Re-imagine the Negative Prompt Algorithm: Transform 2D Diffusion into 3D, alleviate Janus problem and Beyond

Although text-to-image diffusion models have made significant strides in generating images from text, they are sometimes more inclined to generate images like the data on which the model was trained rather than the provided text. This limitation has hindered their usage in both 2D and 3D applications. To address this problem, we explored the use of negative prompts but found that the current implementation fails to produce desired results, particularly when there is an overlap between the main and negative prompts. To overcome this issue, we propose Perp-Neg, a new algorithm that leverages the geometrical properties of the score space to address the shortcomings of the current negative prompts algorithm. Perp-Neg does not require any training or fine-tuning of the model. Moreover, we experimentally demonstrate that Perp-Neg provides greater flexibility in generating images by enabling users to edit out unwanted concepts from the initially generated images in 2D cases. Furthermore, to extend the application of Perp-Neg to 3D, we conducted a thorough exploration of how Perp-Neg can be used in 2D to condition the diffusion model to generate desired views, rather than being biased toward the canonical views. Finally, we applied our 2D intuition to integrate Perp-Neg with the state-of-the-art text-to-3D (DreamFusion) method, effectively addressing its Janus (multi-head) problem.Comment: Our project page is available at https://PerpNeg.github.io

Mycobacterium tuberculosis Detection based on mpt64 amplification by Nested-PCR in Sputum samples

Introduction: Tuberculosis (TB) is an old problem that is currently considered as a great challenge, mostly in developing countries. It may be a lethal disease. Thus, rapid diagnosis of Mycobacterium tuberculosis (MTB) infection plays a critical role in controlling the spread of TB, whereas conventional methods may take up to several weeks or longer to diagnose the infection. Hence, nested polymerase chain reaction (NCR) assay was applied for direct identification of the MTB DNA presence in sputum samples. The aim of the study was the development of a direct NCR method using mpt64 specific primers for rapid diagnosis of MTB infection. Materials and Methods: To development of study, eight positive and negative sputum specimens obtained from Masih Daneshvari hospital pulmonary TB center, were studied. After smear preparation genomic DNA was extracted and mpt64 was amplified using NCR method. While doing work we paying attention to PCR standardization and precautions to avoid sample contamination. Results: After evaluation gained appropriate results from purified genomic DNA by AGE and biophotometer, the standardized NCR products were evaluated by Agarose Gel Electrophoresis. Five of 7 positive samples were positive, and one of the negative samples was negative using our NCR assay. Conclusion: Based on the results of this study, we could be successful in the NCR technique’s optimization to our system for disese detection, while it can be apply as a more rapid, accurate, inexpensive, and specific diagnostic assay for direct detection of MTB DNA

Overhead-controlled contention-based routing for VANETs

Routing of VANETs is a challenging issue that has attracted many attentions of researchers recently. Contention based routing protocols have good congruity with high mobility of nodes in this kind of networks. Prevention from forwarding duplicate packets is an important challenge in such routing protocols. Indeed, such duplications can reduce scalability and efficiency of contention based routing protocols. On the other hand, the prevention method can affect advantages of such routing protocols. In this paper, we proposed 2 new routing protocols by adding 2 new methods to an existing contention based routing protocol to decrease overhead of duplications. Simulation results show that overhead decreases significantly while preserving end-to-end delay and delivery ratio in suitable values

The effect of pyrithione zinc in emollient base in the treatment of psoriasis

چکیده: زمینه و هدف: پسوریازیس یک بیماری مزمن التهابی شایع پوست است که به بسیاری از روش های درمانی مقاوم می باشد. این مطالعه با هدف بررسی اثر فرمولاسیون موضعی پیریتیون زینک در پایه نرم کننده در مقایسه با پایه نرم کننده به تنهایی در درمان پسوریازیس انجام شد. روش بررسی: در این مطالعه کارآزمایی بالینی دوسوکور بیماران مبتلا به پسوریازیس با گرفتاری کمتر از 10 سطح پوست به روش دردسترس انتخاب و بطور تصادفی به دو گروه، آزمون و کنترل تقسیم شدند. گروه آزمون با کرم 25/0 پیریتیون زینک در پایه نرم کننده و گروه کنترل با پایه نرم کننده به تنهایی روزانه دو بار به مدت 3 ماه درمان و پیگیری شدند. قبل و بعد از درمان شدت ایندوراسیون، اریتم و پوسته ریزی بر اساس PASI score (Psoriasis Area Severity Index) محاسبه گردید. اطلاعات به دست آمده بین دو گروه با کمک آزمون آماری t مستقل و t زوجی مقایسه گردید. یافته ها: PASI score قبل و بعد از درمان در گروه آزمون 8/1±4/3 و 3/1±9/0 (01/0P) بود. میانگین کاهش PASI score در گروه آزمون و کنترل به ترتیب 1/0±4/0 و 2±4/2 بود (01/0

Relationship between Phosphatase and Tensin Gene Expression and Clinicopathologic Features of Breast Cancer in Patients who Underwent Biopsy or Breast Surgery

Background: Phosphatase and tensin (PTEN) gene is a tumor suppressor gene on chromosome 10q23 that is composed of 11 exons. Several studies have shown that loss of PTEN function is a common occurrence in breast cancer in particular in triple negative type, and it is significantly associated with age and higher stage of cancer. In this study, the expression of this gene in malignant breast cancer tissue samples and their correlation with clinicopathologic parameters was studied.Methods: In this retrospective study, 65 malignant tissue samples were chosen for immunohistochemistry (IHC) test. Other information about clinicopathologic features were collected from pathology reports and patients’ medical records. IHC on the selected paraffin blocks was performed, and the collected data were analyzed using SPSS software and chi-square test. P < 0.0500 was considered statistically significant.Results: PTEN expression rate in malignant breast tissue was 50.8% of the cases (33 out of 65 samples). Lack of PTEN expression had significant correlation with involvement of the lymph node sent by the sample, vascular or perineural invasion, metastasis and chemotherapy background, spontaneous malignancy presence, familial history, negative progesterone receptor, negative estrogen receptor, and positive her2/neu. No relationship was observed between the expression of PTEN with patients’ age, tumor size, age group of the patients after categorization into two groups of under 50 years and over 50 years, lesion location (left or right breast), and tumor grade.Conclusions: The results showed PTEN loss as a frequent event in breast cancer that is closely associated with progression and poor prognosis. PTEN loss might predict more aggressive behavior and worse outcomes in patients with breast cancer

ChronoR: Rotation Based Temporal Knowledge Graph Embedding

Despite the importance and abundance of temporal knowledge graphs, most of the current research has been focused on reasoning on static graphs. In this paper, we study the challenging problem of inference over temporal knowledge graphs. In particular, the task of temporal link prediction. In general, this is a difficult task due to data non-stationarity, data heterogeneity, and its complex temporal dependencies. We propose Chronological Rotation embedding (ChronoR), a novel model for learning representations for entities, relations, and time. Learning dense representations is frequently used as an efficient and versatile method to perform reasoning on knowledge graphs. The proposed model learns a k-dimensional rotation transformation parametrized by relation and time, such that after each fact's head entity is transformed using the rotation, it falls near its corresponding tail entity. By using high dimensional rotation as its transformation operator, ChronoR captures rich interaction between the temporal and multi-relational characteristics of a Temporal Knowledge Graph. Experimentally, we show that ChronoR is able to outperform many of the state-of-the-art methods on the benchmark datasets for temporal knowledge graph link prediction