effect greatly, it will result in higher transmission energy depletion as using more transmit antennas. Lots of schemes have been proposed to reduce such energy cost by transmit antenna selection. However, most of those only consider QoS requirements between neighbor nodes while ignoring QoS requirements for entire network. In this paper, we investigate an energy-efficient transmit antenna selection problem for many-to-one communications, while guaranteeing a certain packet drop loss ratio from any node to the sink. We prove that this problem (Transmit Antenna Selection Problem, TASP) is NP-hard. Thus, a greedy algorithm TASA (Transmit Antenna Selection Algorithm) is proposed to solve this problem. This paper also prove the correctness of the proposed algorithm and the time complexity of TASA algorithm is O(|E|+|V|-MT), where |V| represents the number of nodes, |E| indicates the number of links and MTis the number of transmit antennas on each node, respectively. Our numerical results show that in a 100 node network, TASA can reduce transmission energy cost by 44.3% on average.

Relay node placement is an important issue because it greatly affects the performance of wireless ad hoc sensor networks. The pervious works mainly pay attention on placing the minimum number of relay nodes to connect all the sensor nodes. But we find that this placement scheme may result in QoS reduction of the network, such as transmission delay, energy cost, etc. Therefore, this work studies the minimum relay node placement problem to guarantee both the connectivity and geometric spanner properties. We first show that there is no algorithm with the performance O(opt) for this problem, where opt is the minimum number of relay nodes to connect the sensor nodes. Thus, this paper presents an efficient placement algorithm MSGP with O(max{opt, n}) relay nodes, where n is the number of sensor nodes. Specially, if any two sensor nodes cannot communicate directly, the algorithm obtains O(1)-approximation bound. Then, as data gathering is one of the most important operations in sensor networks, we also study the spanner-aware placement problem for this communication scheme. The simulation results show the efficiency of the proposed algorithm. For example, MSGP algorithm can reach the spanner factor 3 with the additional 30% relay nodes compared with MST-based placement algorithm.