Towards Automatic Boundary Detection for Human-AI Hybrid Essay in Education

Human-AI collaborative writing has been greatly facilitated with the help of modern large language models (LLM), e.g., ChatGPT. While admitting the convenience brought by technology advancement, educators also have concerns that students might leverage LLM to partially complete their writing assignment and pass off the human-AI hybrid text as their original work. Driven by such concerns, in this study, we investigated the automatic detection of Human-AI hybrid text in education, where we formalized the hybrid text detection as a boundary detection problem, i.e., identifying the transition points between human-written content and AI-generated content. We constructed a hybrid essay dataset by partially removing sentences from the original student-written essays and then instructing ChatGPT to fill in for the incomplete essays. Then we proposed a two-step detection approach where we (1) Separated AI-generated content from human-written content during the embedding learning process; and (2) Calculated the distances between every two adjacent prototypes (a prototype is the mean of a set of consecutive sentences from the hybrid text in the embedding space) and assumed that the boundaries exist between the two prototypes that have the furthest distance from each other. Through extensive experiments, we summarized the following main findings: (1) The proposed approach consistently outperformed the baseline methods across different experiment settings; (2) The embedding learning process (i.e., step 1) can significantly boost the performance of the proposed approach; (3) When detecting boundaries for single-boundary hybrid essays, the performance of the proposed approach could be enhanced by adopting a relatively large prototype size, leading to a $22$\% improvement (against the second-best baseline method) in the in-domain setting and an $18$\% improvement in the out-of-domain setting.Comment: 9 pages including references, 2 figure

Contextualized End-to-End Speech Recognition with Contextual Phrase Prediction Network

Contextual information plays a crucial role in speech recognition technologies and incorporating it into the end-to-end speech recognition models has drawn immense interest recently. However, previous deep bias methods lacked explicit supervision for bias tasks. In this study, we introduce a contextual phrase prediction network for an attention-based deep bias method. This network predicts context phrases in utterances using contextual embeddings and calculates bias loss to assist in the training of the contextualized model. Our method achieved a significant word error rate (WER) reduction across various end-to-end speech recognition models. Experiments on the LibriSpeech corpus show that our proposed model obtains a 12.1% relative WER improvement over the baseline model, and the WER of the context phrases decreases relatively by 40.5%. Moreover, by applying a context phrase filtering strategy, we also effectively eliminate the WER degradation when using a larger biasing list.Comment: Accepted by interspeech202

Boosting the Transferability of Adversarial Attacks with Global Momentum Initialization

Deep neural networks are vulnerable to adversarial examples, which attach human invisible perturbations to benign inputs. Simultaneously, adversarial examples exhibit transferability under different models, which makes practical black-box attacks feasible. However, existing methods are still incapable of achieving desired transfer attack performance. In this work, from the perspective of gradient optimization and consistency, we analyze and discover the gradient elimination phenomenon as well as the local momentum optimum dilemma. To tackle these issues, we propose Global Momentum Initialization (GI) to suppress gradient elimination and help search for the global optimum. Specifically, we perform gradient pre-convergence before the attack and carry out a global search during the pre-convergence stage. Our method can be easily combined with almost all existing transfer methods, and we improve the success rate of transfer attacks significantly by an average of 6.4% under various advanced defense mechanisms compared to state-of-the-art methods. Eventually, we achieve an attack success rate of 95.4%, fully illustrating the insecurity of existing defense mechanisms

Adaptive Contextual Biasing for Transducer Based Streaming Speech Recognition

By incorporating additional contextual information, deep biasing methods have emerged as a promising solution for speech recognition of personalized words. However, for real-world voice assistants, always biasing on such personalized words with high prediction scores can significantly degrade the performance of recognizing common words. To address this issue, we propose an adaptive contextual biasing method based on Context-Aware Transformer Transducer (CATT) that utilizes the biased encoder and predictor embeddings to perform streaming prediction of contextual phrase occurrences. Such prediction is then used to dynamically switch the bias list on and off, enabling the model to adapt to both personalized and common scenarios. Experiments on Librispeech and internal voice assistant datasets show that our approach can achieve up to 6.7% and 20.7% relative reduction in WER and CER compared to the baseline respectively, mitigating up to 96.7% and 84.9% of the relative WER and CER increase for common cases. Furthermore, our approach has a minimal performance impact in personalized scenarios while maintaining a streaming inference pipeline with negligible RTF increase

Co-infusion of haplo-identical CD19-chimeric antigen receptor T cells and stem cells achieved full donor engraftment in refractory acute lymphoblastic leukemia

Abstract Background Elderly patients with relapsed and refractory acute lymphoblastic leukemia (ALL) have poor prognosis. Autologous CD19 chimeric antigen receptor-modified T (CAR-T) cells have potentials to cure patients with B cell ALL; however, safety and efficacy of allogeneic CD19 CAR-T cells are still undetermined. Case presentation We treated a 71-year-old female with relapsed and refractory ALL who received co-infusion of haplo-identical donor-derived CD19-directed CAR-T cells and mobilized peripheral blood stem cells (PBSC) following induction chemotherapy. Undetectable minimal residual disease by flow cytometry was achieved, and full donor cell engraftment was established. The transient release of cytokines and mild fever were detected. Significantly elevated serum lactate dehydrogenase, alanine transaminase, bilirubin and glutamic-oxalacetic transaminase were observed from days 14 to 18, all of which were reversible after immunosuppressive therapy. Conclusions Our preliminary results suggest that co-infusion of haplo-identical donor-derived CAR-T cells and mobilized PBSCs may induce full donor engraftment in relapsed and refractory ALL including elderly patients, but complications related to donor cell infusions should still be cautioned. Trial registration Allogeneic CART-19 for Elderly Relapsed/Refractory CD19+ ALL. NCT0279955

