Who Would be Interested in Services? An Entity Graph Learning System for User Targeting

With the growing popularity of various mobile devices, user targeting has received a growing amount of attention, which aims at effectively and efficiently locating target users that are interested in specific services. Most pioneering works for user targeting tasks commonly perform similarity-based expansion with a few active users as seeds, suffering from the following major issues: the unavailability of seed users for newcoming services and the unfriendliness of black-box procedures towards marketers. In this paper, we design an Entity Graph Learning (EGL) system to provide explainable user targeting ability meanwhile applicable to addressing the cold-start issue. EGL System follows the hybrid online-offline architecture to satisfy the requirements of scalability and timeliness. Specifically, in the offline stage, the system focuses on the heavyweight entity graph construction and user entity preference learning, in which we propose a Three-stage Relation Mining Procedure (TRMP), breaking loose from the expensive seed users. At the online stage, the system offers the ability of user targeting in real-time based on the entity graph from the offline stage. Since the user targeting process is based on graph reasoning, the whole process is transparent and operation-friendly to marketers. Finally, extensive offline experiments and online A/B testing demonstrate the superior performance of the proposed EGL System.Comment: Accepted by ICDE 202

Think-in-Memory: Recalling and Post-thinking Enable LLMs with Long-Term Memory

Memory-augmented Large Language Models (LLMs) have demonstrated remarkable performance in long-term human-machine interactions, which basically relies on iterative recalling and reasoning of history to generate high-quality responses. However, such repeated recall-reason steps easily produce biased thoughts, \textit{i.e.}, inconsistent reasoning results when recalling the same history for different questions. On the contrary, humans can keep thoughts in the memory and recall them without repeated reasoning. Motivated by this human capability, we propose a novel memory mechanism called TiM (Think-in-Memory) that enables LLMs to maintain an evolved memory for storing historical thoughts along the conversation stream. The TiM framework consists of two crucial stages: (1) before generating a response, a LLM agent recalls relevant thoughts from memory, and (2) after generating a response, the LLM agent post-thinks and incorporates both historical and new thoughts to update the memory. Thus, TiM can eliminate the issue of repeated reasoning by saving the post-thinking thoughts as the history. Besides, we formulate the basic principles to organize the thoughts in memory based on the well-established operations, (\textit{i.e.}, insert, forget, and merge operations), allowing for dynamic updates and evolution of the thoughts. Furthermore, we introduce Locality-Sensitive Hashing into TiM to achieve efficient retrieval for the long-term conversations. We conduct qualitative and quantitative experiments on real-world and simulated dialogues covering a wide range of topics, demonstrating that equipping existing LLMs with TiM significantly enhances their performance in generating responses for long-term interactions

Growth of millimeter-sized high-quality CuFeSe2_2 single crystals by the molten salt method and study of their semiconducting behavior

An eutectic AlCl$_3$/KCl molten salt method in a horizontal configuration was employed to grow millimeter-sized and composition homogeneous CuFeSe$_2$ single crystals due to the continuous growth process in a temperature gradient induced solution convection. The typical as-grown CuFeSe$_2$ single crystals in cubic forms are nearly 1.6$\times$1.2$\times$1.0 mm3 in size. The chemical composition and homogeneity of the crystals was examined by both inductively coupled plasma atomic emission spectroscopy and energy dispersive spectrometer with Cu:Fe:Se = 0.96:1.00:1.99 consistent with the stoichiometric composition of CuFeSe$_2$. The magnetic measurements suggest a ferrimagnetic or weak ferromagnetic transition below T$_C$ = 146 K and the resistivity reveals a semiconducting behavior and an abrupt increase below T$_C$

Thiophene Disubstituted Benzothiadiazole Derivatives: An Effective Planarization Strategy Toward Deep-Red to Near-Infrared (NIR) Organic Light-Emitting Diodes

As one of the three primary colors that are indispensable in full-color displays, the development of red emitters is far behind the blue and green ones. Here, three novel orange-yellow to near-infrared (NIR) emitters based on 5,6-difluorobenzo[c][1,2,5]thiadiazole (BTDF) namely BTDF-TPA, BTDF-TTPA, and BTDF-TtTPA were designed and synthesized. Density functional theory analysis and photophysical characterization reveal that these three materials possess hybridized local and charge-transfer (HLCT) state feature and a feasible reverse intersystem crossing (RISC) from the high-lying triplet state to the singlet state may conduce to an exciton utilization exceeding the limit of 25% of traditional fluorescence materials under electrical excitation. The insertion of thiophene with small steric hindrance as π-bridge between the electron-donating (D) moiety triphenylamine (TPA) and the electron-accepting (A) moiety BTDF not only results in a remarkable 67 nm red-shift of the emission peak but also brings about a large overlap of frontier molecular orbitals to guarantee high radiative transition rate that is of great significance to obtain high photoluminescence quantum yield (PLQY) in the “energy-gap law” dominated long-wavelength emission region. Consequently, an attractive high maximum external quantum efficiency (EQE) of 5.75% was achieved for the doped devices based on these thiophene π-bridged emitters, giving a deep-red emission with small efficiency roll-off. Remarkably, NIR emission could be obtained for the non-doped devices, achieving an excellent maximum EQE of 1.44% and Commission Internationale de l'Éclairage (CIE) coordinates of (0.71, 0.29). These results are among the highest efficiencies in the reported deep-red to NIR fluorescent OLEDs and offer a new π-bridge design strategy in D-π-A and D-π-A-π-D red emitter design