Expert Consensus on Microtransplant for Acute Myeloid Leukemia in Elderly Patients -Report From the International Microtransplant Interest Group

Recent studies have shown that microtransplant (MST) could improve outcome of patients with elderly acute myeloid leukemia (EAML). To further standardize the MST therapy and improve outcomes in EAML patients, based on analysis of the literature on MST, especially MST with EAML from January 1st, 2011 to November 30th, 2022, the International Microtransplant Interest Group provides recommendations and considerations for MST in the treatment of EAML. Four major issues related to MST for treating EAML were addressed: therapeutic principle of MST (1), candidates for MST (2), induction chemotherapy regimens (3), and post-remission therapy based on MST (4). Others included donor screening, infusion of donor cells, laboratory examinations, and complications of treatment

Prospecting for Microelement Function and Biosafety Assessment of Transgenic Cereal Plants

Microelement contents and metabolism are vitally important for cereal plant growth and development as well as end-use properties. While minerals phytotoxicity harms plants, microelement deficiency also affects human health. Genetic engineering provides a promising way to solve these problems. As plants vary in abilities to uptake, transport, and accumulate minerals, and the key enzymes acting on that process is primarily presented in this review. Subsequently, microelement function and biosafety assessment of transgenic cereal plants have become a key issue to be addressed. Progress in genetic engineering of cereal plants has been made with the introduction of quality, high-yield, and resistant genes since the first transgenic rice, corn, and wheat were born in 1988, 1990, and 1992, respectively. As the biosafety issue of transgenic cereal plants has now risen to be a top concern, many studies on transgenic biosafety have been carried out. Transgenic cereal biosafety issues mainly include two subjects, environmental friendliness and end-use safety. Different levels of gene confirmation, genomics, proteomics, metabolomics and nutritiomics, absorption, metabolism, and function have been investigated. Also, the different levels of microelement contents have been measured in transgenic plants. Based on the motivation of the requested biosafety, systematic designs, and analysis of transgenic cereal are also presented in this review paper

Super-hydrophobic PTFE hollow fiber membrane fabricated by electrospinning of Pullulan/PTFE emulsion for membrane deamination

Polytetrafluoroethylene (PTFE) is increasingly used in membrane applications due to its excellent thermal stability, resistance to chemical degradation and strong hydrophobicity. However, it is difficult to prepare high flux membranes by conventional technology due to its high melt viscosity and solvent resistance. Here, a environment-friendly technology is used to fabricate PTFE hollow fiber membrane via emulsion electrospinning. Pullulan is dope into the PTFE emulsion and employ as the binder of PTFE particles to facilitate the formation of the as-spun PTFE-Pullulan hollow fiber membrane. The expected PTFE hollow fiber membrane is obtained after sintering of the as-spun membrane. No organic solvent is used and no pollutant is discharged during the preparation process. The prepared PTFE hollow fiber membrane shows excellent properties such as superhydrophobicity (contact angle > 150 degrees), high porosity (85%) and excellent mechanical properties (Young's modulus of 39 MPa and fracture strain of 245%). In the deamination test, the experimental mass transfer coefficient of the PTFE hollow fiber membrane reaches 2.4*10(-5) m/s when the pH is 11, which is 1.6-2.4 times as that of the commercial membrane. The self-made PTFE hollow fiber membrane shows great potential in the application of membrane deamination

DNA-Conjugated Amphiphilic Aggregation-Induced Emission Probe for Cancer Tissue Imaging and Prognosis Analysis

Detection of an ultralow concentration of mRNA is important in the prognosis of gene-related diseases. In this study, a DNA-conjugated amphiphilic aggregation-induced emission probe (TPE-R-DNA) was synthesized for cancer tissue imaging and prognosis analysis based on an exonuclease III-aided target recycling technique. TPE-R-DNA comprise two components: a hydrophobic component that serves as the “turn-on” long wavelength fluorescence imaging agent (TPE-R-N₃); and a hydrophilic single DNA strand (Alk-DNA) which acts as specific recognition part for target mRNA. In the absence of target mRNA, TPE-R-DNA had almost no fluorescence because of its high water solubility. Conversely, the TPE-R-DNA was digested by exonuclease III (Exo III) in the presence of MnSOD mRNA to release the hydrophobic fluorogens (TPE-R-AT). Subsequently, TPE-R-AT formed aggregates, and therefore, fluorescence signal was distinctly observed. For the first time, the structure of the hydrolysis product (TPE-R-AT), containing two bases A and T, was proved by the mass spectrum (MS) and high-performance liquid chromatography (HPLC). Moreover, the detection limit toward mRNA could be achieved in as low as 0.6 pM. Furthermore, the fluorescent signal can be used to confirm the MnSOD mRNA expression level in cancer tissue. The MnSOD mRNA expression in renal cancer was lower than in renal cancer adjacent tissue. In particular, the expression level was analyzed to predict prognosis of cancer patients. Our results demonstrate that a shorter survival time was evident among patients in lower MnSOD mRNA expression. Thereby, it indicates great potential for the development of an ultrasensitive biosensing platform for the application in disease prognosis