BPLLDA: Predicting lncRNA-Disease Associations Based on Simple Paths With Limited Lengths in a Heterogeneous Network

In recent years, it has been increasingly clear that long noncoding RNAs (lncRNAs) play critical roles in many biological processes associated with human diseases. Inferring potential lncRNA-disease associations is essential to reveal the secrets behind diseases, develop novel drugs, and optimize personalized treatments. However, biological experiments to validate lncRNA-disease associations are very time-consuming and costly. Thus, it is critical to develop effective computational models. In this study, we have proposed a method called BPLLDA to predict lncRNA-disease associations based on paths of fixed lengths in a heterogeneous lncRNA-disease association network. Specifically, BPLLDA first constructs a heterogeneous lncRNA-disease network by integrating the lncRNA-disease association network, the lncRNA functional similarity network, and the disease semantic similarity network. It then infers the probability of an lncRNA-disease association based on paths connecting them and their lengths in the network. Compared to existing methods, BPLLDA has a few advantages, including not demanding negative samples and the ability to predict associations related to novel lncRNAs or novel diseases. BPLLDA was applied to a canonical lncRNA-disease association database called LncRNADisease, together with two popular methods LRLSLDA and GrwLDA. The leave-one-out cross-validation areas under the receiver operating characteristic curve of BPLLDA are 0.87117, 0.82403, and 0.78528, respectively, for predicting overall associations, associations related to novel lncRNAs, and associations related to novel diseases, higher than those of the two compared methods. In addition, cervical cancer, glioma, and non-small-cell lung cancer were selected as case studies, for which the predicted top five lncRNA-disease associations were verified by recently published literature. In summary, BPLLDA exhibits good performances in predicting novel lncRNA-disease associations and associations related to novel lncRNAs and diseases. It may contribute to the understanding of lncRNA-associated diseases like certain cancers

Original experimental platform system and application of underground coal mine reservoirs

Coal is the main energy source in China, and the western region in China is the main coal production area. The protection and utilization of coal mine water is the major technical challenge in the coal mining. The coal green mining research team of China Energy Group has pioneered the technology of underground coal mine reservoirs, which has been widely implemented in the Shendong mining area in Western China, providing over 95% of water needed for mining and ensuring the sustainable development of the mining area. To further enrich and improve the theoretical and technical system of underground coal mine reservoirs, this technology has been promoted and applied under different geological and working conditions in the western coal mining areas to ensure national energy security and water resources protection. The research team has built the original technology experimental platform system for coal mine underground reservoirs, including the comprehensive intelligent experimental platform for underground water transport and protection, the physical modelling platform for coal mine underground reservoirs under multiple coal seams, the dam structure experimental platform for coal mine underground reservoirs, the simulation experimental platform for deep well construction in western areas, the water-rock coupling mechanism experimental platform for underground water reservoirs, the integrated water treatment process experimental platform, and the impact experimental platforms for coal mine underground reservoirs, etc. On these platforms, the researches can be carried out consisting of the transport law of underground water, the optimization of dam structure parameters, the safety and stability of reservoirs, water-rock coupling mechanism, the optimization of mine water treatment, and the impact of caving rock mass on the dam structure under different coal seam occurrence conditions in the western mining areas of China. These platform systems provide a theoretical support and technical verification for the construction, operation, and safety of coal mine underground reservoirs. By utilizing these experimental platforms, various experimental research studies have been conducted, including the stability and seepage of coal mine underground reservoirs under multiple coal seams, the structural safety of coal mine underground reservoirs dam, and the water-rock coupling mechanism of coal mine underground reservoirs. These research results have been applied in actual engineering practice to ensure the safe and stable operation of coal mine underground reservoirs

Association Between Cerebral Hypoperfusion and Cognitive Impairment in Patients With Chronic Vertebra-Basilar Stenosis

Objective: This study aimed to investigate the association between cognitive impairment and cerebral haemodynamic changes in patients with chronic vertebra-basilar (VB) stenosis.Methods: Patients with severe posterior circulation VB stenosis and infarction or a history of infarction for more than 2 weeks from January 2014 to January 2015 were enrolled (n = 96). They were divided into three groups, namely, the computed tomography perfusion (CTP) normal group, the CTP compensated group, and the CTP decompensated group. Cognitive function was assessed using a validated Chinese version of the Mini-Mental State Examination (MMSE), the Frontal Assessment Battery (FAB), and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Regression models were used to identify independent risk factors for cognitive impairment.Results: The MMSE and FAB scores of patients in the CTP decompensated group were significantly lower than those of patients in the CTP normal and CTP compensated groups (all p < 0.05). The RBANS total and its domain scores, including immediate memory, visual acuity, and delayed memory, in the CTP compensated and CTP decompensated groups were significantly lower than those in the CTP normal group (all p < 0.05). Multiple regression analyses showed that CTP compensation, CTP decompensation, severe VB tandem stenosis, and multiple infarctions were independent risk factors for cognitive impairment.Conclusions: Low perfusion caused by severe VB stenosis can lead to extensive cognitive impairments in areas such as immediate memory, visual span, and delayed memory